Stability and synchronization of fractional-order reaction-diffusion inertial time-delayed neural networks with parameters perturbation.

This study is centered around the dynamic behaviors observed in a class of fractional-order generalized reaction-diffusion inertial neural networks (FGRDINNs) with time delays. These networks are characterized by differential equations involving two distinct fractional derivatives of the state. The global uniform stability of FGRDINNs with time delays is explored utilizing Lyapunov comparison principles. Furthermore, global synchronization conditions for FGRDINNs with time delays are derived through the Lyapunov direct method, with consideration given to various feedback control strategies and parameter perturbations. The effectiveness of the theoretical findings is demonstrated through three numerical examples, and the impact of controller parameters on the error system is further investigated.

Delaying high school start times impacts depressed mood among students: evidence from a natural experiment.

PURPOSE: Delaying high school start times prolongs weekday sleep. However, it is not clear if longer sleep reduces depression symptoms and if the impact of such policy change is the same across groups of adolescents. METHODS: We examined how gains in weekday sleep impact depression symptoms in 2,134 high school students (mean age 15.16 ± 0.35 years) from the Minneapolis metropolitan area. Leveraging a natural experiment design, we used the policy change to delay school start times as an instrument to estimate the effect of a sustained gain in weekday sleep on repeatedly measured Kandel-Davies depression symptoms. We also evaluated whether allocating the policy change to subgroups with expected benefit could improve the impact of the policy. RESULTS: Over 2 years, a sustained half-hour gain in weekday sleep expected as a result of the policy change to delay start times decreased depression symptoms by 0.78 points, 95%CI (-1.32,-0.28), or 15.6% of a standard deviation. The benefit was driven by a decrease in fatigue and sleep-related symptoms. While symptoms of low mood, hopelessness, and worry were not affected by the policy on average, older students with greater daily screen use and higher BMI experienced greater improvements in mood symptoms than would be expected on average, signaling heterogeneity. Nevertheless, universal implementation outperformed prescriptive strategies. CONCLUSION: High school start time delays are likely to universally decrease fatigue and overall depression symptoms in adolescents. Students who benefit most with respect to mood are older, spend more time on screens and have higher BMI.

Fully distributed consensus of discrete-time linear multi-agent systems with large input and communication delays.

This paper investigates the consensus problem for discrete-time leader-following multi-agent systems subject to large time delays. Building upon two assumptions, a novel fully distributed protocol is devised by utilizing a normalized weighting matrix, depending solely on the relative output measurement. It is shown that, for arbitrarily large yet bounded input and communication delays that are constant and exactly known, the consensus problem can be effectively addressed by both the proposed protocol and its truncated version. Assuming further that followers incorporate solely input delays, then the permitted delays can be time-varying and different. The proposed protocols do not rely on global information of the directed communication topology, thus ensuring robustness against alterations in the communication topology. A numerical example is employed to validate the effectiveness of the suggested approach.

Sliding mode control for uncertain fractional-order reaction-diffusion memristor neural networks with time delays.

This paper investigates a sliding mode control method for a class of uncertain delayed fractional-order reaction-diffusion memristor neural networks. Different from most existing literature on sliding mode control for fractional-order reaction-diffusion systems, this study constructs a linear sliding mode switching function and designs the corresponding sliding mode control law. The sufficient theory for the globally asymptotic stability of the sliding mode dynamics are provided, and it is proven that the sliding mode surface is finite-time reachable under the proposed control law, with an estimate of the maximum reaching time. Finally, a numerical test is presented to validate the effectiveness of the theoretical analysis.

Advanced stability analysis of a fractional delay differential system with stochastic phenomena using spectral collocation method.

In recent years, there has been a growing interest in incorporating fractional calculus into stochastic delay systems due to its ability to model complex phenomena with uncertainties and memory effects. The fractional stochastic delay differential equations are conventional in modeling such complex dynamical systems around various applied fields. The present study addresses a novel spectral approach to demonstrate the stability behavior and numerical solution of the systems characterized by stochasticity along with fractional derivatives and time delay. By bridging the gap between fractional calculus, stochastic processes, and spectral analysis, this work contributes to the field of fractional dynamics and enriches the toolbox of analytical tools available for investigating the stability of systems with delays and uncertainties. To illustrate the practical implications and validate the theoretical findings of our approach, some numerical simulations are presented.

Identification, influencing factors and outcomes of time delays in the management pathway of diabetic foot: A systematic review.

OBJECTIVE: A systematic review was conducted to evaluate the time delays in the management of diabetic foot and explore influencing factors of these delays and potential outcomes. METHODS: The researchers searched several electronic databases (Pubmed, Web of Science, Cochrane Library, EMbase, CNKI, WanFang, CBM and VIP) for English and Chinese studies that examined time delays in the management pathway of diabetic foot. Two authors independently screened and extracted data, and assessed the quality of the included studies using the Newcastle-Ottawa Scale and the Agency for Health Research and Quality checklist. Due to heterogeneity among the studies, descriptive analysis was performed. RESULTS: The review included 28 articles, comprising 20 cohort studies and 8 cross-sectional studies, that met the inclusion criteria. Among these, 14 were deemed of high quality. The median times from symptom onset to primary health care or specialist care varied from 3 to 46.69 days. The median delay in referral by primary care specialists ranged from 7 to 31 days, and subsequent median times to definitive treatment ranged from 6.2 to 56 days. Multiple complex factors were found to contribute to these delays, including patient demographics (older age, lower education level and income level) and poor patient health-seeking behaviors (inaccurate self-treatment, incorrect recognition and interpretation of symptoms), inaccurate assessment or initial treatment by health primary professionals, complex referral pathways and clinical characteristics of diabetic foot (number of foot ulcers, Wagner grade scale, and hemoglobin A1c index). Negative outcomes associated with these delays included increased risk of major amputation and mortality, decreased wound healing rate, prolonged hospital stay, and increased hospital costs. CONCLUSIONS: Time delays in the diabetic foot management pathway were both common and serious, contributing to negative health outcomes for patients with diabetic foot. Many complex factors related to patient's poor patient health-seeking behaviors, health system, and clinical characteristics of diabetic foot are responsible for these delays. Therefore, it is necessary to develop new strategies for standard referral practices and strengthen patient awareness of seeking care.

Asynchronous sampled-data control for connected vehicles with heterogeneous delays.

This study investigates an asynchronous sampled-data control problem of vehicular platoons, with heterogeneous sampling, subjected to actuator delays. Without a synchronized clock, a completely asynchronous sampled-data controller is designed for each follower, where the state of the ith follower itself and its neighboring vehicles are sampled at their own sampling time instants. The caused closed-loop tracking error dynamics for the entire platoon considering the effect of the nonuniform sampling time intervals, heterogeneous vehicle dynamics, inter-vehicle topology and heterogeneous time delays. To simplify the stability analysis and controller design, the tracking-error dynamics of the entire platoon are decomposed into individual subsystems with reduced-order dynamics. Based on Lyapunov stability theory, the optimal conditions are explored to design an asynchronous sampled-data controller to guarantee the desired stability performance. Moreover, the exact values for the maximum allowable sampling interval and time delay are calculated for each follower using the designed feedback controller gain. The proposed asynchronous sampled-data control method is extended to a vehicular platoon using an event-based sampling scheme. Numerical examples are used to verify the effectiveness of the proposed sampled-data control method.

Asynchronous adaptive event-triggered fault detection for delayed Markov jump neural networks: A delay-variation-dependent approach.

This paper presents a delay-variation-dependent approach to fault detection of a discrete-time Markov jump neural network (MJNN) with a time-varying delay and mismatched modes. The goal is to detect the potential fault of delayed MJNNs by constructing an appropriate adaptive event-triggered and asynchronous H∞ filter. By choosing a delay-product-type Lyapunov-Krasovskii (L-K) functional with a delay-dependent matrix and exploiting some matrix polynomial inequalities, bounded real lemmas (BRLs) are obtained on the existence of suitable adaptive event generator and filters. These BRLs are dependent not only on the delay bounds but also on the delay variation rate. Simulation results are given to show the validity of the proposed theoretical method.

Adaptive guaranteed cost control for nonlinear systems with unknown parameters and time delays based on fully actuated system approaches.

This paper investigates the adaptive guaranteed cost stabilization (AGCS) problems for two classes of high-order nonlinear systems with unknown parameters (vector) and time delays. Firstly, based on the high-order fully actuated (HOFA) system approaches, the Lyapunov-Krasovskii functional (LKF) and the guaranteed cost control (GCC), a new AGCS strategy is proposed for HOFA nonlinear system with unknown parameter vector and time delays. Then, based on the above result, another AGCS controller for a class of strict-feedback systems (SFSs) with unknown parameters and time delays is obtained. Two designed controllers ensure that all of the states of two closed-loop systems are global boundedness, and preset arbitrarily the upper bound of cost functions (UBCFs) characterizing the output performance. More importantly, the UBCFs are independent of system initial values, unknown parameters (vector), and even time delays, which is difficult to achieve by using existing control methods. To do this, this paper introduces a local smooth nonlinear function (LSNF), and gives its corresponding lemma, which provide an important mathematical tool. Finally, three simulation examples, including an application in the electromechanical system, are given to prove the effectiveness and the practicability of our proposed control method.

Stochastic tumor-immune interaction model with external treatments and time delays: An optimal control problem.

Herein, we discuss an optimal control problem (OC-P) of a stochastic delay differential model to describe the dynamics of tumor-immune interactions under stochastic white noises and external treatments. The required criteria for the existence of an ergodic stationary distribution and possible extinction of tumors are obtained through Lyapunov functional theory. A stochastic optimality system is developed to reduce tumor cells using some control variables. The study found that combining white noises and time delays greatly affected the dynamics of the tumor-immune interaction model. Based on numerical results, it can be shown which variables are optimal for controlling tumor growth and which controls are effective for reducing tumor growth. With some conditions, white noise reduces tumor cell growth in the optimality problem. Some numerical simulations are conducted to validate the main results.

Almost Surely Exponential Convergence Analysis of Time Delayed Uncertain Cellular Neural Networks Driven by Liu Process via Lyapunov-Krasovskii Functional Approach.

As with probability theory, uncertainty theory has been developed, in recent years, to portray indeterminacy phenomena in various application scenarios. We are concerned, in this paper, with the convergence property of state trajectories to equilibrium states (or fixed points) of time delayed uncertain cellular neural networks driven by the Liu process. By applying the classical Banach's fixed-point theorem, we prove, under certain conditions, that the delayed uncertain cellular neural networks, concerned in this paper, have unique equilibrium states (or fixed points). By carefully designing a certain Lyapunov-Krasovskii functional, we provide a convergence criterion, for state trajectories of our concerned uncertain cellular neural networks, based on our developed Lyapunov-Krasovskii functional. We demonstrate under our proposed convergence criterion that the existing equilibrium states (or fixed points) are exponentially stable almost surely, or equivalently that state trajectories converge exponentially to equilibrium states (or fixed points) almost surely. We also provide an example to illustrate graphically and numerically that our theoretical results are all valid. There seem to be rare results concerning the stability of equilibrium states (or fixed points) of neural networks driven by uncertain processes, and our study in this paper would provide some new research clues in this direction. The conservatism of the main criterion obtained in this paper is reduced by introducing quite general positive definite matrices in our designed Lyapunov-Krasovskii functional.

Stability and synchronization for complex-valued neural networks with stochastic parameters and mixed time delays.

In this paper, a class of complex-valued neural networks (CVNNs) with stochastic parameters and mixed time delays are proposed. The random fluctuation of system parameters is considered in order to describe the implementation of CVNNs more practically. Mixed time delays including distributed delays and time-varying delays are also taken into account in order to reflect the influence of network loads and communication constraints. Firstly, the stability problem is investigated for the CVNNs. In virtue of Lyapunov stability theory, a sufficient condition is deduced to ensure that CVNNs are asymptotically stable in the mean square. Then, for an array of coupled identical CVNNs with stochastic parameters and mixed time delays, synchronization issue is investigated. A set of matrix inequalities are obtained by using Lyapunov stability theory and Kronecker product and if these matrix inequalities are feasible, the addressed CVNNs are synchronized. Finally, the effectiveness of the obtained theoretical results is demonstrated by two numerical examples.

Perceived patient safety culture and its associated factors among clinical managers of tertiary hospitals: a cross-sectional survey.

BACKGROUND: Patient safety is a global challenge influenced by perceived patient safety culture. However, limited knowledge exists regarding the patient safety culture perceived by hospital clinical managers and its associated factors. This study aims to investigate the perceptions of patient safety culture and associated factors among clinical managers of tertiary hospitals in China. METHODS: A cross-sectional survey was conducted from June 19 to July 16, 2021, involving 539 clinical managers from four tertiary hospitals in Changsha City of Hunan Province. The Hospital Survey on Patient Safety Culture (HSOPSC) was utilized to assess perceived patient safety culture. Bivariate, multivariable linear regression, and logistic regression analyses were performed. RESULTS: The mean score for the total HSOPSC was 72.5 ± 7.6, with dimensional scores ranging from 62.1 (14.9) to 86.6 (11.7). Three dimensions exhibited positive response rates (PRRs) < 50%, indicating areas that need to be improved: "nonpunitive response to errors" (40.5%), "staffing" (41.9%), and "frequency of events reported" (47.4%). Specialized hospitals (ß = 1.744, P = 0.037), female gender (ß = 2.496, P = 0.003), higher professional title (ß = 1.413, P = 0.049), a higher education level (ß = 1.316, P = 0.001), and shorter time delays per shift (ß=-1.13, P < 0.001) were correlated with higher perceived patient safety culture. Education level, work department, "teamwork within a unit", "management support for patient safety", "communication openness", and "staffing" dimensions were associated with patient safety grades (all P < 0.05). Years worked in hospitals, occupation, education level, work department, hospital nature, professional title, "communication openness", and "handoffs & transitions" were associated with the number of adverse events reported (all P < 0.05). CONCLUSIONS: Our study revealed a generally low level of patient safety culture perceived by clinical managers and identified priority areas requiring urgent improvement. The associated factors of patient safety culture provide important guidance for the development of targeted interventions in the future. Promoting patient safety by optimizing the patient safety culture perceived by clinical managers should be prioritized.

Fixed-time adaptive dynamic event-triggered control of flexible-joint robots with prescribed performance and time delays.

In this study, a dynamic event-triggered control strategy was proposed for n-link flexible-joint robots with prescribed tracking performance and time delays. First, an adaptive fixed-time filter was designed to prevent "differential explosion", and a given-time prescribed performance method was introduced. Then, an auxiliary system and Lyapunov-Krasovskii functionals were designed to compensate for input and full-state delays. After that, neural networks were introduced to handle the unknown dynamics and a dynamic event-triggered controller was designed. The closed-loop system was demonstrated fixed-time stability without Zeno behaviors. Finally, simulations were presented to confirm the effectiveness of the proposed scheme.

Dynamic Reconfiguration of Dominant Intrinsic Coupling Modes in Elderly at Prodromal Alzheimer's Disease Risk.

Large-scale human brain networks interact across both spatial and temporal scales. Especially for electro- and magnetoencephalography (EEG/MEG), there are many evidences that there is a synergy of different subnetworks that oscillate on a dominant frequency within a quasi-stable brain temporal frame. Intrinsic cortical-level integration reflects the reorganization of functional brain networks that support a compensation mechanism for cognitive decline. Here, a computerized intervention integrating different functions of the medial temporal lobes, namely, object-level and scene-level representations, was conducted. One hundred fifty-eight patients with mild cognitive impairment underwent 90 min of training per day over 10 weeks. An active control (AC) group of 50 subjects was exposed to documentaries, and a passive control group of 55 subjects did not engage in any activity. Following a dynamic functional source connectivity analysis, the dynamic reconfiguration of intra- and cross-frequency coupling mechanisms before and after the intervention was revealed. After the neuropsychological and resting state electroencephalography evaluation, the ratio of inter versus intra-frequency coupling modes and also the contribution of ß1 frequency was higher for the target group compared to its pre-intervention period. These frequency-dependent contributions were linked to neuropsychological estimates that were improved due to intervention. Additionally, the time-delays of the cortical interactions were improved in {δ, θ, α2, ß1} compared to the pre-intervention period. Finally, dynamic networks of the target group further improved their efficiency over the total cost of the network. This is the first study that revealed a dynamic reconfiguration of intrinsic coupling modes and an improvement of time-delays due to a target intervention protocol.

Dynamics of a cross-superdiffusive SIRS model with delay effects in transmission and treatment.

We investigate the dynamics of a SIRS epidemiological model taking into account cross-superdiffusion and delays in transmission, Beddington-DeAngelis incidence rate and Holling type II treatment. The superdiffusion is induced by inter-country and inter-urban exchange. The linear stability analysis for the steady-state solutions is performed, and the basic reproductive number is calculated. The sensitivity analysis of the basic reproductive number is presented, and we show that some parameters strongly influence the dynamics of the system. A bifurcation analysis to determine the direction and stability of the model is carried out using the normal form and center manifold theorem. The results reveal a proportionality between the transmission delay and the diffusion rate. The numerical results show the formation of patterns in the model, and their epidemiological implications are discussed.

Fixed-deviation stabilization and synchronization for delayed fractional-order complex-valued neural networks.

In this paper, we study fixed-deviation stabilization and synchronization for fractional-order complex-valued neural networks with delays. By applying fractional calculus and fixed-deviation stability theory, sufficient conditions are given to ensure the fixed-deviation stabilization and synchronization for fractional-order complex-valued neural networks under the linear discontinuous controller. Finally, two simulation examples are presented to show the validity of theoretical results.

Quasi-projective and complete synchronization of discrete-time fractional-order delayed neural networks.

This paper presents new theoretical results on quasi-projective synchronization (Q-PS) and complete synchronization (CS) of one kind of discrete-time fractional-order delayed neural networks (DFDNNs). At first, three new fractional difference inequalities for exploring the upper bound of quasi-synchronization error and adaptive synchronization are established by dint of Laplace transform and properties of discrete Mittag-Leffler function, which vastly expand a number of available results. Furthermore, two controllers are designed including nonlinear controller and adaptive controller. And on the basis of Lyapunov method, the aforementioned inequalities and properties of fractional-order difference operators, some sufficient synchronization criteria of DFDNNs are derived. Because of the above controllers, synchronization criteria in this paper are less conservative. At last, numerical examples are carried out to illustrate the usefulness of theoretical upshots.

Dynamic behavior analysis of an SVIR epidemic model with two time delays associated with the COVID-19 booster vaccination time.

Since the outbreak of COVID-19, there has been widespread concern in the community, especially on the recent heated debate about when to get the booster vaccination. In order to explore the optimal time for receiving booster shots, here we construct an SVIR model with two time delays based on temporary immunity. Second, we theoretically analyze the existence and stability of equilibrium and further study the dynamic properties of Hopf bifurcation. Then, the statistical analysis is conducted to obtain two groups of parameters based on the official data, and numerical simulations are carried out to verify the theoretical analysis. As a result, we find that the equilibrium is locally asymptotically stable when the booster vaccination time is within the critical value. Moreover, the results of the simulations also exhibit globally stable properties, which might be more beneficial for controlling the outbreak. Finally, we propose the optimal time of booster vaccination and predict when the outbreak can be effectively controlled.

Distributed neurodynamic optimization for multi-energy management with time-varying external disturbances considering time-varying emission limitations and load demand in multi-microgrid.

An emerging time-varying distributed multi-energy management problem (MEMP) considering time-varying load and emission limitations for resisting time-varying external disturbances and communication time delays in the multi-microgrid (MMG) system is investigated. Each microgrid (MG) contains some smaller microgrids (SMGs), which are connected by energy routers (ERs) of the system and can monitor energy in real-time with each other. In addition, a time-varying multi-energy management optimization model (MEMOM) is proposed in this paper in order to minimize the total cost of the MEMP which considers environmental cost, renewable energy cost and fuel cost. Furthermore, time-varying distributed neurodynamic optimization algorithms are proposed for solving the above MEMP based on consensus theory and sliding mode control technique. Compared with the optimization algorithms which consist of symbolic functions proposed in traditional energy management problems, algorithms consisting of hyperbolic tangent functions proposed in this paper can effectively reduce the oscillation of the algorithms and improve the stability of algorithms. Furthermore, the algorithm can converge the optimal trajectory of optimization problems with time-varying external disturbances and communication time delays. Meanwhile, the stability and convergence of the algorithms are proved theoretically by constructing appropriate Lyapunov functions. Finally, the performance evaluation results of numerical simulations show that the proposed algorithms can efficiently handle energy trading under time-varying load and maintain excellent stability with time-varying external disturbances and communication time delays.