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.

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.

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.

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.

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.

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.

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.

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.

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.

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.