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In view of the facts in the infection and propagation of COVID-19, a stochastic reaction–diffusion epidemic model is presented to analyse and control this infectious diseases. Stationary distribution and Turing instability of this model are discussed for deriving the sufficient criteria for the persistence and extinction of disease. Furthermore, the amplitude equations are derived by using Taylor series expansion and weakly nonlinear analysis, and selection of Turing patterns for this model can be determined. In addition, the optimal quarantine control problem for reducing control cost is studied, and the differences between the two models are compared. By applying the optimal control theory, the existence and uniqueness of the optimal control and the optimal solution are obtained. Finally, these results are verified and illustrated by numerical simulation.

Infectious diseases have for centuries been the leading causes of death and disability worldwide and the environmental fluctuation is a crucial part of an ecosystem in the natural world. In this paper, we proposed and discussed a stochastic SIRI epidemic model incorporating double saturated incidence rates and relapse. The dynamical properties of the model were analyzed. The existence and uniqueness of a global positive solution were proven. Sufficient conditions were derived to guarantee the extinction and persistence in mean of the epidemic model. Additionally, ergodic stationary distribution of the stochastic SIRI model was discussed. Our results indicated that the intensity of relapse and stochastic perturbations greatly affected the dynamics of epidemic systems and if the random fluctuations were large enough, the disease could be accelerated to extinction while the stronger relapse rate were detrimental to the control of the disease.

Time delay, where it depends on the current state and on the past situation, is often occurred in biological activities, for example, the process by which microorganism consume nutrients into their available biomass is not instantaneous. This investigation inspects the dynamic behavior of stochastic turbidostat model coupled with distributed delay and degenerate diffusion, including sufficient conditions of the extinction and the existence of a unique stationary distribution. What’s more, the existence and uniqueness of globally positive equilibrium of the exploited model are studied. The findings manifest that the turbidostat system is ergodic only when the intensity of white noise is very small. Finally, some numerical examples are proposed to indicate the validity of the theoretical results.

This paper concerns the estimation of stationary probability of ergodic semi-Markov chains based on an observation over a time interval. We derive asymptotic properties of the proposed estimator, when the time of observation goes to infinity, as consistency, asymptotic normality, law of iterated logarithm and rate of convergence in a functional setting. The proofs are based on asymptotic results on discrete-time semi-Markov random evolutions.

In this manuscript, a new approach of a stochastic predator-prey interaction with protection zone for the prey is developed and studied. The considered mathematical model consists of a system of two stochastic differential equations, SDEs, describing the interaction between the prey and predator populations where the prey exhibits a social behavior called also by “herd behavior.” First, according to the theory of the SDEs, some properties of the solution are obtained, including: the existence and uniqueness of the global positive solution and the stochastic boundedness of the solutions. Then, the sufficient conditions for the persistence in the mean and the extinction of the species are established, where the extinction criteria are discussed in two different cases, namely, the first case is the survival of the prey population, while the predator population goes extinct; the second case is the extinction of all prey and predator populations. Next, by constructing a suitable stochastic Lyapunov function and under certain parametric restrictions, it has been proved that the system has a unique stationary distribution which is ergodic. Finally, some numerical simulations based on the Milstein’s higher-order scheme are performed to illustrate the theoretical predictions.

The deterministic rate equation has been considered as an approximate description of the dynamics of the gene regulatory network, derived in the limit of vanishing stochastic fluctuations. When stochastic fluctuations are introduced, the stable fixed points of the deterministic equation usually turn into local maxima of the stationary distribution. Here, I consider a simple genetic regulatory network with a positive feedback and a cooperative binding, where the stationary distribution does not have a local minimum at a stable fixed point. I show that a transient peak appears at the stable fixed point instead, whose life-time grows exponentially as the stochastic fluctuation decreases. The result shows that the order of the limits is important for retrieving the deterministic dynamics.

In this work, we investigate some qualitative properties of a stochastic dynamical model for tuberculosis with case detection. Using appropriately formulated stochastic Lyapunov functions, we derive sufficient conditions for the existence (and uniqueness) of an ergodic stationary distribution of the positive solutions of the model, guaranteeing persistence of the disease in the presence of case detection. We also obtained conditions that will allow for the eradication of the disease from the population. Using numerical simulations, we were able to illustrate the analytical results obtained herein.

This paper considers the global exponential synchronization almost surely (GES a.s.) in an array of two-dimensional (2-D) discrete-time systems with Markovian jump topology. Considering the fact that external disturbances are inevitable and communication resources are limited, mode-dependent quantized output control with actuator fault (AF) is designed. Sufficient conditions formulated by linear matrix inequalities (LMIs) are given to ensure the GES a.s. Control gains of the controller without AF is designed by solving the LMIs. It is shown that the stationary distribution of the transition probability of the Markov chain plays an important role in our study, which makes it possible that some of the modes are not controlled. Numerical examples are given to illustrate the effectiveness of theoretical analysis.

This paper considers a stochastic chemostat model with degenerate diffusion. Firstly, the Markov semigroup theory is used to establish sufficient criteria for the existence of a unique stable stationary distribution. The authors show that the densities of the distributions of the solutions can converge in L1 to an invariant density. Then, conditions are obtained to guarantee the washout of the microorganism. Furthermore, through solving the corresponding Fokker-Planck equation, the authors give the exact expression of density function around the positive equilibrium of deterministic system. Finally, numerical simulations are performed to illustrate the theoretical results.

In this paper, we study the dynamics of stochastic time-delayed predator–prey model with modified Leslie–Gower and ratio-dependent schemes including additional food for predator and prey with Michaelis–Menten type harvest. We introduce the effects of time delay, Michaelis–Menten type harvest and stochastic perturbation under the structure of the original model to make the model more consistent with the actual system. We first prove that the system has a globally unique positive solution. Secondly we obtain conditions for the persistence in mean and extinction of the system. Besides, we verify that the system is stochastic permanence under certain conditions. In addition to that, we prove that the system has an ergodic stationary distribution when the parameters satisfy certain conditions. Finally, some numerical simulations were performed to verify the correctness and validity of the theoretical results.

A stochastic waterborne disease model is analyzed to reveal the effects of the white noise and telegraph noise. For this stochastic model, a critical quantity $$R_0^s$$ is derived, which depends on not only model parameters but also environmental noises. If $$R_0^s>1$$ , then this model admits an ergodic stationary distribution. One of the most important contributions is the suitable Lyapunov function depending on the state of Markov chains is constructed. Theoretical results and numerical simulations provide a clear insight of the impact of environmental noises on the dynamical behavior of the model.

Recently, considering the temporary immunity of individuals who have recovered from certain infectious diseases, Liu et al. (Phys A Stat Mech Appl 551:124152, 2020) proposed and studied a stochastic susceptible-infected-recovered-susceptible model with logistic growth. For a more realistic situation, the effects of quarantine strategies and stochasticity should be taken into account. Hence, our paper focuses on a stochastic susceptible-infected-quarantined-recovered-susceptible epidemic model with temporary immunity. First, by means of the Khas’minskii theory and Lyapunov function approach, we construct a critical value \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathscr {R}}_0^S$$\end{document}R0S corresponding to the basic reproduction number \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathscr {R}}_0$$\end{document}R0 of the deterministic system. Moreover, we prove that there is a unique ergodic stationary distribution if \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathscr {R}}_0^S>1$$\end{document}R0S>1. Focusing on the results of Zhou et al. (Chaos Soliton Fractals 137:109865, 2020), we develop some suitable solving theories for the general four-dimensional Fokker–Planck equation. The key aim of the present study is to obtain the explicit density function expression of the stationary distribution under \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathscr {R}}_0^S>1$$\end{document}R0S>1. It should be noted that the existence of an ergodic stationary distribution together with the unique exact probability density function can reveal all the dynamical properties of disease persistence in both epidemiological and statistical aspects. Next, some numerical simulations together with parameter analyses are shown to support our theoretical results. Last, through comparison with other articles, results are discussed and the main conclusions are highlighted.

In this paper we introduce the concept of a new metric-type space named as extended JS-generalized metric space and establish some Banach-type fixed point theorems on such space. We also discuss about the topology of such spaces and prove a theorem like Cantor’s intersection theorem therein. Some examples are given in strengthening the hypothesis of our theorems. Moreover our fixed point result is applied to Markov process for finding a stationary distribution.

In this contribution, two delayed commensalism models with noise coupling and interval biological parameters are constructed, in which the positive and negative intrinsic growth rates of commensal species are respectively considered. Two sets of sufficient conditions for the persistence and extinction of each species are obtained as well as the existence of a unique ergodic stationary distribution and three pathwise estimations of the solutions. Meticulous numerical simulations are performed to validate our theoretical analysis in detail. The effects of white noises, imprecise biological parameters and delay on the above long-time behaviors are also revealed.

In this paper, we investigate a stochastic multi-stage model to evaluate the influence of treatment intensification with the integrase inhibitor raltegravir on viral load and 2-LTR dynamics in HIV patients under suppressive therapy. Firstly, it is proven that the model has a unique global positive solution. Secondly, by constructing a Lyapunov function, we establish sufficient conditions for the existence of a unique ergodic stationary distribution if $$R_{0}^{S}>1$$ . Thirdly, we obtain sufficient criterions $$R_{0}^{s}

The noise-induced transition and stochastic resonance (SR) phenomenon of Brusselator system under the excitation of Levy noise is investigated in this paper. The Janicki–Weron algorithm is used to simulate Levy noise, and the stationary probability density is obtained by solving the system equation through the fourth-order stochastic Runge–Kutta algorithm. The influences of the noise intensity, stability index and skewness parameter on the stationary probability density are analyzed. The numerical results imply that the stability index, skewness parameter and noise intensity can induce the transition. Moreover, the effects of the stability index and skewness parameter of Levy noise, as well as the amplitude of external signal on the signal-to-noise ratio are discussed. The research results show that the larger stability index, skewness parameter and signal amplitude are beneficial to the occurrence of SR.

The stochastic resonance (SR) phenomenon for an underdamped monostable system with multiplicative and additive noise is investigated. The expression for the stationary probability density is obtained under the condition of the detailed balance and weak noise. The signal-to-noise ratio (SNR) for the monostable system is derived based on two-state theory. The result shows that the SR phenomenon can be observed when the SNR varies with the intensities of the multiplicative and additive white noise, as well as varies with the amplitude of the additive dichotomous noise. One resonance peak can be found when the SNR changes with the damping coefficient and with other system parameters.

Machine learning models assume that data is drawn from a stationary distribution. However, in practice, challenges are imposed on models that need to make sense of fast-evolving data streams, where the content of data is changing and evolving over time. This change between the distributions of training data seen so-far and the distribution of newly coming data is called concept drift. It is of utmost importance to detect concept drifts to maintain the accuracy and reliability of online classifiers. Reactive drift detectors monitor the performance of the underlying machine learning model. That is, to detect a drift, feedback on the classifier output has to be given to the drift detector, known as prequential evaluation. In many real-life scenarios, immediate feedback on classifier output is not possible. Thus, drift detection is delayed and gets out of context. Moreover, the drift detector output is in the form of a binary answer if there is a drift or not. However, it is equally important to explain the source of drift. In this paper, we present the Statistical Drift Detection Method (SDDM) which can detect drifts by monitoring the change of data distribution without the need for feedback on classifier output. Moreover, the detection is quantified and the source of drift is identified. We empirically evaluate our method against the state-of-the-art on both synthetic and real life data sets. SDDM outperforms other related approaches by producing a smaller number of false positives and false negatives.

Numerous studies have demonstrated the important role of noise in the dynamical behavior of a complex system. This work is devoted to investigating the constructive role of combined feedbacks and recycling random excitation on stochastic bifurcations and its regularization in a birhythmic oscillator. This possesses a bistability mode with the coexistence of two stable limit cycles in the deterministic case. However, so far, these combining effects on the birhythmic dynamic system have not been reported. Relying on the approximate methods, the harmonic approximation is adopted to drive the delay self-control feedback to state variables without delay noise. The dynamical behavior of the model is analyzed analytically using Hopf bifurcation theorem and numerically such as two-dimensional bifurcation diagrams with respect to two feedback parameters are obtained. Then, Fokker–Planck–Kolmogorov (FPK) equation and stationary probability density function (PDF) for amplitude are obtained by applying the stochastic averaging method. Based on these results, the influence of related parameters of recycling noise and damping coefficient on the stochastic P-bifurcation is studied.

We analyze a multi-processor two-stage tandem call center retrial queueing network in which the processors are subject to active breakdowns and repairs at stage-I. A level-dependent quasi-birth-and-death (LDQBD) process is formulated and a sufficient condition for ergodicity of the system is discussed. Under the stability condition, the stationary distribution of the number of calls in the system, the mean number of calls in the orbit, the mean waiting time of calls in the orbit and the mean busy period of the system along with other descriptors of the system are determined by using the matrix-analytic techniques. Besides, the availability analysis of the processors is also studied. Further, we have also discussed the characteristics of the first-passage time to reach the orbit critical level, the number of calls served in the call center during this period and their corresponding moments. Finally, extensive numerical results are presented to highlight the impact of the system parameters on the performance measures of the two-stage tandem call center retrial queueing system under investigation.

We consider a Markov Chain in which the state space is partitioned into sets where both transitions within sets and between sets have a special structure. Transitions within each set constitute a finite level dependent quasi-birth-and-death-process (LDQBD), and transitions between sets are restricted to six types of transitions. These latter types are needed to preserve the sets structure in the reduction step of our algorithm. Specifically, we present a successive censoring algorithm, based on matrix analytic methods, to obtain the stationary distribution of this system of connected LDQBD-processes.

The mathematical model with time delay is often more practical because it is subject to current and past state. What remains unclear are the details, such as how time delay and sudden environmental changes influence the dynamic behavior of systems. The purpose of this paper is to analyze the long-time behavior of a stochastic Nicholson’s blowflies model, which includes distributed delay and degenerate diffusion. The application of the Markov semigroups theory is to prove that there exists a unique stationary distribution. What’s more, the expression of probability density function around the unique positive equilibrium of the deterministic model is briefly described under a certain condition. The results of this paper can be used to find that the weaker white noise can guarantee the existence of a unique stationary distribution and the stronger mortality rate can cause the extinction of Nicholson’s blowflies. Some numerical examples are also given to explain the effect of the white noise.

We propose a class of evolutionary models that involves an arbitrary exchangeable process as the breeding process and different selection schemes. In those models, a new genome is born according to the breeding process, and then a genome is removed according to the selection scheme that involves fitness. Thus the population size remains constant. The process evolves according to a Markov chain, and, unlike in many other existing models, the stationary distribution -- so called mutation-selection equilibrium -- can be easily found and studied. The behaviour of the stationary distribution when the population size increases is our main object of interest. Several phase-transition theorems are proved., Comment: 38 pages, 5 figures

This paper is concerned with the dynamical behaviors of a model of syphilis transmission disturbed by both white noises and telegraph noises. Multiple infections and treatment stages are considered, which include and extend the existing ones. The existence and ergodicity of the stationary distribution are obtained by constructing a suitable Lyapunov function, which determines a critical value $$R_0^*$$ corresponding to the control reproduction number $$R_c$$ of the corresponding determined system. In addition, a sufficient criteria for extinction of the diseases is derived. Finally, the numerical simulations illustrate our theoretical results, which show that, the stronger white noises can result in the extinction of the diseases and telegraph noises can strength the stability of the system.

In this paper, a tristable viscoelastic isolation system with stochastic excitation under both displacement and velocity delayed feedback control is considered. Firstly, the theoretical expressions of the mean first-passage time (MFPT) to measure the activated escape between different potential wells are derived. Induced nonlinear transition dynamics due to the noise and time delays are mainly discussed. It is found that the delay-induced behaviours affect the transitions between the equilibrium points of the system, the corresponding phenomenon of the delay-enhanced stability is observed. In this respect, the existence of the maxima of the MFPT1 and the MFPT2 is found in one period. Meanwhile, the MFPT1 and the MFPT2 show the monotonic behaviour with the increase of the noise intensity. Additionally, the stationary probability density of the amplitude and the stationary mean amplitude are derived. The influence regimes of the system parameters on both stationary probability density of the amplitude and the stationary mean amplitude are explored. This paper establishes the relationship between system parameters and dynamical properties of the tristable viscoelastic isolation system. This provides a fundamental guidance for the optimization of the viscoelastic isolation by utilizing the technique of delayed feedback control.

In this paper, a stochastic multi-group AIDS model with saturated incidence rate is studied. We prove that the system is persistent in the mean under some parametric restrictions. We also obtain the sufficient condition for the existence of the ergodic stationary distribution of the system by constructing a suitable Lyapunov function. Our results indicate that the existence of ergodic stationary distribution does not rely on the interior equilibrium of the corresponding deterministic system, which greatly improves upon previous results.

This work is concerned with a novel stochastic competitive model with saturation effect and distributed delay, in which two coupling noise sources are incorporated and the interspecific competition delayed terms show saturation effect. A good understanding of exponential extinction, extinction, persistence in the mean and permanence in time average of two species are gained. Also, with the help of Lyapunov function and the global attraction of positive solution, we derive the existence and uniqueness of stationary distribution. Our main results reveal that the coupling noise sources can significantly change the survival results of two species and affect the existence of a unique stationary distribution.

With the rapid development of technology, the issues about blockchain are getting more and more attention from scholars. In order to study the operation of the blockchain system, this paper simulated the transaction confirmation process of a blockchain system with two miners. Combining with practice, factors such as adverse transactions, impatience phenomena of transactions, fault repairable conditions of the mining process and a spare miner are considered. An M/(M $$_1$$ ,M $$_2$$ )/1 vacation queueing model with negative customers, impatient customers, optional services, repairable faults and a spare server is established. The stationary distribution of the system is obtained by using the matrix-geometric solution method, and expressions of the average confirmation time of transactions and other performance indexes are given. The influence of each parameter on the performance indexes of blockchain system is analyzed by using MATLAB software. Revenue function and equilibrium sleep rate of miners are discussed for optimizing the blockchain system.

Van der Pol–Duffing oscillator, which can be used a model for many dynamical system, has been widely concerned. However, most of the systems by scholars are either stable steady states or limit cycles. Here, the self-sustained oscillator with the coexistence of steady state and limit cycles, which is famous for describing the flutter of airfoils with large span ratio in low-speed wind tunnels, is treated in this paper. Using the energy balance method, the deterministic bifurcation of the tristable system with time-delay feedback is investigated. The presence of time-delay feedback expands the bifurcation range of the parameters, making the bifurcation phenomenon more abundant. In addition, according to the stationary probability density function obtained by the stochastic averaging method, stochastic bifurcation of the system with time-delay feedback and noise is explored theoretically. The numerical results confirm the correctness of the theoretical analysis. Transition between the unimodal structure, the bimodal structure and the trimodal structure is found. Many rich bifurcations are available by adjusting the time-delay and noise intensity, which may be conductive to achieve the desired phenomenon in the real-world application.

We consider a (Q, r) perishable inventory system with state-dependent compound Poisson demands with a random batch size, general lead times, exponential shelf times, and lost sales. We assume $$r

Modern authors have identified a variety of striking economic patterns, most importantly those involving the distribution of incomes and profit rates. In recent times, the econophysics literature has demonstrated that bottom incomes follow an exponential distribution, top incomes follow a Pareto, and profit rates display a tent-shaped distribution. This paper is concerned with the theory underlying various explanations of these phenomena Traditional econophysics relies on energy-conserving “particle collision” models in which simulation is often used to derive a stationary distribution Those in the Jaynesian tradition rely on entropy maximization, subject to certain constraints, to infer the final distribution. This paper argues that economic phenomena should be derived as results of explicit economic processes For instance, the entry and exit process motivated by supply decisions of firms underlies the drift-diffusion form of wage, interest and profit rates arbitrage. These processes give rise to stationary distributions that turn out to be also entropy maximizing. In the arbitrage approach, entropy maximization is a result. In the Jaynesian approach, entropy maximization is the means.

This article studies a predator–prey model with modified Leslie–Gower and Holling type II schemes under white-noise disturbances. The sensitivity analysis of the considered ecosystem is performed to investigate the uncertainty of the model. Based on the theory of the Markov diffusion process, we propose a numerical technique to study the stochastic dynamic behaviors of the ecosystem. We reveal the effects of the excitation intensities, the system parameters, the original states and the critical states on the stationary probability density functions and the first-passage failure. Through Monte Carlo simulations, we verify the accuracy, the efficiency and the applicability of the proposed methodology.

In the microorganism cultivation process, delay and stochastic perturbations are inevitably accompanied, which results in complicated dynamical behaviors for microorganisms. In this paper, a mathematical model with discrete delay and random perturbation is constructed to understand how the dynamics of microorganisms in the turbidostat can be characterized. The existence, uniqueness and boundedness of the positive solution are first determined for the mathematical model. Furthermore, sufficient conditions for microorganism extinction and permanence in the turbidostat are obtained with the theory of stochastic differential equations. The system has the stationary distribution under a low-level intensity of stochastic perturbation from the environment; that is, microorganism in the turbidostat is persistent fluctuating around a positive value. On the contrary, microorganisms will be extinct with a strong enough intensity of noise. Several numerical simulations are applied to validate the theoretical results for the dynamics of the system.

In this work, we focus on the stationary analysis of a specific class of continuous time Markov-modulated reflected random walks in the quarter plane with applications in the modelling of two-node Markov-modulated queueing networks with coupled queues. The transition rates of the two-dimensional process depend on the state of a finite state Markovian background process. Such a modulation is space homogeneous in the set of inner states of the two-dimensional lattice but may be different in the set of states at its boundaries. To obtain the stationary distribution, we apply the power series approximation method, and the theory of Riemann boundary value problems. We also obtain explicit expressions for the first moments of the stationary distribution under some symmetry assumptions. An application in the modelling of a priority retrial system with coupled orbit queues is also presented. Using a queueing network example, we numerically validated the theoretical findings.

The stationary probability density function (PDF) solution of a variable-mass system is calculated under Gaussian white noises and Poisson white noises, respectively. For small mass disturbance, the corresponding Fokker–Planck–Kolmogorov equation and Kolmogorov–Feller equation of the system are derived. The solution procedure based on the exponential–polynomial closure (EPC) method is formulated to obtain and study the probabilistic solutions of the strongly nonlinear variable-mass system subjected to Gaussian white noises and Poisson white noises. Both odd and even nonlinear variable-mass systems are considered. Compared with Monte Carlo simulation results, good agreement is achieved with the EPC method in the case of sixth-order polynomial. For large mass disturbance, the PDFs and logarithmic PDFs of displacement and velocity are numerically calculated via the fourth-order Runge–Kutta algorithm.

Analytical and numerical studies of multi-degree-of-freedom (MDOF) nonlinear stochastic or deterministic dynamic systems have long been a technical challenge. This paper presents a highly-efficient method for determining the stationary probability density functions (PDFs) of MDOF nonlinear systems subjected to both additive and multiplicative Gaussian white noises. The proposed method takes advantages of the sufficient conditions of the reduced Fokker-Planck-Kolmogorov (FPK) equation when constructing the trial solution. The assumed solution consists of the analytically constructed trial solutions satisfying the sufficient conditions and an exponential polynomial of the state variables, and delivers a high accuracy of the solution because the analytically constructed trial solutions capture the main characteristics of the nonlinear system. We also make use of the concept from the data-science and propose a symbolic integration over a hypercube to replace the numerical integrations in higher-dimensional space, which has been regarded as the insurmountable difficulty in the classical method of weighted residuals or stochastic averaging for high-dimensional dynamic systems. Three illustrative examples of MDOF nonlinear systems are analyzed in detail. The accuracy of the numerical results is validated by comparison with the Monte Carlo simulation (MCS) or the available exact solution. Furthermore, we also show the substantial gain in the computational efficiency of the proposed method compared with the MCS.

Classification of a disease often depends on more than one test, and the tests can be interrelated. Under the incorrect assumption of independence, the test result using dependent biomarkers can lead to a conflicting disease classification. We develop a copula-based method for this purpose that takes dependency into account and leads to a unique decision. We first construct the joint probability distribution of the biomarkers considering Frank’s, Clayton’s and Gumbel’s copulas. We then develop the classification method and perform a comprehensive simulation. Using simulated data sets, we study the statistical properties of joint probability distributions and determine the joint threshold with maximum classification accuracy. Our simulation study results show that parameter estimates for the copula-based bivariate distributions are not biased. We observe that the thresholds for disease classification converge to a stationary distribution across different choices of copulas. We also observe that the classification accuracy decreases with the increasing value of the dependence parameter of the copulas. Finally, we illustrate our method with a real data example, where we identify the joint threshold of Apolipoprotein B to Apolipoprotein A1 ratio and total cholesterol to high-density lipoprotein ratio for the classification of myocardial infarction. We conclude, the copula-based method works well in identifying the joint threshold of two dependent biomarkers for an outcome classification. Our method is flexible and allows modeling broad classes of bivariate distributions that take dependency into account. The threshold may allow clinicians to classify uniquely individuals at risk of developing the disease and plan for early intervention.

A Markovian single-server queue is studied in an interactive random environment. The arrival and service rates of the queue depend on the environment, while the transition dynamics of the random environment depend on the queue length. We consider in detail two types of Markov random environments: a pure jump process and a reflected jump diffusion. In both cases, the joint dynamics are constructed so that the stationary distribution can be explicitly found in a simple form (weighted geometric). We also derive an explicit estimate for the exponential rate of convergence to the stationary distribution via coupling.

Stochasticity in gene expression can result in fluctuations in gene product levels. Recent experiments indicated that feedback regulation plays an important role in controlling the noise in gene expression. A quantitative understanding of the feedback effect on gene expression requires analysis of the corresponding stochastic model. However, for stochastic models of gene expression with general regulation functions, exact analytical results for gene product distributions have not been given so far. Here, we propose a technique to solve a generalized ON-OFF model of stochastic gene expression with arbitrary (positive or negative, linear or nonlinear) feedbacks including posttranscriptional or posttranslational regulation. The obtained results, which generalize results obtained previously, provide new insights into the role of feedback in regulating gene expression. The proposed analytical framework can easily be extended to analysis of more complex models of stochastic gene expression., This papar has been accepted for publication in SCIENCE CHINA Mathematics

We consider the regularity and ergodic properties of the Branching Collision Process with Immigration (BCIP) in this paper. We establish an easy checking sufficient condition under which the Feller minimal BCIP is honest. We provide some good conditions under which the Feller minimal BCIP is positive recurrent and then establish an analytic form of the generating function of the stationary distribution. The closely associated expected hitting times are also considered. Examples and numerical calculations are provided to illustrate our results.

A result of Ward and Glynn (Queueing Syst 50(4):371–400, 2005) asserts that the sequence of scaled offered waiting time processes of the $$GI/GI/1+GI$$ queue converges weakly to a reflected Ornstein–Uhlenbeck process (ROU) in the positive real line, as the traffic intensity approaches one. As a consequence, the stationary distribution of a ROU process, which is a truncated normal, should approximate the scaled stationary distribution of the offered waiting time in a $$GI/GI/1+GI$$ queue; however, no such result has been proved. We prove the aforementioned convergence, and the convergence of the moments, in heavy traffic, thus resolving a question left open in 2005. In comparison with Kingman’s classical result (Kingman in Proc Camb Philos Soc 57:902–904, 1961) showing that an exponential distribution approximates the scaled stationary offered waiting time distribution in a GI / GI / 1 queue in heavy traffic, our result confirms that the addition of customer abandonment has a non-trivial effect on the queue’s stationary behavior.

We compute explicitly the distribution of the point of closest reach to the origin in the path of any d-dimensional isotropic stable process, with d ≥ 2. Moreover, we develop a new radial excursion theory, from which we push the classical Blumenthal–Getoor–Ray identities for first entry/exit into a ball (cf. Blumenthal et al. Trans. Amer. Math. Soc., 99, 540–554 1961) into the more complex setting of n-tuple laws for overshoots and undershoots. We identify explicitly the stationary distribution of any d-dimensional isotropic stable process when reflected in its running radial supremum. Finally, for such processes, and as consequence of some of the analysis of the aforesaid, we provide a representation of the Wiener–Hopf factorisation of the MAP that underlies the stable process through the Lamperti–Kiu transform. Our analysis continues in the spirit of Kyprianou (Ann. Appl. Probab., 20(2), 522–564 2010) and Kyprianou et al. (2015) in that our methodology is largely based around treating stable processes as self-similar Markov processes and, accordingly, taking advantage of their Lamperti-Kiu decomposition.

The vibroimpact systems with bilateral barriers are often encountered in practice. However, the dynamics of the vibroimpact system with bilateral barriers is full of challenges. Few closed-form solutions were obtained. In this paper, we propose a novel method for random vibration analysis of single-degree-of-freedom (SDOF) vibroimpact systems with bilateral barriers under Gaussian white noise excitations. A periodic approximate transformation is employed to convert the equations of the motion to a continuous form. The probabilistic description of the system is subsequently defined through the corresponding Fokker-Planck-Kolmogorov (FPK) equation. The closed-form stationary probability density function (PDF) of the response is obtained by solving the reduced FPK equation and using the proposed iterative method of weighted residue together with the concepts of the circulatory probability flow and the potential probability flow. Finally, the versatility of the proposed approach is demonstrated by its application to two typical examples. Note that the solution obtained by using the proposed method can be used as the benchmark to examine the accuracy of approximate solutions obtained by other methods.

Consider a discrete–time, time–homogeneous Markov chain on states $$1, \ldots , n$$ whose transition matrix is irreducible. Denote the mean first passage times by $$m_{jk}, j,k=1,\ldots , n,$$ and stationary distribution vector entries by $$v_k, k=1, \ldots , n$$ . A result of Kemeny reveals that the quantity $$\sum _{k=1}^n m_{jk}v_k$$ , which is the expected number of steps needed to arrive at a randomly chosen destination state starting from state j, is–surprisingly–independent of the initial state j. In this note, we consider $$\sum _{k=1}^n m_{jk}v_k$$ from the perspective of algebraic combinatorics and provide an intuitive explanation for its independence on the initial state j. The all minors matrix tree theorem is the key tool employed.

This study examines the dynamic control of an M/M/2 machine repair system with R operating machines, S standbys, and two unreliable repairmen, wherein standbys are subject to switching failures. We apply the matrix analytic method to derive explicit expressions of the stationary probability distributions as well as matrix expressions of the system performance measures. We construct a cost model and determine the optimal dynamic operating policy to minimize the expected cost function per unit time. Sensitivity analysis is conducted using numerical examples. The results of sensitivity analysis indicate that the optimal thresholds and corresponding minimum expected cost increase as the number of operating machines or standbys increases. Moreover, the minimum expected cost is sensitive to the mean arrival and repair rates. The results provide managers with decision reference for productivity improvement and cost reduction.

In this paper we study gradient estimation for a network of nonlinear stochastic units known as the Little model. Many machine learning systems can be described as networks of homogeneous units, and the Little model is of a particularly general form, which includes as special cases several popular machine learning architectures. However, since a closed form solution for the stationary distribution is not known, gradient methods which work for similar models such as the Boltzmann machine or sigmoid belief network cannot be used. To address this we introduce a method to calculate derivatives for this system based on measure-valued differentiation and simultaneous perturbation. This extends previous works in which gradient estimation algorithm’s were presented for networks with restrictive features like symmetry or acyclic connectivity.

Unlike many particle systems, Activated Random Walk has nontrivial behavior even in one spatial dimension. We prove inner and outer bounds on the spread of n activated random walkers from a single source in Z. The inner bound involves a comparison with the stationary distribution of activated random walkers on a finite interval, while the outer bound involves a comparison with the stabilization of an infinite Bernoulli configuration of activated random walkers on Z., Comment: 26 pages, 5 figures

BackgroundCombinatorial drug therapy for complex diseases, such as HSV infection and cancers, has a more significant efficacy than single-drug treatment. However, one key challenge is how to effectively and efficiently determine the optimal concentrations of combinatorial drugs because the number of drug combinations increases exponentially with the types of drugs.ResultsIn this study, a searching method based on Markov chain is presented to optimize the combinatorial drug concentrations. In this method, the searching process of the optimal drug concentrations is converted into a Markov chain process with state variables representing all possible combinations of discretized drug concentrations. The transition probability matrix is updated by comparing the drug responses of the adjacent states in the network of the Markov chain and the drug concentration optimization is turned to seek the state with maximum value in the stationary distribution vector. Its performance is compared with five stochastic optimization algorithms as benchmark methods by simulation and biological experiments. Both simulation results and experimental data demonstrate that the Markov chain-based approach is more reliable and efficient in seeking global optimum than the benchmark algorithms. Furthermore, the Markov chain-based approach allows parallel implementation of all drug testing experiments, and largely reduces the times in the biological experiments.ConclusionThis article provides a versatile method for combinatorial drug screening, which is of great significance for clinical drug combination therapy.

In this paper, we propose a stochastic HIV-1 infection model with distributed delay and logistic growth. Firstly, we transfer the stochastic model with weak kernel case into an equivalent system through the linear chain technique. Then, we establish sufficient conditions for the existence of a stationary distribution of the model by constructing a suitable stochastic Lyapunov function. Moreover, we obtain sufficient criteria for extinction of the infected cells; that is, the uninfected cells are survival and the infected cells are extinct. Our results show that the smaller white noise can ensure the existence of a stationary distribution when the basic reproduction number $$R_{0}^{S}$$ of the stochastic system is bigger than one, while the larger white noise can lead to the extinction of the infected cells when the basic reproduction number $$R_{0}$$ of the deterministic system is smaller than one.

In this paper, we introduce two perturbations in the classical deterministic susceptible-infected-susceptible epidemic model with two correlated Brownian motions. We consider two perturbations in the deterministic SIS model and formulate the original model as a stochastic differential equation with two correlated Brownian motions for the number of infected population, based on the previous work from Gray et al. (SIAM J Appl Math 71(3):876–902, 2011) and Hening and Nguyen (J Math Biol 77:135–163, 2017. https://doi.org/10.1007/s00285-017-1192-8 ). Conditions for the solution to become extinction and persistence are then stated, followed by computer simulation to illustrate the results.