Steady-state Probability Distribution Research Articles

Growth, poverty trap and escape

The well-known Solow growth model is the workhorse model of the theory of economic growth, which studies capital accumulation in a model economy as a function of time with capital stock, labour and technology-based production as the basic ingredients. The capital is assumed to be in the form of manufacturing equipment and materials. Two important parameters of the model are: the saving fraction s of the output of a production function and the technology efficiency parameter A , appearing in the production function. The saved fraction of the output is fully invested in the generation of new capital and the rest is consumed. The capital stock also depreciates as a function of time due to the wearing out of old capital and the increase in the size of the labour population. We propose a stochastic Solow growth model assuming the saving fraction to be a sigmoidal function of the per capita capital kp . We derive analytically the steady state probability distribution P(kp) and demonstrate the existence of a poverty trap, of central concern in development economics. In a parameter regime, P(kp) is bimodal with the twin peaks corresponding to states of poverty and well-being, respectively. The associated potential landscape has two valleys with fluctuation-driven transitions between them. The mean exit times from the valleys are computed and one finds that the escape from a poverty trap is more favourable at higher values of A. We identify a critical value of Ac below (above) which the state of poverty (well-being) dominates and propose two early signatures of the regime shift occurring at Ac . The economic model, with conceptual foundations in nonlinear dynamics and statistical mechanics, shares universal features with dynamical models from diverse disciplines like ecology and cell biology.

Bi-objective optimization of a retrial model with synchronous working vacation interruption

ABSTRACT The present research considers a multiserver retrial model with synchronous working vacation, vacation interruption and impatience customers under a constant retrial policy. In such a system, as soon as no customer is in orbit at the completion of the service, all servers will go on vacation synchronously and also provide service for customers at a relatively low-rate service rate. The vacation will be interrupted as the low-rate service is completed and the system satisfies the administrative policy. When all servers are occupied, arriving customers may enter orbit or depart the system without service. Steady-state probability distribution has been derived in matrix-form. Some performance measures are assessed numerically. A bi-objective optimization analysis is performed using MATLAB software to minimize the expected system operating cost and the expected waiting time of customers in orbit. An application of the proposed analytical method is illustrated in the manufacturing system. This makes it possible to achieve an appropriate balance between system operating cost and service quality.

Inertial active harmonic particle with memory induced spreading by viscoelastic suspension.

We investigate the self-propulsion of an inertial active particle confined in a two-dimensional harmonic trap. The particle is suspended in a non-Newtonian or viscoelastic suspension with a friction kernel that decays exponentially with a time constant characterizing the memory timescale or transient elasticity of the medium. By solving the associated non-Markovian dynamics, we identify two regimes in parameter space distinguishing the oscillatory and non-oscillatory behavior of the particle motion. By simulating the particle trajectories and exactly calculating the steady-state probability distribution functions and mean square displacement; interestingly, we observe that with an increase in the memory time scale, the effective temperature of the environment increases. As a consequence, the particle becomes energetic and spread away from the center, covering larger space inside the confinement. On the other hand, with an increase in the duration of the activity, the particle becomes trapped by the harmonic confinement.

Multi-objective optimization of production system with staggered production

In the manufacturing industry, timely order fulfilment from diverse customers is important for operational profitability, given the constraints of limited production capacity. This paper proposes a novel queueing system for a production system, in which order processing times are categorised as regular or rush based on customer preference. The proposed queueing system employs variable service rates and service strategies in conjunction with staggered production to deal with heterogeneous arrivals. We compute the steady-state probability distribution and develop system performance indicators. The lower and upper bounds of the expected sojourn time for rush and regular orders are derived. Numerical results show that an appropriate switching policy can reduce rush orders by approximately 35% while regular orders increase by less than 10%, demonstrating significant improvement in order fulfilment management. We formulate a multi-objective optimisation problem and implement the NSGA-II algorithm to obtain Pareto optimal solutions. Finally, three regression models are proposed to determine the minimum cost and the associated optimal service rates, given the maximum acceptable value of the expected number of rush orders. The established models can explain at least 80% of the dataset of the optimal solutions and thus our numerical findings provide useful guidance for decision-makers and production managers.

Equilibrium analysis of the fluid model with two types of parallel customers and incomplete fault

Abstract This article considers the individual equilibrium behavior and socially optimal strategy in a fluid queue with two types of parallel customers and incomplete fault. Assume that the working state and the incomplete fault state appear alternately in the buffer. Different from the linear revenue and expenditure structure, an exponential utility function can be constructed to obtain the equilibrium balking thresholds in the fully observable case. Besides, the steady-state probability distribution and the corresponding expected social benefit are derived based on the renewal process and the standard theory of linear ordinary differential equations. Furthermore, a reasonable entrance fee strategy is discussed under the condition that the fluid accepts the globally optimal strategies. Finally, the effects of the diverse system parameters on the entrance fee and the expected social benefit are explicitly illustrated by numerical comparisons.

Steady-State Supersaturation Distributions for Clouds under Turbulent Forcing

Abstract The supersaturation equation for a vertically moving adiabatic cloud parcel is analyzed. The effects of turbulent updrafts are incorporated in the shape of a stochastic Lagrangian model, with spatial and time correlations expressed in terms of turbulent kinetic energy. Using the Fokker–Planck equation, the steady-state probability distributions of supersaturation are analytically computed for a number of approximations involving the time-scale separation between updraft fluctuations and phase relaxation, and droplet or ice particle size fluctuations. While the analytical results are presented in general for single-phase clouds, the calculated distributions are used to compute mixed-phase cloud properties—mixed fraction and mean liquid water content in an initially icy cloud—and are argued to be useful for generalizing and constructing new parameterization schemes. Significance Statement Supersaturation is the fuel for the development of clouds in the atmosphere. In this paper, our goal is to better understand the supersaturation budget of clouds embedded in a turbulent environment by analyzing the basic equations of cloud microphysics. It is found that the turbulent characteristics of an air parcel substantially affect the cloud’s supersaturation budget and hence its life cycle. This is also shown in the context of mixed-phase clouds where, depending on the turbulent regime, different liquid-to-ice ratios are found. Consequently, the theoretical approach of this paper is crucial to develop tools to parameterize small-scale atmospheric features, like clouds, into global circulation models to improve climate projections for the future.

HETEROGENEOUS SERVER RETRIAL QUEUEING MODEL WITH FEEDBACK AND WORKING VACATION USING ARTIFICIAL BEE COLONY OPTIMIZATION ALGORITHM

This research delves into the dynamics of a retrial queueing system featuring heterogeneous servers with intermittent availability, incorporating feedback and working vacation mechanisms. Employing a matrix geometric approach, this study establishes the steady-state probability distribution for the queue size in this complex heterogeneous service model. Additionally, a range of system performance metrics is developed, alongside the formulation of a cost function to evaluate decision variable optimization within the service system. The Artificial Bee Colony (ABC) optimization algorithm is harnessed to determine service rates that minimize the overall cost. This work includes numerical examples and sensitivity analyses to validate the model's effectiveness. Also, a comparison between the numerical findings and the neuro-fuzzy results has been examined by the adaptive neuro fuzzy interface system (ANFIS).

Optimal management strategies of renewable energy systems with hyperexponential service provisioning: an economic investigation

The current research proposes optimal management strategies for queueing modeling-based renewable energy systems with hyper-exponentially distributed maintenance/repair under the assumption of an admission control policy. Using the concept and steps of the matrix-analytical method, the steady-state probability distribution associated with energy systems is explicitly presented. A relatively straightforward computation that can help with modeling wind energy generation, investigating wind farm performance, optimizing energy based on system storage, reliability inspection, service maintenance planning, and numerous other purposes can be employed to mathematically derive several system performance indicators. The investigation findings are validated via quantitative outcomes, illustrative possesses, and a step-by-step recursive methodology for efficient management of the renewable energy system. Additionally, considering multiple governing parameter values, the nature-inspired optimization technique, Cuckoo Search (CS), is employed to demonstrate the optimum anticipated cost of renewable energy system. A comparison with other metaheuristics and semi-classical approaches is also presented to establish the best convergence results. In order to help system designers, policymakers, engineers, and researchers, several numerical examples are also provided to construct more practical strategies based on the production of energy, storage, and system management. The economic, parametric, and performance investigation findings are highlighted, and the opportunities and recommendations for further research are provided. In a nutshell, the outcomes of the present analysis can be adopted to formulate the most effective economic strategies and regulate decision-making processes in the energy sectors.

Performance analysis and ANFIS computing of an unreliable Markovian feedback queueing model under a hybrid vacation policy

This study covers the stationary analysis of an unreliable Markovian queueing system with feedback based on a hybrid vacation policy. Customers arrive at the station at random using a Poisson process. Service time is distributed exponentially. The necessary and sufficient requirements for system stability have been demonstrated. Many performance metrics of this queueing system, such as the stationary queue length distribution, have been obtained from the steady-state probability distribution of the queueing model and the steady-state queue length using the matrix geometric method. To establish the model’s performance indicators, a few formulas have also been developed. The Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to develop rules from the input datasets and generate different performance indices. Finally, the impact of system parameters and cost analysis was studied using numerical examples.

Cross-layer modeling and analysis of TCP communications over turbulent FSO links

This paper addresses the accurate characterization of the performance of transmission control protocol (TCP) for end-to-end transport services over free-space optical (FSO) links. Unlike previous works on this topic, we introduce a second-order Markovian assumption for the variation of the TCP congestion window to capture the memory from turbulence channels and further propose a Markov chain model that maps two consecutive congestion windows along time into the state space to interpret TCP operations. The analytical expression of steady-state probability distribution vector of the proposed model in general cases where relay routers exist behind the TCP connection is derived, based on which the throughput efficiency of TCP is formulated. The high accuracy of the proposed model is verified by Monte-Carlo simulations and experiments. We further analyze the impact of different link/channel parameters on the TCP performance, and discuss briefly the effectiveness of bundle protocol-based schemes for improving the performance of FSO networks from a transport layer perspective.

A run-and-tumble particle around a spherical obstacle: the steady-state distribution far-from-equilibrium

We investigate the steady-state distribution function of a run-and-tumble particle (RTP) evolving around a repulsive hard spherical obstacle. We demonstrate that the well-documented activity-induced attraction translates into a delta-peak accumulation at the obstacle’s surface accompanied by an algebraic divergence of the density profile close to the obstacle. We obtain the full form of the distribution function in the regime where the typical distance run by the particle between two consecutive tumbles is much larger than the obstacle’s size. This finding provides an expression for the low-density pair distribution function of a fluid of highly persistent hard-core RTP. It also advances an expression for the steady-state probability distribution of highly ballistic active Brownian particles and active Ornstein–Uhlenbeck particles around hard spherical obstacles.

Efficient and accurate Lyapunov function-based truncation technique for multi-dimensional Markov chains with applications to discriminatory processor sharing and priority queues

Online service providers aim to satisfy the tail performance requirements of customers through Service-Level Objectives (SLOs). One approach to ensure tail performance requirements is to model the service as a Markov chain and obtain its steady-state probability distribution. However, obtaining the distribution can be challenging, if not impossible, for certain types of Markov chains, such as those with multi-dimensional or infinite state-space and state-dependent transitions. Examples include M/M/1 with Discriminatory Processor Sharing (DPS) and preemptive M/M/c with multiple priority classes and customer abandonment.To address this fundamental problem, we propose a Lyapunov function-based state-space truncation technique that leverages moments or bounds on moments of the state variables. This technique allows us to obtain tight truncation bounds while ensuring arbitrary probability mass guarantees for the truncated chain. We highlight the efficacy of our technique for multi-dimensional DPS and M/M/c priority queue with abandonment, demonstrating a substantial reduction in state space (up to 74%) compared to existing approaches. Additionally, we present three practical use cases that highlight the applicability of our truncation technique by analyzing the performance of the DPS system.

Equationof state for active matter.

We characterize the steady states of a suspension of two-dimensional active Brownian particles (ABPs). By approximating the first-order correction to the steady-state probability distribution to lowest order in Peclet number, we show that macroscopic quantities can be calculated in analogous way to equilibrium systems using this probability distribution. We then derive expressions for the macroscopic pressure and position-orientation correlation functions. We check our results by direct comparison with extensive numerical simulations. A key finding is the importance of many-body effective interactions even at very low densities.

Analysis of a Heterogeneous Queuing Model with Intermittently Obtainable Servers under a Hybrid Vacation Schedule

This paper investigates the concept of a Markovian queueing model with heterogeneous, intermittently available servers with feedback under a hybrid vacation policy. Both the asymmetric transition representation and the hybrid vacation policy are addressed in this article. The necessary and sufficient conditions for system stability are presented. In addition, the steady-state probability distribution of the queueing model was derived by employing the matrix geometric method. Furthermore, a few formulae were constructed to determine the model’s performance indicators. Finally, the influence of system parameters was also investigated using some numerical examples.

Optimal strategy analysis of a Markovian queue with variable vacation and vacation interruptions under unobservable cases

ABSTRACT This paper makes a game-theoretic analysis of an M/M/1 queue with variable vacation and vacation interruptions. The Nash equilibrium mixed strategy and social utility maximization are derived based on a non-cooperative game theory and an optimization theory under different information precision levels, namely almost unobservable and fully unobservable cases. The explicit solutions of the entrance probabilities and the steady-state probability distribution of the system are investigated using the probability generating function and nonhomogeneous linear difference equations. Furthermore, the effects of the information levels and diverse system parameters on the equilibrium strategies, the arrival rates, and the expected benefits are explicitly illustrated by numerical comparisons. The research results can provide a theoretical basis and performance analysis tool for the optimal design in the wireless transmission and network communication system.

The connection between dissipative chaos and nonequilibrium

Strong nonequilibrium conditions often give rise to dissipative chaos. However, the underlying connection between dissipative chaos and nonequilibrium remains unclear. Herein, we study this connection from the framework of the nonequilibrium potential-flux landscape. The nonequilibrium system dynamics are determined in part by the potential landscape associated with the steady-state probability distribution. The other part of the driving force for nonequilibrium dynamics comes from the steady-state probability flow, leading to the breaking of detailed balance. We find that dissipative chaos is driven mainly by the nonequilibrium probability flow. Using the Lorenz strange attractor model, we quantitatively demonstrate the close connection between the kinematic measures of the appearance of dissipative chaos and the degree of nonequilibrium. This work reveals the dynamic and thermodynamic origins of dissipative chaos beyond the interpretation based on nonlinearity. This study also provides a new understanding of the phase transitions in nonequilibrium systems.

Emergent Criticality in Coupled Boolean Networks

Early embryonic development involves forming all specialized cells from a fluid-like mass of identical stem cells. The differentiation process consists of a series of symmetry-breaking events, starting from a high-symmetry state (stem cells) to a low-symmetry state (specialized cells). This scenario closely resembles phase transitions in statistical mechanics. To theoretically study this hypothesis, we model embryonic stem cell (ESC) populations through a coupled Boolean network (BN) model. The interaction is applied using a multilayer Ising model that considers paracrine and autocrine signaling, along with external interventions. It is demonstrated that cell-to-cell variability can be interpreted as a mixture of steady-state probability distributions. Simulations have revealed that such models can undergo a series of first- and second-order phase transitions as a function of the system parameters that describe gene expression noise and interaction strengths. These phase transitions result in spontaneous symmetry-breaking events that generate new types of cells characterized by various steady-state distributions. Coupled BNs have also been shown to self-organize in states that allow spontaneous cell differentiation.

A study of Michaelis–Menten type harvesting effects on a population in stochastic environment

In this paper, a study of Michaelis–Menten type harvesting effects on a population with outbreak characteristic in stochastic environment is done formulating stochastic model. The Ludwig model (Ludwig et al., 1978) is chosen as the base mathematical model for the population. Steady state probability distribution and stationary potential function of the population are determined. P-bifurcation analysis has been presented. The impact of the noises and cross correlations strength on outbreak and extinction of the population are measured numerically using probability. It is observed that random rate of Michaelis–Menten type harvesting may extinct the population. The choice of noise or type of harvesting process to population control cannot be uniquely defined and deeply depends on the exogenous factors (extrinsic and intrinsic noise), a contradiction to the earlier reports. Various complex ecological phenomena like noise enhance stability, phase regime shift, etc., are identified in the system.

Analysis on D2D Heterogeneous Networks with State-Dependent Priority燭raffic

In this work, we consider the performance analysis of state dependent priority traffic and scheduling in device to device (D2D) heterogeneous networks. There are two priority transmission types of data in wireless communication, such as video or telephone, which always meet the requirements of high priority (HP) data transmission first. If there is a large amount of low priority (LP) data, there will be a large amount of LP data that cannot be sent. This situation will cause excessive delay of LP data and packet dropping probability. In order to solve this problem, the data transmission process of high priority queue and low priority queue is studied. Considering the priority jump strategy to the priority queuing model, the queuing process with two priority data is modeled as a two-dimensional Markov chain. A state dependent priority jump queuing strategy is proposed, which can improve the discarding performance of low priority data. The quasi birth and death process method (QBD) and fixed point iteration method are used to solve the causality, and the steady-state probability distribution is further obtained.Then, performance parameters such as average queue length, average throughput, average delay and packet dropping probability for both high and low priority data can be expressed. The simulation results verify the correctness of the theoretical derivation. Meanwhile, the proposed priority jump queuing strategy can significantly improve the drop performance of low-priority data.

Effect of color cross-correlated noise on the growth characteristics of tumor cells under immune surveillance.

Based on the Michaelis-Menten reaction model with catalytic effects, a more comprehensive one-dimensional stochastic Langevin equation with immune surveillance for a tumor cell growth system is obtained by considering the fluctuations in growth rate and mortality rate. To explore the impact of environmental fluctuations on the growth of tumor cells, the analytical solution of the steady-state probability distribution function of the system is derived using the Liouville equation and Novikov theory, and the influence of noise intensity and correlation intensity on the steady-state probability distributional function are discussed. The results show that the three extreme values of the steady-state probability distribution function exhibit a structure of two peaks and one valley. Variations of the noise intensity, cross-correlation intensity and correlation time can modulate the probability distribution of the number of tumor cells, which provides theoretical guidance for determining treatment plans in clinical treatment. Furthermore, the increase of noise intensity will inhibit the growth of tumor cells when the number of tumor cells is relatively small, while the increase in noise intensity will further promote the growth of tumor cells when the number of tumor cells is relatively large. The color cross-correlated strength and cross-correlated time between noise also have a certain impact on tumor cell proliferation. The results help people understand the growth kinetics of tumor cells, which can a provide theoretical basis for clinical research on tumor cell growth.