Research and application of Stochastic Resonance in Quad-stable Potential System

To solve the problem of low weak signal enhancement performance in the quad-stable system, a new Quad-stable potential Stochastic Resonance (QSR) is proposed. Firstly, under the condition of adiabatic approximation theory, the Stationary Probability Distribution (SPD), the Mean First Passage Time (MFPT), the Work (W) and the power Spectrum Amplification Factor (SAF) are derived, and the impacts of system parameters on them are also deeply analyzed. Secondly, numerical simulations are performed to compare QSR with the Classical Tri-stable Stochastic Resonance (CTSR) by using the Genetic Algorithm (GA) and the fourth-order Runge-Kutta algorithm. It shows that the Signal-to-Noise Ratio (SNR) and Mean Signal-to-Noise Increase (MSNRI) of QSR are higher than CTSR, which indicates that QSR has superior noise immunity than CTSR. Finally, the two systems are applied in the detection on real bearing faults. The experimental results show that QSR is superior to CTSR, which provides a better theoretical significance and reference value for practical engineering application.

Statistical Mechanics of Unconfined Systems: Challenges and Lessons

Motivated by applications of statistical mechanics in which the system of interest is spatially unconfined, we present an exact solution to the maximum entropy problem for assigning a stationary probability distribution on the phase space of an unconfined ideal gas in an anti-de Sitter background. Notwithstanding the gas’ freedom to move in an infinite volume, we establish necessary conditions for the stationary probability distribution solving a general maximum entropy problem to be normalizable and obtain the resulting probability for a particular choice of constraints. As a part of our analysis, we develop a novel method for identifying dynamical constraints based on local measurements. With no appeal to a priori information about globally defined conserved quantities, it is therefore applicable to a much wider range of problems.

Statistical Analysis of Cropland Area in Canada using the Autoregressive Hidden Markov Time Series Model

Crop production and other agricultural activities are as old as human existence and becoming increasingly intensive, spatially concentrated and specialized. However, diversification in economic activities and recent development in technology in many developed countries have led to significant increase in land use. Thereby, resulting to huge reduction in the total land area available for agricultural activities especially crop production. This study examines the distribution of cropland area in Canada in relation to three contributing factors using the Autoregressive Hidden Markov time series Model (AR-HMM) due to the limitations of the ordinary Autoregressive model in the accuracy of its parameter estimation. Expectation-Maximization (E-M) algorithm method was used to estimate the model parameters so as to investigate the effects of the factors on cropland distribution using secondary data from Food and Agriculture Organisation (FAO). Jarque-Bera and D'Agostino normality tests were carried out to examine the normality of the series. Augmented Dickey Fuller (ADF) and the KPSS tests established the stationarity of the series. The ideal stationary probability distribution for transition was at AR (3)-HMM with the minimum Bayesian Information Criterion (BIC) of 16270.62. The prior transition states for the HMM are 0.462, 0.260 and 0.278 respectively. In conclusion, this study suggests that deforestation and other land use activities as a result of commercial and technological advancements should be minimized to ensure more available cropland area.

A probabilistic model for risk assessment and predicting the health risk of occupational exposure to pesticides in agriculture

Introduction. The main point is the influence of a complex of chemical and physical stressors on agricultural machine operators. The processes of occurrence and interaction of harmful factors are probable. Markov processes are a convenient model that can describe the behaviour of physical processes with random dynamics. Purpose of the work: was to develop a probabilistic model of risk assessment for agriculture workers during the application of pesticides based on Markov processes’ theory and evaluate with the help of the developed model the probability of occurrence, the degree of severity and the prediction of the different influence of adverse factors on the operator. Materials and methods. The mechanized treatment of pesticide is presented in the form of a system, the states of which are ranked according to the degree of danger to the operator: from non-dangerous to dangerous. The transition occurs under the influence of negative factors and is characterized by the probability of pij transition. Based on the marked graph of the system states, a stochastic matrix P[ij] of transition probabilities was constructed in one step. There are formulas by which it is possible to calculate the state of systems in k steps for a homogeneous and non-homogeneous Markov chain. Results. Based on Markov chains’ theory, the system’s behaviour is modelled when using single-component preparations based on imidacloprid for rod spraying of field crops. Received vector of probabilities of possible hazardous conditions for the employee after each hour of spraying within 10 hours. After 6 hours of working, the probabilistic risk for the operator to stay in a non-dangerous state is about 50 %, and the probability risk of going into a dangerous - at 24 %. The stationary probability distribution results show the inevitability of the transition to a hazardous state of the system if enough steps have been taken. Conclusion. With this model, you can supplement the operator’s health risk assessment system, analyze, compare and summarize the results of years of research. The calculated statistical probabilities can be used in the development of new hygiene regulations with using pesticides.

Quasi-stationary probability distribution for the SIR epidemic model

In this paper, we propose two new approximations to the joint quasi-stationary distribution (QSD) of the number susceptible and infected individuals in the Susceptible-Infected-Recovered (SIR) stochastic epidemic model and we derive the marginal QSD of the infected individuals. These two approximations depend on the basic reproduction number [Formula: see text] and give a positive probability of the QSD to all the transient states. Numerical comparisons are presented to check the accuracy of these approximations.

Analysis of SIS-SI Stochastic Model with CTMC on the Spread of Malaria Disease

Various mathematical models have been developed to describe the transmission of malaria disease. The purpose of this study was to modify an existing mathematical model of malaria disease by using a CTMC stochastic model. The investigation focused on the transition probability, the basic reproduction number (R0), the outbreak probability, the expected time required to reach a disease-free equilibrium, and the quasi-stationary probability distribution. The population system will experience disease outbreak if R0>1, whereas an outbreak will not occur in the population system if R0≤1. The probability that a mosquito bites an infectious human is denoted as k, while θ is associated with human immunity. Based on the numerical analysis conducted, k and θ have high a contribution to the distribution of malaria disease. This conclusion is based on their impact on the outbreak probability and the expected time required to reach a disease-free equilibrium.

On Stochastic Differential Equations Generating Non-gaussian Continuous Markov Process

Continuous Markov processes widely used as a tool for modeling random phenomena in numerous applications, can be defined as solutions of generally nonlinear stochastic differential equations (SDEs) with certain drift and diffusion coefficients which together governs the process’ probability density and correlation functions. Usually it is assumed that the diffusion coefficient does not depend on the process' current value. For presentation of non-Gaussian real processes this assumption becomes undesirable, leads generally to complexity of the correlation function estimation. We consider its analysis for the process with particular pairs of the drift and diffusion coefficients providing the given stationary probability distribution of the considered process

Non-Maxwellian distribution in linear systems and its applications

Bimodality is a typical behavior of bistable nonlinear stochastic differential equations. In this work, we find an exact result for calculating the dynamical and stationary probability distribution function in a simple linear system driven by an asymmetric Markovian dichotomous noise. The results show that asymmetric dichotomous noise leads to a unimodal-bimodal distribution transition, exhibiting eight different non-Maxwellian stationary probability distribution profiles in the parameter space. The noise-induced transitions depend on the correlation time, which characterizes the asymmetric dichotomous noise. The calculations are performed using a linear configuration; but applications to other systems governed by nonlinear equations such as single species population growth models are discussed. In the proper limits, the symmetric case, including the Gaussian white noise limit, is recovered. Numerical simulations show good agreement with analytical results. Finally, a possible experimental setup is proposed.

A Finite State Method in the Performance Evaluation of the Bernoulli Serial Production Lines

Research on the performance measure evaluation of Bernoulli serial production lines is presented in this paper. Important aspects of the modeling and analysis using transition systems within the Markovian framework are addressed, including analytical and approximation methods. The “dimensionality curse” problems of the large scale and dense transition systems in the production system engineering field are pointed out as one of the main research and development obstacles. In that respect, a new analytically-based finite state method is presented based on the proportionality property of the stationary probability distribution across the systems’ state space. Simple and differentiable expressions for the performance measures including the production rate, the work-in-process, and the probabilities of machine blockage and starvation are formulated. A finite state method’s accuracy and applicability are successfully validated by comparing the obtained results against the rigorous analytical solution.

Semi-Classical Einstein Equations: Descend to the Ground State

The time-dependent cosmological term arises from the energy-momentum tensor calculated in a state different from the ground state. We discuss the expectation value of the energy-momentum tensor on the right hand side of Einstein equations in various (approximate) quantum pure as well as mixed states. We apply the classical slow-roll field evolution as well as the Starobinsky and warm inflation stochastic equations in order to calculate the expectation value. We show that, in the state concentrated at the local maximum of the double-well potential, the expectation value is decreasing exponentially. We confirm the descent of the expectation value in the stochastic inflation model. We calculate the cosmological constant Λ at large time as the expectation value of the energy density with respect to the stationary probability distribution. We show that Λ ≃ γ 4 3 where γ is the thermal dissipation rate.