scholarly journals Confinement Time Required to Avoid a Quick Rebound of COVID-19: Predictions From a Monte Carlo Stochastic Model

2020 ◽  
Vol 8 ◽  
Author(s):  
Társilo Girona
Keyword(s):  
Monte Carlo ◽  
Stochastic Model ◽  
Confinement Time ◽  
Time Required

A Simple Statistical Theory for Grain Growth in Materials.

1990 ◽  
Vol 209 ◽  
Author(s):  
P. Mulheran ◽  
J.H. Harding
Keyword(s):  
Growth Rate ◽  
Monte Carlo ◽  
Statistical Theory ◽  
Stochastic Model ◽  
Grain Growth ◽  
Three Dimensional ◽  
Monte Carlo Procedure ◽  
Short Time ◽  
2D And 3D ◽  
Good Agreement

A Monte Carlo procedure has been used to study the ordering of both two and three dimensional (2d and 3d) Potts Hamiltonians, further to the work of Anderson et al. For the 3d lattice, the short time growth rate is found to be much slower than previously reported, though the simulated microstructure is in agreement with the earlier studies. We propose a new stochastic model that gives good agreement with the simulations.

scholarly journals A Novel Theoretical Probabilistic Model for Opportunistic Routing with Applications in Energy Consumption for WSNs

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8058
Author(s):  
Christian E. Galarza ◽  
Jonathan M. Palma ◽  
Cecilia F. Morais ◽  
Jaime Utria ◽  
Leonardo P. Carvalho ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Energy Consumption ◽  
Stochastic Model ◽  
Opportunistic Networks ◽  
Opportunistic Routing ◽  
Opportunistic Network ◽  
Expected Number ◽  
Successful Transmission ◽  
Network Parameters ◽  
Proposed Model

This paper proposes a new theoretical stochastic model based on an abstraction of the opportunistic model for opportunistic networks. The model is capable of systematically computing the network parameters, such as the number of possible routes, the probability of successful transmission, the expected number of broadcast transmissions, and the expected number of receptions. The usual theoretical stochastic model explored in the methodologies available in the literature is based on Markov chains, and the main novelty of this paper is the employment of a percolation stochastic model, whose main benefit is to obtain the network parameters directly. Additionally, the proposed approach is capable to deal with values of probability specified by bounded intervals or by a density function. The model is validated via Monte Carlo simulations, and a computational toolbox (R-packet) is provided to make the reproduction of the results presented in the paper easier. The technique is illustrated through a numerical example where the proposed model is applied to compute the energy consumption when transmitting a packet via an opportunistic network.

Operational Risk Evaluation of an Ice Feature Impact

1990 ◽  
Vol 112 (1) ◽  
pp. 96-101
Author(s):  
A. B. Dunwoody
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probability Distribution ◽  
Stochastic Model ◽  
Risk Evaluation ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Offshore Structure ◽  
Simple Stochastic Model ◽  
General Method

The risk of impact by a particular ice feature in the vicinity of an offshore structure or stationary vessel is of concern during operations. A general method is presented for calculating the risk of an impact in terms of the joint probability distribution of the forecast positions and velocities of the ice feature. A simple stochastic model of the motion of an ice feature is introduced for which the joint probability distribution of ice feature position and velocity can be determined as a function of time. The risk of an impact is presented for this model of the motion of an ice feature. Predictions of the distributions of the time until impact and the drift speed upon impact are also presented and discussed. Predictions are compared against results of a Monte Carlo simulation.

Monte Carlo Model of Nonlinear Chromatography:  Correspondence between the Microscopic Stochastic Model and the Macroscopic Thomas Kinetic Model

2002 ◽  
Vol 74 (24) ◽  
pp. 6269-6278 ◽  
Author(s):  
Alberto Cavazzini ◽  
Francesco Dondi ◽  
Alain Jaulmes ◽  
Claire Vidal-Madjar ◽  
Attila Felinger
Keyword(s):  
Monte Carlo ◽  
Kinetic Model ◽  
Stochastic Model ◽  
Monte Carlo Model ◽  
Nonlinear Chromatography ◽  
Microscopic Stochastic Model
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