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In this article we present a Bayesian Markov model for investigating environmental spread processes.
In this study, the orientation effects during inclined downward flame spread processes were thoroughly investigated by experimental and theoretical methods.
For studies focused on the preservation of biodiversity, graph-based methods provide an interesting framework to investigate the landscape influence on population spread processes.
The simulation results from DISPATCH and CA are more similar than those from the percolation algorithm; however, the latter appeared to be a better representative of observed fire spread processes due to its underlying assumption of fire spread mechanisms.
The simulation study and case study provide evidence that the model presented here is both robust and flexible; and is therefore appropriate for a wide range of spread processes in epidemiology and ecology.
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Finally, the influence spread process stops at a time slot when there is no newly activated node.
With this common modus operandi, we reveal the most fundamental and wide spread process-structure-property relationship in biominerals.
The algorithms for simulating the fire spread process in buildings and calculating dynamic probability of fire spread for each compartment at each time step of simulation are proposed.
We propose a novel influence spread model called Fluidspread, using the fluid dynamics theory to reveal the time-evolving influence spread process.
For example, in a 1000 node network, allocating 100 seeds in (G') requires roughly 100,000 simulations of the spread process across a 1000 node graph.
A planner applying (text {OAS}_{text {mg}}) may significantly overspend when the spread process is similar to a Linear Threshold Model.
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