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For particles smaller than 20 nm or larger than 200 nm, FIMS transfer function and mobility resolution can be well represented by the calculated ones based on simulated particle trajectories in the FIMS.
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We present two nature-inspired algorithms for this problem, the first based on simulated annealing, and the second on particle swarm optimization.
First, we present a comparative study with the method proposed by Law et al. [41] based on simulated data to judge the efficiency of particle filter approach for moving load identification.
All simulations were based on simulated sibling pairs only, and the simulated marker was assumed to be the actual QTL.
This paper presents an optimization algorithm based on Simulated Annealing.
The proposed algorithm is based on Simulated-Annealing (SA).
Similarly, oceanographic models based on environmental data can simulate particle movements in ocean currents and make quantitative estimates of larval connections between populations possible [5 9].
Black lines in the map are the boundaries of the zones based on the concentration of impact craters, as reported by Kaneko et al. (2016) in order to compare simulated particle distribution with actual distribution of ballistic impact craters.
The study was based on a simulated outbreak; therefore, the participants' reactions were to a simulated, not an actual, crisis.
In this article we present an implementation of accelerating a Monte Carlo method based on the Inverse scheme for simulating particle coagulation on the GPU.
Based on the simulated results, an engineering-oriented method was demonstrated to be effective in assessing indoor particle pollution by coal combustion and its health effect.
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