Examining oxidation in β-NiAl and β-NiAl+Hf alloys by stochastic cellular automata simulations

We present results from a stochastic cellular automata (CA) model developed and employed for examining the oxidation kinetics of NiAl and NiAl+Hf alloys. The rules of the CA model are grounded in diffusion probabilities and basic principles of alloy oxidation. Using this approach, we can model the oxide scale thickness and morphology, specific mass change and oxidation kinetics as well as an approximate estimate of the stress and strains in the oxide scale. Furthermore, we also incorporate Hf in the grain boundaries and observe the “reactive element effect”, where doping with Hf results in a drastic reduction in the oxidation kinetics concomitant with the formation of thin, planar oxide scales. Interestingly, although we find that grain boundaries result in rapid oxidation of the undoped NiAl, they result in a slower-growing oxide and a planar oxide/metal interface when doped with Hf.

Testing more and earlier = better control of the epidemic and lower social costs.

In this note we explore the effect of the number of daily tests on an epidemics control policy purely based on testing and selective quarantine, and the impact of these actions depending on the time their application starts. Surprisingly, the results not only confirm that increasing the number of tests lowers the number of infected individuals, but also that it has a very beneficial effect limiting the number of quarantined individuals, and thus the socio-economical costs of the epidemics. The results also show that the timing in the application of the measures is as important as the measures themselves. The results suggest that fast decision making and investments to increase testing capabilities are highly rewarded not only from the public health viewpoint, but also from the socio-economical one. The study is carried out in simulation using stochastic cellular automata representing a community of 50'000 individuals. The selection of the tested individuals is carried out based on a contact tracing strategy focused on the closer contacts.

Mining stochastic cellular automata to solve density classification task in two dimensions

Density Classification Task (DCT) is a well-known problem that researchers have been tackling for more than two decades, where the main goal is to build a cellular automaton whose local rule gives rise to emergent global coordination. We describe the methods used to identify new cellular automata that solve this problem. The design of our cellular automata was carried out by a parallel genetic algorithm, specifically instantiated for this task. Our approach identifies both the neighborhood and its stochastic rule using a dataset of initial configurations that covers in a predefined and balanced way the full range of densities in DCT. We compare our results with some models currently available in the field. In some cases, our models show better performance than the best solution reported in the literature, with efficacy of 0.842 for datasets with uniform distribution around the critical density. The best-known cellular automaton achieves 0.832 in the same datasets. Tests are carried out in datasets of diverse lattice sizes and sampling conditions; we focused the analysis on the performance of our model around critical densities. Finally, by a statistical non-parametric test, we demonstrate that there are no significant differences between our identified cellular automata and the best-known model.