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The mean escape time of Chinook salmon differed significantly between the two versions examined.
We quantify this behaviour by identifying the mean escape time from the region.
We conclude that, broadly, the mean escape time varies exponentially in the potential barrier and that it is smooth and monotonically decreasing in both λ and α.
To understand how the mean escape time, E , varies as a function of model parameters, we consider both numerical and approximate analytic results for comparison.
When the noise amplitude α increases, or as the excitability parameter λ → 1, the mean escape time decreases, or the 'seizure rate' increases.
The mean escape rate can be estimated from the mean escape time
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Our quasi-analytic results demonstrate a clear relationship between network structure and the mean escape time--which we think of as seizure frequency--in our phenomenological model.
Mean escape latency (the time between being placed in the water and finding the hidden platform) was calculated which is considered as an index of acquisition.
The numerical results come from calculating the sample mean of the escape times from a large number of numerically generated trajectories.
Treatment with LCDT not only decreased the mean escape latency but also increased the time spent in the target quadrant and the numbers of platform crossings.
In both cases, the evacuation of the rooms and corridor is represented by means of the stepped time functions displaying the escape (i.e. escape time behaviour) of people from EA.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com