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We show that the problem of finding the expected squared distance between two individuals and finding the variance of the squared distance can be reduced for a wide range of population models to finding the mean and mean square coalescence times.
Then, we calculate the expected squared distance from cluster members to the CH.
The expected squared distance (track segment length) is therefore E(Li2) = v2Ti2+4DTi.
The energy model of [6] only focuses on the energy consumed in transmitting data and derives the expected squared distance from a sensor node to its cluster-head using a simple stochastic method.
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Mean square error of prediction (MSEP) indicates expected squared Euclidian distance between predicted and observed phenotypes, R expressed the proportion of variance explained by the model and Q evaluates the prediction quality of the model.
Observed Minus Expected Squared is described in detail elsewhere [10], [15].
Then, the sum over a long time T of expected squared family sizes is N TF.
The mean dwell time (τD, eq 7 ) of CTxB in autocorrelations prior to cross-linking above planarized apertures was 6 ± 1 ms. 2D Brownian diffusers are expected to diffuse a mean squared distance, ⟨ r⟩, in the time Δ t according to ⟨ r⟩ = 4 DΔ t.
The chi squared distance (dchi squared) is based on a comparison between the distributions themselves.
If Brownian diffusion is assumed, CTB would be expected to take a time t to diffuse a mean squared distance r according to t = r/4 D. For the spot size used in this study, ∼1 mm, it would take CTB approximately 2.5 × 10 s to diffuse through the illumination area.
We expect descriptive representative conformers may minimize the mean of the squared distance of the clique within clusters (MSQw) and to maximize the mean of the squared distance of the clique between the clusters (MSQb).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com