f 2 l, fixed charges for distribution center l (Rs/period) (120, 150, 120, 150).
Bifurcation diagram of the average slow system (6a)–(6b) with respect to (c_{mathrm{tot}}) with l fixed at 0.94.
Using slow averaged dynamics, we have elucidated how the transition from bursting to tonic spiking occurs and why (ca_{i}) jumps to larger values as (c_{mathrm{tot}}) increases for l fixed.
In applications, we do not compute these distributions on the fly but during preprocessing, for all l≤ L fixed.
Results were obtained by calculating SNP effects for a single replicate of the training data; FR-LS: fixed regression-least squares; RR-BLUP: random regression-BLUP; Bayes-R: Bayesian regression; SVR: support vector regression; PLSR: partial least squares regression.
The training data set included animals born before 1998; ASI: Australian Selection Index; PPT: protein percentage; FR-LS: fixed regression-least squares; RR-BLUP: random regression-BLUP; Bayes-R: Bayesian regression; SVR: support vector regression; PLSR: partial least squares regression.
ASI: Australian Selection Index; PPT: protein percentage; FR-LS: fixed regression-least squares; RR-BLUP: random regression-BLUP; Bayes-R: Bayesian regression; SVR: support vector regression; PLSR: partial least squares regression.
This leads to a 2D matrix C k, l (i, j), which is basically the slice of C i, j, k, l) for fixed k, l.
In this paper, N : L → L is fixed.
Once τ l and λ l are fixed, we need to estimate only the exponential amplitudes u l, ij and v l, i.
The area is a square of L × L m2 and L is fixed to 10 m.
