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In the proposed modelling approach each stone block is modelled as a discrete element which is discretized by triangular finite elements.
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Consequently, a given pattern observation, numbered i, at some position x i in a given block, is modeled as: y ( x i ) = y B ( x i ) + a i b i p + ε i + n i (2).
The channel impulse response (CIR) of the j th information block is modeled as an FIR filter with coefficients h j = [h j [0],..., h j [L]]T, where L denotes the corresponding channel memory length.
The curve was re-sampled to give 60 values as opposed to the original 12 so that learning within a block was modelled as smoothly increasing during blocks rather than increasing stepwise from block to block.
Block was modelled as the random independent variable.
Infection status was modeled as a fixed effect, while block was modeled as a random effect.
The BOLD response during each experimental task block was modeled as a boxed-car covariate of variable length in SPM8 using a human canonical hemodynamic function.
In this second GLM, the entire duration of each block was modeled as an epoch, from the onset of the first trial to the offset of the last trial.
The scheduling of queuing packets to access the memory blocks is modeled as a bipartite matching problem.
The instruction screen between blocks was modelled as a separate regressor.
Each mini-block was modeled as an epoch of 12 s and convolved with a canonical hemodynamic response function.
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