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MD simulation produced a less accurate result due to neglecting electronic motions.
With the influent loading, the solid retention time, and sludge settleability carefully controlled to mimic the experimental conditions, a BioWin simulation produced a biomass concentration and effluent quality consistent with experimental results.
Only when the sensitivity of exposure to smoking was as low as 0.50, the simulation produced a non-significant result.
Each simulation produced a quasi-stationary state within the 1.000 generation time frame applied, suggesting that the underlying dynamics may admit attracting fixed point equilibria.
The value of μ in this spreadsheet was varied until the simulation produced a frequency of mutant cells equal to the median frequency observed experimentally (after a number of virtual generations equal to those required to produce the number of cells that initiate the selection experiment).
However, bearing in mind that the simulation produced a correlation of r =.63 under the precondition that all non-significant evidence, i.e. 87% of all evidence remained unpublished, publication bias cannot account for the whole correlation, but perhaps for a substantial amount.
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A subsequent 20 ns unbiased MD simulation produced an ensemble of TibA models that matched the EM density with a best overall CCC value of 0.87.
The simulation produces a predicted or simulated dataset (curves labeled Predicted Data).
The simulation produces a motion of the tail in the (x, y) plane, with the tail oscillating as a rigid body in the form of a sinusoidal wave.
Each simulation produces a distinct forecast output (ensemble member), which is a representative sample of the possible future atmospheric states for probability assessment [4].
Thus the simulation produces a 2D diffraction pattern at each position in the 2D scan grid, resulting in a 4D output, the size of which can become considerable.
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