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The predicted molar fractions out of the process were compared with the real results and good agreement was observed between the real data and simulated results.
The simulation results demonstrate that the TTLS can inhibit the pathological effects on the magnetometer measurements well, and that the residual between the real data and the data after TTLS calibration is less than in other calibration results.
The order of the proposed system is a free parameter which can be used to have a better fit between the real data and a theoretical formulation of the solutions.
Figure 1 shows the reading of actual values scattered closely the line of predicted values for the removals of phenol, 4-CP and COD which underscored the agreement between the real data and the ones gained from the models.
Also in this case we can observe a good agreement between the real data and the simulations.
The distribution of the age of head was used inside the generation process, but the stochastic extractions from this distribution were spaced out from various tests; for this reason we can expect some discrepancy between the real data and the generated population.
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When the sample size was set to 500, the error proportion in the total gene clusters, either between the DG result and the real data or between any two sample results from the DG algorithm, were <0.1% in all test populations.
The validation process shows that all the models successfully pass the hypothesis test, which means that there were no significant differences between the model and the real data.
Because the amplitudes are different between the synthetic and the real data, the threshold values are different.
Two different methods are commonly used to measure the similarity between the models and the real data: nonlinear least-square curve fitting and cross correlation (CC).
Note the similarity in apparent noise level between the simulation and the real data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com