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The model response was checked for the FD grid refinement, while attempting to minimize run time.
The model response was dramatic; as can be seen in Fig. 5 (A), the model indicates suitable habitat for tsetse would be found throughout the heavily populated higher elevations of Kenya.
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The model response is computed in a simulator solving a set of partial differential equations.
From Table 2, the most influencing variables on the model response were X2 (Molar ratio of D-isoascorbic to palmitic acid) and X4 (ultrasonic power).
The model response is fitted best to the data with a gradient-based optimisation strategy, requiring the sensitivity analysis of the micromechanical model.
In short rivers point and nonpoint sources play a key role in shaping the model response, being as important as the self-purification dynamics.
The entry J i, j in J shows how sensitive the model response is at the i-th data point for a change in the j-th parameter.
The resultant model response is shown for each combination of coherence and duration in F. DOI: http://dx.doi.org/10.7554/eLife.00699.005 10.7554/eLiFigure99.006 Figure 3 Figure supplement 1. Temporal coherence models for other SFG stimuli.
The model responses were calculated based on (11).
The various model responses were compared with the experimental responses.
This figure demonstrates that the fitting between the observed data and model responses are reasonable.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com