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Input multiplicity is a strange situation in which different input concentrations can give the same output concentration.
Input multiplicity, where multiple input values give the same output, leads to "wrong" control action under feedback control severely compromising control system robustness.
This means that CL≥ and CL= formalize intensional notions of functions, where "intensionality" implies that functions that give the same output on the same input may remain distinguishable.
However, even when both runs give the same output, the final points of the trace (or/and the initial) can be wrongly matched as seen in Fig. 9, so GEMMA is run for the first and last few points (about 5) of the trace just to confirm that the output given by Marchal's is correct.
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We have applied the methodology to show that we can make two alternative model frameworks for cardiac contraction give the same outputs, and that we can generate reduced versions of both these models using this approach.
Each of these algorithms gives the same output for the computation of PH, so we only give a brief overview and references to these algorithms, as one does not need to know them to compute PH with one of the publicly-available software packages.
A series of RVef, EDV and heart rate combinations can give the same cardiac output; monitoring or targeting cardiac output alone ignores this reality.
A dual-voltage LED lamp can give the same light output at 240 VAC, 20 VAC or 24 VDC.
The identifiability problem is based on the determination of all parameter sets which give the same input-output response.
Obviously, with discrete phenotypes it is possible to exclude all discrepantly coded individuals, and all +A and A+ individuals (Table 4) could be removed to give exactly the same output from a GWAS.
If we define the measurement step for the Block Set Formulation similarly to the Quantum Query Model, then we prove that both formulations give the same Gram matrix of output states, although the Block Set Formulation allows a much more explicit form.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com