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The weighting factor (w) was iteratively estimated, with the optimal w value determined by minimization of the relative signal difference ([Formula: see text]) and signal-difference-to-noise ratio (SDNR) between material (or bone) and the background.
This w value determines the posterior probability of entity of interest.
It returns W value which is summarized with its previous WeNB value.
For each w value, we run the AOF method, and get a Pareto point.
If W value is too high, the length of history frames will be too long; if W value is set too low, the history for adaptation will be too short.
To get the W value for the second step of the calculation using Equation 2, AHP questionnaires were distributed.
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In each panel of Figure 2, the FL2-W value of each cell (vertical axis) is plotted against its relative fluorescence intensity (horizontal axis).
All parameters demonstrated a W-value above 0.9 and were, therefore, considered normally distributed.
For each cell in Table 9, the number before the slash is the p-value and the number after the slash is the W-value.
Transforming the intercept from a log to a natural scale gives a selection rate constant (i.e. w-value, see equation 2) of 100.4221 = 1.53.
Especially in Experiment CM2_Ex4_E2, the most complicated one, we obtained very small p-value and W-value when comparing QML-AiNet (MM) with the other two algorithms.
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