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In conclusion, Table 2 resumes the behavior of our scheduling approach with different situations of dynamicity.
In conclusion, Table 3 summarizes the strengths and weaknesses of both cameras tested and evaluated relative to our application constraints.
While no individual metric can quantitively demonstrate the improved risk assessment, there are qualitative data supporting this conclusion (Table 2).
The study with ligand 1L2S offered the same conclusion (Table 3 and Figure 7).
An additional test used in this study, coalescent simulation (CS) analysis, supports their conclusion (Table 3).
However, the many orthogonal and corroborating pieces of evidence we collected and, equally important, the absence of any conflicting observations, cumulatively support this conclusion (Table S2, see also discussion points below).
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Multivariable analysis with independent factors led to the same conclusions (Table 2).
Previous studies on trans-boundary or trans-regional PM inventories vary in measuring scales and final conclusions (Table 1).
From these tables, we can draw some conclusions: Table 1 CDF difference in dB between Monte Carlo simulations (SIM) on the one hand and CLCFM and FWBM on the other hand at 5, 50 and 90% (σ = 3, 4, 6 dB, * means greater than 3 dB).
We have also considered models with exponentially distributed infectious periods, and arrived at the same conclusions (Table S2).
The seven reviews had remarkably different conclusions Table 2, ranging from 'consistent evidence' [22], 'extensively researched and generally well established' [25], to 'moderate evidence', and 'limited evidence' in the two latest reviews [31], [32].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com