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Table 4 shows the percentage of clinical asthmatics associated with each of the non-weighted score thresholds, illustrating that ranking according to non-weighted scores does not demonstrate the ranking in order of probability of asthma that is achieved by the weighted scoring system.
To demonstrate the ranking scheme's effectiveness, we have used the Spearman's Foot rule [17] distance as the measure of similarity between proposed system ranking and user-human ranking.
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The other experimental work demonstrates the ranking of noise components from a riding electric scooter.
For calculating the values of the index, Table 2 demonstrates the ranking of the water quality.
Figure 2 shows screenshots of the HGCS online platform, demonstrating the ranking of 284 genes from WES data for an HSE patient, using TLR3 as the core gene.
To demonstrate the usefulness of rank aggregation in this setting we performed the following study.
We demonstrate that the ranking is stable under alternative grouping and weighting schemes.
These examples demonstrate the advantage of using rank size analysis at different spatial scales for having a complete understanding of the dynamics behind the observed empirical data.
Our results support their general conclusion that the whole peak lists from all programs show significant proportion of the canonical binding motif and also demonstrate the significance of peak rank in recovering high confidence motif sites.
We also demonstrate the advantages of using low-rank third-order moment estimates for blind system estimation.
Numerical experiments with the linear advection diffusion equation, and the non-linear Lorenz-95 model demonstrate the effectiveness of a low-rank Krylov subspace solver when compared to a traditional solver.
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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