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To capture potential fire spread along with irregular landscape connectivity considerations, we use a graph-theoretical approach that allows us to exploit graph connectivity measures (e.g., the number of connected components) as optimization objectives.
Due to these considerations, we use the 2D subspace method rather than the projected gradient method to solve (14).
Based on above considerations, we use MATLAB to implement the proposed association mining method.
On the basis of these considerations, we use the compactness-scalable fuzzy K-means clustering algorithm described in Du et al. (2005).
To model the two mentioned considerations, we use a Gaussian distribution with an expected value of zero: where s is the standard deviation for the size of deletions or insertions.
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Based on these considerations, we used inferred WBC counts as covariates in downstream analyses.
For sample size considerations, we used published literature employing the 4th generation Roche hsTnT assay (Elecsys) [ 44].
In the present study, we used the same long definitions as did Wagner et al but because of space considerations, we used brief descriptions in the tables.
For all other further considerations, we used minimal initial conditions in which only Vfr, C1 G1, C3 G3, and C5 G3 are initially set to one.
Other considerations we used in evaluating the kits were ease of use, measured by the number of steps involved in the protocol and cost of each labeling reaction (Table 2).
Based on these considerations, we used separate logistic regression analysis models to examine cumulative lifetime home and workplace ETS exposure, rather than including both exposures in the same model.
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