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We apply this approach to solve two problems we have encountered in practice: (i) synthesizing models for relevant library functions, and (ii) removing dead code that causes imprecision in the analysis.
For that, it is possible to: parse the models for relevant features to the application of the functional technique, like the input variables and their types.
This has been a matter of some controversy, since not only is the ternary relation unfamiliar to those whose exposure is primarily to modal logics with a simpler binary accessibility relation between possible worlds, but also because of the novelty of the treatment of negation in models for relevant logics.
Rather than using NHANES sample weights, we adjusted all models for relevant covariates (age, sex, race/ethnicity, and sample cycle).
Since the high humiliation group had N = 65, we could only enter six variables into the multivariate analysis, and we therefore tested several models for relevant variables to be entered.
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In this paper, we use JADE algorithm together with MLP audio-visual model (for relevant source selection) as the video assisted JADE (denoted by JADE-AV).
All models adjusted for relevant individual, household, and neighborhood characteristics.
We tested for exposure-response relationships using logistic regression models, controlling for relevant covariates.
These results remained significant in multivariate logistic regression models controlling for relevant variables including PTSD, MDD, and TBI.
These relations were not observed for urine Cd in models adjusted for relevant covariates.
The estimates were similar in models adjusted for relevant confounders (i.e., smoking and early menopause).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com