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In the subsequent models, we added influence factors at the personal and institutional level.
Moreover, to ensure that the synthesised slip distributions resemble the original slip models, we added a constraint on the location and extent of the rectangular high-slip patch (asperity zone).
In further models, we added two variables describing the home neighborhood of the student participant, the median household income of the neighborhood and the percentage of the neighborhood population that is White and finally variables representing the geographic regions of the country.
To further establish the quality of the neural and behavioral models, we added cross-validation tests.
In subsequent models, we added baseline hypertension and diabetes mellitus as covariates.
In addition to factors common to both procedure models, we added selected covariates to specific procedure volume analyses.
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In the context of general models, we add the additional axiom schema for choice: ∀n ∃X φ(n,X) → ∃Y ∀n φ(n, {t | Ynt}) prefixed by universal quantifiers as needed.
In addition to the constructional selection, which can be illustrated by the NK model, we added environmental selection into the simulation and explored technological innovation as the result of the interaction between these two kinds of selection.
Because age was included in the second-best model, we added age to unpacked models in a separate analysis (not shown).
To this model we added the MAMC intervention, cost parameters and details on ART.
In the second model, we added potential confounding variables.
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