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The structure of the simulated wall flames is studied in terms of a classical mass-mixing variable, the fuel air based mixture fraction, and a less familiar heat loss variable, the excess enthalpy variable, introduced to provide a measure of nonadiabatic behavior due to wall cooling.
The blood loss variable had the most missing values (n = 6/3%).
Material loss variable was divided into three levels: none to minimum loss = 0, moderate loss = 1, and severe loss = 2.
Table 3 reports the estimated effects of a 1-unit (kg or weight cycle) change in the weight loss variable on diabetes incidence and glucose concentrations, HOMA-IR, SBP, and triglyceride concentrations at 2 years.
This is caused by the lack of definitions and describing criteria used for this condition (e.g., quantification of blood loss, variable cutoff limits for estimated blood loss, and linkage to the mode of delivery).
We initially considered an interaction term between post-earthquake financial/material aid (two-level) and earthquake related material loss (three-level), however, as the effect of material loss among those with none to minimum loss and moderate loss were statistically similar, they were collapsed into a single category resulting into a two-level material loss variable.
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A neuro-fuzzy approach was developed to relate the input (flow rate and pipe diameter) and output (CHW and friction loss) variables.
Empirical relationships between loss variables and hazard and exposure factors are presented in this section.
The human loss variables are the number of homeless and injured people, and the fatalities, while considering the affected population (POP.Unit) as an exposure-related variable.
Considering the whole dataset of earthquakes and intensities as the predictor variables (Fig. 4b, Table 7), low-fit models were derived, with adjusted R 2 of 0.23, 0.34, and 0.42 for the H, I, and F human loss variables, respectively.
CatPCA benefits from optimal scaling, handles together nominal (e.g. initial symptoms), ordinal (e.g. cognitive status), and interval (e.g. age at ambulation loss) variables, and is suitable for data recorded with uncertain units (e.g. MMT scores) [21].
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