In terms of the CO2 capture process, the DBN model predicts CO2 production rate and CO2 capture level using the following variables as model inputs: inlet flue gas flow rate, CO2 concentration in inlet flue gas, pressure of flue gas, temperature of flue gas, lean solvent flow rate, MEA concentration and temperature of lean solvent.
We estimated agricultural use of methyl bromide near each woman's residence using the following variables from the 1999 2000 PUR data: pounds of active ingredient applied, application date, and location.
Due to the high percentage (13.1%) of missing information on birth weight, we performed the multiple imputation procedure by logistic regression to correct for this loss of information, using the following variables in the estimation of missing data: gender, age, education, leg and trunk lengths, and maternal education.
The model revealed that highest concentration factors were gained using the following variables and variable interactions: cationic cetyl trimethylammonium bromide (CTAB), lower surfactant concentrations, and CTAB combined with high column heights.
Based on the optimized condition, highest conversion of 97.5% was predicted using the following variables; reaction temperature = 423 K, reaction time = 3 h, molar ratio of oil to methanol = 1 12 and amount of catalyst = 8 g* g−1.
This was evaluated using a definitive screening design (DSD) approach in which fourteen batches were produced under predefined and controlled conditions using the following variables: addition of SiO2 nanoparticles, mixing speed, cooling rate, heating temperature, container filling temperature and isothermal mixing at the filling temperature.
These equations are a function of retro-time τ, which is the time-to-go, obtained using the following variable change: tau=t_{f}-t (7).
These equations are a function of retro-time τ, which is the time-to-go, obtained using the following variable change: tau=t_{f}-t (7) where t f is the termination time of the game.
Finally, we also control for the self-assessed income level of respondents as a proxy for their socio-economic status (Chung and Meuleman 2017) using the following variable: Q57.
Clustering was performed using the following variable parameters: minimum percent identity for overlaps (94%), minimum overlap length (30 nt) and maximum length of unmatched overhangs (30 nt).
Database searches were performed using the following variable modifications: SILAC heavy arginine: C6-N4 (MW = 10.01), and SILAC heavy lysine: C6-N2 (MW = 8.01).
