This paper reviews in details the state-of-the-art related to analysis, detection, dynamical modeling and elimination of the slug within the offshore oil & gas Exploration and Production (E&P) processes.
The papers address all current issues in automated deduction and theorem proving based on resolution, superposition, model generation and elimination, or connection tableau calculus, in first-order, higher-order, intuitionistic, or modal logics, and describe applications to geometry, computer algebra, or reactive systems.
The overall aim of our investigation was to develop a procedure of system identification in which the parameter inference acts as tool of model refinement and elimination of false positive interconnections.
Univariate predictors with a two-sided p-value ≤0.2 were included in the multivariate model, and backward elimination was applied to obtain a final model; two-sided p-values ≤0.05 were considered significant.
By implementing the SBL model and backward elimination (BE) procedure, we obtained the candidate CNA regions.
We used a simple 1-compartment, first-order pharmacokinetic model and assumed elimination rates as previously estimated from a worker population (11).
For the copy number data in the first dataset, we used a sparse Bayesian learning (SBL) model and backward elimination (BE) procedure to find candidate CNA regions.
At the start, all variables were included in the model and backward elimination process was employed to remove non-significant variables.
Once the most parsimonious main-effects model was identified, all two-factor interactions were introduced into the model and stepwise elimination of non-significant terms was undertaken (again based on the model deviance statistic) until the final model was obtained.
However, the regression of the melanoma growth, which was induced by depletion of Foxp3+ Tregs, was critically dependent on the presence of CD8+ T cells in this model and additional elimination of those cells resulted in ongoing tumor progression [ 46].
