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This paper aims to address this challenge by presenting a stochastic geological modeling framework for uncertainty quantification of stratigraphic profiles using sparsely located observation information from geotechnical site investigations.
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This article explains the formation of polarized advocacy or discourse coalitions in this complex phenomenon by presenting a dynamic, stochastic, and discrete agent-based model based on graph theory and local optimization.
This paper mainly focuses on addressing such a complex scheduling problem by presenting a general two-stage stochastic programming formulation, which embeds uncertainty into the qualification management problem.
The effectiveness of the method is demonstrated by presenting a numerical example of a non-linear system subject to stochastic ground acceleration.
In this paper, we attempt to address these issues by presenting an anomaly detection scheme, called SCAN (Stochastic Clustering Algorithm for Network Anomaly Detection), that has the capability to detect intrusions with high accuracy even with incomplete audit data.
We present a stochastic revenue optimization model for CDMA networks inspired by bid pricing models from the airline industry.
In this paper, we present a stochastic programming model to cope with uncertainty brought by cellulosic ethanol technology evolvement in transportation fuel portfolio design problems.
We present a stochastic regime switching SIS epidemic model with vaccination driven by a Lévy noise.
Section 3 presents a stochastic weather model, including global warming.
We present a stochastic approach for solving the problem.
In this study, we present a stochastic multi-objective formulation of the otherwise single objective groundwater optimization problem by considering minimization of prediction uncertainty as an additional objective.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com