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The proposed methodology utilizes an approximate steady state model that is assumed to be known a-priori.
The proposed Security Vulnerability Assessment, Prevention and Prediction (SVAPP) methodology utilizes an existing safety barrier approach and adapts it to suit the security facet.
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The underlying methodology utilizes a cyclic control algorithm based on a mean orbital elements feedback.
The methodology utilizes a Delaunay triangulation to represent surface fire spread within the landscape.
Our methodology utilizes a hybrid genetic algorithm neural network strategy (GA ANN).
The methodology utilizes a nitrogen-containing plasma polymer to achieve linker-free binding of a layer of biorecognition molecules.
Our methodology utilizes a cyclic-order solution encoding, which maps a permutation of the customer set to a collection of many possible VRP solutions.
The proposed methodology utilizes a variable leaky factor to eschew wavering of weights involved in the estimation and extraction process of current references.
The proposed control methodology utilizes a backstepping procedure integrated with multiple estimation models to estimate failure induced parametric uncertainties and unknown system parameters.
The proposed methodology utilizes a multi-tier mechanism to detect SQL attacks while maintaining the speed and user experience of the web application.
This methodology utilizes a modified Heliosat-2 model, and applies for all sky conditions; it also introduces a horizon calculation of the DTM points and accounts for the effect of snow covers.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com