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In this paper, a methodology of extracting the globally optimal EF trajectory from dynamic programming (DP) solution is proposed for the design of EF adaptation strategies.
The methodology of extracting and analysing our simulated data is directly analogous to the process that an experimental cell biologist would routinely use in the laboratory and highlights the versatility of the agent-based paradigm and its natural relationship to the experimental world.
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In order to achieve this, we first investigate appropriate methodologies of extracting features from Cloud servers and explore potential features that are suitable for key generation.
It is of particular interest to develop a methodology capable of extracting information of a system under changing oscillations and determine parameters that characterize the structural behavior.
The main results of the paper are to provide a general methodology for extracting parameters of linear models from an experimentally measured scalar function – the transfer function – and a framework for the identifiability analysis of complex model structures using linked models.
We have developed a methodology for extracting characteristic properties of a fitness landscape of interest by analyzing fitness data on an in vitro molecular evolution.
In this article, we demonstrated that the experimental data is consistent with our theoretical equations quantitatively, and that our methodology for extracting characteristic properties of a fitness landscape may be effective.
This paper presents a novel methodology for extracting the underlying trends of signals based on the separations of consecutive empirical mode decomposition (EMD) components in the Hilbert marginal spectrum.
Despite the large number of published maps, a systematic methodology for extracting biological insights remains a major challenge.
Effectiveness of methodology to extract significant subnetworks for networks obtained using variable dataset sources is proved by the comparison results showed in Table 10 and Figure 6.
This paper describes and evaluates a methodology to extract mentions of such risk factors from diabetic clinical notes, which was a task of the i2b2/UTHealth 2014 Challenge in Natural Language Processing for Clinical Data.
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