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The key issues to implement such a model updating are discussed such as sampling with design of experiments, selecting the significant updating parameters and constructing a quadratic polynomial response surface.
The key issues to implement such a model updating are discussed in this paper, such as design of experiment, parameter screening, construction of high-order polynomial response surface model, optimization methods and precision inspection of RS model.
Therefore, our aim is to implement such a model with our experimental data on the surface free energy of supported and embedded Au NCs on/in SiO2 to simulate (i.e. to predict) their cohesive energy, melting temperature and vacancy formation energy as a function of the surface free energy and size.
The CHWs in the Gauteng case studies lacked the resources to implement such a model.
The provision of staff development and support to implement such a model (including training in skilled communication to identify and respond to emotional distress), and structured case review for complex or challenging cases.
A Bayesian Markov chain Monte Carlo (MCMC) algorithm appears necessary to implement such a model, by extending the work of Rannala and Yang (2003) and Hey and Nielsen (2004).
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However, implementing such a model within a data centre (having homogenous and/or heterogenous resources) is very difficult since it needs calibration whenever a new hardware is installed within the existing servers.
Yet, implementing such a model may require deep changes in the phylogenetic algorithm.
A recent work by (Paisley and Carin, 2009) implemented such a model for factor mixtures using a beta process prior.
The effect of implementing such a model in clinical practice is an important empirical question for future research.
This is the first attempt to create and implement such a holistic model of range-wide conservation for a large carnivore species.
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