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Within a computer-aided molecular design (CAMD) framework, a characterization based method was combined with chemometric techniques in a reverse problem formulation to design ionic liquid (IL) structures corresponding to particular physical properties.
Within the computer-aided molecular design (CAMD) framework, a characterization based method was combined with chemometric and property clustering techniques in reverse problem formulation to the design of ionic liquid (IL) structures corresponding to particular physical properties.
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Within a computer-aided molecular design (CAMD) framework, a characterization based method is combined with chemometric and property clustering techniques in a reverse problem formulation for the design of ionic liquid (IL) structures with specific physical properties.
The consistency based diagnosis method is combined with a test selection procedure to produce an original iterative diagnosis method for hybrid systems that reduces diagnosis ambiguity at each iteration.
Moreover, these Wavelet based methods can be combined with polynomial and differentiation based methods to get superior results [38].
As a result, these methods are combined to achieve more robust tracking results such as tracking algorithm based on combining the color and boundary-based methods [7, 8].
Immunochemical and DNA based methods have been combined as PCR- LCR- EIA (Polymerase Chain Reaction - Ligase Chain Reaction- Enzyme Immunoassay) to detect one specific nucleotide present in bovine milk as adulterant in ewe, goat and buffalo milk (Klotz & Einspanier, 2001).
Methylated DNA immunoprecipitation (MeDIP) is a popular enrichment based method and can be combined with sequencing (termed MeDIP-seq) to interrogate the methylation status of cytosines across entire genomes.
Ginter proposed an unsupervised method, based on hidden Markov models, which was combined with latent semantic analysis to define topics of interest without necessarily data annotation; this method could also be used to identify short segments (Ginter et al. 2009).
We use the generalized polynomial chaos based stochastic Galerkin (gPC-SG) method, which is combined with the micro macro decomposition based deterministic AP framework in order to handle efficiently the diffusive regime.
The results of the base selectors are combined using different combination methods, also called aggregators, and a practical subset is selected according to several different threshold values (traditional values based on fixed percentages, and more novel automatic methods based on data complexity measures).
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