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In the past years several studies have been conducted on nanofluids based on mixtures of nitrates as PCM where KNO3 is one of the components [28 31].
Industrial catalysts for benzene oxidation are based on mixtures of V2O5 and MoO3.
These solvents generally are based on mixtures of a quaternary ammonium or phosphonium salts and an uncharged hydrogen bond donor (HBD), such as an amide, acid, or alcohol.
Polymer electrolytes based on mixtures of lithium trifluoromethylsulfonylimide, LiTFSI and lithium oligoether sulfates dissolved in poly(oxyethylene) were studied.
Other existing approaches, based on mixtures of Gaussian random functions estimated via the expectation-maximization algorithm, are limited to reasonable sample sizes and a small number of variables.
They are based on Mixtures of Truncated Exponentials (MTE) designed to deal with discrete and continuous variables in the same network simultaneously without any restriction.
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Both problems can be tackled by limiting the anodic voltage cutoffs in lithium cells and by the use of innovative electrolytes based on mixture of an ionic liquid and the standard carbonate-based solvent blend.
Six PCMs were used including paraffin wax (as reference material), two materials based on mixture of inorganic hydrated salts, two materials based on mixture of organic substances and one material based on a mixture of both organic and inorganic materials.
While the SR estimation is based on mixture of two GLDs and MLE is based on a single GLD, this comparison is not unfair.
This paper presents a novel method for bioprocess hybrid parametric/nonparametric modelling based on mixture of experts (ME) and the expectation maximisation (EM) algorithm.
In this paper, we provide an advanced parametric fit, based on mixture of Weibull distributions, and compare it with the single Weibull distribution model obtained with the Maximum Likelihood Estimation (MLE) method.
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