Ab initio calculations and X-ray diffraction experiments were carried out to study the structure of solutions of calcium chloride in water and methanol. Ab initio calculations were performed at MP2 level and density functional calculations at B3LYP level on calcium-water and calcium-methanol clusters yielding the formation of stable calcium-water clusters with up to eight water molecules and calcium-methanol clusters with up to seven methanol molecules. The experiments were performed in a wide concentration range both in water and in methanol (1-6 M and 1-2 M, respectively). The coordination number of the cation in low-concentration (1 M) aqueous and methanol solutions could only be determined with great uncertainty due to the low weights of cation-solvent contributions to the X-ray scattering intensity for both series of solutions. It was found that in 1 M solutions the Ca 2+ ion is surrounded by eight (five to ten) water and six (four to seven) methanol molecules, respectively. The coordination numbers decrease with an increase in concentration. The accuracy of the coordination numbers determined increases with increasing concentration. The solvation shell of Clion is composed of six solvent molecules in each solution. We have found evidence of both contact and solvent-separated Ca-Cl ion pair formation at higher concentrations. On the basis of the stoichiometry of the solution and structural parameters obtained, different models are suggested to explain the liquid structure of the solutions.

To determine the structure of aqueous sodium hydroxide solutions, results obtained from x-ray diffraction and computer simulation (molecular dynamics and Car-Parrinello) have been compared. The capabilities and limitations of the methods in describing the solution structure are discussed. For the solutions studied, diffraction methods were found to perform very well in describing the hydration spheres of the sodium ion and yield structural information on the anion’s hydration structure. Classical molecular dynamics simulations were not able to correctly describe the bulk structure of these solutions. However, Car-Parrinello simulation proved to be a suitable tool in the detailed interpretation of the hydration sphere of ions and bulk structure of solutions. The results of Car-Parrinello simulations were compared with the findings of diffraction experiments.

Molecular dynamics simulation has been performed to study the structure of water-methanol mixtures. Besides the evaluation of partial radial distribution functions describing the hydrogen-bonded structure of the mixtures with different composition, the statistical analysis of configurations was introduced resulting in a new insight in the clustering properties and topology of hydrogen-bonded network. The results have shown that mixtures of methanol and water exhibit extended structures in solution. At low methanol concentration the water molecules form a percolated network, the methanol molecules are incorporated as monomers or short chains and together form a percolated system. In methanol-rich mixtures short water chains and longer methanol chains build up the hydrogen-bonded clusters in the system. On the basis of the statistical analysis of configurations obtained from molecular dynamics simulation it has been found that more methanol molecules are connected to non-cyclic entities, while more water molecules form rings that might have been predicted on the basis of the stoichiometry of the mixtures. This finding can be explained by the presence of microscopic configurational inhomogeneity in water-methanol mixtures.

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In this paper we present our initial results in articulatory-toacoustic conversion based on tongue movement recordings using Deep Neural Networks (DNNs). Despite the fact that deep learning has revolutionized several fields, so far only a few researchers have applied DNNs for this task. Here, we compare various possible feature representation approaches combined with DNN-based regression. As the input, we recorded synchronized 2D ultrasound images and speech signals. The task of the DNN was to estimate Mel-Generalized Cepstrum-based Line Spectral Pair (MGC-LSP) coefficients, which then served as input to a standard pulse-noise vocoder for speech synthesis. As the raw ultrasound images have a relatively high resolution, we experimented with various feature selection and transformation approaches to reduce the size of the feature vectors. The synthetic speech signals resulting from the various DNN configurations were evaluated both using objective measures and a subjective listening test. We found that the representation that used several neighboring image frames in combination with a feature selection method was preferred both by the subjects taking part in the listening experiments, and in terms of the Normalized Mean Squared Error. Our results may be useful for creating Silent Speech Interface applications in the future.