Effects of microstructure factors on the acoustic performance of open-cell foams can be characterized numerically by a microstructure-based approach. To this regard, the numerical homogenization approach and the equivalent-fluid theory are employed to study the acoustic behavior of random open-cell foams within their Voronoi tessellation-based Representative Volume Elements (RVE). As a validation step, the numerical predictions are compared with the reference findings to either verify the finite element procedure or demonstrate that the constructed RVE can capture both the local geometrical characteristics and the acoustic macrobehavior of cellular solid foams. It can be seen from the obtained results that the morphological characteristics of open-cell foams could be controlled to achieve the desired sound absorbing behavior. In addition, the analytical expressions, formulating the relationship between the geometry of foam absorbers and their target absorption performance, are established to design sound absorbing foam layers.

Electrocatalytic gas sensors belong to the family of electrochemical solid state sensors. Their responses are acquired in the form of I-V plots as a result of application of cyclic voltammetry technique. In order to obtain information about the type of measured gas the multivariate data analysis and pattern classification techniques can be employed. However, there is a lack of information in literature about application of such techniques in case of standalone chemical sensors which are able to recognize more than one volatile compound. In this article we present the results of application of these techniques to the determination from a single electrocatalytic gas sensor of single concentrations of nitrogen dioxide, ammonia, sulfur dioxide and hydrogen sulfide. Two types of classifiers were evaluated, i.e. linear Partial Least Squares Discriminant Analysis (PLS-DA) and nonlinear Support Vector Machine (SVM). The efficiency of using PLS-DA and SVM methods are shown on both the raw voltammetric sensor responses and pre-processed responses using normalization and auto-scaling