The presented work deals with the creation of a new radial basis function artificial neural network-based model of dynamic thermo-mechanical response and damping behavior of thermoplastic elastomers in the whole temperature interval of their entire lifetime and a wide frequency range of dynamic mechanical loading. The created model is based on experimental results of dynamic mechanical analysis of the widely used thermoplastic polyurethane, which is one of the typical representatives of thermoplastic elastomers. Verification and testing of the well-trained radial basis function neural network for temperature and frequency dependence of dynamic storage modulus, loss modulus, as well as loss tangent prediction showed excellent correspondence between experimental and modeled data, including all relaxation events observed in the polymeric material under study throughout the monitored temperature and frequency interval. The radial basis function artificial neural network has been confirmed to be an exceptionally high-performance artificial intelligence tool of soft computing for the effective predicting of short-term viscoelastic behavior of thermoplastic elastomer systems based on experimental results of dynamic mechanical analysis.

The article deals with the possibility of natural fibers and textiles coating, especially of cotton. The coating methods most used in industrial sector are: PVD and CVD. Cotton is one of the most important natural cellulosic fibers in terms of processing volume and properties. Creating PVD coatings on non-metal fabrics is a new unconventional approach, which is not very investigated. It's starting area of new materials creating, and this article deal with it. The coating is based on the releasing of coated material from the source target and transfer to the coated object surface and create a thin film. PVD coating can be performed by several methods: Arc, Sputtering, Vacuum evaporation, Cathode sputtering, Magnetron sputtering. By coating of textile materials it is possible to made new materials with specific properties like fabric electrically and thermally conductive, fabric that filter bacteria and viruses, fabric which gives tires the new features in form of embedded sensors

The application of nitrogen fertilizers allows increasing crop yields and partially increase the natural soil fertility. They have a negative influence on the environment due to the significant release of nitrogen. Hence, the technology for decreasing of N-release is proposed in this work. The...

In this study, a new generalized regression neural network model for predicting the curing characteristics of rubber blends with different contents of carbon black filler cured at various temperatures is proposed for the first time The carbon black contents in the rubber blend and cure temperature were used as input parameters, while the minimum and maximum elastic torque, scorch time, and optimal cure time, obtained from the analysis of 11 rheological cure curves registered at 10 various temperatures, were considered as output parameters of the model. A special pre-processing procedure of the experimental input and target data and the training algorithm is described. Less than 55% of the experimental data were used to significantly reduce the total number of input and target data points needed for training the model. Satisfactory agreement between the predicted and experimental data, with a maximum error in the prediction not exceeding 5%, was found. It is concluded that the generalized regression neural network is a powerful tool for intelligently modelling the curing process of rubber blends even in the case of a small dataset, and it can find a wide range of practical applications in the rubber industry.

Journal of Nano- and Electronic Physics. Scientific journal. ISSN: 2077-6772. Journal abbreviation: J. Nano- Electron. Phys. No page charges. All articles are freely available on-line. Issued 4 times per year. Publisher: Sumy State University. (Sumy, Ukraine)