Zinc cadmium oxide (ZnCdO) synthesis of zinc-oxide-based nanomaterials with specific properties is a great challenge due to its excellent industrial applications. We report the production of zinc cadmium oxide thin films as a first step for developing a n-ZnCdO/p-Si heterojunction.

The study of the cross sections of the knocking out nucleons provides important infor-mation about the asymptotic behaviour of the scattering amplitude. During the interac-tion of the incident particle with nucleus, its function is distorted. In this case, we are talking about those components of the wave function for which the probability of inter-action with the target is small, i.e. during the collision, only a small part of the target is excited (an elastic collision with only one of its constituent particles). To do this, it is necessary to use the "point" component of the wave function of the incident particle.

The influence of the silicon nanoparticles on the structure and optic properties of the polystyrene-based nanocomposites was investigated. PS+Si nanocomposites were prepared by combinations of the solution blending and hot pressing methods. The EDS spectrum of Silicon characterizes the high purity of nanoparticles. The XRD study of the nanocomposite samples shows that silicon nanoparticles play not only the role of filler but also have a structure-formatted function in the nanocomposite. PL properties of the polymer nanocomposite also prove that silicon nanoparticles, play the role of the center of the structure to format a more ordered arrangement in the polymer matrix. It was clear that the PL emission of the nanocomposite enhanced with the introduction of the silicon nanoparticles at the 450-550 nm region. This increase results in the expansion of the emission region of the pure polystyrene by the addition of a few amounts of silicon nanoparticles. This PS+Si nanocomposite could be an effective material for biomedical applications as a fluorescent marker for drug delivery systems.

Soft magnetic amorphous materials are considered a new type of magnets. Although amorphous materials have a wide scope in modern times, new areas of application can be obtained by improving their properties. In this study, as a result of heat treatment of the composition Fe59Ni19Si9B13 and Fe39Ni39Si9B13, materials with properties suitable for operation were obtained.

In the work, two multivariate analysis methods Neural Network (NN) and Boosted Decision Tree (BDT) were used to separate the ZH(bb ̅) signal from the background and the results obtained from them were compared. The list of input variables for BDT and NN is similar to those used in the analysis in the ATLAS experiment. Up to 0.8 million signals and the same number of background events were used for training and testing. The settings used in the ATLAS analysis, which has the best performance, were chosen to tune the BDT hyperparameters. Various number of events (0.1M, 0.2M and 0.8M) are trained and different settings for NN are obtained, providing performance that exceeds that of BDT. It turns out that for any number of training events, it is possible to find corresponding NN settings with better performance than BDT. The problem with NN training is that it is computationally intensive compared to BDT.