Ecology factor and Venom of snake Macrovipera lebetina obtusa

In this article presents experimental data, the basic composition of the venom of Macrovipera lebetina obtusa, captured from different regions of Azerbaijan, differing in degree of contamination by industrial emissions was studied. γ – radiospectrometric studies showed that the samples of venom also contain radionuclides as Ra228, Ra226, K40 and 137 Cs. It was established that the radiation dose (up to dose 1.35 kGy) for 3 minutes did not cause structural changes in the samples venom of vipera, but rather contribute to the stabilization of both toxicity and pharmacological activity while increasing the shelf life of aqueous solutions of vipera venom. At high doses (2.7, 4.05 and 5.4 kGy) γ-irradiation for 3 minutes there was a gradual decrease in toxicity (pharmacological activity of enzymes) of snake venom. We can assume that these data can be used in the identification of zootoxins and their metabolites, and these criteria can serve as a theoretical basis for the development of effective methods for diagnosis of poisoning zootoxins

Influence of Different Types of Radiation on the Crystal Structure of Silicon Monocrystals n-Si

In this work, the influence of alpha particles, protons and gamma rays on the crystal structure and structural characteristics of n-type silicon (n-Si) single crystals was studied using X-ray diffraction. N-type silicon (KEF-40) was used for the study. The samples were irradiated with protons with a dose of 9×1014 cm-2 with an energy of 600 keV and a current of 1÷1.5 µA, irradiated with alpha particles with a dose of 6×1014 cm-2 with an energy of 800 keV and a current of 0.5÷1 µA and γ− 60Co quanta with a flux intensity of ~ 3.2×1012 quantum/cm2·s. Based on the results of X-ray diffraction analysis, it was established that distortions, vacancies and amorphization of lattice parameters that arose after irradiation lead to an increase in lattice parameters

Effect of the C/N ratio modification on the corrosion behavior and performance of carbonitride coatings prepared by cathodic arc deposition

This study focuses on investigating carbonitride coatings, specifically CNTi-(Zr, ZrNb, and ZrSi), as promising candidates for enhancing the durability and efficiency of Ti6Al4V materials used in nuclear fusion technology. X-ray diffraction analysis identified distinct phases, including TiN, ZrN, ZrC, and TiC. The corrosion studies showed complete degradation of the TiN, ZrC, and ZrN phases in the TiZrCN coating after tests, while the TiC phase exhibited relative stability. The surface morphologies and elemental mapping analysis demonstrated the loss of homogeneity in element distribution after corrosion process. The addition of Si and Nb elements into TiZrCN significantly influenced the coatings' corrosion behavior, with breakaway corrosion observed in CNTi- (Zr and ZrSi) coatings and localized corrosion in CNTi-(ZrNb) coatings. Notably, the CNTi-(ZrSi) coating formed an oxide phase in the presence of NaCl, whereas the CNTi-(ZrNb) coating exhibited continuous resistance and a low corrosion rate. Irradiation was carried out for the generation of active isotopes, showing that no radioactive isotopes were formed in any of the investigated samples

A multi-biometric iris recognition system based on a deep learning approach

YesMultimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris- V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person

Application of an electronic nose coupled with fuzzy-wavelet network for the detection of meat spoilage

Food product safety is one of the most promising areas for the application of electronic noses. During the last twenty years, these sensor-based systems have made odour analyses possible. Their application into the area of food is mainly focused on quality control, freshness evaluation, shelf-life analysis and authenticity assessment. In this paper, the performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillets stored either aerobically or under modified atmosphere packaging, at different storage temperatures. A novel multi-output fuzzy wavelet neural network model has been developed, which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the relevant quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population. Comparison results against advanced machine learning schemes indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology

Peculiarities of TP-e.m.f. caused by the heating of charge carriers by an electric field in a layered semiconductor n-InSe

The effect of the external and intracrystalline factors (temperature, light, and the magnitude of the initial dark resistivity of the sample, electric field, chemical nature, and amount of the impurities) on the main characteristics of layered n-InSe crystals was investigated. The thermophoto-e.m.f. (TP-e.m.f.) was observed due to the heating of free charge carriers by an electric field. It has been established that the obtained experimental results differ significantly from spatially homogeneous semiconductors. This deviation increases with an increase in the value of the initial dark resistivity of the sample (ρD0) which depends nonmonotonically on the concentration of the impurity (NREE). Undoped (with the lowest ρD0) and rare-earth-doped (NREE ≥ 5·10–2 at.%) samples were studied under all conditions, as well as at high T0 and I0, and it was determined that the TP-e.m.f. characteristics of hot current carriers (HCC) are the most stable and reproducible. The obtained results satisfactorily correlate with the provisions of the theory of TP-e.m.f. of HCC in spatially homogeneous semiconductors. The dependence of the characteristics of TP-e.m.f. of HCC from ρD0 and NREE clearly explains the deviations compared to spatially homogeneous semiconductors considering the presence of random macroscopic defects in the samples

Key to the rust fungi of the Kazakhstan

For identification of the 259 species from 21 genera of rust fungi of Kazakhstan, a key based on the systematic position of the host plant, symptoms of infected plants and microscopic features of fungal pathogens was prepared. 786 species (Polypodiophyta – 3 species, Pinophyta – 8, Magnoliophyta – 775) are recorded as host plants. Six species of fungi that cause rust (Puccinia acanthii P. Syd. et Syd., Puccinia calthae Link, Pucciniastrum goodyerae (Tranzschel) Arthur, Uredinopsis macrosperma (Cooke) Magnus, Uromyces cobresiae Korbonsk., Uromyces lineolatus (Desm.) J. Schröt.), and 29 species of host plants are new to Kazakhstan.</p

Effect of Si and Nb additions on carbonitride coatings under proton irradiation: A comprehensive analysis of structural, mechanical, corrosion, and neutron activation properties

In the present study, understoichiometric TiZrCN, TiZrNbCN, and TiZrSiCN coatings were produced using the cathodic arc technique with a C/N ratio of approximately 0.5 to investigate their potential use in nuclear technology. The coatings were evaluated for their corrosion resistance in 3.5 % NaCl and neutron activation. The effect of adding Si and Nb to the quaternary TiZrCN system was also investigated. The results showed that the addition of Si (∼4.64 at.%) to the matrix of TiZrCN improved their electrochemical properties in NaCl solution, the protective efficiency was 92%, while the Nb addition (∼5.5 at%) lead to the decrease in corrosion resistance by 1.39 times comparing with TiZrCN. Furthermore, after fast neutron irradiation at a nominal power of 1450 kW, none of the coatings were activated, indicating good radiation resistance. It was determined from the structural analysis that the Ti6Al4V substrate before corrosion consists of hexagonal and cubic space groups with different lattice parameters. By adding Si and Nb, a small amount of ZrO2 and Si3N4 was detected along with the main phases in the TiZrCN structure