Chelyabinsk State University

Ambiguity intolerance is a relatively new psychological phenomenon, which manifests its relevance in the conditions of in-stability of the modern world. Intolerance to ambiguity that is often not distinguished with the intolerance to uncertainty, refers to an individual's predisposition to react aversely to ambiguous situations. This tendency manifests across three domains: cognitively, through dichotomous (black-and-white) thinking; affectively, through experiences of anxiety and discomfort; and behaviorally, through the avoidance of ambiguous scenarios. This study aims to fill the existing gap in methodological development of this phenomenon in the Russian context. The aim was to identify psychometric characteristics and adapt the Short Ambiguity Intolerance Scale (SAIS-7) on a Russian sample. The study involved 2,709 respondents aged between 18 and 87 years (M = 34.27, r = 14.73) with different sociodemographic characteristics. Confirmatory factor analysis found the final (ex post) model of the scale, having a single-factor structure in which the indices of consent best meet the original data after adding links between errors of variables: CMIN = 100.909; df = 12; p = .000; GFI = .974; CFI = .990; RMSEA = .052; Pclose = .325. The results of the study allow us to establish acceptable indicators of the internal reliability and convergence. The identified patterns support that the Short Ambiguity Intolerance Scale (SAIS-7) is a reliable and robust tool, extending the scope for empirical studies on the intolerance to ambiguity, including cross-cultural design.

Thermolysis stages of Nb forms of polyantimonic acid that crystallize in the pyrochlore structure and composites based on polyantimonic acid containing Nb5+ ions have been identified in the temperature range 24–700°C. In the INTRODUCTION section, we depict distinctive structural features of polyantimonic acid: the presence of water molecules on particular crystallographic sites and adsorbed water molecules. In connection with this, the objectives of this work were to determine the amount of water of hydration in H2Sb2–хNbxO6∙nH2O samples and model the thermolysis process. Using a number of experimental techniques—X-ray diffraction, thermogravimetry, and IR spectroscopy—we have identified thermolysis stages, determined the phase composition of the thermolysis products, and proposed structural formulas of the synthesized compounds with allowance for the thermolysis model. The amount of water of hydration in the sample with the highest dopant concentration, x = 0.4, has been shown to be n = 5.34; the undoped sample, with x = 0, has n = 3.10. The content of water of hydration in the x = 0.6 composite, having the best proton-conducting properties, is n = 4.85. It seems likely that the proton conduction mechanism is influenced by the nonequivalence of the proton-containing groups, as evidenced by thermal analysis and IR spectroscopy data.

The paper analyzes the results of numerical magnetohydrodynamic (MHD) modeling of the gravitational fragmentation of molecular filaments with a longitudinal magnetic field. Based on the simulations, we study the evolution of the magnetic field of the cores formed as a result of the fragmentation at the filament edges and inside the filament. The simulations show that, during the evolution of the filament, its magnetic field bends towards the boundaries of the gravitationally bound cores. Inside the cores, the magnetic field retains its initial geometry, and its strength anisotropically increases towards the core center. The magnetic field of the cores ranges from 0.18 to 6.3 mG, which agrees with the observed values in the Orion A filament. In the supercritical clouds with a weak magnetic field, the initial stage of core collapse is characterized by a “kinematic” compression across the magnetic field lines and an increase in magnetic field strength proportional to the gas density. In the subcritical clouds with a strong magnetic field, the cores collapse along the filament axis with virtually no increase in the magnetic field strength. In both cases, further increase in gas density and the formation of gravitationally bound cores is accompanied by an anisotropic radial compression of the cores, such that the magnetic flux density depends on the gas density as B ∝ρ^1/3 . The magnetic field distributions in the cores at the filament edges and inside the filament have different degrees of anisotropy, which points to different pathways for the formation of stars in the corresponding parts of the observed filaments.

Assessment of atomic structure of material, depending on the task, usually require computationally expensive numerical simulations or some form of direct human participation. One possible alternative approach is involving different kinds of artificial neural networks, which may, in certain problems, offer a satisfactory compromise between precision, performance and reproducibility. Sample of differently compressed aluminum crystals was considered. Based on generated pictures of residual distribution of defects in their post-compression relaxed state, two tasks of predicting spall strength and identifying prevailing type of lattice at maximum compression were solved by means of convolutional neural networks (CNNs). It is shown that machine learning based method that described in paper is efficient for solving such tasks, even if the algorithm and form of data representation are probably not optimal and dataset size is relatively small, and have perspective for further improvements via pure quantitative enhancements.

A model of chronic predatory stress was effectively reproduced in Danio rerio. It was found that during chronic stress, the fish used an active coping strategy. The results of the novel tank diving test indicate the presence of anxiety and phobic disorders in stressed fish. Behavioral disorders are associated with an increase in serotonin levels with a simultaneous decrease in Z-MAO activity and a decrease in the level of cortisol in stressed fish.