Microscopic dynamic mechanism of equilibration in crystals: one-dimensional model

The dynamics of free vibrations of a chain of atoms is investigated taking into account the retardation of interactions. It is shown that all oscillations of the circuit are damped. The dynamics of forced vibrations of this chain of atoms is investigated. It is shown that, regardless of the initial conditions, the system passes into a stationary state of dynamic equilibrium with an external field, which depends both on the properties of the system and on the parameters of the external field. A non-statistical dynamic mechanism of the process of irreversible establishment of the state of thermodynamic equilibrium in both many-body and few-body systems is proposed.

Microscopic Dynamic Mechanism of Irreversible Thermodynamic Equilibration of Crystals

The dynamics of free and forced vibrations of a chain of particles are investigated in a harmonic model taking into account the retardation of interactions between atoms. It is found that the retardation of interactions between particles leads to the non-existence of stationary free vibrations of the crystal lattice. It is shown that in the case of a stable lattice, forced vibrations, regardless of the initial conditions, pass into a stationary regime. A non-statistical dynamic mechanism of the irreversible thermodynamic equilibration is proposed.

Improving the quality of identification and filtering of micro-object images based on neural networks

Constructive approaches, principles, and models for optimizing the identification of micro-objects have been developed based on the use of combined statistical, dynamic models and neural networks with mechanisms for filtering noise and foreign particles of images of medical objects and pollen grains. Algorithms for learning neural networks under conditions of a priori insufficiency, uncertainty of parameters, and low accuracy of data processing are investigated. The mechanisms of contour selection, segmentation, obtaining the boundaries of segments with hard and soft thresholds, filtering using the morphological features of the image have been developed [1]. Mechanisms for recognition and classification of images, adaptation of parameter values, tuning of the network structure, approximation and smoothing of random emissions, bursts in the image contour are proposed. A mechanism for suppressing impulse noise and noise is implemented based on various filtering methods, preserving the boundaries of objects and small-sized parts. Mathematical expressions are obtained for estimating the identification errors caused by nonstationarity, inadequacy of approximation, interpolation, and extrapolation of the image contour. A software package for the recognition and classification of micro-objects has been developed. The results were obtained for correct, incorrect recognition, as well as rejected pollen samples, which were synthesized with cubic, biquadratic, interpolation spline-functions and wavelet transforms.

Technique for assessing the probability of survival of structural members with delaminations

The paper presents a technique of quantitative assessment of probability of failure-free operation of composite structures containing an ensemble of defects in the form of delamination. This method is based on approaches of general reliability theory. The proposed method was validated relative to the assessment of reliability of a composite conical bay with multiple delaminations generated under loads at the stage of inserting the launch vehicle into orbit. The statistical dynamic problem was solved by using Monte Carlo method which was implemented by means of algorithms developed by the author in ANSYS software. Nonlinear analysis of the conical bay buckling was carried out for each implementation. Quantitative assessment of survival probability was conducted on the basis of the hypothesis of the law of Gaussian distribution of load-bearing capacity and using the graphical method of reliability calculation. Good agreement of the results obtained by both methods was noted.

Continuous components of solar activity oscillation spectrum and forecasting of solar activity

<p>Spectrum of solar activity oscillations contains apart from the well-known 11-year activity cycle a continuous component, which includes, in particular, quasy-biennual oscillations as well as long-term oscillations including so-called Gleisberg cycle.  We suggest to consider the mid-term solar variability in terms of statistical dynamic of fully turbulent systems, where solid arguments are required to accept an isolated dominant frequency in a continuous (smooth) spectrum. What about the timescales longer than the Schwabe cycle, we consider them as a presence of long-term memory in solar dynamo and discuss statistical test for veryication of this interpretation. Sequences for statistical long-term forecast of solar activity are discussed.</p>

THE CURRENT TRENDS AND FACTORS IN THE DEVELOPMENT OF MORTGAGE HOUSING LENDING IN RUSSIA

The presented work contributes to the development of the studies evaluating the key determinants of mortgage housing lending development in Russia. Despite a relatively well-developed body of research in the field of mortgage housing lending, devoted to revealing the essence, functional purpose, and implementation mechanism, the works, aimed at identifying the factors influencing its development in the current economic environment in Russia, are under-represented in the Russian scientific field. This study aims to assess the dynamics of mortgage housing lending in the contemporary economic conditions and to identify the determinants of its defining. The research methodology is based on the use of statistical dynamic analysis and correlation-regression analysis. The work is based on the application of methods of statistical dynamic analysis and correlation-regression analysis. Assessment of the dynamics has shown the general trend towards an increase in the volume of mortgage housing loans. However, some periods have been noted to have a negative dynamic primary related to the negative external environment and stagnation of the economy, due to the COVID-19 pandemic. Additionally, this article presents the assessment of the qualitative indicators characterizing the level of penetration and the degree of aggressiveness of lending institutions’ policies on housing mortgage lending. The authors have built a panel data model that has allowed identifying the key determinants of the development of residential mortgage lending in Russia. The results have revealed that the state of the mortgage lending sector in Russia is influenced by such factors, as the commissioning of residential buildings, the cost in the primary real estate market, weighted average interest rates, and the unemployment rate.

Multiyear Statistical Prediction of ENSO Enhanced by the Tropical Pacific Observing System

The theoretical predictability limit of El Niño–Southern Oscillation has been shown to be on the order of years, but long-lead predictions of El Niño (EN) and La Niña (LN) are still lacking. State-of-the-art forecasting schemes traditionally do not predict beyond the spring barrier. Recent efforts have been dedicated to the improvement of dynamical models, while statistical schemes still need to take full advantage of the availability of ocean subsurface variables, provided regularly for the last few decades as a result of the Tropical Ocean–Global Atmosphere Program (TOGA). Here we use a number of predictor variables, including temperature at different depths and regions of the equatorial ocean, in a flexible statistical dynamic components model to make skillful long-lead retrospective predictions (hindcasts) of the Niño-3.4 index in the period 1970–2016. The model hindcasts the major EN episodes up to 2.5 years in advance, including the recent extreme 2015/16 EN. The analysis demonstrates that events are predicted more accurately after the completion of the observational array in the tropical Pacific in 1994, as a result of the improved data quality and coverage achieved by TOGA. Therefore, there is potential to issue long-lead predictions of this climatic phenomenon at a low computational cost.