The Welfare and Public Health of the Population of Russia: Adaptation To Economic Volatility

In the article, the results of the research of correlation of welfare and public health of the population of Russia in the conditions of economic instability are presented. The review of performance indicators of development of public sentiments of society applied both in Russian and foreign practice is submitted. The concept content of the “social and psychological potential of a region” as an indicator of public health of the population is opened. On the basis of this concept, the potential pattern is created. The evaluation method of social and psychological potential of a region is developed, its main idea is an integrated assessment of both the potential of a region in general and its separate components. The assessment of the condition of potential in territorial subjects of the Russian Federation is given. Character and power of correlation between indicators of welfare and level of social and psychological potential of territorial subjects of the Russian Federation on the basis of development of correlation matrixes are revealed, also, the regional consistent patterns and tendencies are determined.The study was funded by a grant from the Russian Science Foundation (project No. 14-18-00574, “Anti-crisis”, an Information Analysis System “: Diagnostics of the Regions, Estimation of Threats and Scenario Forecasting in Order to Maintain and Reinforce Economic Security and Enhance the Well-Being of Russia)

Pattern Transitions in Unstable Viscous Convective Medium

Convection in a thin layer of liquid (gas) with temperature dependent viscosity between poorly heat conducting boundaries is studied within framework of the Proctor-Sivashinsky model. This model is examined in order to study both the flow pattern formation and the second-order structural phase transitions as between patterns with translational invariance as well as between structures with broken translational invariance but keeping a long-range order. The spatial spectrum of arising patterns and estimation of their visual defectiveness are analyzed. The relation between the density of pattern defects and spectral characteristics of the pattern is found. We also discuss the noise effects on the formation of pattern defects. The influence of temperature dependence of viscosity on the process of pattern formation and structure transformations is also discussed. It is shown that the temperature dependence of viscosity inhibits structural transition from regular rolls to square cells

Economic tomography: the possibility to anticipate and respond to socio-economic crises

The article discusses an approach based on an original hypothesis related to the peculiarities of Russia’s development (on the one hand, its scale, the Russian mentality and a certain closeness of the economy; on the other hand, a significant dominant resource and human potential, and, as a consequence, a genuine role in the global economic community), the diagnosis of which (at the level of the well-being of individuals and inhabited areas) can be used to identify crises, provide an early assessment of threats to socio-economic development of regions as well as help to evaluate the state of the region over a 3 to 5 year period. In other words, in order to ensure that executives have enough time to mount a sufficiently rapid response to the crises and administrative errors and to reduce the impact of emerging threats. The aim of this paper is to present theoretical and methodological tools for the recognition of the early stages of emerging threats, allowing fewer losses to be experienced during the crisis period. Simulation experiments were carried out for the purpose of classifying previously occurring social and economic crises (9 possible variants were reviewed) and mathematically processed trajectories of change in the main indicators for the well-being of individuals and inhabited areas, taking the influence of various factors into account. On the basis of the authors’ proposed approach (referred to as economic tomography) an attempt is made to comprehensively assess the state of sample representative regions of Russia.The research has been supported by the Russian Science Foundation (project № 14–18–00574 'Information-analytical system "Anticrisis:" diagnostics of the regions, threat assessment and scenario forecasting for the preservation and strengthening of economic security and well-being of Russia')

Compact Precomputed Voxelized Shadows Construction on GPU

We consider the problem of producing high-quality shadows in real-time for 3D computer graphics software. In [1, 4] authors have proposed new data structure for object geometry representation by binary voxel grid. This binary data was packed to directed acyclic graph — traditional sparse voxel octree with merged identical subtrees. This approach has been extended to shadowing by voxelizing shadow volumes instead of object geometry [2, 3]. Obtained structure enables high-quality filtered shadows to be reconstructed for any point in the scene in real-time. In [1–4] authors have used CPU-based bottom-up algorithm that reduces sparse voxel octree to minimal directed acyclic graph. In the present paper we construct new parallel algorithm for such reduction that runs entirely on GPU

Trainable Regularization in Dense Image Matching Problems

This study examines the development of specialized models designed to solve image-matching problems. The purpose of this research is to develop a technique based on energy tensor aggregation for dense image matching. This task is relevant within the framework of computer systems since image comparison makes it possible to solve current problems such as reconstructing a three-dimensional model of an object, creating a panorama scene, ensuring object recognition, etc. This paper examines in detail the key features of the image matching process based on the use of binocular stereo reconstruction and the features of calculating energies during this process, and establishes the main parts of the proposed method in the form of diagrams and formulas. This research develops a machine learning model that provides solutions to image matching problems for real data using parallel programming tools. A detailed description of the architecture of the convolutional recurrent neural network that underlies this method is given. Appropriate computational experiments were conducted to compare the results obtained with the methods proposed in the scientific literature. The method discussed in this article is characterized by better efficiency, both in terms of the speed of work execution and the number of possible errors. Doi: 10.28991/HIJ-2023-04-03-011 Full Text: PD