Unconstrained Astrometric Orbits for Hipparcos Stars with Stochastic Solutions

A considerable number of astrometric binaries whose positions on the sky do not obey the standard model of mean position, parallax and linear proper motion, were observed by the Hipparcos satellite. Some of them remain non-discovered, and their observational data have not been properly processed with the more adequate astrometric model that includes nonlinear orbital motion. We develop an automated algorithm based on "genetic optimization", to solve the orbital fitting problem with no prior information about the orbital elements is available (from, e.g., spectroscopic data or radial velocity monitoring). We test this method on Hipparcos stars with known orbital solutions in the catalog, and further apply it to stars with stochastic solutions, which may be unresolved binaries. At a confidence level of 99%, orbital fits are obtained for 65 stars, most of which have not been known as binary. A few of the new probable binaries with A-type primaries with periods 444-2015 d are chemically peculiar stars, including Ap and \lambda Boo type. The anomalous spectra of these stars are explained as admixture of the light from the unresolved, sufficiently bright and massive companions. We estimate the apparent orbits of four stars which have been identified as members of the 300 Myr-old UMa kinematic group. Another four new nearby binaries may include low-mass M-type or brown dwarf companions. Similar astrometric models and algorithms can be used for binary stars and planet hosts observed by SIM PlanetQuest and Gaia

Giant acceleration in slow-fast space-periodic Hamiltonian systems

Motion of an ensemble of particles in a space-periodic potential well with a weak wave-like perturbation imposed is considered. We found that slow oscillations of wavenumber of the perturbation lead to occurrence of directed particle current. This current is amplifying with time due to giant acceleration of some particles. It is shown that giant acceleration is linked with the existence of resonant channels in phase space

Application of Supercomputer Technologies for Simulation of Socio-Economic Systems

To date, an extensive experience has been accumulated in investigation of problems related to quality, assessment of management systems, modeling of economic system sustainability. The studies performed have created a basis for formation of a new research area — Economics of Quality. Its tools allow to use opportunities of model simulation for construction of the mathematical models adequately reflecting the role of quality in natural, technical, social regularities of functioning of the complex socioeconomic systems. Extensive application and development of models, and also system modeling with use of supercomputer technologies, on our deep belief, will bring the conducted researches of social and economic systems to essentially new level. Moreover, the current scientific research makes a significant contribution to model simulation of multi-agent social systems and that isn’t less important, it belongs to the priority areas in development of science and technology in our country. This article is devoted to the questions of supercomputer technologies application in public sciences, first of all, — regarding technical realization of the large-scale agent-focused models (AFM). The essence of this tool is that owing to increase in power of computers it became possible to describe the behavior of many separate fragments of a difficult system, as social and economic systems represent. The article also deals with the experience of foreign scientists and practicians in launching the AFM on supercomputers, and also the example of AFM developed in CEMI RAS, stages and methods of effective calculating kernel display of multi-agent system on architecture of a modern supercomputer will be analyzed. The experiments on the basis of model simulation on forecasting the population of St. Petersburg according to three scenarios as one of the major factors influencing the development of social and economic system and quality of life of the population are presented in the conclusion

Simulation of population’s reproductive behaviour patterns within an agent-oriented regional model

The study focuses on the research on how the unevenness of demographic transition affects the social and demographic characteristics and their dynamics of a region’s population. The research was conducted by means of computerized experiments (simulations) set within an original agent-oriented model. The study features the structure of the model represented by an artificial society, with its members (agents) being attributed their personal characteristics in such a way that they would imitate the gender and age of the region’s population. The agents are divided into two groups which differ in their reproductive strategy. Agents from Group 1 adhere to the traditional strategy characterized by a high birth rate, while the agents from Group 2 follow the modern strategy resulting in a markedly low birth rate. With the application of probabilistic mechanisms, the natural birth-death processes are imitated within the model. The extinction of agents occurs in accordance with the death rates adjusted for age and gender but remaining the same for the whole population. In the model, the appearance of new agents (birth of children) results from the choice made by reproductive-aged female agents, and their choice is influenced by the subjective traits determined by their group. The age and social structure of the regional population are generally formed as a result of the aggregation of particular agents’ activity. The model has been applied in a range of experiments on forecasting the number and structure of the population in an assumed region. The results showed that despite the apparent simplification of the reality, the developed agent-oriented model correctly represents both the initial condition of the regional population including the gender, age and social structure and the dynamics of the population’s basic characteristics.The research has been supported by the Russian Science Foundation (Project № 14-18-01968)