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The article discusses the problems of implementation of strategic plans for development of industry, in particular, the State Program «Development of the industry and increase its competitiveness», emerged in the economy at the beginning of 2014 as a result of foreign policy events. Home of the problems - is a possible reduction in the size of government funding critical to the country's state program, which in non-realization of schedule may extend the technological backwardness of Russian industry to the world level. In order to evaluate the possible consequences of this situation for the economy, the article suggests a model tool that allows to evaluate these effects, both on the macro economy, and the sheer size of the public funding of the state program. In particular, modeling tools allowed us to determine the threshold level of underfunding, the excess of which will lead to the practical impossibility of the state program in the given parameters. Modeling tools based on two concepts - analysis «cost-benefit» analysis and influence, and is based on the unit of production functions. All calculations are made for the subroutine «Chemical industry» government program. Selecting this subroutine was based on the importance of the chemical complex as a key component of the process of economic development. The article describes in detail the results of the model calculations.

Utilities have long struggled with establishing resource planning criteria that ensure adequate resources to meet loads at low cost. Historically, many utilities used planning reserve margin criteria. The onset of deregulation brought about a paradigm shift in which it was expected that markets would provide a more efficient mechanism for maintaining resource sufficiency in the course of system demand growth. Major power shortages in the Midwest and California in the wake of deregulation led to a reexamination by most regions of the need for centralized resource planning and integrated resource plans. Reserve margin techniques continue to be used by many resource planners to ensure resource adequacy. Simulation-based probabilistic assessments can provide a more direct measure of adequacy but are quite intensive computationally and therefore only allow exploring a limited number of scenarios. In this paper, we suggest a simple analytical probabilistic approach to maintaining resource adequacy and calculating peak load carrying capability of incremental generating units. The methodology targets a level of system adequacy, rather than a specified reserve margin, under system expansion. It provides a powerful technique for simply calculating probability-based load carrying capability of resource additions without iteratively running computationally intensive stochastic computer models. The technique also provides a simple but effective method for developing portfolios of resources with comparable contributions to system adequacy. The latter may be employed in capacity expansion algorithms as a simpler, more efficient, and more accurate method of determining least-cost resource additions than targeting planning reserve margins. Applications of these techniques to the IEEE Reliability Test System illustrate the methods and verify the results with a stochastic model. Index Terms--Power generation planning, power system availability, power system planning, power system reliability.

Mathematical models were devised to evaluate the dynamics of hereditary pathology frequencies, based on registration of spontaneous abortions, congenital malformations and Down disease in the city of Angarsk during 12 years. The models provide an opportunity to detect the fact of the presence or absence of temporary tendencies. The average frequency of spontaneous abortions and units of hereditary pathology have not been changed for 12 years, the frequency of Down disease being decreased, while congenital malformations seem to change with certain periodicity. In cases of insufficient sample sizes, the prolongation of observation for a period of several years does not facilitate the task of construction of models which could adequately describe multifactor-conditioned processes.