As a result of the climate change the situation in Arctic area leads to several important consequences. On the one hand, fossil fuels can be exploited much easier than before. On the other hand, their excavation leads to serious potential threats to fishing by changing natural habitats which in turn creates serious damage to the countries’ economies. Another set of problems arises due to the extension of navigable season for shipping routes. Thus, there are already discussions on how should resources be allocated among countries. In Aleskerov and Victorova (An analysis of potential conflict zones in the Arctic Region, HSE Publishing House, Moscow, 2015) a model was presented analyzing preferences of the countries interested in natural resources and revealing potential conflicts among them. We present several areas allocation models based on different preferences over resources among interested countries. As a result, we constructed several allocations where areas are assigned to countries with respect to the distance or the total interest, or according to the procedure which is counterpart of the Adjusted Winner procedure. We consider this work as an attempt to help decision-making authorities in their complex work on adjusting preferences and conducting negotiations in the Arctic zone. We would like to emphasize that these models can be easily extended to larger number of parameters, to the case when some areas for some reasons should be excluded from consideration, to the case with ‘weighted’ preferences with respect to some parameters. And we strongly believe that such models and evaluations based on them can be helpful for the process of corresponding decision making.

We propose a new machine learning concept called Randomized Machine Learning, in which model parameters are assumed random and data are assumed to contain random errors. Distinction of this approach from “classical” machine learning is that optimal estimation deals with the probability density functions of random parameters and the “worst” probability density of random data errors. As the optimality criterion of estimation, randomized machine learning employs the generalized information entropy maximized on a set described by the system of empirical balances. We apply this approach to text classification and dynamic regression problems. The results illustrate capabilities of the approach.

Рассматриваются проблемы управления в области обеспечения безопасности дорожного движения, развивается системный подход к решению этих проблем на основе программно-целевого управления. Дается описание математических моделей и механизмов обеспечения безопасности дорожного движения. Это, в первую очередь, механизмы комплексного оценивания применительно к оценке деятельности органов Государственной инспекции безопасности дорожного движения, методы разработки программ повышения уровня безопасности дорожного движения с учетом фактора надежности (вероятности реализации программы).

An asynchronous discrete model of nonsynaptic chemical interactions between neurons is proposed. The model significantly extends the previous work [5,6] by novel concepts that make it more biologically plausible. In the model, neurons interact by emitting neurotransmitters to the shared extracellular space (ECS). We introduce dynamics of membrane potentials that comprises two factors: the endogenous currents depending on the neurons of firing type, and the exogenous current, depending on the concentrations of neurotransmitters that the neuron is sensitive to. The firing type of a neuron is determined by the individual composition of endogenous currents. We consider three basic firing types: oscillatory, tonic and reactive. Each of them is essential for modeling central pattern generators, i.e. neural ensembles generating rhythmic activity in the absence of external stimuli. Variability of endogenous currents of different neurons leads to asynchronous neural interactions and significant fluctuations of phase durations in the activity patterns present in simple neural systems. An algorithm computing the behavior of the proposed model is provided.

Considered system consists of two renewable channels that connected in parallel. The system operates in a random environment having k states. The functioning of both components are described by two continuous time alternating processes. The sojourn time in the state 0 (work state) of both channels has exponential distribution with parameters μ1,i and μ2,i if the random environment has state i. The sojourn times in the state 1 (failed state) have general absolute continuous distributions. These sojourn times are independent and doesn’t depend on the random environment state too. The system is working at time t if at least one channel is working. The system reliability on given time interval is calculated for the known initial states of the components.