Mathematical information models have been developed to support managerial decision-making to ensure the necessary technical characteristics of monitoring objects in multifunctional complexes with unmanned aerial vehicles. The integration of methods for modelling stochastic dynamic objects and methods of expert assessments at the initial stage of their life cycle is proposed.

It has been shown in previous papers that traditional approaches to feature selection can lead to learning a two-tier decision tree, which significantly loses in accuracy to a enumerate of all non-empty subsets of features containing no more than three features. In this paper, on a real, public dataset, we calculated how often such an accuracy loss can occur. It is shown that there is a statistically significant correlation between the coefficient of determination of a two-tier decision tree, constructed without any feature selection, and the magnitude of such a loss in accuracy. The characteristic values for the loss of accuracy in the construction of a two-tier tree without any feature selection, compared to a full enumeration of all nonempty subsets of features containing no more than three features, for a real dataset in some domain are calculated.

IT­ infrastructure of a modern enterprise usually includes a number of very powerful automation and control systems. Such functionality as real­time data collection, aggregation and storing do not belong to outstanding or extraordinary activities since last couple of decades. The main focus is now moving away from accumulating data to its ‘intellectual’ processing and analysis. IT (information technology), BI (business intelligence) and other managing and analytical departments are trying to bring additional insights and value to their enterprises by inventing new data­driven control approaches. This paper describes one approach how the real­time historical data may be utilized during so called “what ­if” enterprise scenario forecast. In our research we conduct the “what­if” research on a power plant and the problem setting with the scenario modelling stages look as follows: a. Based on the available historical trends from SCADA (supervisory control and data acquisition system) approximate the output power characteristics of the power generating units with a piecewise linear dependencies for different ambient conditions. The approximation is conducted with a least squares (LSQ) method and the resulting model strongly depends on the data captured into the LSQ scope. b. Calculate cross­correlation for all output powers with all available measurements collected by power station SCADA system. The influencing factors belong to several major reasons: ambient conditions change, operators accuracy when following the generating plan, assets basic state characteristics. c. Create several scenarios for research – for this purpose we take a random historical day with known power generating plan and calculated level of power plant margin. Alternative scenarios contain certain plan modifications and the modelling session objective is to define whether each plan deviation could provide additional margin to the power plant (define the better possible generating plan). d. For each possible approximation generated in p.a and all combinations of influencing parameters of p.b recalculate the day margin with the new plan. Considering all the possible variants we get the Monte­Carlo alike procedure of calculations. The resulting effect of each scenario is estimated by comparison of mean values of distribution sets obtained after Monte­Carlo modelling session. The number of data points used to create a power plant model for this experiment is calculate as the number of parameters multiplied by observation timeframe duration and frequency of the SCADA trending in events per minute. As it is done in the paper, considering 100 parameters during 10 years with the frequency of 1 measurement per 1 minute leads to a 500 million points analysis at the model generation stage. During scenario modelling stage the typical number of Monte­Carlo iterations is usually around 1 million. Both stages assume massive computations and may be considered as big data problems when conducted on a standard server platform. On the whole the paper targets to show a real practical case of such buzz notions of modern industrial IT as ‘digitalization’ and ‘big data’.

This paper is devoted to developing discrete microfluidics using multidimensional network models (MNM). The method provides the possibility of generating a set of possible variants of the invention in the form of MNMs and selecting the best design variant using analyzing both the structure and the features of the MNMs. For example, the paper reports results of designing the microfluidic three-stage generator, including the design MNM and the experimental model.

The paper presents an approach to studying the key laws (as regularities) of the development of the global computer environment based on a universal model of seamless-programmable and cybersecurity distributed computing in a mathematically homogeneous algorithmic space of arbitrarily large computer networks. The proposed approach made it possible to identify the fundamental factors of the joint informational influence of human and computer environments on the processes of digital transformation of large-scale sociotechnical systems. The basis of this approach is the general bicentric model and the corresponding computer-network architectures of its implementation, which open up opportunities for the agreed functioning and sustainable development of large-scale systems of new generations in the conditions of global information strongly-connectedness.