The paper considers a multi-criteria approach to create a management system to detect and reduce the risks of aviation accidents within the responsibility of experimental and civil aviation institutions. The principal purpose of such a system shall be risk detection, risk management, simulation of various fatalities and accidents, creation of predictive models and decision support systems for both operating personnel and different levels of decision-makers. Although there is a considerable amount of information on various risk factors in aviation accumulated, many parameters cannot be digitalized and expressed only in terms of “high-low” or “positive-negative.” Thus, the safety control system’s creation shall be built based on the full range of advanced intelligent methods, including fuzzy logic, neuro-fuzzy networks, and genetic algorithms.

The paper observes the most updated concepts of Smart Cities. Problem of sustainable development of the cities is presented. An overview of transformation from Smart Cities to Smart Countries is given. Intelligent management system is proposed in the paper enhancing efficiency in the application of intelligent e-Government and Smart City projects in all areas. Integrated Operation Center is introduced – a new concept of operational intelligence, being able to digitize, monitor, predict, act, make better and faster decisions and engage with their citizens. Proposed concept of smart city (smart region, smart country) management system would allow if implemented to make a breakthrough change, improve efficiency in the management and operation of a city or a region.

Статья посвящена системному анализу эволюции здравоохранения и отраслей медицины в Российской Федерации, роли и места автоматизации и интеллектуализации больших данных (Big Data и Data Mining), являющихся ключевыми технологиями, применяемыми для сбора, обработки и хранения больших, как правило, плохо структурированных массивов разнотипных данных. Определены и объяснены основные направления и возможности Big Data в решении задач обработки интегрируемых баз и децентрализованных данных медицины, фармацевтики и медицинского страхования в отечественной практике. Актуальность проведенного авторского исследования связана с необходимостью цифровизации медицины и здравоохранения, а также социально-экономических систем, необходимостью актуализации процессов и операций обработки больших массивов, децентрализованных и неструктурированных данных. Такие данные не только сложно структурированы и имеют большой объем, но и требуют оперативной обработки с визуализацией процессов. Big Data, вместе с Data Mining являются инструментарием автоматизации и интеллектуализации, идентификации скрытых связей, актуализируемых (сбора, обработки, представления и хранения результатов) данных...

From the standpoint of control theory, branched oil pipelines definitely meet the definition of complex systems, which therefore determines the multicomponent structure of their control systems and algorithms. In addition, the fact that such infrastructural objects play critical role in economy and industry brings extra requirements to their performance regimes. Specifically this means that the pipeline network is functioning strictly according to the predefined schedule designed to satisfy the requirements of all suppliers and consumers in all nodes of the network in given times. The typical hierarchical control system for a branched pipeline was discussed earlier in a separate publication where authors analyzed different problem settings utilized for operational scheduling. According to the hierarchical approach, operational scheduling is a lower level detailed optimization process used to generate everyday timetable for all assets of the pipeline. nary optimization results and gives recommendations for corrections. The designed approach is positioned to be the top level planning algorithm in a pipeline multistage hierarchical scheduling system. This solution block is utilized for setting the parameters of input orders from oil suppliers: in case the feasible solution exists the new coming order is included in the list with no changes in its parameters; in case the feasibility with initial parameters is not reachable the optimization algorithms suggests new parameters (mainly new deadlines) to definite orders. At this publication, the basic version of single-machine optimization is considered.