Purpose – The purpose of the article is to research a semantically invariant method of modeling subject domains based on the use of primary mental abstractions of identification, generalization, and association. The modeling process does not depend on any subject domain, but is determined only by human abstraction abilities. Design/methodology/approach – Notions are modeled, but not concepts. A notion is an abstract objective concept, and a concept is a concrete subjective notion. There are different notions belonging to the same concept. A notional model consists of a notional structure and descriptions of notions included in it. The notional structure is defined as a set of notions formed by mental abstractions of identification, generalization, and association. A description of a notion is a set of other previously defined notions. Findings – Using the primary mental abstractions allows increasing the level of model abstraction, improving the scalability of information systems and developing an information system, which requires a small number of common algorithms with a small computational complexity. These algorithms do not depend on the subject domain as they are formulated in universal operations on notions. Originality/value – The refusal to describe associations as relations with different semantic meanings makes the conceptual model semantically invariant (independent of subject domains). This effect is because an association between notions in notional models is a notion that can be used to define other notions. Research/ Practical/ Social/ Environment implications – The semantic invariance of the modeling method increases the efficiency of describing complex subject domains, as well as the updatability and usability of the notional models. The use of an information system with a notional model increases the transparency of the company's business processes and reduces the risks of the ownership of the information system. Research limitations – The main difficulty in using the notional models is the need to master the new methodology and technology of modelling, knowledge representation and knowledge inference. Keywords: subject domain, mental abstractions, identification, association, generalization, notional structure, notional model, information system, knowledge representation, knowledge inference.

In this paper, the decomposition procedure of synthesis of nonlinear local feedbacks is developed for an electromechanical plant, operating in conditions of parametric and external disturbances. The developed procedure provides tracking by the output variable of the given signal with some accuracy in presence of constraints on state variables and control action. Results of numerical simulation are presented.

The work contains new concepts in system structural identification developed by the author of this work. All the developed concepts reflect the significant properties of a specific type of structural identification, which has the following property. All specific structural identifications related to this type are part of creating a real automatic control system with the required properties under prior structural uncertainty conditions. The work includes a meaningful formulation of the risk problem arising from the practical use of the Mallows' method or its parametric generalization. The work contains the results of a preliminary theoretical study of the risk problem.

The study contributes to the literature by developing a method for analyzing and processing available statistical information in the field of environmental protection in order to design an optimal plan of environmental measures. Decision-making is proposed to be carried out based on comparing the indicators of regional statistics with target indicators corresponding to the "ideal" state of the region’s economy and its environmental management system, computing by Data Envelopment Analysis model. The method can be easily incorporated in everyday practice of environmental management and can become an important element of information support system for environmental management in the regions.