I B Mamai’s research while affiliated with Russian Academy of Sciences and other places

Determining the priorities for sustainable development involves critical problems identifying, the conditions creating necessary for successfully solving such problems, and identifying a set of methods and tools to ensure that these conditions are met. The existing quantitative approaches to the priorities definition do not consider the problems of resource provision of scientific priorities. The developed expert system makes it possible to use effectively multilevel information-logical structures for the systematic analysis of scientific priorities according to many criteria. It allows ensuring the transition from research goals to methods of achieving these goals, the choice of specific means and the development of the necessary research methodology.

Modern decision support systems in ecology are aimed at resolving the contradiction between the inability of megacities ecosystems to meet the growing needs of society. Scientific and methodological approaches have been developed to determine the anthropogenic load of large urban agglomerations in the context of digitalization based on cellular automata and network models of quantitative and qualitative characteristics of ecosystems. A comparative analysis of environmental and socio-economic indicators has been carried out, which makes it possible to assess the level of variation of the analyzed target indicators to ensure the sustainable development of megacities ecosystems. KeywordsDecision makerEcosystemDigital transformationDecision support system

Natural systems and techno-systems form ecosystems of urban areas that are holistic and ordered in time and space. In the context of digitalization, the assessment of the quality of the urban environment necessitates a system analysis, which will make it possible to trace the changes in each factor of anthropogenic load, and to determine the dynamics, patterns and vectors of these changes. The existing approaches to the analysis of the urban environment have a low classification accuracy due to the a priori fuzzy partitioning of the space of anthropogenic load signs. Mathematical and software support for an intelligent system for determining anthropogenic load in the digital economy based on methods of evolutionary calculations and pattern recognition has been developed.

The high anthropogenic load on the ecosystems of megalopolises necessitates the search for non-trivial ways to maintain and preserve the quality of life based on intelligent decision support systems. The introduction of knowledge maps into the ecosystems of megalopolises is associated with the need to develop conceptual decision-making models. It is proposed to use neuro-fuzzy networks as a basis for constructing such models. Scientific and methodological approaches to the evolutionary construction of decision rules for knowledge maps based on a system of criteria are developed. The proposed approaches were implemented in the form of a program that allows building knowledge maps based on the resolution of environmental and socio-economic factors of load on the ecosystems of megalopolises.

The current state of social and economic development of regions requires new approaches to increasing the efficiency of their activities, and above all scientific approaches to forecasting, as one of the main components of the strategy of transformative changes. It is proposed to use an architecture based on neuro-fuzzy networks for forecasting regional development, which is characterized by a high learning rate due to the linear dependence of outputs on adjustable weights. Scientific and methodological approaches are developed to determine the global minimum of the learning criterion, taking into account the decision rules “if-then”.

Data envelopment analysis is an effective method for assessing the relative sustainability of ecosystems in megalopolises, which are characterized by a variety of environmental and socio-economic indicators. The existing models do not allow adapting the parameters of data envelopment analysis models to the preferences of the decision-maker and do not provide a consistent assessment of the effectiveness of complex socio-economic systems. The most important characteristics of complex socio-economic systems have to be calculated by indirect methods by solving a number of optimization problems. Adaptive data envelopment analysis models are developed to determine the sustainability of ecosystems in megalopolises on the basis of ecological and socio-economic indicators of anthropogenic load. In the proposed adaptive models, intermediate results at the previous stage of the functioning of the megalopolis ecosystem are only partially consumed at the next stage. A part of the input parameters of the functioning of the ecosystems of the megalopolis can be freely distributed. Additional input parameters are directly consumed at the next stage of the functioning of the megalopolis ecosystem. The optimal values of the parameters of the models are determined on the basis of the dialogue procedure of sequential cutting of multidimensional sets using affine subspaces. Indicators of sustainability of ecosystems of megalopolises, which characterize a low level of anthropogenic load, are calculated.

Evaluation of the effectiveness of organizational systems refers to semi-structured decision-making tasks. In such tasks, it is difficult to give numerical estimates of the criteria, as well as to assess the dependencies between them. As a methodological basis, it is proposed to use the verbal analysis of decisions based on frames. Algorithms for information-analytical processing of frames have been developed to assess the effectiveness of organizational systems. During the information and analytical processing of frames, the purpose of processing is set and, therefore, the composition and form of the situational framework, which reflects the vision of the effectiveness of the organizational system. The identification of frames is carried out by comparing the properties of the marked-up data on the activities of the organizational system with the properties of the elements of the approach-result frame. It is proposed to sequentially select from the general data array segments that include links and attributes. Then frames are selected that have associative and cause-and-effect relationships, then frames that include attributes without relationships. Removal of unconnected frames from the segment is carried out, and a semantic network is built on the basis of related frames. A knowledge base has been implemented for storing a set of frames and algorithms for their information and analytical processing to support decision making. #COMESYSO1120.

The formation of a smart city is a dynamic process that involves the implementation of systemic steps that transform the city into a comfortable environment for living. Smart cities are evolving on the basis of a flexible telecommunications architecture for IoT devices. Existing sustainability technologies require a large amount of computing power to process IoT data. For effective detection and localization of dysfunctions of complex socio-technical systems of smart cities, it is proposed to use an approach based on a parametric representation of objects of interest. In order to eliminate the influence of the variability of the Internet of Things on the classification accuracy, it is proposed to use a combination of optimality principles, taking into account the parameters of energy consumption, processor and memory usage.

Environmental projects have a high degree of uncertainty and risk of their implementation. In the process of assessing their effectiveness, it is necessary to take into account the interests of all the participants in the environmental project. The environmental projects effectiveness evaluation is formalized in the form of a mathematical problem of nonlinear programming with Boolean variables with constraints such as equality and inequality. To solve it, the interactive genetic algorithm for the environmental projects implementation trajectory formation was developed.

The main idea of digitalization is the transition to a new technological order and, as a result, gaining competitive advantages, therefore, measuring the diffusion of scientific knowledge in the digital economy is especially relevant. The existing articles do not offer general models for the transfer of knowledge, regardless of the digitalization industry. The authors of the article have developed a conceptual model for measuring the diffusion of scientific knowledge, which allows, according to the characteristics of knowledge carriers, to assess the proximity of their paradigms, as well as the likelihood of delays in the transfer of knowledge due to the need to relay knowledge, depending on the paradigms they use. Criteria to assess the reliability of the scientific knowledge transfer in the context of digitalization are proposed.