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A high level of urbanization, the growing role of cities, and the increasing urban population have led to a rise in the relevance of the problem of selecting investment projects in urban construction. Along with the usual factors considered in such a selection, regional peculiarities of conducting economic activity in the field of urban construction are gaining particular importance. The necessity of taking them into account requires an improvement in decision-making methods. This study develops a multi-stage adaptive method for multi-criteria project selection in urban construction. The method integrates regulatory requirements, the customer’s vision, and retrospective data on previously implemented projects in the region. It comprises the following sequential stages: the elimination of projects that do not meet the requirements; the construction of integral criteria (weighting functions) using logarithmic transformation; and an iterative reduction in the set of criteria. An experimental verification of the developed method demonstrated its application and revealed its potential for practical use. The proposed method can be effectively employed in urban planning systems and the smart management of urban spaces. Full article

The article presents the results of a study performed and substantiated based on the principles of a new method of diagnostics of technical conditions of a hybrid powertrain regardless of the structural diagram and design features of a hybrid vehicle. The presented new technology of the diagnostics of hybrid powertrains allows an objective complex assessment of their technical condition by diagnostic parameters in contrast to existing diagnostic methods. In the proposed method, a mechanism for the general standardization of diagnostic parameters has been developed as well as for determining the numerical values of the parameters of the powertrain. The control subset was used to control the learning error. As a result of debugging the system, the scatter of experimental and calculated points has decreased, which confirms the quality of debugging the tested fuzzy model. As a result of training the artificial neural network, the standard deviation of the error in the control sample was 0.012·P_k. A symmetry method of diagnostics of the technical state of a hybrid propulsion system was developed based on the concept of a neural network together with a neuro-fuzzy control with an adaptive criteria based on the method of training a neural network with reinforcement. The components of the vector functional include the criteria for control accuracy, the use of traction battery energy, and the degree of toxicity of exhaust gases. It is proposed to use the principle of symmetry of the guaranteed result and the linear inversion of the vector criterion into a supercriterion to determine the technical state of a hybrid powertrain on a set of Pareto-optimal controls under unequal conditions of optimality. Full article