1 Публікацію підготовлено за виконання НДР "Формування "розумної" спеціалізації в економіці України" (№ держреєстрації 0117U006045).
This article discusses a sampling algorithm for machine learning in order to capture the trend of the cumulative deterioration of the characteristics of a hydraulic pump (cumulative degradation), which affects the efficiency of its operation and manifests itself in the form of a drop in volumetric efficiency. To generate data, a simulation model of a typical station for the supply of working fluid in technological complexes, developed in the SimulationX program, is used. The transient processes of pressure change in the system are described, from the analysis of which a tendency of a decrease in the average component of the pressure signal is traced, which is used as a diagnostic feature - an indicator of the state of the system. An example is also considered that describes the possibility of assessing the residual life of the system based on data characterizing the past state of the system, and can be adapted when forming a more complex base, taking into account the use of artificial neural networks.
The full-scale war started by Russia in Ukraine has caused many challenges for economic development, being the latter hardly imaginable without the contribution of research and innovation. Rebuilding R&I becomes another challenge for Ukrainian policymakers. Thus, the purpose of the paper is to analyze the R&I policy of Ukraine during the war caused by Russia and to develop policy recommendations for the postwar recovery. To achieve it, we used several methods, in particular expert opinion generalization, relevant scientific and policy literature analysis, and statistical analysis. The paper considers three approaches to innovation policy-making at crisis time: produce; procure; repurpose. Currently, Ukraine uses mainly the second one, by buying and receiving modern armament and equipment. Meanwhile, there were some innovative developments in Ukraine, which are not produced in sufficient quantities. After the war, Ukraine will no longer be able to buy armament due to fiscal constraints. Thus, in the short-run period, the government should reorient efforts toward repurposing current developments. However, such an approach is not sustainable in the long-run period, when the development of a broader S&T base is required to create a solid base for further repurposing in emergency cases. The war has caused massive damage to Ukrainian R&I potential, which by now is not fully measured and quantified. There are two types of damage: physical loss of research and innovation infrastructure (e.g. research facilities, and high-tech enterprises) and «brain drain», both of which should be the focus for R&I policymakers. Therefore, a set of policy measures is proposed to address the war-led challenges in R&I.
Introduction. Euro-integration determines the need to harmonize the innovation policy of Ukraine in line with smart specialization (SS). SS is a quite new tool to facilitate knowledge-based growth in regions.Problem Statement. SS aims at stimulating new economic activities that emerge at the intersection of interests of many different stakeholders. The SS implementation in Ukraine started from pilot activities in 3 regions, in 2017. Their results were not considered properly while incorporating SS into regional development strategies. Despite technical support from the European Commission, many regions were not able to change the policy making process and to ensure proper triple helix (TH) interactions. The under-involvement of the state in the process is among the reasons thereof, so the role of the state in SS has been explained in the paper too.Purpose. The purpose of this research is to assess the SS implementation in Ukraine in the context of TH interaction between innovation stakeholders.Materials and Methods. The study is based on the data obtained by the authors during the elaboration of smart specialization for Kyiv city and Kyiv Oblast, the analysis of the regulatory framework, as well as other analytical materials and research papers. The expert opinion generalization, content and statistical analysis methods have been used.Results. The first steps in the implementation of the SS concept in the strategies of regional development have been assessed, the problems and ways to address them have been identified in order to facilitate the SS and to strengthen TH cooperation in Ukraine. The current situation with the implementation of SS has been analyzed. The analysis has shown paths for assessing the Ukrainian innovation capacity, international developments, and the development of the existing essential tools to control the progress in the promotion of smart specialization in the country. The report on the development of SS in Kyiv and Kyiv Oblast with an emphasis on the problems related to the involvement of stakeholders has been prepared.Conclusions. The key barriers for the SS implementation, namely, inconsistency of the legislation, underdeveloped innovation and industrial policies have been identified and the ways for overcoming these barriers have been proposed.
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