2782-6333 1 15 2025 1-96

RAR RUS 8-26 Vladivostok State University, Vladivostok, Russian Federation Grenkin Gleb Vladivostok State University, Vladivostok, Russian Federation Doroshenko Sergey Vladivostok State University, Vladivostok, Russian Federation Dutov Dmitry Galimzyanova Kseniya

Vladivostok State University, Vladivostok, Russian Federation

Modelling the Dependence of Employee Burnout on Their Expectations Satisfaction by Machine Learning Methods As part of the current task of predicting employee burnout, machine learning models are being developed to predict burnout based on data regarding the fulfillment of employee expectations from the corporate well-being program. The source data consists of survey results from employees of large companies. To predict the degree of burnout, a classification model based on fuzzy levels of burnout is built. The correspondence matrix between the fuzzy ranges of the integral indicator of expectation fulfillment and the fuzzy levels of burnout is optimized using the criterion of entropy minimization. The task of binary classification for predicting the presence of burnout is also addressed. For this purpose, a set of rules is formed that provides an explanation for the machine learning model. Each rule uses a couple of features. The machine learning model includes 10 decision rules and achieves an accuracy of 80% for burnout prediction. Based on the constructed model, it is concluded that the nature of burnout differs depending on the implementation of corporate well-being activities for different clusters of employees based on expectation level. At the same time, the assignment of an employee to a particular cluster is related to his value priorities. Thus, the study allows for the identification of hidden factors that are determined by the values of employees and affect their burnout. Текст статьи находится в процессе верстки. 10.48554/SDEE.2025.1.1 331.44 well-being corporate program burnout values machine learning fuzzy approach https://sustainable.spbstu.ru/article/2025.15.1/ sdee_2025_1_1.pdf

RAR RUS 27-40 S-8668-2017 57194696524 orcid.org/0000-0001-7171-7357 Peter the Great St.Petersburg Polytechnic University, Saint-Petersburg, Russia Skhvediani Angi shvediani_ae@spbstu.ru

Polytechnicheskaya 29

Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation Davydenko Elizaveta Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation Egorova Maya Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation Yablonskaya Karina Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation Mokhov Egor Gravity Model: An Econometric Analysis of the Foreign Trade Dynamics between Russia and Belarus with the BRICS Countries The study examines the relationship between key macroeconomic indicators and the dynamics of foreign trade relations between Russia and Belarus with BRICS countries from 2004 to 2023. For this purpose, econometric methods and the gravity model of foreign trade were used. The logarithmic values of exports and imports of Russia and Belarus to partner countries were used as dependent variables, while independent variables included the GDP of exporting and importing countries, GDP per capita, bilateral exchange rates and the ratio of trade volume to GDP of the countries in question. The results of the analysis indicated that the export volume of Russia and Belarus with BRICS partner countries was positively related to the GDP volumes of these countries and the GDP per capita volumes of exporting countries. The import volume of Russia and Belarus from other countries was also positively related to the GDP of those countries, the GDP per capita and the exchange rate of importing countries. The study revealed that the ratio of foreign trade turnover to the GDP of partner countries was inversely proportional to the import volume of Russia and Belarus from those countries. This confirmed the hypothesis that the dynamics of exports and imports of Belarus and Russia were largely influenced by the economic development of their partners and the scale of their own economies. In this regard, it is recommended that the governments of Russia and Belarus continue to develop trade relations with BRICS countries to enhance competitiveness in the global market and achieve higher economic development indicators. Further research could consider factors such as distance between countries, technological development and others. Текст статьи находится в процессе верстки. 10.48554/SDEE.2025.1.2 339.5 gravity model foreign trade macroeconomic factors panel data model BRICS Union State https://sustainable.spbstu.ru/article/2025.15.2/ sdee_2025_1_2.pdf

RAR RUS 41-57 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation Sharikov Nikita LLC Gazpromneft Information Technology Operator, St. Petersburg, Russian Federation Poliakova Polina Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation Kudryavtsev Arsenii Modeling the Factors of Economic Growth of the Provinces of Thailand The article examines the relationship between socio-economic factors and the dynamics of economic development in Thailand’s provinces within the context of sustainable development and pronounced territorial heterogeneity. The aim of the study is to assess the factors of provincial economic development using econometric modeling methods. The analysis covers 77 provinces of Thailand, studied on the basis of panel data for the period 2013–2022. The methodology involves the application of fixed and random effects models, logarithm of variables and Hausman, Breusch–Pagan and Pesaran tests to evaluate model quality. The modeling results revealed statistically significant relationships: population size and the level of industrial investment per employee are negatively related to net provincial product per capita. The negative contribution of these factors indicated the need to reconsider their role and apply spatial analysis to account for regional differentiation and interprovincial interactions. The importance of time-fixed effects allows for consideration of the impact of macroeconomic and political events, including the 2014 coup d'état, the COVID-19 pandemic and structural reforms. The developed model confirms the necessity of a targeted approach to the formation of state policy and regional planning strategies. The findings can be used to develop mechanisms for stimulating sustainable development and improving the efficiency of territorial governance. The study fills a gap in empirical research and can serve as a foundation for further scientific and applied developments. Текст статьи находится в процессе верстки. 10.48554/SDEE.2025.1.3 332.122 economic development panel data spatial econometrics Thai provinces regional development econometric modeling https://sustainable.spbstu.ru/article/2025.15.3/ sdee_2025_1_3.pdf

RAR RUS 59-79 Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russian Federation Levina Anastasia Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russian Federation Kalyazina Sofia Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russian Federation Trifonova Nina Admiral Makarov State University of Maritime and Inland Shipping, Saint Petersburg, Russian Federation Antonov Alexander Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russian Federation Abramov Valery Agent-Based Decision Support Model for Project Portfolio Management Contemporary conditions of project activity management are characterized by a high degree of dynamism, uncertainty and competition for limited resources. This requires adaptive and intelligent approaches to decision support, especially in the context of project portfolio management. This article proposes an agent-based decision support model based on a multi-agent system (MAC), in which each object of the project environment (portfolio, project, task, resource) is represented as a software agent with autonomous behavior logic. The developed architecture and operational algorithms of the system ensure decentralized decision-making, coordination of agents’ actions and adaptation to changing conditions of project implementation. Special attention is paid to the formalization of agent types, scenarios of their interaction, events that initiate decision-making and appropriate algorithms for resource redistribution and task rescheduling. Model verification was conducted through comparison with traditional centralized approaches. The results confirm the effectiveness of the agent-based approach to increase adaptability, consistency and strategic validity of decisions in a multi-project environment. Текст статьи находится в процессе верстки. 10.48554/SDEE.2025.1.4 004.81 project portfolio management; multi-agent systems; agent-based modeling; decision support; adaptive management; resource allocation; project environment modeling; intelligent management systems https://sustainable.spbstu.ru/article/2025.15.4/ sdee_2025_1_4.pdf

RAR RUS 80-94 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation Abramova Arina W-8013-2019 57203897426 0000-0002-9703-5079 Peter the Great St. Petersburg Polytechnic University, Russian Federation Gintciak Aleksei gintsyak_am@spbstu.ru 57211475098 Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia Redko Sergey Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation Lundaeva Karina Development of a Typical Life Cycle Model of a Knowledge-Intensive Project at Oil and Gas Industry Enterprises This article is devoted to the development of a standard model of the life cycle of a knowledge-intensive project at oil and gas enterprises in order to obtain accurate forecasts of project deadlines. The relevance of the work lies in improving the accuracy of the forecast models used by oil and gas enterprises in project management and the corresponding increase in competitive advantages by reducing the risks of project deadline failures. The study analyzed the main stages of the life cycle of knowledge-intensive projects and the specifics of each stage. To develop a standard model, a system-dynamic approach was chosen to visualize cause-and-effect relationships between the variables of the system state and to describe these relationships in the form of structured functional dependencies. The results of the study present a standard system-dynamic model of the life cycle of a knowledge-intensive project in the oil and gas industry, containing the main stages of project implementation from the process of requirements analysis to the implementation and support of project results. The stages of passing the audit review are reflected in the model as auxiliary processes. The model can be used to estimate the project execution time depending on the established values of the influencing parameters at the enterprise under study, for the purpose of making management decisions in project management. The prospects of the study are the introduction of an accounting of the occurrence of investment risks into the model, as well as the possibility of a quantitative assessment of their occurrence. 10.48554/SDEE.2025.1.5 519.876.5 project life cycle knowledge-intensive projects oil and gas industry life cycle model project management model simulation modeling system dynamics https://sustainable.spbstu.ru/article/2025.15.5/ sdee_2025_1_5.pdf