Scientific hub Dnipro University of Technology

Introductions. The banking sector plays an important role in financing the economic system of the country, because, on the one hand, it accumulates investment resources of households, and on the other hand, it makes them available to other economic entities as financial means for business development, being an important intermediary in the financial resources market. A banking institution, striving to achieve the efficiency of its own economic activities, must offer the most favorable conditions for cooperation for participants in the financial and credit resources market. Thus, the study of the efficiency of the banking sector is an important aspect of regulating the system of monetary and credit relations of the country. Aim. Generalization of methodological approaches and development of proposals based on them for managing the efficiency of the economic activities of a banking institution.

The article considers the statistics of sales of wristwatches of various types and the actual stock of these products in a specialised store. To determine the optimal number of stocks of these goods, the ABC-XYZ inventory method, regression analysis and the method of optimisation through modelling the cumulative consumption were used. It is proved that actual records can be reduced by an average of 20%. The advantage of the statistical optimisation method is shown as the fastest and easiest to use.

The issue of Russia's invasion of Ukraine and its impact on countries' economic growth is critical in today's global landscape. The conflict has caused severe economic disruption, including disruptions to trade routes, investment uncertainty, a decline in the country's GDP, a rise in inflation and a significant increase in unemployment rates, both in Ukraine and in neighbouring countries. The purpose of the article is to analyse the impact of the war in Ukraine on the economic growth of Lithuania, which plays an important role in the sovereignty and territorial integrity of Ukraine, the article covers the data for 2002-2023. In order to find a model of economic growth for Lithuania during the war in Ukraine, the following parameters were analysed: the number of immigrants from Ukraine in Lithuania, Lithuania's military spending, the long-term interest rate, the level of business confidence, LithuaniaUkraine exports, Lithuania-Russia imports, Ukraine-Lithuania exports, Ukraine-Lithuania imports, foreign direct investment in Lithuania, financial help to Ukraine from Lithuania, renewable energy consumption, unemployment rate, and inflation. The subsequent factors have been selected on the basis of a review of the extant literature and an analysis of the research conducted by foreign and domestic scholars. The analysis of Lithuania's economic growth has been conducted using the Harrod-Domar model, which focuses on investments and savings as a means of increasing economic growth. The hypothesis concerning the factors has been tested using correlation analysis to ascertain the relationship between the dependent and independent variables, and to identify the parameters with the most significant impact on economic growth. A thorough regression analysis reveals a substantial non-linear association between Lithuania's economic growth, its military expenditure, the influx of immigrants from Ukraine, energy consumption and its provision of military assistance to Ukraine. To optimise the model's parameters, an assessment of expert opinions was conducted, utilising linear programming to maximise economic growth in Lithuania. As Lithuania is geographically proximate to both Ukraine and Russia, and has historically maintained robust trade relations with both nations, it is imperative to ascertain the potential ramifications of the war in Ukraine on Lithuania's economic growth. The research findings and the models developed can serve as a valuable analytical framework for the study of other post-Soviet economies, facilitating the evaluation of their economic development in the context of geopolitical conflicts.

Introduction: This study explores the role of Artificial Intelligence (AI) in financial analysis and forecasting, focusing on its application in the banking sector. AI's ability to process large datasets and enhance prediction accuracy is critical for improving financial decision-making, particularly in forecasting stock prices, currency rates, and market trends. Methods: The research employed traditional statistical methods such as ARIMA models and machine learning algorithms like Gradient Boosting Machines and Random Forests. These methods were applied to financial data sets to assess the impact of AI on forecasting accuracy and risk assessment. Data preprocessing and model training were conducted using R statistical software. Results: Integrating AI models improved forecasting accuracy by 30% compared to traditional methods, and risk assessment accuracy increased by 20%. Gradient Boosting Machines outperformed other models in identifying investment portfolio risks, while Random Forests provided robust predictions of trading volumes. Conclusions: AI has the potential to revolutionize financial analysis by increasing the efficiency and accuracy of forecasts. However, data privacy, algorithmic bias, and ethical concerns must be addressed to ensure fair and responsible AI use in finance. Collaboration among researchers, financial experts, and policymakers is essential for maximizing AI's benefits while mitigating risks

Для зниження ударних навантажень, які при традиційних способах подрібнення пере-вищують величини, необхідні для руйнування руд, пропонується заміна їх дотичними (тангенціальними), що призводить до значного зменшення витрати енергії на подрібнення руд. На основі нового типу млинів розроблена технологічна схема, дозволяє значно знизити кількість обладнання на збагачувальній фабриці і знизити витрату енергії на подрібнення. Показано, що використання вертикальних роликових млинів в майбутньому може призвести до радикальної зміни технологій на гірничо-збагачувальних підприємствах з точки зору капітальних витрат, енергозбереження та охорони навколишнього середовища.

Розглянуто проблему шлаків, що лишаються після спалювання твердих побутових відходів. Виконано оцінку екологічної небезпеки шлаків за допомогою біоіндикаторів. Наведено дані про токсичну і мутагенну активність шлаків спалювання ТБВ. Рассматривается проблема шлаков, остающихся после сжигания твердых бытовых отходов. Выполнена оценка экологической опасности шлаков с помощью биоиндикаторов. Приведены данные о токсичной и мутагенной активности шлаков сжигания ТБО. The problem of municipal solid waste incineration slag arc considered. The estimation of ecological danger of slag are given with the help of bioindicators. Data about toxic and mutagenous activity of MSW incineration slag are given.