Dmytro Zherlitsyn

The ongoing war in Ukraine has posed unprecedented challenges to traditional education systems, disrupting learning and affecting education quality. As universities adapt to these challenges, the growing reliance on distance learning strategies becomes crucial for maintaining academic management processes. This paper investigates the role of distance learning tools in addressing wartime challenges and enhancing university academic management.
Utilizing a mixed-methods approach, the study combines quantitative data analysis of student performance with qualitative insights from educators and students affected by the war. The results prove the effectiveness of distance learning tools in maintaining education quality during the war while also addressing the unique challenges faced by universities in conflict areas.
The findings reveal that distance learning tools serve as a valuable resource for universities to mitigate the negative impact of the war on education quality as part of academic management processes. However, specific challenges such as developing digital competencies, ensuring access to technology, and designing effective distance learning materials must be addressed in war-related disruptions.
The quantitative analysis of student performance data highlights the potential of innovative distance learning tools in maintaining education quality during crises and wars. However, the efficiency of their use during the large-scale war in Ukraine has shown a decline and thus necessitates further research. Nevertheless, these insights provide valuable guidance for educators and academician managers to support students and educators during challenging times.

The study reviews the application of machine learning tools in financial investment portfolio management, focusing on cluster analysis for asset allocation, diversification, and risk optimization. The paper aims to explore the use of clustering analysis to broaden the concept of portfolio diversification beyond traditional volatility metrics. An open dataset from Yahoo Finance includes a ten-year historical period (2014–2024) of 130 actively traded securities from international stock markets used. Dataset selection prioritizes top liquidity and trading activity. Python analytical tools were employed to clean, process, and analyze the data. The methodology combines classical Markowitz optimization with clustering analysis techniques, highlighting variance-return trade-offs. Various asset characteristics, including annualized return, standard deviation, Sharpe ratio, correlation with indices, skewness, and kurtosis, were incorporated into the clustering models to reveal hidden patterns and groupings among financial assets. Results show that while clustering enhances insights into asset diversity, classical approaches remain historically superior in optimizing risk-adjusted returns. This study concludes that clustering complements, rather than replaces, classical methods by broadening the understanding of diversification and addressing many diversity factors, such as metrics of the technical, graphical, and fundamental analysis. The paper also introduces the diversity rate based on clustering, which measures the variance balance by all features within and between clusters, providing a broader perspective on diversification beyond traditional metrics. Future research should investigate dynamic clustering techniques, integrate fundamental economic indicators, and develop adaptive models for effective portfolio management in evolving financial markets.

The correlation between macroeconomic dynamics and the inflation rate is the subject of many economic studies. The principles of monetary policy are developed in classical economics studies, which are based on the theories of Keynes, Phillips, Campbell, etc. However, classic approaches require practical validation, especially with regard to modern economic trends in times of crisis and emerging economies. Therefore, the purpose of the paper is to investigate and summarize the impact of inflation targeting and other key monetary policy instruments on fundamental economic indicators in Ukraine during periods of stability and crises. An empirical analysis is based on official statistics from Ukraine for 2011–2019. This study uses econometric methods (multivariate regression and simultaneous equation model), which are applied for the general and transmission impact of inflation on the estimation of economic growth. The results prove that inflation does not affect (less than 0.46 linear correlation) fundamental economic indicators during periods of real GDP growth and a quarterly CPI level of less than 2%. On the other hand, there are significant simultaneous regressions (more than 0.8 coefficients of determination) between unemployed, spending on real final consumption, hryvnia exchange rate and monetary policy instruments (discount rate, international reserves, amount of government bonds, M3 monetary aggregate) for periods when the quarterly CPI (consumer price index) is more than 2%. Therefore, the traditional monetary policy implications are discussed for emerging economies.