http://hdl.handle.net/10563/1000009 2026-04-07T06:05:00Z http://hdl.handle.net/10563/1012786 Reactions of captive adult great tits toward aposematic prey: effects of personality Adamová-Ježová, Dana; Fuchsová, Lucie; Štys, Pavel; Šilarová, Eva; Drent, Pieter Jan; van Oers, Kees; Exnerová, Alice Individual variation in reactions to novel aposematic prey is common in avian predators. In wild adults, this variation may be caused by differences among individuals in experience with various prey, but similar variation exists in naive juveniles, and this is linked to personality-a complex of correlated, partly heritable behavioral traits that are consistent across time. Along the extremes on an axis of early exploratory behavior in great tits (Parus major), fast explorers are bold, aggressive, and routine-forming, whereas slow explorers are shy, less aggressive, and more innovative. We tested the effect of personality on innate wariness toward aposematic prey in adult hand-reared great tits from 2 lines selected for opposite levels of early exploratory behavior (fast vs. slow). The birds were offered aposematic firebugs (Pyrrhocoris apterus) over 2 d. Birds from both selection lines showed a similar degree of innate wariness toward the firebugs on the first day, but on the second day, fast explorers approached the firebugs significantly faster and more frequently than slow birds. Whether the birds attacked the firebugs was also dependent on their personality. Thus, personality-related individual differences in reactions of great tits toward the aposematic prey were maintained in the adult life stage. Personality affects avian responses to aposematic prey. In great tits, fast explorers are bolder, slow explorers more cautious. We tested hand-reared adults from lines selected for fast versus slow exploration. Both fast and slow birds were initially wary of aposematic firebugs, but fast explorers approached and attacked firebugs more frequently in repeated test. Although adults were less likely to attack aposematic prey than juveniles tested in a previous study, personality-related differences were maintained over time. 2026-01-01T00:00:00Z http://hdl.handle.net/10563/1012772 Sustainability and green management in hospitals Heinzová, Romana; Hoke, Eva Public sector organizations are also increasingly focusing on assessing the environmental impact of their activities. Sustainable principles in hospitals mean ensuring high-quality, safe, cost-effective care that minimizes negative impacts on society and the environment. Efficiency and rationalization of production resources are essential conditions for sustainable healthcare. The approach of hospitals to sustainability is thus changing. Hospital management is adopting management and strategy development approaches that integrate elements of green management. Despite the growing importance of environmental management in hospitals, this area remains insufficiently researched in the Czech Republic, representing a significant research gap. The article, therefore, aims to map and assess the extent to which elements of green management are used in hospitals in the Czech Republic, focusing on environmental strategy, energy management, and employee training in sustainability issues. The research was nationwide in scope and covered all hospitals in the Czech Republic. Fisher's exact test was used for statistical verification of the proposed hypotheses. The results showed no statistically significant correlation between hospital size and the presence of an environmental policy or strategy. Although hospitals with a defined environmental strategy are more likely to address energy savings, the difference between the groups was not statistically significant. On the contrary, it was confirmed that hospitals with an environmental policy significantly more often and more systematically implement training or education of employees in the area of sustainability. 2025-01-01T00:00:00Z http://hdl.handle.net/10563/1012775 Do machine learning techniques outperform autoregressive distributed lag models in inflation forecasting? Oancea, Bogdan; Simionescu, Mihaela; Pospíšil, Richard Following the COVID-19 pandemic, Romania and other Central and Eastern European (CEE) countries faced some of the highest inflation rates in the European Union, creating a pressing need for accurate short-term forecasts to guide monetary policy. This study compares modern machine learning (ML) methods-Long Short-Term Memory (LSTM) neural networks, Random Forests (RF) and Support Vector Regression (SVR)-with traditional Autoregressive Distributed Lag (ARDL) models in forecasting Harmonised Index of Consumer Prices. Using quarterly data for Romania (2006Q1-2023Q4) and monthly data for nine CEE economies (2006M1-2025M3), we incorporate unemployment and sentiment indicators derived from the Romanian Central Bank reports and the European Commission's Economic Sentiment Indicator (ESI). We further evaluate model performance through simulation experiments that include high persistence, moving-average non-invertibility, nonlinear regimes, and structural breaks. Across both empirical and LSTM and SVR models-they frequently deliver lower forecast errors than ARDL, with LSTM achieving up to 53% reductions in mean squared error relative to na & iuml;ve benchmarks. However, ARDL remains competitive when sentiment indices are the main predictor. These findings highlight that while advanced ML models can capture nonlinear dynamics and regime changes, traditional econometric tools still provide valuable robustness, particularly in sentiment-driven contexts. Overall, integrating ML, econometric approaches, and sentiment analysis offers a more reliable toolkit for short-horizon inflation forecasting under economic uncertainty. 2025-01-01T00:00:00Z http://hdl.handle.net/10563/1012747 Employment in the public sector and its impact on the economic performance of V4 countries Krajčo, Karol; Hoke, Eva The aim of this study is to analyze the impact of public sector employment on the economic performance of the Visegrad Four (V4) countries—the Czech Republic, Hungary, Poland, and Slovakia—over the period from 2010 to 2023. The research specifically focuses on employment in key public service sectors, including public administration, defense, education, healthcare, and social services. These sectors are examined in relation to three fundamental macroeconomic indicators: Gross Domestic Product (GDP), Gross Value Added (GVA), and collective government consumption. A quantitative research approach was adopted, utilizing comprehensive data from Eurostat. The methodology includes time series analysis, descriptive statistics, regression and correlation analysis, as well as ANOVA tests to assess the statistical significance of the observed relationships. The findings reveal a consistently strong and positive correlation between the number of public sector employees and the selected macroeconomic indicators across all four countries. This suggests that public sector employment plays a significant role in supporting and potentially enhancing economic performance. The study addresses a notable research gap, as most existing literature tends to focus on national-level analyses and often overlooks the broader, comparative perspective. Furthermore, the role of the public sector as a direct contributor to economic value creation is frequently underrepresented in economic discourse. By offering a cross-country comparison, this research contributes a novel viewpoint on how public employment influences economic outcomes. In addition, the study lays the groundwork for future research, particularly in exploring how ongoing trends such as digitalization and the integration of artificial intelligence may reshape employment structures and productivity within the public sector. These developments could have profound implications for both employment policy and economic strategy in the V4 region. 2025-01-01T00:00:00Z