Hydrological drought projections are crucial for climate-resilient water management; however, many basins lack calibrated process-based models that can readily be forced with climate scenarios. This study develops a purely data-driven framework to forecast the Streamflow Drought Index (SDI) from standardized meteorological indices and to assess future drought regimes under different emission pathways. We used a 60-year monthly record (1961-2020) of the Standardized Precipitation Index (SPI), the Standardized Temperature Index (STI), the Standardized Precipitation-Evapotranspiration Index (SPEI), and the SDI for the Sava River Basin. Correlation analysis showed that the SDI is primarily controlled by the short-lag SPI (0-1 months), whereas the STI and SPEI play a minor role. Several machine learning models were tested for one-month-ahead SDI prediction; a Random Forest (RF) with hyperparameters optimized by TimeSeriesSplit cross-validation, combined with linear-scaling bias correction, clearly outperformed XGBoost, Elastic Net, support vector regression, and a multilayer perceptron. On the independent test period (2009-2020), the RF achieved MAE approximate to 0.62, RMSE approximate to 0.83, NSE approximate to 0.49, and KGE approximate to 0.65. Using SPI/STI/SPEI projections from RCP2.6, RCP4.5, and RCP8.5, the RF produced monthly SDI projections for 2021-2050, revealing increasingly frequent, severe, and persistent streamflow droughts with higher emissions. The results demonstrate that carefully tuned ensemble tree models driven solely by standardized climate indices can provide skilful and interpretable SDI projections for drought risk assessment, supporting sustainable, climate-resilient water resources planning and adaptation in this transboundary basin.
Diversity-oriented recruitment practices are increasingly recognized as a crucial component of human resource management, particularly in the context of globalization. However, the implementation of these practices varies significantly across organizational contexts and economic systems. This study examines the adoption of diversity-oriented recruitment practices in post-socialist European countries, where historical legacies and transitional economic conditions shape workforce diversity policies. Using data from the CRANET research network, the study analyzes responses from 1,270 companies in 11 post-socialist economies. A combination of descriptive statistics, Mann-Whitney U tests, and hierarchical regression analysis is employed to assess the influence of organizational type (domestic vs. multinational), sector (public vs. private), and size on the implementation of diversity-oriented recruitment practices. The findings indicate that multinational and private sector organizations are more likely to adopt diversity-focused recruitment strategies compared to domestic and public sector entities. Contrary to expectations, organizational size did not significantly moderate these relationships, suggesting that other factors play a more critical role. This study contributes to the literature by providing empirical evidence on diversity recruitment in transitional economies, a topic that remains underexplored. The findings offer insights for organizations and policymakers aiming to develop more inclusive recruitment strategies in contexts shaped by post-socialist institutional legacies. Implications for Central European audience: The findings of this study offer valuable insights for organizations operating in post-socialist Central European economies, where historical legacies continue to shape human resource management. The research highlights the role of multinational corporations in driving diversity-oriented recruitment, emphasizing the need for domestic firms to adopt more inclusive hiring strategies. Additionally, the limited impact of organizational size suggests that cultural and strategic factors may be more influential than scale. These insights can guide policymakers and business leaders in developing policies that foster workplace diversity, improve talent acquisition, and enhance competitiveness in increasingly globalized labor markets.
Biological invasions, driven by the spread of non-native species, have become a critical global issue because of their far-reaching ecological and socioeconomic impacts. Effective communication of the risks of biological invasions is essential for implementing robust policy and legislation and gaining public support for conservation efforts. However, current policies often suffer from fragmentation and ineffectiveness, largely due to inadequate risk communication and complex multi-level governance. To address this challenge, we develop a global framework designed to enhance clearer communication about biological invasion risks. The framework contextualizes key terms across three domains in invasion science: species invasiveness, risk analysis, and decision support tools. Using both diffusion-of-English and ecology-of-language paradigms, and following a three-step process involving preliminary consensus, AI querying, and ground-truthing with final consensus, we validate the framework in 70 non-English languages which, together with English, have official status in at least one country and collectively cover all 195 countries worldwide. Our findings reveal that while terminology for risk analysis is well established, terminology for species invasiveness and, especially, for decision support tools remains underdeveloped in many languages, hindering effective communication and policy implementation. Our framework underscores the importance of cultural and political neutrality. By promoting clearer risk communication among scientists, policymakers, and the public globally, we aim to reduce policy fragmentation and foster enhanced collaboration in risk mitigation. We recommend expanding multilingual decision support tools to include the full risk analysis process: risk identification, risk assessment, and risk management. This will support intergovernmental mitigation efforts and promote a unified global response to biological invasions.
Interpopulation variation was investigated using seed samples originating from twenty-six European beech (Fagus sylvatica L.) populations across the Balkan Peninsula, a part of the species' distribution range characterized by high ecological heterogeneity in key climatic factors, such as temperature (5.8-10.6 degrees C), precipitation (648-1632 mm), and elevation (185-1410 m a.s.l.). The statistical significance of intrapopulation differences was confirmed by analysis of variance (ANOVA) for all seed traits analyzed: seed weight (g), length (mm), width (mm), thickness (mm), eccentricity and flatness indices, and germination capacity (%). Multivariate principal component analysis (PCA) was applied to examine seed traits in relation to environmental variables of the maternal site, such as mean temperature and precipitation in September and October (the seed maturation period), revealing distinct patterns of relationships among the variables studied. Seed traits were significantly positively correlated with mean temperatures of the maternal site in September and October, indicating that temperature during the seed-filling period affects seed mass. Germination capacity was associated with precipitation during the same period, though the correlation coefficient was not statistically significant; a shorter vector length in the PC biplot suggests a weaker contribution to population separation. Elevation of the site of origin showed a significant negative correlation with temperature, precipitation, and seed traits. Agglomerative hierarchical clustering analysis identified three distinct population clusters. Higher temperature and precipitation values did not necessarily result in higher seed trait values or higher germination percentages. The population with the highest seed mass exhibited the lowest germination capacity (32%) during seed maturation under the lowest precipitation. Conversely, the population characterized by the lowest seed mass showed a higher germination rate of 68% in environments with high precipitation. These results provide valuable insights into the reproductive ecology of European beech, suggesting that other factors beyond those analyzed here may have a more substantial influence on seed germination. The variation in seed traits across habitats that are either drier and hotter or colder and wetter, along the elevation gradient of the studied populations, paves the way for future research and breeding efforts to enhance the species' survival and reproductive success amid anticipated climate change scenarios.
Early in 2020, the WHO recommended that existing drugs be evaluated as a repurposed resource to fight the SARS-CoV-2 pandemic. Here, we investigate the trends of using repurposed and off-label drugs among people living with HIV in Central and Eastern Europe (CEE). From November 2020 to May 2021, data on the clinical outcomes of HIV-positive patients diagnosed with COVID-19 were collected on eCRFs (SurveyMonkey (R) platform, Inc. San Mateo, CA, USA). Factors associated with the off-label drugs available at this time (chloroquine, hydroxychloroquine, favipiravir, oseltamivir, and lopinavir/ritonavir) were identified using logistic regression models. Of the 557 HIV-positive patients assessed with COVID-19 disease, 67 (12.0%) received off-label drugs, as well as 11.6% (16/138) of hospitalized and 12.2% (51/419) of ambulatory patients (p = 0.8564). In the adjusted logistic regression model, higher odds of off-label drug use were found in patients who had their diagnoses confirmed by an RT PCR test (aOR 5.08 [95%CI 1.17-22.0], p = 0.0396), and who came from a non-EU region (aOR 6.79 [95%CI 3.51-13.1], p < 0.0001). The only factor decreasing the odds of off-label drug use was co-infection (aOR 0.31 [95%CI 0.10-0.94], p < 0.0395). In a cohort of HIV patients from the CEE, 12% were prescribed off-label drugs for COVID-19. Symptomatic patients with confirmed SARS-CoV-2 infection or who were from non-EU countries were more likely to receive a repurposed drug. Drug repurposing is an immediate solution to emerging pandemics. All data regarding the safety and effectiveness of such use should be monitored, reported, and publicly available. Access patterns within and outside the EU should be analyzed to prevent potential inequalities in access to care during epidemics in European settings.
