This paper addresses two open questions posed in Xu et al. (2021) regarding the balanced domination number in graphs. We show that three new classes of graphs-those of convex polytopes An, Dn, and R '' n-are d-balanced. Further, we provide a characterization of d-balancedness for rooted trees with two levels of descendants and prove that each full binary tree is d-balanced. Additionally, several results for caterpillar graphs are established. Moreover, we determine and prove the exact balanced domination number on grid graphs. Finally, we conclude the paper by providing several new problems of interest.
This paper addresses the Longest Filled Common Subsequence (LFCS) problem, a challenging NP-hard problem with applications in bioinformatics, including gene mutation prediction and genomic data reconstruction. Existing approaches, including exact, metaheuristic, and approximation algorithms, have primarily been evaluated on small instances, which provide limited insight into their scalability. In this work, we introduce a new benchmark dataset with significantly larger instances and demonstrate that existing datasets lack the discriminative power needed to meaningfully assess algorithm performance at scale. To solve large instances efficiently, we utilize an adaptive Construct, Merge, Solve, Adapt (CMSA) framework that iteratively generates promising subproblems via component-based construction and refines them using feedback from prior iterations. Subproblems are solved using an external black-box solver. Extensive experiments on both standard and a newly introduced set of large-sized instances prove that the designed adaptive CMSA achieves state-of-the-art performance, outperforming four known approaches. Notably, among 1510 problem instances with known optimal solutions, our approach matches the proven optimum on 1486 instances—corresponding to 98.4% of these instances. These results demonstrate substantially improved scalability on large problem instances. As an engineering contribution, we propose a novel application of LFCS to song identification from degraded audio excerpts, using real-world energy-profile instances derived from popular music. Finally, we conduct an empirical explainability analysis to identify critical feature combinations influencing algorithm performance, revealing the key problem features associated with the success or failure of the approaches across different instance types.
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.
