Urban forests contribute significantly to human well-being and climate resilience, yet they may also act as reservoirs of contaminants such as radionuclides, heavy metals, and persistent organic pollutants (POPs). These pollutants accumulate in soils and can pose chronic exposure risks to surrounding communities. This study quantified radionuclides, heavy metals and POPs in soils of the protected Mount Avala forest near Belgrade, Serbia, and evaluated the associated health risks. Natural radionuclide activities exceeded the global averages, particularly for 40K, while 137Cs behavior appeared to be highly influenced by site-specific forest conditions and post-depositional processes. Mean radiological excess lifetime cancer risk (ELCR) was ∼60 % above the global average. Strong correlations among 226Ra, 232Th, Co, and Ni indicate a predominantly lithogenic origin, whereas the strong correlation between 137Cs and DDE suggests similar dynamics of these contaminants under forest-specific conditions. The homogeneous distribution of 137Cs and other surface-deposited contaminants indicates downward migration and redistribution over time, driven by post-depositional processes. Soils at AT6 showed extreme enrichment in As and Pb, whereas AT8 exhibited elevated PCB 52 and DDT metabolites. While radiological hazard indices remained within safety limits, certain metals and POPs exceeded non-carcinogenic and carcinogenic intervention criteria at identified hotspots. Spatial heterogeneity in pollutant concentrations reflects both geological controls and localized anthropogenic inputs. Integrating radionuclide- and contaminant-focused monitoring, hotspot-targeted remediation, and risk-informed urban planning is essential to ensure that urban green infrastructure remains safe, sustainable, and health-supportive.
Purpose-This study investigates the relationship between digital literacy and hotel guests' perceptions of AI chatbot services, focusing on three key dimensions: enjoyment, satisfaction, and loyalty. Drawing on empirical data from a field survey conducted in Serbia and Montenegro, the research explores how varying levels of digital literacy influence guests' evaluations of AI chatbot-assisted interactions and their behavioral intentions. Methodology-A purposive sampling approach was used to recruit participants with prior experience using hotel AI chatbots during booking, check-in, or customer service inquiries. Descriptive statistics, multiple regression analysis, and comparative mean testing were conducted using IBM SPSS Statistics 26.0. Findings-The results reveal that digital literacy significantly influences enjoyment, satisfaction, and loyalty toward AI chatbot customer services in hotels. Serbian respondents consistently reported higher mean scores across all dimensions, with the most pronounced difference observed in loyalty. Implications-The findings contribute to the theoretical discourse on customer-AI interaction by positioning digital literacy as an important explanatory factor in shaping user experience. Practically, the results offer actionable insights for hotel managers and service designers, highlighting the importance of tailoring AI-based service strategies to guests' digital capabilities to optimize satisfaction and encourage repeat usage.
The objective of this study is to examine the impact of transformational and transactional leadership styles on Corporate Social Responsibility (CSR) and Organizational Innovation (OI) within Serbian organizations. Using responses from 204 employees across small and medium, domestic and foreign companies, the research explores how leadership behaviors influence CSR initiatives and innovative practices. Data were collected through an online survey using a structured questionnaire and analyzed in SPSS with descriptive statistics, reliability testing, and multiple regression to examine relationships. Stratified sampling was applied to ensure representation across company size and origin. Two hypotheses were tested and proven highlighting significant differences of this influence based on organizational size and origin, with transformational leadership proving more effective than transactional leadership as a predictor of CSR and OI outcomes. The research findings point to the importance of culturally aligned leadership approaches in fostering sustainable and innovative practices, offering valuable insights for leadership development in transitional economies.
Bioeconomy represents an economic model based on the sustainable use of renewable biological resources for the production of food, materials, energy and industrial products. In the agricultural sector of the Balkan region, bioeconomy plays an increasingly important role due to the availability of biomass resources, traditional agricultural production and the growing need for environmentally sustainable solutions. The aim of this review paper is to analyze the concept of bioeconomy and to synthesize examples of good practice in agriculture across the Balkan region. The paper is based on the analysis of scientific literature, policy documents and case studies from several Balkan countries. Particular attention is given to the utilization of agricultural residues, development of biogas plants, recycling of organic waste and implementation of circular production models in rural areas. The reviewed literature and analyzed practice examples indicate that the use of agricultural biomass for energy production, composting of organic waste and valorization of by-products from food processing can contribute to improving resource efficiency and reducing environmental pollution. At the same time, the analysis conducted in this paper suggests that the transfer of good practices in the Balkans depends not only on resource availability, but also on institutional support, local capacities, investment and cooperation between farmers, local communities and research institutions.
The distance to instability provides a natural robustness measure for stable continuous-time dynamical systems, but its direct computation can become computationally expensive for large non-normal matrices. In this paper, we develop a structure-aware framework for obtaining certified lower bounds for the distance to instability by exploiting block decompositions of the system matrix. Building on an existing block comparison result, we formulate a recursive two-block procedure in which local stability margins can be supplied by different certified estimates. A new theoretical ingredient is a Frobenius-resolvent lower estimate which, for the class of substochastic network blocks considered here, leads to an explicit DTI bound depending only on the block dimension and the model parameters, thereby avoiding singular-value and resolvent computations for such blocks. The resulting framework admits adaptive and hybrid implementations: individual blocks may be treated by direct computation, explicit estimates, or further partitioning according to the quality and computational cost of the available local information. Particular attention is given to hierarchical and nearly triangular interaction patterns, for which directional coupling can substantially simplify the admissibility condition. Numerical experiments show that finer partitions do not necessarily produce sharper bounds and that carefully selected coarse, possibly highly unbalanced partitions can provide a favorable balance between accuracy and computational cost. A large hierarchical socio-economic network example illustrates the ability of the explicit local estimate to replace direct DTI computation for a high-dimensional subsystem while retaining a substantial portion of the corresponding block bound.
