Kastamonu University

Chloroplast genome analyses provide critical insights into plant genetic diversity, evolution, and responses to abiotic stress. In Beta vulgaris L., a major crop contributing approximately 20

The brown bear (Ursus arctos) occurs across several regions of T & uuml;rkiye and occasionally damages beehives near rural settlements. This study examines temporal data and the spatial arrangement of beehive damage incidents recorded in the Ilgaz district of & Ccedil;ank & imath;r & imath;, T & uuml;rkiye during 2023-2024. The temporal data were evaluated across lunar phases. A chi-square test showed that incidents did not distribute evenly. A higher frequency was found during the Waxing Crescent phase. Spatial intensity was mapped using Kernel Density Estimation (KDE), where bandwidth selection followed a cross-validation procedure. KDE results showed clear concentrations of incidents in the southern and southwestern parts of the district, while other areas recorded few or none. A Decision Tree (DT) classifier based on eleven environmental variables was used to identify predictors of incident presence. The DT achieved an AUC of 0.808. It identified "distance to settlement" as the primary separating variable, followed by "distance to road", "distance to forest", and the "Human Footprint Index". Beehive damage followed a non-random temporal pattern across lunar phases. It clustered near settlements. Conflict timing followed both environmental conditions and human activity. The findings provide an empirical basis for reducing apiary losses and improving coexistence measures between local communities and brown bears in the Ilgaz region.

In recent years, transition to renewable energy has emerged as a vital strategy for achieving sustainable development and reducing environmental degradation. Only when backed by a solid institutional and macroeconomic context can economic growth serve as a stimulus for the development of renewable energy. In the same way, reducing income inequality increases the affordability, accessibility, and societal support needed for a just energy transition. Environmental protection expenditures play a complementary role by stimulating innovation, reducing regulatory uncertainty, and enhancing institutional capacity for long-run sustainability. Furthermore, indicating environmental stress, a decreasing load capacity factor emphasizes how urgent it is to accelerate the use of renewable energy. This study looks at how transition to renewable energy in OECD economies is affected by economic growth, income inequality, environmental protection expenditures, and load capacity factor between 2000 and 2021 using the common correlated effects mean group (CCEMG) estimator and its regularized extension (rCCE). The results indicate that while economic growth has a detrimental impact on green energy transition in Italy, Estonia, and Ireland, it has a favorable impact in Belgium. Income inequality has a detrimental impact on Belgium's and the Netherlands' green energy transition. The Czech Republic's green energy transition is positively impacted by environmental protection expenditures; however, Germany and Ireland are negatively impacted. In Spain and the United Kingdom, load capacity factor has a favorable impact on green energy transition. On the other hand, this element has a detrimental impact on Italy's shift to green energy. The study includes country-based policy recommendations within the findings.

This study investigates how national artificial intelligence (AI) readiness influences sustainable development performance across four Sustainable Development Goals (SDGs): Good Health and Well-Being (SDG 3), Quality Education (SDG 4), Industry, Innovation and Infrastructure (SDG 9), and Climate Action (SDG 13). Using cross-country data from the Oxford Insights AI Readiness Index and the Sustainable Development Report, we apply ensemble machine learning methods and select boosting based on predictive performance. SHAP values and Partial Dependence Plots reveal nonlinear and interaction effects across institutional, digital, and economic dimensions. The findings indicate that AI readiness functions as a strategic systems-level capability with domain-specific impacts. Data availability and ecosystem maturity shape health outcomes; digital infrastructure and economic scale influence education; technology sector strength supports innovation; and climate performance depends on economic-digital complementarities. The study positions AI capability as a structural enabler of national competitiveness and coordinated development strategy.

The increasing demand for sustainable materials has accelerated the development of environmentally friendly filaments for fused deposition modeling (FDM). In this study, the surface roughness and thermal degradation behavior of sustainable PLA-based filaments, including PLA, recycled PLA (Re-PLA), and wood-filled PLA (Wood-PLA), were systematically investigated under different FDM printing conditions. A full factorial experimental design was employed to identify the dominant processing parameters and optimize surface quality. Surface roughness was evaluated using values Ra, Rz, and Rq parameters measured on three different surface orientations (top surface at 0 degrees, top surface at 45 degrees, and side surface). Scanning electron microscopy (SEM) was used to examine the relationship between roughness measurements and surface morphology, while thermogravimetric analysis (TGA) was performed to evaluate the thermal degradation behavior of the filaments in relation to printing temperature. The results have shown that filament material is the most important parameter affecting surface roughness. While Wood-PLA exhibited the highest roughness due to fiber-induced surface heterogeneity, recycled Re-PLA showed moderate surface irregularities resulting from degradation compared to pure PLA. Despite a rougher filament surface prior to production, recycled PLA exhibited a surface morphology similar to that of pure PLA after printing, influenced by the processing parameters. Furthermore, SEM findings indicated that the Ra parameter predominantly reflects macro-scale surface topography, while local microstructural heterogeneity can be better characterized by complementary roughness parameters such as Rz. These findings support optimizing printing conditions to improve surface quality and more widespread use of sustainable FDM filaments in applications where surface roughness is critical.