Karamanoğlu Mehmetbey University

Microplastics (MPs) are increasingly reported in terrestrial environments worldwide. However, studies investigating their occurrence, spatial distribution, and polymer characteristics in soils across different land-use types in Türkiye remain limited. This study investigated the abundance, morphology, color, and polymer composition of MPs in soils from eight different land-use types in Karaman, Türkiye: three residential areas, an industrial area, mulched agricultural area, wastewater-irrigated agricultural area, roadside, and landfill surroundings. A total of 72 soil samples (n = 9) were collected and analyzed through sequential density separation (NaCl and ZnCl2) and organic matter removal using 35

Advances in artificial intelligence (AI) and machine learning (ML) are increasingly central to predicting student achievement in science and STEM education. This study uses a quantitative bibliometric design to map the conceptual, methodological, and thematic structure of research in this domain. The dataset comprises 1,073 English-language peer-reviewed journal articles indexed in the Web of Science (WoS) between 1993 and 2025, identified through PRISMA-informed screening and analyzed using Bibliometrix/Biblioshiny. Student achievement is operationalized via outcome-oriented constructs reflected in article metadata (e.g., grades, test performance, persistence/retention, and dropout risk), rather than through primary data synthesis. Findings show strong publication growth after 2010 and a recent decline in average citation impact, largely attributable to shorter citation windows for newer publications. The United States and China lead publication output, while Saudi Arabia, the United Kingdom, and Mexico show high international collaboration. Purdue University and Texas A M University are among the most productive institutions. IEEE Access, Computers Education, and Education and Information Technologies emerge as core outlets, and D. Gasevic is identified as the most influential author. Thematic and co-word analyses indicate three dominant knowledge clusters: AI/ML and predictive modelling methods, science/STEM learning contexts, and student-related factors, alongside an emerging axis focused on ethics, explainability, and large language models (LLMs). Overall, the map clarifies dominant themes, evolving trends, and gaps that can inform future comparative studies and responsible AI integration. Limitations include reliance on a single database (WoS) and English-only publications, which may constrain the representativeness of the findings.

Cyanide is a highly toxic anion. Humans are commonly exposed to cyanide through the consumption of drinking water or by eating plants that contain cyanogenic glucosides, such as cassava, a key carbohydrate source in many diets. While numerous methods exist for cyanide detection, few are both cost-effective and simple enough for use by individuals without specialized training. In this work, we designed and synthesized a hemicyanine-based fluorescent sensor ZM-FES for the detection of cyanide (CN−). Among the anions tested, the sensor showed high selectivity towards CN−. The addition of CN− to ZM-FES in aqueous solution (ACN/H2O (1:1, v/v)) caused a dramatic decrease in the absorbance and fluorescence intensities. The detection mechanism was based on nucleophilic addition between CN− and the indolium group, which was confirmed by 1H NMR and mass spectral analysis. The fluorescence intensity plot as a function of CN− concentration showed a good linear relationship in the range 0–10 µM, and the detection limit was calculated as 0.195 µM. Furthermore, the proposed detection approach can operate over a wide pH range from 2.1 to 9.3. Finally, ZM-FES was successfully utilized to detect CN− in food samples and satisfactory results were obtained.

This study develops a robust, multi-algorithm, performance-weighted ensemble modeling framework to predict cutting forces and temperatures during the machining of baseline jute/epoxy and 10

Access to clean and safe freshwater is essential for human health and welfare in both developed and developing countries. Therefore, ecological monitoring of aquatic ecosystems is crucial for all living organisms dependent on these resources. Beyşehir Lake, largest freshwater lake in Türkiye, is a vital natural resource with ecological, economic importance and its sustainability is a national concern. This study investigates seasonally collected water, sediment, and fish (Carassius gibelio) from Beyşehir Lake to assess elemental distributions and potential risks to ecosystem and public health. Data for 21 parameters were compared with national and international regulations and findings from similar studies. Average concentrations showed that Be (2.67 µg L⁻1), Cr (2.77 µg L⁻1), Cu (3.25 µg L⁻1), and Pb (0.94 µg L⁻1) levels in water samples, and Cd (0.07 mg kg⁻1) and Sb (0.69 mg kg⁻1) in fish samples exceeded certain regulatory limits. In contrast, all elements in sediment samples were within acceptable limits. Bioconcentration factor (BCF) evaluation indicated that these elements have limited potential to accumulate in fish tissues. In the risk assessment for fish consumption, estimated daily intake (EDI) values for Cd, Fe and Ni exceeded tolerable daily intake (TDI) thresholds, indicating the need for monitoring. Despite these exceedances, the total target hazard quotient (TTHQ) value of 0.343 suggests that fish consumption does not pose a significant non-carcinogenic health risk. According to principal component analysis (PCA), lithogenic–anthropogenic rock weathering and natural mineralogical composition were identified as the main factors influencing the elemental content of water and sediment samples.