Yozgat Bozok University

Geopolymer technology offers a sustainable pathway for reducing the environmental impact of construction materials through the valorization of industrial wastes. Given this, waste marble powder (WMP), generated in large quantities worldwide, represents a promising alternative precursor to conventional aluminosilicate sources. The primary aim of this study is to evaluate the feasibility and performance of natural zeolite (NZ)–based geopolymers incorporating WMP, with particular emphasis on the effects of precursor replacement level, silicate modulus (MS), and curing temperature. Geopolymer composites were produced by partially replacing NZ with WMP at levels of 0–30

To contribute to sustainable environmental protection studies, the durability and strength properties of geopolymer, which is known as more eco-friendly than ordinary Portland cement, have been a phenomenon among many searchers in recent years. In this study, the durability properties of geopolymer mortars containing C Class fly ash (FA) added with silica fume (SF) were investigated under the influence of sodium sulfate (NS) and magnesium sulfate (MS). Within the scope of the study, FA geopolymer mortar samples were produced with fixed ratios of potassium hydroxide (KOH) and sodium hydroxide (NaOH) and 3 different ratios of silica fume additive (5%, 10%, 15%). The samples were kept at room temperature for up to 28 days after production. Their physic-mechanical properties were examined. The samples were placed in NS and MS solution. Length and weight changes, flexural and compressive strengths of the samples were measured for 30 days, 90 days and 180 days. As a result of the experiments, it was observed that the samples produced by activating with NaOH didn't lose strength at a high ratio, while the compressive strength (CS) of the samples produced by activating with KOH and under the influence of sulfate increased on the 30th day. It was determined that the CS of the samples under the influence of NS reached 75.14 MPa at 30 curing days and the samples under the influence of MS reached 64.31 MPa. In general, the produced samples were found to be resistant to sulfate effects.

In this study, antimicrobial, antioxidant, and hepatoprotective activities of silver nanoparticles (AgNPs) synthesized with grape seed extract (GSE) were evaluated. AgNPs were characterized by UV-visible spectroscopy, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). SEM, and dynamic light scattering, it was determined that the particles were in spherical form and 67 +/- 5 nm in size. FTIR spectra showed the existence of bioactive compounds involved synthesis process of AgNPs. Using XRD, the crystal structure of the nanoparticles were determined. AgNPs have been found effective against bacterial and yeast strains. Antioxidant activities of AgNP and GSE at high concentrations were determined as 83.19 +/- 0.85% and 95.29 +/- 0.87%, respectively. It has been determined that AgNP and GSE reduce inflammation and apoptosis in liver damage caused by lipopolysaccharide and provide equal protection on the liver. The use of biosynthetic AgNPs in bactericidal, antioxidant, hepatoprotective, and other medical fields may be an alternative to minimize the toxic effects of existing drugs.

This study examined how nine wheat cultivars (Glosa, Cömert 2, Bagira, Lucilla, Esperia, Alp 1, Pehlivan, Tosunbey, Hüseyinbey) affect the biological performance and population dynamics of Diuraphis noxia (Russian wheat aphid) (Hemiptera: Aphididae) using the age-stage, two-sex life table approach. The results showed that the pest’s developmental duration, survival patterns, reproductive traits, and population growth parameters differed significantly among host cultivars. Glosa and Cömert 2 were the most favourable hosts for D. noxia, with short pre-adult development, high daily and lifetime fecundity, extended adult longevity, and high intrinsic rates of increase (r). In contrast, Hüseyinbey, Tosunbey, Pehlivan, and Alp 1 were poorly suitable hosts, displaying prolonged development, reduced reproductive output, delayed onset of oviposition, and notably low values of r, λ, and R0. Analyses of age-specific survival (lx), age-stage life expectancy (exj), and reproductive value (vxj) revealed significant reductions in both survival probability and reproductive potential on the more resistant cultivars. In the population projection simulations, a cohort initiated with 10 nymphs expanded to several million individuals on Glosa by day 60, whereas on Hüseyinbey it reached only about 1900 aphids. These projections clearly demonstrate that host-plant resistance is a key factor limiting long-term population growth. The findings indicate that resistant wheat cultivars can be an effective means to reduce reliance on chemical insecticides in managing D. noxia. Overall, the results emphasise that incorporating resistant hosts into integrated pest management (IPM) programmes is essential for sustainable crop production and provides valuable groundwork for future breeding efforts.

Medical image segmentation is employed to separate regions in images based on color and shape differences for disease diagnosis or localization of pathological areas. It can be performed manually or automatically. Automatic segmentation methods leverage machine learning and deep learning techniques, with domain-specific models developed to enhance performance; U-Net-based architectures can achieve effective results even with limited and imbalanced medical datasets. However, U-Net models may face training challenges such as vanishing gradients in deep networks, which can be addressed by ResNet architectures providing deeper structures. The hybrid ResUNet combines the segmentation capabilities of U-Net with the residual connections of ResNet, thus exploiting the advantages of deep networks while mitigating training difficulties. In this study, for automatic liver tumor segmentation, the hybrid ResUNet was applied to channel-based fused data obtained using Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT). Channel-based data fusion preserves the unique and distinctive patterns of each channel, enriching feature representations, and both PCA-and DWT-based fusion methods transform the data into different spaces, enhancing the model's ability to differentiate various structures. The results demonstrate that both methods achieve comparable performance, yielding similar dice similarity coefficient values across two different datasets.