Muş Alparslan University

The cement industry is one of the most energy-intensive sectors and is responsible for a considerable share of industrial emissions. In cement production, the pre-calcination stage plays a critical role in fuel consumption and pollutant formation. Therefore, replacing coal with cleaner fuels such as liquefied natural gas (LNG) and optimizing combustion conditions are important for improving energy performance and reducing environmental impacts. This study investigates the effects of different LNG flow rates and excess air ratios on the energy efficiency, emission characteristics, environmental impact, and enviroeconomic performance of an LNG-fueled rotary burner used for cement pre-calcination. Experiments were conducted at LNG flow rates ranging from 5 to 30 L/min under different air excess ratio conditions. The results showed that increasing the LNG flow rate reduced both energy efficiency and environmental sustainability. The highest overall energy efficiency of 95.66% was achieved at λ = 1.15 with an LNG flow rate of 5 L/min, while the efficiency generally showed a decreasing trend at higher flow rates. Increasing lambda to 1.15 decreased CO, HC, and soot emissions by approximately 60%, 38%, and 60%, respectively. However, CO2 and NOx emissions increased by 6% and 29%, respectively. Due to the dominant contribution of CO2 and NOx, their combined share in the total environmental impact and enviroeconomic cost reached 99%. Overall, within the investigated operating range, the 5 L/min LNG condition provided the most favourable thermo-environmental performance due to its higher energy efficiency, lower heat-transfer losses, and lower absolute environmental and enviroeconomic burdens, whereas higher LNG flow rates increased the environmental and economic loads.

Honey bees (Apis mellifera L.) are essential for ecosystem stability and agricultural production; however, one of the most destructive threats to colonies is the mite Varroa destructor. Although conventional chemical acaricides can provide short-term control, resistance development and residue concerns remain major challenges. Plant-derived essential oils have therefore attracted attention as alternative control agents. In this study, the field efficacy of nanoemulsions (NEms) prepared from peppermint (Mentha piperita; P-NEms), thyme (Thymus vulgaris; T-NEms), and eucalyptus (Eucalyptus globulus; E-NEms) oils against Varroa mites was evaluated at three doses (50, 100, and 200 ppm), and compared with a positive control (amitraz) and a negative control (no treatment). All tested nanoemulsion formulations significantly reduced Varroa mite density, with the highest efficacy (percentage reduction relative to initial mite counts) observed at the 200 ppm dose. The T-NEms200 treatment exhibited the strongest acaricidal activity (75.3%). Peppermint and eucalyptus NEms also achieved high efficacy. The positive control, amitraz, showed 53.5% efficacy and was less effective than the higher NEms doses, while mite loads increased in untreated colonies. The nanoscale droplet structure of the formulations may have contributed to enhanced contact between active compounds and mites. Overall, these findings demonstrate that essential oil-based nanoemulsions reduced Varroa mite populations in a concentration-dependent manner at the colony level. However, the present study focused on efficacy, and further research is required to evaluate potential effects on bee health, brood development, and residue levels in hive products before practical recommendations can be made.

This study presents a statistically validated machine learning approach for predicting unmeasured optoelectronic responses at an Au/SiO2/n-Si semiconductor interface incorporating a graphene oxide (GO) and poly(3-cyclohexyl-4-methyl-2,5-thiophene) (P3C4MT) hybrid interfacial layer. Although GO–polymer hybrid interfaces exhibit promising photoactive properties, comprehensive experimental characterization of current–light intensity relationships is often constrained by the high cost and time demands associated with high-illumination measurements. The experimental dataset used for model training was obtained by measuring current–voltage characteristics at room temperature, with voltage varied from − 2 to + 2 V in 0.02 V increments under illumination intensities ranging from 20 to 100 mW/cm2 at 20 mW/cm2 intervals. Five ensemble and tree-based supervised regression models (Random Forest, Gradient Boosting, LightGBM, HistGradientBoosting, and ExtraTrees) were employed using voltage and light intensity as input features to predict current responses at intermediate and extended illumination levels (10, 30, 50, 70, 90, and 110 mW/cm2). Model performance was evaluated using R2, mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), with all models achieving high predictive accuracy (R2 > 0.99). Among them, the ExtraTrees regression model consistently produced the lowest prediction errors across all illumination conditions. To ensure statistical rigor, a two-way ANOVA was conducted to examine the effects of model type and illumination intensity, revealing statistically significant main and interaction effects (p < 0.001). Post hoc Tukey HSD analysis further confirmed the statistically superior performance of the ExtraTrees model. These results demonstrate that reliable interpolation and controlled extrapolation of optoelectronic behavior can be achieved using sparse experimental data, providing a cost-effective, scalable, and statistically robust methodology for the characterization and design of hybrid semiconductor interfaces in photonic, photovoltaic, and photosensor applications.

In this study, novel Co(II) and Ni(II) complexes of a pyridine-substituted Schiff base (L) were synthesized and characterized using spectroscopic, thermal, and physicochemical methods. The results suggested that both complexes adopted a distorted octahedral geometry and exhibited non-electrolytic behavior. The biological activities of the Schiff base and its novel Co(II) and Ni(II) complexes, along with previously synthesized Pd(II) and Ru(II) metal complexes, were evaluated for their antioxidant and pancreatic lipase inhibitory properties. While Ru(II) and Pd(II) complexes showed notable antioxidant activity, Co(II) and Ni(II) complexes exhibited stronger pancreatic lipase inhibition. Overall, the findings indicate that these metal complexes possess promising enzyme inhibitory potential along with moderate antioxidant activity.

Core Stabilization Exercises (CSE) and myofascial release therapy (MRT) are commonly used methods for musculoskeletal pain. The combined use of these two methods may provide long-term and sustained relief or reduction of pain in both deep and superficial tissues. This study aimed to evaluate the effects of MRT using a roller combined with CSE in adults with chronic neck pain (CNP). A total of 58 participants were initially enrolled and randomly divided into 2 groups. Thirteen participants dropped out during the intervention period, leaving forty-five participants whose data were included in the analyses. The CSE program was applied to participants in the CSE group 3 days per week for 6 weeks. In addition to CSE, MRT with a roller massager was performed 3 days per week for 6 weeks for participants in the CSE + MRT group. In the CSE + MRT group, functionality increased significantly (p < 0.001). The increase in lateral–medial dynamic balance was not statistically significant (p = 0.066). The combination of CSE and MRT is more effective than CSE alone in increasing the pressure pain threshold and reducing pain in individuals with CNP.