https://hdl.handle.net/20.500.12960/17 2026-04-21T10:45:43Z https://hdl.handle.net/20.500.12960/1819 A review on recent developments in electrospun polymeric nanofibers for oil–water separation Zembat, Ahmet Alp; Cansoy, C Elif Oil–water separation is an important process used to reduce pollution and recover valuable resources in many industrial applications. Electropun nanofibers with varying chemical composition and dimensions are commonly used to remove pollutants from water. Various nanoadditives, such as clays, metal nanoparticles, and C-based nanoparticles, can also be introduced into the polymeric nanofiber matrix to improve the removal capacity and flux of the prepared membranes. Various studies in the literature have investigated the use of these polymeric nanofibers in the separation of oil–water mixtures and oil–water emissions, and very good separation efficiencies have been supported by experimental studies. This review briefly summarises the recent developments on polymeric nanofibres used in oil–water separation. The reviewed studies showed that wettability, fiber diameter, chemical structure, and composition of the nanofibers are important parameters for the removal of contaminants, and polymeric nanofibers produced by tailoring their chemical composition and dimensions are promising candidates for many oil–water separation applications. 2026-01-01T00:00:00Z https://hdl.handle.net/20.500.12960/1817 Evaluation of using different metals and working fluids on the thermal performance of nano heat pipes Huang, He; Dheyaa, J. Jasim; Sawaran Singh, Narinderjit Singh; Ahmad, Nafis; Saydaxmetova, Shaxnoza; Smerat, Aseel; Salahshour, Soheil; Sajadi, S. Mohammad; Emami, N. The precise and effective management of heat produced by micro-scale devices, such as electronic processors, is of utmost importance. Heat pipes (HPs) are among the instruments utilized for this objective. The incorporation of nanofluids can significantly improve the thermal performance of HPs at smaller scales. This study examines the impact of spherical nanoparticles on the working fluid of a micro flat-plate HP. A variety of metals and working fluids were utilized, and molecular dynamics (MD) simulations were performed. The findings indicate that, for any specified nanoparticle volume fraction (φ), the highest and lowest evaporation rates correspond to EtOH and H2O, respectively. Platinum (Pt) and aluminum (Al) exhibit the lowest and highest evaporation rates, respectively. In general, an increase in φ leads to enhancements in both mass transfer and heat flux. The maximum condensation rate (79%) is achieved with Cu-EtOH at φ = 1.05, while the minimum (65%) is observed with Pt-H2O at φ = 0.35. The highest mass transfer rate (40%) is recorded for AlAr at φ = 1.05, whereas the lowest (26%) is noted for Pt-H2O at φ = 0.35. The minimum heat flux (1613 W/cm2) is associated with Pt-EtOH, while the maximum (2092 W/cm2) is linked to Cu-H2O. The body material and the working fluid play a crucial role in determining the heat flux within the HP. 2026-01-01T00:00:00Z https://hdl.handle.net/20.500.12960/1816 Prediction of traffic accidents trend with learning methods: a case study for Batman, Turkey Bakış, Enes; Erçetin, Mehmet Ali; Acar, Emrullah; Gökalp, İslam; Yılmaz, Musa Assessing the trend of fatalities in recent years and forecasting road accidents enables society to make appropriate planning for prevention and control. This study analyses the road traffic accident data between the years 2013 and 2022 obtained for the province of Batman in Turkey, where it has not been considered before. The scope of the data analysed includes the fatalities and injuries of drivers, passengers and pedestrians. The road accident forecast for the next ten years up to 2032 is the focus of this study and numerous analyses using learning methods such as State Space Models (SSM), Artificial Neural Networks (ANN), Autoregressive Integrated Moving Average (ARIMA) and hybrid models (CNN + LSTM and Attention + GRU) have been performed on the available data. The predictions made with the above models give results with acceptable accuracy. However, they give different results depending on the parameters used. The models created with the data studied show that the number of road accidents and the related deaths and injuries will continue to increase over the next 10 years, starting in 2022. If the causes of road accidents are not eliminated and the situation remains stable as it is in 2022, the number of accidents, deaths and injuries is expected to double by 2032. 2025-01-01T00:00:00Z https://hdl.handle.net/20.500.12960/1813 Levy flight-assisted hybrid Sine-Cosine Aquila optimizer for solving chemical equilibrium problems through the Gibbs free energy minimization technique Turgut, Oğuz Emrah; Genceli, Hadi; Asker, Mustafa; Baniasadi, Ehsan; Çoban, Mustafa Turhan This research proposes a novel hybrid metaheuristic optimization framework that combines the Aquila Optimization algorithm with the Sine-Cosine Optimizer to find equilibrium points of reacting components under specified operational reaction conditions. The method aims to address the exploitative limitations of the standard Aquila algorithm by incorporating oscillatory sine-cosine movements into the hybrid optimizer, which is one of the significant drawbacks of the base Aquila algorithm that should be addressed. The effectiveness of the hybrid approach is thoroughly tested on a suite of 100 multidimensional unimodal and multimodal benchmark cases, with results compared to those from well-known literature optimizers. Additionally, twenty-eight 30-dimensional benchmark functions from the 2013 Congress on Evolutionary Computation competition are used to evaluate the prediction performance. Three multidimensional constrained engineering design problems are also solved, and their results are compared with those from other literature optimizers. The findings show that the hybrid algorithm produces the best estimates and ranks first among competing algorithms based on average ranking results. To further verify its robustness and accuracy, three more complex chemical equilibrium problems are solved using the Gibbs Free Energy minimization method. The predictions are benchmarked against recent metaheuristic algorithms for each case, demonstrating that the proposed hybrid effectively overcomes the challenges of highly nonlinear and non-convex free energy surfaces, achieving higher solution consistency while finding minimum objective function values across different chemical equilibrium scenarios. 2025-01-01T00:00:00Z