https://hdl.handle.net/20.500.12960/5 PubMed Indexed Publications Collection Sun, 10 May 2026 12:32:37 GMT 2026-05-10T12:32:37Z 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. Wed, 01 Jan 2025 00:00:00 GMT https://hdl.handle.net/20.500.12960/1816 2025-01-01T00:00:00Z https://hdl.handle.net/20.500.12960/1815 The role of environmental technologies in carbon decoupling: A pathway to zero emissions in Turkiye Şahin, Suna; Doğan, Metin; Kaplan, Emin Ahmet This study investigates the direct impact of environmental technologies on carbon emissions in Turkiye (1988–2022) to evaluate the progress of carbon decoupling—the process of separating economic growth from environmental degradation. By analyzing this relationship, the study provides critical insights into the pathways for achieving broader sustainable development goals in emerging economies. The study employs Instrumental Variable Quantile Regression to address endogeneity and analyze how environmental technologies, renewable energy supply, and environmental innovations affect carbon emissions. Key findings indicate that both environmental technologies and renewable energy supply are powerful drivers in reducing carbon emissions, providing strong evidence for their role in the decoupling process. In contrast, environmental innovations exhibit a minor, counterintuitive short-term increase in emissions, suggesting a potential adjustment period or implementation lag before their full environmental benefits are realized. Furthermore, the analysis reveals that the emission-reducing impact of these technologies is significantly stronger at higher levels of pollution, highlighting their critical importance for countries with substantial environmental challenges. The findings suggest that emerging economies should prioritize investments in environmental technologies and renewable energy infrastructure while maintaining a long-term perspective on environmental innovations and implementing progressive environmental policies to achieve more effective decoupling. Wed, 01 Jan 2025 00:00:00 GMT https://hdl.handle.net/20.500.12960/1815 2025-01-01T00:00:00Z https://hdl.handle.net/20.500.12960/1814 Peripheral heterochromatin tethering is required for chromatin-based nuclear mechanical response Attar, Ali Göktuğ; Paturej, Jaroslaw; Sarıyer, Ozan Sabahattin; J Banigan, Edward; Erbaş, Aykut The cell nucleus is a mechanically responsive structure that governs how external forces affect chromosomes. Chromatin, particularly transcriptionally inactive heterochromatin, resists nuclear deformations through its mechanical response. However, chromatin also exhibits liquid-like properties, casting ambiguity on the physical mechanisms of chromatin-based nuclear elasticity. To determine how heterochromatin strengthens nuclear mechanical response, we performed polymer physics simulations of a nucleus model validated by micromechanical measurements and chromosome conformation capture data. The attachment of peripheral heterochromatin to the lamina is required to transmit forces directly to the chromatin and elicit its elastic response. Thus, increases in heterochromatin levels increase nuclear rigidity by increasing the linkages between chromatin and the lamina. Crosslinks within heterochromatin, such as HP1α proteins, can also stiffen nuclei, but only if chromatin is peripherally tethered. In contrast, heterochromatin affinity interactions that may drive liquid-liquid phase separation do not contribute to nuclear rigidity. When the nucleus is stretched, gel-like peripheral heterochromatin can bear stresses and deform, while the more fluid-like interior euchromatin is less perturbed. Thus, heterochromatin's internal structure and stiffness may regulate nuclear mechanics via peripheral attachment to the lamina, while also enabling nuclear mechanosensing of external forces and external measurement of the nucleus' internal architecture. Wed, 01 Jan 2025 00:00:00 GMT https://hdl.handle.net/20.500.12960/1814 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. Wed, 01 Jan 2025 00:00:00 GMT https://hdl.handle.net/20.500.12960/1813 2025-01-01T00:00:00Z