The Namik Kemal University was founded 2006 under the administration of the Trakya University Rectorate Enver Duran. The University is based on a strong and old background of 26 years. Faculty of Agriculture, opened in 1982 and Çorlu Faculty of Engineering, founded in 1992, build up the academic foundation with their education, researches and publications. The University has four faculties, three institutes, ten Vocational schools and a school of health, with nearly 16000 students, more than 360 academic staff and 200 administrative staff. Namık Kemal University Vocational School of Technical Sciences has two building, with nearly 9500 students(Formal 5000 Students, Night Students 4500) Stationery, Canteen and Common Square. The University is a member of the Balkan Universities Network, and was named after the prominent Turkish nationalist and intellectual Namık Kemal.
Escalating global production and consumption are driving rapid growth in energy demand, increasing pressure on finite natural resources. In response, this study proposes a data-driven framework that integrates deep learning-based electricity demand forecasting with economy-wide input-output material footprint analysis to support long-term energy planning and policymaking. The innovative aspect of this framework is its ability to jointly assess future electricity generation and related material requirements within a single analytical structure. A comparative analysis is conducted for T & uuml;rkiye, Germany, and Spain, evaluating the material footprint of electricity generation across renewable and fossil-based energy sources under business-as-usual (BAU) and alternative energy development scenarios. The forecasting models demonstrate strong predictive performance, achieving Mean Absolute Percentage Error (MAPE) values of 1.39% for T & uuml;rkiye, 4.39% for Germany, and 3.90% for Spain, significantly outperforming conventional statistical methods. Scenario-based results indicate that sustainability-oriented pathways (ST and GCA) can reduce material requirements by approximately 20-30% compared to the BAU scenario, particularly for metal-intensive inputs such as iron and refined oil. The findings underscore the importance of integrating material footprint considerations into energy transition strategies and provide practical insights for policymakers seeking to balance energy security with resource sustainability. The study highlights the value of integrated analytical approaches in supporting more resilient and resource-efficient energy systems.
With the increasing demand for eco-friendly textiles, plant cell culture offers a promising alternative to traditional dye sources. In this study, in vitro cell suspension cultures of Rubia tinctorum were investigated as a sustainable source of anthraquinone pigments. The effects of varying concentrations of KNO₃, NH₄NO₃, KH₂PO₄, and vitamins in Murashige and Skoog (MS) medium, as well as the initial medium pH, were evaluated for their influence on biomass accumulation and pigment production. The highest fresh weight (57.45 g FW) and dry weight (2.78 g DW) were achieved in the media supplemented with 0.5× and 2.0× vitamins, respectively. The highest alizarin content (718.42 µg·g⁻¹ DW; 13.6-fold higher than the control) was obtained in the MS medium supplemented with 1× KNO₃ and 3× NH₄NO₃, which also resulted in a 51
The durability and effectiveness of antibacterial agents are a major factors for consumer use. In this study, copper(I)oxide and copper(II)oxide<5 & micro;m particles were used as antibacterial agents. These particles were applied to cotton fabrics with five different structures of polycarboxylic acid crosslinkers such as CA, DL-malic, fumaric, itaconic, BTCA and also ethylmetacrylate via knife-over coating method and antibacterial properties were imparted. The aim is to impart permanent antibacterial properties to cotton fabric by using copper(I)oxide and copper(II)oxide<5 & micro;m particles with six different structures of cross-linkers and to compare the aid of cross-linkers in terms of the particles antibacterial properties. The best result for gram-negative bacteria Klebsiella pneumonia (ATCC 70063) was obtained by using copper(I)oxide<5 & micro;m particles with CA crosslinker treated cotton fabric samples at inhibition zone of 31.53 mm as well as for gram-positive bacteria Staphylococcus aureus (ATCC 43300) the best result was obtained by using copper(I)oxide<5 & micro;m particles with CA crosslinker on treated cotton fabric samples at inhibition zone of 31.02 mm even after 20 repeated washing cycles.
In this study, unavailable inflow data for the Sar & imath;oglan Dam were supplemented using data from the neighboring Sar & imath;msakl & imath; Dam, which shares similar climate and geographical characteristics, to construct an extended dataset for long-term inflow prediction. This comprehensive dataset was analysed using advanced machine learning techniques. Initially, missing inflow data for the Sar & imath;oglan Dam were reconstructed using Sar & imath;msakl & imath; Dam records. The historically extended inflow dataset was then subjected to machine learning algorithms for the purpose of forecasting study, including Bidirectional Long Short-Term Memory (BiLSTM)-based deep neural networks, Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Artificial Neural Networks (ANN). To improve prediction accuracy, data sub-band decomposition techniques were applied, including Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), and Maximal Overlap Discrete Wavelet Transform (MODWT). According to the obtained results, the BiLSTM model combined with EMD had the best accuracy, especially for short-and medium-term forecasts with an R2 value greater than 0.9. This study proposes a robust framework for inflow modeling and forecasting by first augmenting the dataset and subsequently applying hybrid machine learning approaches. This provides a method for developing decision support systems in dam operation and for the sustainable management of water resources.
This study aimed to evaluate the impact of extraordinary events and disasters on migration in Turkey in 2023. Multiple hazards, and accompanying social vulnerabilities and disasters have caused notable shifts in internal migration patterns. The research investigated the spatial and quantitative relationship between disasters and migration, emphasizing how different hazard types shape population mobility across regions. Data were obtained from multiple official sources: extraordinary event and disaster records from the Disaster and Emergency Management Presidency (AFAD) Integrated Disaster Management Platform (AYDES) database, internal migration and population statistics from the Turkish Statistical Institute (TÜİK), and spatial data analyzed through Geographic Information Systems (GIS). The 2017 Socio-Economic Development Index (SEGE) was also used to explore the connection between post-disaster migration and provincial development levels. The analysis employed correlation, multiple regression, and GIS-based spatial mapping techniques to examine links between disaster frequency, affected populations, migration rates, and socioeconomic indicators. Results show that not only disasters but also extraordinary events—which do not reach official disaster thresholds—significantly influence migration dynamics. Large-scale disasters such as earthquakes, floods, and fires triggered intense migration flows, while spatial analysis revealed distinct regional disparities, particularly across Eastern Anatolia, the Black Sea, and the Mediterranean regions. These findings highlight that disasters extend beyond physical destruction, reshaping demographic, social, and economic recovery processes. The study underscores the need for an integrated policy approach linking disaster management and migration planning to support resilient urban development and sustainable recovery.
