WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Identification of Turkish Extra Virgin Olive Oils Produced in Different Regions With Volatile Compounds(Innovhub SSI-Area SSOG, 2025) Sevim, Didar; Koseoglu, Oya; Ertan, Hasan; Ozdemir, Durmun; Ulan, MehmetThis study aims to characterize the composition of the volatile compounds in Turkish extra virgin olive oils (EVOOs) produced from three cultivars-Ayvalik, Gemlik, and Memecik-harvested in the South Marmara, South Aegean, and North Aegean regions during the 2014/15 and 2015/16 crop seasons. A total of 135 EVOO samples were obtained using industrial-scale 2-phase and 3-phase extraction systems. These samples were then analyzed using solid-phase microextraction (SPME) coupled with gas chromatography (GC). Among the twelve volatiles identified, trans-2-hexen-1-ol and cis-2-penten-1-ol exhibited the highest levels of abundance across all samples and seasons. Subsequently, 1-penten-3-one, hexanal, and cis-3-hexenyl acetate were identified, and it was determined that these contribute to the green and fruity sensory profile of high-quality olive oil. Two- and three-factor analyses of variance (ANOVA) revealed that volatile concentrations were significantly influenced by variety, harvest season, and extraction system. It is significant that 1-penten-3-one was found to be significantly influenced by both season and variety (p < 0.05), while 1-penten-3-ol exhibited a multifactorial dependency, with significant two-way interactions (season x variety, season x system, variety x system). Furthermore, PLS-DA-based classification successfully distinguished samples according to olive variety, indicating that volatile profiles could serve as reliable markers for authenticity and geographic origin. These findings underscore the potential of using volatile compounds as quality indicators and for geographic labelling in the olive oil industry.Article Geothermal Resources of Azerbaijan: A Comprehensive GIS-Based Remapping and Temperature Assessment Review(State Oil Company of Azerbaijan Republic, Oil Gas Scientific Research Project Institute, 2025) Isgandarov, S. M.; Uzelli, T. T.; Mukhtarov, A. N.; Baba, A. S.Azerbaijan has considerable geothermal energy potential. The resources are concentrated in regions such as the Absheron Peninsula, the Greater and Lesser Caucasus, the Kur Basin, and the Pre-Caspian-Guba region. Although the country does not have active volcanoes and geysers, geothermal energy can be extracted from deep wells, abandoned hydrocarbon fields, and natural hot springs. This study analyzes and maps Azerbaijan's geothermal resources using a Geographic Information System (GIS) to assess their potential for power generation and direct use. The main results show that wells such as Jarly-3 field thermal fluids with temperatures of up to 96 degrees C. Other promising sites include Daridagh in Nakhchivan and the Shikh field in Absheron, where geothermal water with a temperature of 68 degrees C. GIS-based interpolation techniques, including Kriging and Empirical Bayesian Kriging were applied to model the subsurface temperature distributions and identify regions with the highest geothermal potential. The study analyzed data from over 500 hot springs and geothermal wells to determine temperature variations at different depths. The results indicate that Azerbaijan's geothermal resources could support applications ranging from electricity generation to heating, agriculture, and industrial processes. Developing these resources could diversify Azerbaijan's energy sector and reduce dependence on fossil fuels. This study highlights the need for further exploration, improved drilling technologies, and investment in geothermal infrastructure to unlock the full potential of Azerbaijan's geothermal reserves.Article The Relation of Geogenic and Antrophogenic Factors with Blood and Hair Lead and Arsenic Levels in Women Living in Can and Bayramic Districts of Canakkale Province(Nobel Ilac, 2019) Baba, Alper; Gunduz, Orhan; Bakar, Coskun; Sulun, Serdar; Save, DilsadObjective: Mining areas and associated industrial activities carry considerable risks for human health due to multi-pathway exposure of heavy metals such as arsenic and lead. The objective of this study was to compare arsenic and lead levels it human blood and hair samples in all industrial mining area in northwestern Turkey with that of non-exposed group demonstrating similar sociocultural characteristics. Material and Method: The population of the study consisted of 674 nonsmoker women over the age of 40 who were selected on random basis from mine region and control area. Venous blood samples were taken and analyzed fur blood lead and arsenic levels in all participants. Hair samples were later collected from 108 women with high levels in blood samples. Results: The results showed that the highest prevalence of occurrences was found in district centers whereas relatively lower values were observed in the villages. Hail arsenic and lead levels were comparably higher in the industrialized area (can Region) where low-quality coal combustion used in power generation and residential heating were dominant. Conclusion: Although high correlations were not found, blood and hair arsenic and lead levels in individuals living in industrial in agricultural areas were found to he high at levels influencing the human health On the other hand, these results should be further supported and verified with advanced and long duration monitoring activities.Article Improving Doppler Radar Performance through Optically-Reconfigurable Unequal Power Division with Semi-Analytical Approach(Taylor & Francis Ltd, 2025) Karatay, Anil; Atac, Enes; Dinleyici, Mehmet Salih; Yaman, FatihThe improvement of the signal-to-noise ratio (SNR) of Doppler radar systems, enabling the detection of targets at greater ranges even with limited power, has been a longstanding focus of research. However, while key limitations such as low target reflectivity and environmental interference are often addressed, the impact of efficient use of the input power remains an overlooked, yet crucial factor in overall sensitivity. Additionally, the power allocation needs to be examined from an analytical perspective for further enhancement. In this study, we present a novel measurement approach, utilizing both semi-analytical analysis and experimental methods, to improve the performance of a dual-antenna CW Doppler radar through the use of an optically reconfigurable unequal microwave power divider which provides well-directed power utilization. Comprehensive grid searches, supported by an analytical approach and considering various loss and noise scenarios, demonstrate the capability of the proposed reconfiguration method. In the Doppler radar experiments where the pendulum and servo motor were used as targets, an SNR increase of 3.04 and 2.11 dB in the radar signal was observed with the proposed method, respectively. This noticeable improvement in the SNR of the time-frequency plots indicates an enhancement in the measurement performance. The unequal power allocation enabled continuous detection of target motion with minimal signal loss, lowering the minimum detectable power level by more than 2 dB compared to the equal power division case. The experimental results show that integrating an optically reconfigurable microwave power divider into the Doppler radar system increases precision in velocity measurements.Article A Comparative Study on Experimental and FEA-Based Simulation of Dry Sliding Wear Behavior of Boronized AISI 304 Stainless Steel at Elevated Temperatures(Pleiades Publishing Ltd, 2025) Gok, Mustafa Sabri; Kucuk, Yilmaz; Khosravi, Farshid; Gunen, Ali; Karakas, Mustafa Serdar; Guden, MustafaIn this study, the influence of boronizing on the high-temperature wear behavior of AISI 304 was examined experimentally and with FEA simulation. Boronizing, conducted at 950 degrees C for 3 h using the powder-pack boronizing technique, showed an approximately 7-fold increase in hardness compared to untreated sample. Boride layer characterization was performed using XRD, SEM, and EDS line analyses. Wear tests were performed at ambient temperatures of 25, 250, and 500 degrees C. While the wear rates of the untreated sample increased dramatically with increasing temperature, those of the boronized samples were significantly limited. FEA simulation using the Johnson-Cook fracture model demonstrated a high degree of consistency with the experimental wear profiles and this alignment enables reliable wear predictions. The oxide layer formation was observed on the worn surface of boronized samples during the tests at elevated temperatures, resulting in less plastic deformation.Article Geographical Classification and Characterization of Turkish Gemlik Virgin Olive Oils From Two Locations (Salihli - Manisa and Gemlik - Bursa) Based on Their Glyceridic Profiles(Innovhub SSI - Stazioni Sperimentali per l'Industria, 2025) Diraman, Harun; Ozdemir, DurmusThe Gemlik olive cultivar (which is grown for its fruit and oil, also known as the Trilya or Tirilye olive) is the major domestic cultivar of the Marmara region and originated in Bursa province on the Gulf of Gemlik. It has also been cultivated widely for over twenty years in other olive growing regions in Turkey and is the source of speculative claims by the domestic sector about the properties of its oil. In this study, VOO samples produced from Gemlik olive cultivar grown over two crop years in the two main locations (Salihli-Manisa n=10 and Gemlik -Bursa n=14) and reference samples from the Olive Research Institute-Borova/Izmir (n=2) were analysed using the common and approved capillary GC (Fatty Acid Composition-FA) and HPLC (Triacylglycerol Profile-TAG) methods. All data from both methods were classified with the most popular chemometrics methods (Principal Component Analysis, PCA and Hierarchical Cluster Analysis, HCA). The results of the glyceridic data from the PCA indicated that the changes of cumulative percentage were the reason for variance levels (based on PC1 and PC2) in VOO samples of between 61.75 and 77.93% for all data over the two crop years. According to the PCA biplot analysis for the two crop years, some major-minor compounds and calculated parameters from FAs and TAGs data played an effective role in the geographical characterisation and classification of Gemlik VOO from two different locations, Manisa and Bursa. Consequently, the FA and TAG profiles could be promising in determining the correct geographical classification of monocultivar Gemlik VOOs.Article The Infinitude of the Primes and Some Coloring Theorems(Taylor & Francis inc, 2025) Adibelli, Azem Berivan; Goral, HaydarWe first prove the infinitude of the primes via a special case of Rado's theorem whose proof is based on the infinite Ramsey theorem. In the proof, we use the colorings of the positive integers introduced by Levent Alpoge [1] and Andrew Granville [2]. Finally, using Rado's theorem for integral domains, we will give another proof for the infinitude of nonassociated prime elements in any unique factorization domain R with a few units.Article New Bifunctional Catalysts for the Synthesis of Dimethyl Ether Via Carbon Dioxide Utilization(Y H Mammadaliyev inst Petrochemical Proc, Natl Acad Sci, Baku, Azerbaijan, 2025) Guliyev, Bilal, V; Zahidova, Aysel; Tuncer, Bashak; Sheker, Erol; Nasirov, Fizuli A.The increasing demand for sustainable energy sources has intensified research into carbon dioxide (CO2) utilization for the synthesis of clean alternative fuels such as dimethyl ether. This study investigates the direct synthesis of dimethyl ether from CO2 using bifunctional copper-based hybrid catalysts (SCR-A, SCR-B, and SCR-C) synthesized via the sol-gel method. These catalysts integrate oxidative and acidic functionalities within a single system, enabling methanol synthesis and subsequent dehydration into dimethyl ether in a one-step process. Experimental evaluations were conducted under varying pressures ranging from atmospheric to 40 bar and temperatures between 200-350 degrees C, using both low-and high-pressure reactors to assess performance. The results indicate that under atmospheric conditions, methanol conversion reached 87%, with 82% dimethyl ether selectivity, demonstrating the bifunctional character of the catalysts. Among them, SCR-A exhibited the most favorable performance in terms of conversion and product distribution. Under high-pressure conditions (5 and 7 bar), CO2 conversion remained constant at 50%, while selectivity was influenced by temperature and reactor pressure. At 40 bar and 300 degrees C, dimethyl ether selectivity reached its peak at 60%, confirming this range as the optimal operational window for maximizing dimethyl ether yield. However, a notable decrease in selectivity was observed at 350 degrees C, likely due to catalyst deactivation or the promotion of undesired side reactions. These findings underline the thermodynamic and operational benefits of direct dimethyl ether synthesis over the conventional two-step route, as it simplifies process design, enhances CO2 utilization, and reduces energy and cost demandsArticle Machine Learning in Flow Boiling: Predicting Bubble Lift-Off Diameter Despite Data Limitations(Yildiz Technical University, 2025) Tabrizi, Atta Heydarpour; Mohammadpourfard, Mousa; Mohammadpourfard, MostafaThis study concentrates on applying machine learning techniques to flow boiling in order to predict the bubble lift-off diameter. This prediction is critical because the diameter plays a key role in understanding boiling dynamics and calculating heat transfer rates. Additionally, accurately predicting this diameter is essential for optimizing thermal systems and enhancing energy efficiency. By evaluating the performance of three different machine learning algorithms: M5 tree, multilinear regression, and random forest, we aimed to assess their effectiveness in providing reliable predictions even with limited experimental data. This research is essential as it demonstrates the potential of machine learning to enhance predictive accuracy in scenarios where obtaining extensive datasets is challenging. Our findings show that these machine-learning techniques are effective for accurate predictions. The results show that the coefficient of determination ranged from 0.64 to 0.94, indicating how well the models fit the data. The root mean square error was between 0.009 and 0.02, and the mean absolute error ranged from 0.0004 to 0.02. Based on the findings, it can be inferred that the machine learning algorithms used in this study are reliable for predicting bubble lift-off diameter. This reliability also extends to other experimental parameters, suggesting that these techniques can be effectively applied in various contexts with limited data. This study demonstrates the potential of machine learning to predict experimental parameters and advances previous research by identifying key factors that influence bubble lift-off diameter. © 2025 Elsevier B.V., All rights reserved.Article The OpenAIRE Guide for Research Institutions(Turkish Librarians Assoc, 2012) Gurdal, Gultekin; Turkfidani, Ata; Kutluturk, Levent; Celik, Sonmez; Keten, BurcuThis text is transcript of OpenAIRE Guide which is prepared in order to help research institutions was released on 13.04.2011and translated with the cooperation of ANKOS Open Access and Institutional Repositories Grup members and OpenAIREplus project team of Turkey which is coordinated from Izmir Institute of Technology Library. OpenAIRE Project aims to support researchers in complying with the European Commission Seventh Framework Programme Open Access Pilot through a European Helpdesk System; support researchers in depositing their research publications in an institutional or disciplinary repository; build up an OpenAIRE portal and e-infrastructure for repository networks. The project will work in tadem with OpeanAIREplus Project which has the principal goal of creating a robust, participatory service for the cross-linking of peer-reviewed scientific publications and associated datasets.