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Corpus-based variationist linguistics
No full textThis paper aims to identify what archaic words/word groups were still known and used both among language speakers and Turkish National Corpus (TNC) as an indication of lexical change in Turkish from 1900 to 2020. The present study explores the diachronic variation of lexical change in Turkish by combining the corpus-based variationist sociolinguistic approach with the perspective of historical sociolinguistics. The words/collocations thought to be outdated from the original version of Eyl & uuml;l novel, written in 1900, were selected and randomly subsampled using a computer-based randomization algorithm. A survey was formed using the outdated words/collocations along with the context. The results indicated that demographical variables did not affect word knowledge and that the archaic words were unfamiliar to all participants uniformly. The overall comparison of words/collocations tested in TNC and survey indicated similar results as the most and the least frequently used words were also the most and least abundantly present in TNC
Experimental investigations on metallization in the surface modified additively manufactured plastic substrates using DC sputtering
No full textThe application of copper surface coating to plastic structures offers numerous advantages, including high thermal and electrical conductivity, improved mechanical properties, good corrosion resistance, decorative applications, and enhancements in working temperatures. Besides these advantages, producing plastic structures with 3D printing and applying surface coating enables the final structures to become functional plastic structures adaptable to different fields. In this study, 3D plastic structures were produced using the fused deposition modeling method. Pristine, dichloromethane dipping, dichloromethane vapor, cold oxygen plasma, and mechanical abrasion surface treatments were applied to determine the optimal surface treatment between copper and the plastic substrate before copper coating. Subsequently, copper coating on plastic structures was completed using the DC sputtering technique. The surface topography, optical, electrical, and structural properties of the produced plastic structures were examined. According to X-ray diffraction analysis results, the (111), (200), (220), and (311) crystal planes confirm the presence of copper. The electrical conductivity values of the plastic structures reached 7.87 x 105 S/m. Contact angle measurement results indicate that the applied surface treatments increased the contact angles to 88.309 degrees, leading the coated plastic structures to exhibit a more hydrophobic behavior.Cukurova University, Unit of Scientific Research and Projects [FBA-2023-16070]The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by Cukurova University, Unit of Scientific Research and Projects (Project No. FBA-2023-16070)
Vibration and damping analysis of functionally graded shells
No full textIn this study, dynamic behaviour of viscoelastic functionally graded shells under time-varying load is investigated. Displacement field is obtained by using higher order shear deformation theory. Equations of motion are obtained in Laplace domain by using the energy method. The equations of motion are solved by the Navier's method. Results are transferred to time domain by implementing Durbin's inverse Laplace algorithm. A parametric study of the damped forced vibration of functionally graded shells considering the effects of shell geometry, rate of material variation, the principal radii of curvature, viscoelastic parameters is carried out. Damping behaviour is investigated via linear standard viscoelastic model. Accuracy of the results are verified by comparison with the literature results
Optical properties of NiO films: Effect of nitrogen-doping, substrate temperature and band gap estimation using machine learning
No full textIn this study, nickel oxide (NiO) thin films were synthesized on glass substrates using RF magnetron sputtering with varying nitrogen (N) doping ratio and substrate temperatures to explore modifications in their structural, morphological, and optical properties. The films were prepared using a high-purity NiO target under controlled sputtering conditions. Structural analysis by X-ray diffraction revealed an improvement in crystallinity in the (2 0 0) direction with increasing N ratio. In contrast, higher N ratio led to the suppression of (1 1 1) and (2 2 0) peaks, indicating a significant influence of N on the crystal structure and orientation. The films’ thickness and morphology, examined using scanning electron microscopy and energy-dispersive X-ray spectroscopy, showed uniform and homogeneous growth with smooth surface topologies. Optical properties, assessed by UV–Vis-NIR spectrophotometry, demonstrated a decrease in transmittance and a redshift in the absorption edge with increased N doping, corresponding to a narrowing of the energy bandgap from 3.7 eV to 3.45 eV. This bandgap reduction is attributed to N incorporation substituting oxygen sites, introducing defect states within the band structure. Additionally, the impact of substrate temperature on film growth enhanced crystallinity and orientation along the (1 1 1) plane at higher temperatures, with a simultaneous reduction in film thickness due to increased adatom mobility and potential thermal decomposition. The evaluation of Kernel Ridge Regression (KRR) and Ridge Regression (RR) models revealed their effectiveness in predicting band gap values for thin films at varying substrate temperatures and thicknesses. While RR excelled in predicting a band gap of 3.6 eV for a film with a substrate temperature of 24 °C and a thickness of 112.7 nm, KRR outperformed in predicting a band gap of 3.65 eV for a film with a substrate temperature of 24 °C and a thickness of 107 nm. These findings elucidate the dual influence of N doping and substrate temperature on enhancing the functional properties of NiO thin films, promising for applications in optoelectronic devices and gas sensors. © 2024 Elsevier B.V.Nanophotonics Research and Application Center at Sivas Cumhuriyet University; Sivas Cumhuriyet University R&D Center; CUNAM; CUTAM; Department of Physics at Cukurova University; Scientific Research Project Fund of Sivas Cumhuriyet University, (F-2021-640
Development of photovoltaic and photodetection characteristics in CdS/ P3HT devices through Al-doping
No full textCdS thin films were deposited by chemical bath deposition onto FTO substrates with Al concentrations of 0, 1, 3, 5 and 6 %. X-ray diffraction revealed introduction of 1 % Al-doping reduced dislocation density and enhanced the crystal quality of CdS. Scanning electron microscopy confirmed a reduction in grain size in Al-doped CdS films compared to CdS. N3 and P3HT layers were spin-coated onto the prepared substrates, respectively. The fabrication of the solar cells was completed using Ag silver paste for the top contact. The lowest photoluminescence peak intensity was achieved for CdS (3 %Al):P3HT solar cell, indicating efficient exciton dissociation. 3 % Al-doped CdS-based device exhibited the highest efficiency at 0.210 %, nearly seven times that of reference device. CdS (3 %Al):P3HT device demonstrated the best photodetection characteristics, with a responsivity of 2.2 x 10-2-2 A/W, detectivity of 3.3 x 108 8 Jones, response time of 13 ms, and recovery time of 12 ms at zero bias voltage.Scientific Research Projects Coordination Unit in Adana Alparslan Turkes Science and Technology University [22103007]We are grateful for the support provided by Scientific Research Projects Coordination Unit in Adana Alparslan Turkes Science and Technology University with a grand number of 22103007
The effect of proniosomal encapsulated hydroxytyrozole on yoghurt quality
No full textLisansüstü Eğitim Enstitüsü, Gıda Mühendisliği Ana Bilim Dalı, Gıda Mühendisliği Bilim DalıZeytin yaprağı polifenolleri, sağlığı koruyucu özelliklerden sorumlu biyoaktif bileşenler olup, oleuropein ve ana hidroliz ürünü hidroksitirozol; antioksidan ve antimikrobiyal aktivite gibi biyoaktif özelliklere sahiptir. Ancak söz konusu bileşikler depolama sırasında faydalı özelliklerini kaybedebilmekte ve gıda ürünlerinde istenmeyen tatlara neden olabilmektedirler. Bu çalışmada, niozomal enkapsülasyonun toz formu olan proniozom yöntemi ile liyofilize zeytin yaprağının sulu kısmından elde edilen ekstraktını içeren nanokapsüller üretilmiş ve farklı miktarlarda fermentasyondan sonra yoğurt örneğine ve fermentasyondan önce süt örneklerine eklenmiştir. Daha sonra hazırlanan yoğurt numuneleri fizikokimyasal özellikleri, toplam fenolik madde, antioksidan kapasite, mikrobiyolojik özellikleri, tekstürel özellikleri, in vitro sindirim sistemi metodu ile biyoaktif bileşiklerin biyoerişilebilirliğinin tespiti, aroma maddeleri analizi ve duyusal özellikleri açısından incelenmiştir. Ayrıca yoğurt numuneleri 14 gün depolama süresince pH, asitlik, sinerezis (su salma), renk ve in vitro sindirim sistemi ile antioksidan kapasitelerindeki değişim açısından incelenmiştir. Üretilen nanokapsüllerin yoğurtta uygulanması antioksidan aktiviteyi arttırmış ve kapsüllenmemiş ekstrakt içeren yoğurttan farklı olarak fizikokimyasal ve duyusal özelliklerde önemli bir değişiklik gözlenmemekle birlikte sinerezis oranı azalmıştır. Ayrıca simüle edilmiş in vitro sindirim sonrasında yoğurdun depolama günlerine göre DPPH ve ABTS yöntemleri ile belirlenen antioksidan kapasite değerleri artmıştır. Sonuç olarak, hidroksitirozol içeriği zengin proniozomal zeytin yaprağı sıvı ekstraktı, yoğurdun fonksiyonel özelliklerini geliştirme özelliğine sahip olma potansiyeli taşımaktadır.Olive leaf polyphenols are bioactive components responsible for health-protective properties and oleuropein and its main hydrolysis product hydroxytyrosol have bioactive properties such as antioxidant and antimicrobial activity. However, these compounds may lose their beneficial properties during storage and may cause undesirable flavors in food products. In this study, nanocapsules containing the extract obtained from the aqueous part of lyophilized olive leaves were produced by the proniosome method, which is the powder form of niosomal encapsulation, and added to yogurt samples after fermentation and milk samples before fermentation in different amounts. The yogurt samples were then analyzed for their physicochemical properties, total phenolic content, antioxidant capacity, microbiological properties, textural properties, bioaccessibility of bioactive compounds by in vitro digestive system method, flavor analysis and sensory properties. In addition, yogurt samples were examined for changes in pH, acidity, syneresis (water release), color and in vitro digestion and antioxidant capacity during 14 days of storage. The application of the produced nanocapsules in yogurt increased the antioxidant activity and decreased the syneresis rate, although no significant change was observed in physicochemical and sensory properties compared to yogurt containing non-encapsulated extract. In addition, after simulated in vitro digestion, the antioxidant capacity values of yogurt determined by DPPH and ABTS methods increased compared to the storage days. In conclusion, proniosomal olive leaf liquid extract rich in hydroxytyrosol has the potential to improve the functional properties of yogurt
Biologically active sodium pentaborate pentahydrate and Hypericum perforatum oil loaded polyvinyl alcohol: chitosan membranes
No full textIn this study, sodium pentaborate pentahydrate (NaB) and Hypericum perforatum (HP) oil were incorporated into polyvinyl alcohol (PVA) and chitosan (CH) polymer blend to obtain membranes by solution casting method. In order to see the synergistic effects of NaB and HP oil on the biological and physical properties of the membranes NaB and HP oil were incorporated into membrane matrix in different ratios. Fourier-transform infrared spectroscopy (FTIR) results showed that no significant bond formation between the bioactive components and the PVA:CH matrix. According to mechanical test results, Young's Modulus and elongation at break decreased from 426 MPa to 346 MPa and 52.23 % to 15.11 % for neat PVA:CH membranes and NaB and HP oil incorporated PVA:CH (PVA:CH@35NaB:HP) membranes, respectively. Antimicrobial activity tests have shown the membranes were over 99 % effective against Escherichia coli, Staphylococcus aureus, and Candida albicans, underlining their potential for infection control. Cytocompatibility assay performed with Human Dermal Fibroblast (HDFa) cells highlight the biocompatibility of the membranes, revealing 74.84 % cell viability after 72 h. The properties of NaB and HP oil doped PVA:CH based membranes obtained from these experiments reveal the promise of a versatile membrane for applications in wound healing, tissue engineering and other biomedical fields.TUBITAK [1919B012100868]The authors thank to TUBITAK 2209 Research Projects (Project no:1919B012100868) for their support. This manuscript was enhanced scientifically with new experiments after poster presentation at EUTERMIS 2023
Derin öğrenme teknikleri kullanılarak sektörel elektrik yük tahmini
No full textLisansüstü Eğitim Enstitüsü, Elektrik-Elektronik Mühendisliği Ana Bilim DalıElektrik yükünün tahmin edilmesi, küresel enerji talebinin artması ve enerjinin şebekelerde etkin yönetimine duyulan ihtiyaç nedeniyle kritik bir konudur. Bu çalışma, elektrik yükünü tahmin etmek için Uzun Kısa Süreli Bellek (UKSB), Evrişimsel Sinir Ağı (ESA) ve hibrit bir ESA-UKSB modelini içeren Derin Öğrenme (DÖ) modellerini kullanmayı amaçlamaktadır. 2016-2023 yılları arasındaki aylık veriler kullanılarak, aydınlatma, konut, sanayi, tarım, ticari ve toplam yük referans alınarak, Adana, Mersin ve Antalya illerinde farklı sektörlerdeki elektrik yükü tahmin edilmektedir. Matlab/Simulink modeli 24 saatlik elektriksel yük profilini gözlemlemek için kullanılmıştır. Tezin ana katkısı, DÖ yaklaşımlarının ve hibrit uygulamalarının karşılaştırmalı bir analizinin incelenmesi ve önerilmesidir. Karşılaştırma sonuçları, DÖ modellerinin stratejik enerji planlamasındaki önemine odaklanan karmaşık kalıpları yakalamada DÖ modellerinin etkinliği açısından dikkate değer bilgiler ortaya koymaktadır. Ortalama Karekök Hata (OKH), R2, Ortalama Mutlak Yüzde Hata (OMYH) ve Ortalama Mutlak Hata (OMH) açısından hibrit modellerin UKSB ve ESA modeline üstünlük sağladığı sonucuna varılmıştır. Sonuçlar, ESA-UKSB hibrit modellerinin sektörel elektrik yüklerinin zamansal dinamiklerini yakalama yeteneğinin, UKSB ve ESA'dan daha etkili olduğunu göstermektedir.Forecasting of electricity load is a critical issue because of the increasing global demands for energy and the need for its efficient management in grids. This study aims to utilize Deep Learning (DL) models, including Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and a hybrid CNN-LSTM model, for forecasting electricity load. Monthly data from 2016 to 2023 is used to predict the electricity load in the provinces of Adana, Mersin, and Antalya in different sectors, concerning lighting, residential, industry, agriculture, commercial, and total load. The Matlab/Simulink model has been used to observe the 24-hour electrical load profile. The main contribution of the thesis is to examine and propose a comparative analysis of DL approaches and their hybrid applications. Comparison results illustrated noteworthy information in terms of the effectiveness of DL models in capturing complex patterns, which focused on the importance of DL models in strategic energy planning. It was concluded that concerning Root Mean Square Error (RMSE), R2, Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE), the hybrid model provides superiority to the LSTM and CNN models. The results show that the capability of CNN-LSTM hybrid models in capturing the temporal dynamics of sectoral electricity loads is more effective than the LSTM and CNN as the case
Data-driven MPPT techniques for optimizing vehicular fuel cell performance in hybrid DC microgrid
No full textThis paper aims to apply data-driven maximum power point tracking (MPPT) techniques specifically tailored for fuel cell vehicle (FCV) supported hybrid DC microgrids to enhance the power harvesting capability of fuel cell (FC) stacks. Compared to existing MPPT techniques, the current study focuses on developing and evaluating data- driven approaches for maximum power extraction by dynamically determining the operating point of FC power sources through a Zeta converter. An in-depth analysis is conducted by considering parameters such as efficiency, tracking accuracy, response time, and robustness to variations in load demand and operating conditions. The performance results validate that the developed three-layer neural network (TNN)-based MPPT gives better performance findings than Gaussian process regression (GPR), support vector regression (SVR), decision tree regression (DTR), and bagging ensemble decision tree (BEDT). In the performance evaluation phase, a vehicular FC with a rating of 1.26 kW is designed and operated within the temperature range of 320 K to 343 K for hydrogen pressure values ranging from 1 bar to 1.8 bar. For these operational conditions, the prediction accuracy value of the proposed TNN method is 99.6% while the performance values GPR, SVR, DTR, and BEDT are 99%, 98.6%, 97.2%, and 96%. In addition, system efficiency is increased by 0.98%, 1.25%, 2.51%, and 3.02% compared to GPR, SVR, DTR, and BEDT, respectively
Combining the power of artificial intelligence and mathematical modelling: A hybrid technique for enhanced forecast of tourism receipts
No full textDespite being one of the most visited countries in the world, Turkiye's share of tourism revenue does not rank among the top ten. Therefore, it would be worth researching tourist expenditures and analysing this data could provide valuable insights. This research develops a novel approach to estimating and modelling tourism receipts by analysing expenditure types. Artificial intelligence-based methods, such as machine learning, have been increasingly used in the tourism literature to improve various aspects of the industry. However, little research has been conducted using a hybrid method to model and estimate tourist expenditure. This paper is the first to combine conventional mathematical analysis, specifically first-order two-variable polynomial equations, with artificial intelligence-based machine learning algorithms in a tourism setting. The research results indicate that expenditure types such as accommodation and food & beverage significantly impact Turkiye's tourism revenue and Turkiye's total tourism revenue will not exceed 45 billion dollars by 2027. This study provides a valuable and practical contribution to improving the accuracy and efficiency of methods for managing tourism economics, particularly in European countries where the economy heavily relies on income generated by tourism. Additionally, it fills a gap in studies focused on tourists' expenditure types by combining artificial intelligence and traditional analysis, making it a unique piece of research