Recep Tayyip Erdoğan Üniversitesi Kurumsal Akademik Arşivi
DSpace@RTEÜ, Recep Tayyip Erdoğan Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve yayınların etkisini artırmak için telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
Endocrine disorders and diabetes mellitus in patients on hemodialysis
(Nova Science Publishers, Inc., 2025) Çiftel, Serpil; Mercantepe, Filiz
The kidney plays an essential role in producing and metabolizing many hormones. Therefore, uremia is accompanied by significant endocrine changes. End-stage renal disease (ESRD) is a significant health problem due to the increased risk of morbidity and mortality. Hemodialysis (HD) provides essential support for restoring kidney functions in these patients. While fluid-electrolyte and acid-base imbalances in the body are controlled with HD treatment, which is life-saving in clinical practice, some endocrinological, physical, psychological, and social disorders may occur in these patients. The endocrine system may be affected by underlying diseases, and managing existing endocrine diseases may be complicated. Disorders in the hypothalamic-pituitary-gonadal axis and changes in the synthesis and clearance levels of many hormones may be observed. Hormonal disorders, especially those caused by kidney failure, can negatively affect endocrine organs such as the parathyroid, thyroid, adrenal glands, pituitary, gonads, and pancreas. In this chapter, endocrine diseases frequently seen in patients on HD and current information on the diagnosis and management of these diseases will be discussed.
Integrating obsidian and silica fume in geopolymer mortars: Strength prediction via meta-ensemble machine learning framework
(Elsevier, 2025) Çakmak, Talip; Ustabaş, İlker; Yılmaz, Erol
Renowned for its durability and structural strength, concrete revives to lead global construction as the material of choice. However, the carbon-intensive nature of cement production demands the pursuit of greener, more sustainable alternatives. Geopolymer mortars derived from industrial by-products like obsidian (OB) and silica fume (SF) offer a sustainable alternative to conventional binders, but accurately assessing their behavior under diverse curing regimes remains a significant challenge. Furthermore, although there are many studies on machine learning (ML) methods and different types of geopolymer in the literature, there is no comprehensive study on predicting the compressive strength of geopolymers containing OB (90–100 %) and SF (0–10 %) using ML-based methods. This study therefore aims to address this gap by predicting the compressive strength of a dataset consisting of 150 data points created by varying the OB and SF ratios. The current research offers a robust ML framework for strength prediction of geopolymer mortars featuring OB and SF additives. Five popular ML techniques covering Gaussian Process Regression, Extremely Randomized Trees, Extreme Gradient Boosting, Bagging, and Decision Tree were tested both individually and in combination through a hybrid meta-model. The combined model delivered the best results, reaching an R2 of 0.979, outperforming the standalone models, which scored between 0.87 and 0.963. The principal factors such as the proportions of OB and SF, curing temperature, and curing duration were examined using Feature Importance and Permutation Feature Importance analyses, with ANOVA confirming their relevance. K-fold cross-validation verified model's robustness, demonstrating ensemble ML methods substantially improve the precision and reliability of strength predictions for geopolymer mortars. These findings advance the design of sustainable construction materials while contributing to reduced carbon emissions in the building industry.
Investigation of latent heat storage performance of a solar collector incorporating dimpled dendritic fins and nano-additive phase change material
(Elsevier, 2026) Gürsoy, Emrehan; Kaya, Hüseyin; Gürdal, Mehmet; Gedik, Engin
In this numerical study, the melting and thermal energy storage performance of phase change material (PCM) integrated into flat-plate solar collector (FPSC) was investigated using dendritic fins with different dimple geometries (spherical, elliptical, trapezoidal), Fe3O4 nanoparticles at different volume concentrations (φ = 0.5, 1.0, 2.0, 3.0 vol%), and metal foam (MF). The low thermal conductivity of PCMs limits the performance of latent heat thermal energy storage (LHTES) systems by prolonging the phase change periods. Therefore, the study aims to optimize the thermal performance by combining the effects of MF, which increases the effective thermal conductivity of the system, dendritic fins that increase the heat transfer surface area, and nanoparticles that improve the thermophysical properties. Numerical computations were managed under the condition of constant heat flux of q" = 1000 W m−2 using the enthalpy-porosity method and the local thermal equilibrium approach in ANSYS Fluent software. Analyses were conducted for total of 25 different cases. The results showed that the addition of nanoparticles suppressed natural convection, decreasing the melting performance, while dimpled dendritic fins increased the melting rate by 79 %. The highest melting rate was achieved with trapezoidal dimpled dendritic fins (complete melting in 21 min). Furthermore, the highest stored energy of 279 kJ kg−1 was obtained with spherical dimpled dendritic fins. The novelty of this study is the use of dimpled dendritic fins for the first time in literature and their integration into FPSC as hybrid system with MF + nano-PCM. This design contributes to the development of next-generation compact and high-efficiency LHTES systems
Berberine alleviated methotrexate-induced oxidative and inflammatory lung injury by modulating Nrf2 signaling in rats
(Elsevier, 2026) Menteşe, Ahmet; Demir, Selim; Yuluğ, Esin; Alemdar, Nihal Türkmen; Durmuş, Tenzile Beyza; Demir, Elif Ayazoğlu; Aliyazıcıoğlu, Yüksel
Methotrexate (MTX), a medication commonly utilised in the management of autoimmune disorders and cancer, has been observed to precipitate pulmonary tissue injury when administered over an extended period. This may result in a range of adverse effects, including psychological and physiological distress, in patients. Berberine (BER), a molecule that has been employed in traditional therapeutic practices for centuries, is an alkaloid derivative with a multitude of pharmacological activities. The objective of the present study was to examine the therapeutic potential of BER in counteracting pulmonotoxicity induced by systemic MTX administration for the first time. In this experimental model, rats were subjected to a single intraperitoneal injection of 20 mg/kg of MTX on the first day in order to induce lung injury. Following this, the rats were then administered BER treatments at doses of 1 mg/kg or 2 mg/kg for a period of three consecutive days. Administration of MTX resulted in a significant suppression of nuclear factor erythroid 2-related factor 2 (Nrf2) levels in the lung tissue of rats when compared to the control group (∼7.0 fold; p < 0.01). Furthermore, MTX administration significantly induced lipid peroxidation, inflammation, and endoplasmic reticulum stress levels in comparison with the control group, leading to severe pulmonary histopathological symptoms (p < 0.001). Conversely, the administration of BER treatments led to a significant alleviation of degenerative biochemical and histopathological findings, achieved by modulating Nrf2 signalling (p < 0.05). Consequently, the present findings imply that BER may exert a positive effect on MTX-induced oxidative pulmonary damage, at least in part, by modulating the Nrf2 pathway.
Resonant tunneling properties of inverse parabolic multibarrier structures: a non-equilibrium green’s function approach
(Springer, 2026) Ukan, Abdurrahman; Hati̇poğlu, Aslı; Batı, Mehmet
We present a theoretical investigation aimed at understanding how external electric fields influence resonant tunneling and quantum transport in inverse parabolic multibarrier semiconductor heterostructures. The main problem addressed is the lack of comprehensive studies describing field-induced localization and miniband modulation in smoothly varying potential profiles. The analysis is carried out using the non-equilibrium Green’s function formalism with the finite element method, which allows accurate determination of transmission spectra, resonant energy levels, and current density-voltage characteristics. Our results highlight the strong dependence of resonant tunneling features on the structural parameters of the system, including the number of barriers, as well as the width of wells and height of barriers. It is found that increasing the number of barriers enhances the complexity of the transmission spectrum, leading to sharper resonant peaks and modified miniband formation. Furthermore, the application of an external electric field introduces a substantial shift in the resonant energy levels and significantly alters the transmission probability. Numerical results indicate that for a field-free structure, unity transmission occurs at specific resonance energies (E 20–250 meV for NB = 2 and 5), while under a high electric field (F = 50 kV/cm), the transmission significantly decreases and resonance peaks vanish due to wave localization. The calculated current–voltage characteristics reveal a pronounced negative differential resistance behavior. As the barrier height increases from 250 meV to 500 meV, the NDR region broadens while the peak-to-valley current ratio decreases. These findings emphasize the tunability of inverse parabolic multibarrier structures and their potential applications in high-frequency nanoelectronic and quantum device technologies.
