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
Broken rotor bar fault detection in induction motors through power spectral density to image method
(Yildiz Technical University, 2025) Okumuş, Hatice; Ergün, Ebru
Induction motors play an important role in a variety of industrial applications but are partic-ularly sensitive to electrical faults, such as rotor-related problems such as broken rotor bars. Eliminating such faults is critical to reducing maintenance costs and preventing serious finan-cial losses. This study presents a method based on detailed feature extraction for identifying broken rotor pull-out faults in induction motors. The process is initiated by generating spec-trograms from sensor-based signals. However, instead of using these spectrograms directly, the resulting power spectral density data is converted into an optimized image format suitable for processing by pre-classified deep neural networks. To utilize these networks’ capabilities, the developed features are fed into nearest neighbor (k-NN) and random forest classifiers for fault detection. The programmatic method was tested on a publicly available dataset of a three-phase step-down motor operating under various load conditions. In particular, the DenseNet201 model’s improved features from the mean pooling structure yielded a remarkable accuracy of 99.75% using the random forest classifier. This result demonstrates a power-ful and sensitive fault detection tool in induction motors by effectively integrating the conven-tional circuit techniques with detailed extraction by the proposed method
Sustainable aviation fuels from bio resources: Technological pathways, prospects, and deployment challenges
(Elsevier, 2025) Mohanty, Asit; Ramasamy, Agileswari; Verayiah, Renuga; Satpathy, Abhaya S.; Mohanty, P.; Mohanty, P; Hakami, Hadi; Cüce, Erdem
Sustainable Aviation Fuels (SAFs) represent a vital strategy for the decarbonization of the aviation sector, significantly reducing greenhouse gas emissions and improving energy resilience. This paper presents a comprehensive assessment of recent technological advancements and emerging trends in SAF production, focusing on advanced biofuels, power-to-liquid synthetic fuels derived from renewable electricity, and process innovations that enhance lifecycle sustainability and fuel efficacy. This review systematically compares pathways including HEFA, Fischer–Tropsch, alcohol-to-jet, and PtL, based on over 100 peer-reviewed studies, techno-economic assessments, and policy reports published from 2015 to 2025. It evaluates lifecycle GHG reductions (50 % to over 90 %), fuel yields (250–650 L/ton), and production costs (0.85–4.00 USD/L). The analysis identifies ongoing disparities between laboratory-scale innovations and their commercial implementation, especially in high-yield lignocellulosic and PtL pathways, where issues of scalability and cost competitiveness are yet to be addressed. This study identifies key leverage points where innovation can improve scalability, such as feedstock flexibility, catalytic conversion pathways, and modular biorefinery designs. Despite these advancements, the widespread adoption of SAF is constrained by high production costs, insufficient infrastructure, competition for feedstocks, and regulatory inconsistencies across jurisdictions. The document underscores the environmental risks associated with large-scale biomass procurement and the urgent need for a robust SAF supply chain. This paper synthesizes the current status of sustainable aviation fuel (SAF) development alongside policy and market dynamics, providing actionable insights to overcome challenges to a low-carbon aviation future and outlining a pathway for aligning SAF adoption with global climate objectives.
BALAD-2 emerges as the most accurate prognostic model in hepatocellular carcinoma: Results from a biobank-based cohort study
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Demirtaç, Coşkun Özer; Eren, Fatih; Yılmaz Karadağ, Demet; Kaldırım Armutcuoğlu, Yasemin; Tolu, Tuğba; Huseyinov, Javid; Özdoğan, Osman Cavit
Background/Objectives: Accurate prognostication of hepatocellular carcinoma (HCC) remains essential for treatment selection and risk stratification. This study aimed to compare the prognostic performance of individual serum biomarkers and composite scoring models, including GALAD, BALAD, BALAD-2, GAAP, ASAP, the Doylestown algorithm, and aMAP, using data from a biobank-based HCC cohort. Methods: This study enrolled 186 patients with confirmed HCC diagnosed between 2019 and 2024. Serum biomarkers (AFP, AFP-L3%, DCP) and composite models were evaluated for their association with overall survival (OS). Prognostic performance was assessed using time-dependent area under the receiver operating characteristic curve (AUROC) at 1-, 2-, 3-, and 5-year intervals and Harrel’s concordance index (c-index). Subgroup analyses were performed based on treatment intent and liver disease etiology. Results: All three biomarkers and composite models were independently associated with OS in multivariate analyses (all p < 0.05). Among all models, BALAD-2 demonstrated the best overall performance (c-index: 0.737), with the highest AUROCs at 1 year (0.827), 2 years (0.846), 3 years (0.781), and 5 years (0.716). BALAD-2 consistently showed superior discrimination in patients treated with curative or noncurative therapies and in the viral etiology subgroup. In the non-viral etiology subgroup, BALAD-2 remained among the top performers, although the GAAP, ASAP, and Doylestown algorithms showed slightly higher metrics. Conclusions: BALAD-2 demonstrated consistent and robust prognostic performance compared with other biomarker-based and clinical models across different patient subgroups, particularly among those receiving curative therapy and viral etiologies. These findings support its integration into clinical risk stratification and decision-making for HCC management.
High fructose corn syrup ınduced liver and heart damage are not reversed with hazelnut consumption: In vivo study
(Public Library of Science, 2025) Toprak-Semiz, Ayça; Yavuz-Bedir, Efsane; Yüzüak, Hakan; Usta, Murat; Şengül, Demet
Hazelnut, antioxidant, anti-inflammatory effects, has an important role in a healthy diet. High fructose corn syrup (HFCS), used as a sweetener in ready-made food, beverages; causes hyperlipidemia, fatty liver, cardiovascular system damages; oxidative stress, inflammation play role in these damages. Based on these data, we aimed to examine liver and heart damage caused by HFCS in rats and to investigate possible role of hazelnut enriched food in preventing/improving these damages. During this process, weight change, food, liquid consumption were recorded. Biochemical parameters were measured with standard enzymatic techniques. Inflammatory cytokines were determined by ELISA. Liver and heart tissues were evaluated histopathologically, changes were scored, graded. HFCS decreased food, increased liquid consumption. Feeding with hazelnut reduced fluid consumption. HFCS increased weight gain, hazelnut did not reverse it. LDH, CK values increased in HFCS group due to heart damage. While damage occurred in livers of HFCS group due to increased levels of TNF-α and IL-1ß, feeding with hazelnut did not change it. In heart, inflammatory cytokines were similar between groups. In histopathological analysis, inflammation was observed both in livers, hearts of HFCS group. In hazelnut group, a significant decrease in damage was observed compared to HFCS, HFCS+H groups. According to our results, hazelnut supplementation reduced liquid intake and showed limited cardiac protection, but did not reverse HFCS-induced hepatic or cardiac injury.
Benchmarking ML approaches for earthquake-induced soil liquefaction classification
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Korkmaz Can, Nuray; Özkat, Erkan Caner; Ceryan, Nurcihan; Ceryan, Şener
Earthquake-induced soil liquefaction represents a critical geotechnical challenge due to its nonlinear soil–seismic interactions and its impact on structural safety. Traditional empirical methods often rely on simplified assumptions, limiting their predictive capability. This study develops and compares six machine learning (ML) classifiers—namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), Random Forest (RF), Decision Tree (DT), and Naïve Bayes (NB)—to evaluate liquefaction susceptibility using an original dataset of 461 soil layers obtained from borehole penetration tests in the Edremit region (Balıkesir, NW Turkey). The models were trained and validated using normalized geotechnical and seismic parameters, and their performance was assessed based on accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC). Results demonstrate that SVM, ANN, and kNN consistently outperformed other models, achieving test accuracies above 93%, F1 scores exceeding 98%, and AUC values between 0.933 and 0.953. In contrast, DT and NB exhibited limited generalization (test accuracy of 84–88% and AUC of 0.78–0.82), while RF showed partial overfitting. In contrast, DT and NB exhibited weaker generalization, with test accuracies of 84% and 88% and AUC values of 0.78 and 0.82, respectively, while RF indicated partial overfitting. The findings confirm the superior capability of advanced ML models, particularly SVM, ANN, and kNN, in capturing complex nonlinear patterns in soil liquefaction. This study provides a robust framework and original dataset that enhance predictive reliability for seismic hazard assessment in earthquake-prone regions.
