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Integrated Quantitative Modelling for the Dimension Stone Quality Evaluation: Implications for Sustainable Resource Management
No full textThe growing demand for dimensional stones in construction and monument conservation requires fast, repeatable and scientifically valid quality assessment procedures. The present study, in this context, established a solid foundation for quantifying the quality of dimension stones by adopting two quantitative methods: the Suitability Index (SI) and Dimension Stone Field Performance Coefficient (DSFPC). Both methods were coded in the MATLAB environment and implemented for 20 different rock types used in various dimension stone applications in Turkey. Evaluations based on the above-mentioned methods demonstrate that the DSFPC provides a more conservative assessment than the SI method. Additionally, engineering interpretations derived from the SI and DSFPC approaches are compared with recently published classification systems developed for the dimension stone industry. Focusing on this comparison, it is concluded that the adopted methods offer a more holistic evaluation framework compared to the approaches based solely on a single input parameter, such as effective porosity (ne), uniaxial compressive strength (UCS), or B & ouml;hme abrasion value (BAV) of rocks. Furthermore, it is concluded that the adopted methods complement each other by yielding supportive outcomes. The coded methods can be adapted to other lithological series and integrated with spatial information systems to support decision-making in mining and construction sectors. From this point of view, the present study may be considered a case study supporting holistic approaches to sustainable resource management in the dimension stone industry
A Potential Hemostatic Chitosan/Gelatin Cryogel Impregnated with Verbascum Thapsus Leaf Extract for Noncompressible Hemorrhage Management
No full textIn this study, we prepared a series of chitosan/gelatin (CS/GEL) cryogels containing Verbascum thapsus (V. thapsus) leaf extract and identified a lead formulation for noncompressible hemorrhage (NCH). Cryogels with average pore diameters ranging from 225 to 478 mu m were fabricated through cryogelation at various CS/GEL ratios. C15 was chosen as the base scaffold due to its homogeneous pore distribution, with a pore size coefficient of variation (CV) of approximately 0.22. Extract loading was 1%, 5%, 10%, and 20% w/v. Functional porosity was reported by the relative accessible void index (RAVI). In PBS, the values relative to neat C15 were 1.00, 0.27, 0.20, 0.13, and 0.09 for concentrations of 0%, 1%, 5%, 10%, and 20% w/v, respectively. In citrated blood, the series was 1.00, 0.29, 0.12, 0.14, and 0.09. After loading, equilibrium swelling decreased and the compressive modulus increased, consistent with partial pore filling in a fixed network. The cryogels maintained an interconnected macroporous network and showed swelling from 300% to 3600% in blood and PBS. Antibacterial activity reached 89% inhibition, and cell viability remained above 80%. Hemolysis was low and within acceptance limits. Clotting improved in whole blood as the blood clotting index decreased from 11.9 to 6.5, and the clotting time was approximately 6 min. The 5% w/v group provided the optimal balance of clotting, antibacterial effects, and biocompatibility. This study presents a novel hemostatic CS/GEL cryogel containing V. thapsus leaf extract that holds strong potential for future applications in NCH management.Turkiye Council of Higher Education; Scientific and Technological Research Council of Turkiye (TUBITAK)We would like to thank the Turkiye Council of Higher Education (YOK 100/2000 program) and the Scientific and Technological Research Council of Turkiye (TUBITAK) (BIDEB 2211-A program) for their valuable support for Ph.D. scholarships to Adile Yuruk
Machine Learning Models With Hyperparameter Optimization for Voice Pathology Classification on Saarbrücken Voice Database
No full textEarly diagnosis and referral are crucial in the treatment of voice disorders. Contemporary investigations have indicated the efficacy of voice pathology detection systems in significantly contributing to the evaluation of voice disorders, facilitating early diagnosis of such pathologies. These systems leverage machine learning methodologies, widely applied across diverse domains, and exhibit particular potential in the realm of voice pathology classification. However, machine learning models and performance metrics employed in these studies vary significantly, making it challenging to determine the optimal model for voice pathology classification. In this study, healthy and pathological voices were classified with state-of-the-art machine learning models, and the performance results of the models were compared. The voice samples employed in our research were sourced from the Saarbrücken Voice Database, a reputable German database. Feature extraction from voice signals was conducted using the Mel Frequency Cepstral Coefficients method. To assess and enhance the models’ performance adequately, we employed hyperparameter optimization and implemented a 10-fold cross-validation approach. The outcomes revealed that the support vector machine model exhibited the highest accuracy, achieving 99.19% and 99.50% accuracies in the classification of male and female voice pathologies, respectively. © 2025 Elsevier B.V., All rights reserved
Shooting a Water Slug into an Air Column with and without Vent
No full textASME Pressure Vessels and Piping DivisionCompressed air is used to shoot a single water slug into an upward sloping pipe with elbow and orifice at its upper end. The experiment concerns a 12 m long pipe of 0.1 m diameter connected to a 0.5 m3 air vessel. The 10 to 50 kg heavy slugs are initially at rest in the lower part of the system. Because the upper end is closed by a flange with orifice, the water slug is expected not to hit the upstream elbow. It causes - like a piston - a fast compression of the air column ahead of it. Sometimes the slug bounces back and forth, which results in a pressure oscillation of serious amplitude. Numerical simulations based on an elementary mathematical model are normally used to interpret the pressure measurements, not all of which are fully understood. Lessons learned are summarised, and suggestions for improved experiments and enhanced simulations are given. The research is of importance, for example, for steam lines where liquid condensates may collect in lower parts after power failure. Start-up of the system will then lead to rapid slug acceleration and potentially damaging impact on elbows, orifices, and machinery. © 2025 Elsevier B.V., All rights reserved
Manyetik Rezonans Görüntülemede Düşük Dereceli Gliom Segmentasyonu için Unet Varyantlarının Karşılaştırmalı Çalışması
No full textBrain tumors originating from glial cells are pathological entities that significantly impact quality of life and are classified based on their malignancy into low-grade gliomas (LGGs) and high-grade gliomas (HGGs). While the more aggressive HGGs have been extensively studied, LGGs are of critical importance for early diagnosis due to their potential progression to HGGs if left untreated. This has driven researchers to develop methods for the rapid and consistent diagnosis of LGGs. In this study, three models—UNet, Transformer UNet, and Super Vision UNet—were comparatively evaluated for the automatic segmentation of LGGs using magnetic resonance imaging (MRI) data. Multimodal MRI scans from 110 patients, retrieved from The Cancer Imaging Archive (TCIA), were used to train the models. Performance was evaluated using Dice Coefficient, Tversky Index, and Intersection over Union (IoU) metrics. The Super Vision UNet achieves the highest Dice (0.9115) and Tversky (0.9154) scores, while the Transformer UNet attains the highest IoU (0.8789). Both advanced models demonstrate superior segmentation performance with lower loss values compared to the conventional UNet. Visual outputs indicate that the modern architectures delineate tumor contours with greater precision. These results highlight the effectiveness and reliability of contemporary UNet-based and Transformer-based architectures in segmenting complex tumor structures such as LGGs. Integrating these models into clinical decision support systems holds promise for enhancing the speed and accuracy of the diagnostic process. © 2025 Elsevier B.V., All rights reserved
Production of High-Grade Antimony Oxide From Smelter Slag via Leaching and Hydrolysis Process
No full textKursunoglu, Sait/0000-0002-1680-5482;This study aimed to investigate the recovery of antimony (Sb) from slag generated in an antimony smelting plant using leaching followed by hydrolysis processes. The leaching behaviors of rare earth elements (REEs) were also examined. The physicochemical properties of the slag were determined using various analytical techniques. The slag (4.12 % Sb) was mainly composed of quartz and minor minerals, including microline, magnetite, heden-bergite, and stibiconite. The Sb types in the slag determined by XPS were found to be in the oxide form. The concentrations of REEs (La, Y, Ce, and Nd) in the slag were 169.21 g/t. Preliminary leaching experiment results indicate that (i) HCl was selected rather than other acids due to its high extraction ability on the Sb from the slag, (ii) a sample with a d50 of 90 %. However, the extraction rate of Sb was negligible in extended times. It was determined that using tartaric acid positively affected La's leaching mech-anism, and the required leaching time for La decreased to 180 min from 20 h with the increase of tartaric acid from 1 g/L to 6 g/L. Hydrolysis tests were conducted using the Taguchi approach (L32, 21 43). The effects of the alkaline type (NH4OH and NaOH), stirring speed (100, 200, 300, and 400 rpm), temperature (50, 60, 70, and 80 C-degrees), and pH (1.5, 2, 2.5, and 3) on the precipitation of Sb from the PLS were investigated. NH4OH was suggested for use in the hydrolysis test to obtain precipitates with higher purities. The product obtained under the optimal conditions comprised 81.43 % Sb, 16.23 % O, and 2.34 % Fe. The product was identified as antimony oxide by XRDScientific and Technological Research Council of Turkey (TUBITAK) [123M062]; TUBITAK; Cukurova University Research Fund [FYL-2022-15229]This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant Number 123M062. The author thanks TUBITAK for their support. Also, the author would like to thank Cukurova University Research Fund for financial support (FYL-2022-15229) . In addition, the authors would like to thank Dr. Burcu Selen CAGLAYAN for her help to evaluate XPS analysis
Fuzzy Logic-Enhanced PMC Index for Assessing Policies for Decarbonization in Higher Education: Evidence from a Public University
No full textHigher education institutions play a critical role in the transition to a low-carbon future due to their research capacity and societal influence. Accordingly, the calculation of greenhouse gas (GHG) emissions and the prioritization of mitigation strategies are of particular importance. In this study, a comprehensive campus-level GHG inventory was prepared for a public university in T & uuml;rkiye in alignment with the ISO 14064-1:2018 standard, and mitigation strategies were evaluated. To prioritize these strategies, both the classical Policy Modeling Consistency (PMC) index and, for the first time in the literature, a fuzzy extension of the PMC model was applied. The results reveal that the total GHG emissions for 2023 amounted to 4888.63 tCO2e (1.19 tCO2e per capita), with the largest shares originating from investments (31%) and purchased electricity (28.38%). While the classical PMC identified only two high-priority actions, the fuzzy PMC reduced score dispersion, resolved ranking ties, and expanded the number of high-priority actions to seven. The top strategies include awareness programs, energy-efficiency measures, virtual meeting practices, advanced electricity monitoring, and improved data management systems. By comparing the classical and fuzzy approaches, the study demonstrates that integrating fuzzy logic enhances the transparency, reproducibility, and robustness of strategy prioritization, thereby offering a practical roadmap for campus decarbonization and sustainability policy in higher education institutions
Bovine Serum Albumin (BSA)-Loaded Polyvinyl Alcohol (PVA) / Chitosan (CH) / Hydroxyapatite (HA) Electrospun Nanofibers for Bone Tissue Regeneration
No full textBozdag, Mehmet Murat/0009-0006-2244-1861; Sahin, Ali/0000-0001-5594-1551The natural bone structure consists of three different nanocomposite layers; a porous polymer ceramic part, a lamellar, and a fiber-matrix composition gives the bone its unique physical and biological properties. During bone tissue regeneration bioactivity, and osteoinductivity are especially important with other parameters such as porosity, degradation rate, and cell adhesion. In this study, hydroxyapatite (HA) and bovine serum albumin (BSA) protein-loaded, polyvinyl alcohol (PVA) and chitosan (CH) nanofibers were fabricated via the electrospinning method. The mean diameters of PVA/CH/HA/BSA-5, PVA/CH/HA/BSA-10, and PVA/CH/HA/BSA-15 nanofibers were measured as 325.39 +/- 77.512 nm, 332.45 +/- 82.251 nm, 447.03 +/- 101.382 nm respectively, required porosity and properties for bone tissue engineering were considered achieved. BSA release profiles of BSA-5, BSA-10, and BSA-15 nanofibers were similar in terms of burst release which continued until the 12th hour, 58 %, 78 %, and 73 % of the BSA were released, respectively. After 72 h 100 % of BSA were released from all nanofibers. Cell viability tests showed that PVA/CH/HA/BSA nanofibers exceeded the control group in terms of cell viability by 119.9 %. In future bone injury treatment, PVA/CH/HA/BSA nanofibers can assist the healing process of cracks and fractures, and decrease the recovery time of bone as an alternative bone healing nanofiber
Short-Term Vs. Long-Term Analysis of Loan Volume and Non-Performing Loans: Turkish Banking Sector Perspective
No full textBu tez, Türkiye bankacılık sektöründe kredi hacmi ile takipteki krediler (TK) arasındaki dinamik ilişkiyi hem kısa hem de uzun vadeli perspektiflerden incelemektedir. 2014'ün Ocak ayından 2024'ün Mart ayına kadar olan veriler kullanılarak, Dinamik Koşullu Korelasyon (DCC) modeli aracılığıyla kredi büyümesi ve ekonomik şokların TK'lere nasıl etki ettiği araştırılmaktadır. Bulgular, uzun vadede kredi hacmi ile TK'ler arasında anlamlı bir ilişki olduğunu ortaya koymaktadır. Öte yandan, 2018 döviz krizi ve COVID-19 pandemisi gibi finansal istikrarsızlık dönemlerinde kayda değer dalgalanmalar yaşandığı da tespit edilmiştir. Çapraz korelasyon analizi, kredi büyümesinin TK'ler üzerindeki etkilerini daha da vurgulayarak, proaktif risk yönetiminin önemini göstermektedir. Araştırma, dengeli kredi büyümesi, geliştirilmiş düzenleyici çerçeveler ve dinamik risk değerlendirme araçlarının önemini vurgulayarak politika yapıcılar için uygulanabilir öneriler sunmaktadır. Bu bulgular, kredi dinamikleri ile finansal istikrar arasındaki ilişkiye dair daha detaylı bir anlayış sunarak literatüre katkı sağlamaktadır. Çalışma ayrıca, bankacılık uygulamalarının iyileştirilmesi yoluyla kapsayıcı ekonomik büyümeyi ve finansal eşitsizliklerin azaltılmasını destekleyen Sürdürülebilir Kalkınma Amaçları'ndan (SKA) 8, 10 ve 17 ile uyum göstermektedir. Anahtar Kelimeler: Takipteki Alacaklar, Kredi Hacmi, Bankacılık SektörüThis thesis investigates the dynamic relationship between loan volume and NPLs in the Turkish banking sector, focusing on both short-term and long-term perspectives. Using weekly data from January 2014 to May 2024 and employing the DCC model, this study explores how credit growth and economic shocks influence NPLs. The findings reveal a significant correlation between loan volumes and NPLs in the long term. On the other hand, the thesis also shows notable fluctuations during financial instability, such as the 2018 currency crisis and the COVID-19 pandemic. The cross-correlation analysis further highlights the effects of loan growth on NPLs following the importance of proactive risk management. The research provides actionable insights for policymakers, emphasizing the need for balanced credit growth, enhanced regulatory frameworks, and dynamic risk assessment tools. These findings contribute to the literature by offering a nuanced understanding of the relationship between credit dynamics and financial stability in an emerging market context. The study also aligns with Sustainable Development Goals (SDGs) 8 10, and 17, supporting inclusive economic growth and reduced financial inequalities and partnership through improved banking practices. Keywords: Non-Performing Loans, Loan Volume, Banking Secto
Defect Classification of Composite Materials Using Transfer Learning Methods
No full textBakir-Gungor, Burcu/0000-0002-2272-6270; Kolukisa, Burak/0000-0003-0423-4595; Gulsen, Abdulkadir/0000-0002-4250-2880Nowadays, composite materials have become prevalent across various sectors, particularly finding usage in large-scale applications such as spaceships, automobiles, and aircrafts. The accurate detection of the defects in these materials is crucial, yet traditional methods often rely on human inspection, which is susceptible to errors. Recent advancements in machine learning have enabled defect detection using ultrasonic non-destructive testing methods. This paper introduces a new dataset named UNDT, which is obtained from the scans of 60 different composite materials, generating a total of 1150 images depicting both defective and non-defective areas. Several transfer learning methods are applied on the newly introduced UNDT dataset as well as the publicly available USimgAIST ultrasonic dataset. Comparative performance assessments illustrate the significance of utilising the transfer learning approach for defect classification on ultrasonic inspection images. Furthermore, the research emphasises the substantial benefits of employing these transfer learning methods. Notably, the DenseNet121 and VGG19 models achieve the highest accuracy rates, with 98.8% and 98.6% on the UNDT and USimgAIST datasets, respectively