ATÜ Kurumsal Akademik Arşivi
DSpace@ATÜ, Adana Alparslan Türkeş Bilim ve Teknoloji 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 telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
Multifunctional POSS-based nanoparticles functionalized with silver, SPIONs, and rhamnolipid for antibacterial applications
(Elsevier, 2026) Kibar, Gunes; Kafali, Melisa; Ozonuk, Olgu Cagan; Oztas, Merve; Usta, Berk; Ercan, Batur
Nano-engineered materials, particularly those featuring bio-based surface modifications, are emerging as effective tools in combating bacterial infections. In this study, polyhedral oligomeric silsesquioxane (POSS) nanoparticles were functionalized with silver nanoparticles (Ag), superparamagnetic iron oxide nanoparticles (SPIONs), and the biosurfactant rhamnolipid (RL)-either individually or in combination-to evaluate their antibacterial and antibiofilm activities against Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa). The modified nanoparticles exhibited sizes ranging from 127 to 227 nm and demonstrated superparamagnetic behavior, offering potential for magnetic targeting. Among the various formulations, the RLcoated, silver- and SPION-decorated POSS nanoparticles (RSMP) exhibited the highest antibacterial efficacy, reducing S. aureus and P. aeruginosa colony growth by approximately 90 % and 66 %, respectively, at a concentration of 0.01 g/L. RSMP nanoparticles also showed strong biofilm inhibition and had the lowest MIC50 values. Notably, these nanoparticles supported the proliferation of human osteoblasts at concentrations up to 0.05 g/L, indicating favorable cytocompatibility. Overall, RSMP nanoparticles present a promising platform for magnetically targetable antibacterial agents, with potential applications in biomedical fields, particularly for managing orthopedic infections.
Peas, natural resources for a sustainable future: a multifaceted review of nutritional, health, environmental, and market perspectives
(Frontiers Media SA, 2026) Nikolic, Nada Cujic; Mutavski, Zorana; Savikin, Katarina; Zivkovic, Jelena; Pavlovic, Suzana; Jones, Petra; Copperstone, Claire; Aytar, Erdi Can; Aydin, Betul; Van Bavegem, Evelien; Kunili, Ibrahim Ender; Ozmen, Ozge; Kusumler, Aylin Seylam; Unal, Derya Ozalp; Gunduz, Selin; Lara, Szymon Wojciech; Akin, Meleksen; Orahovac, Amil; Balazs, Balint; Milesevic, Jelena; Sirbu, Alexandrina; Negrao, Sonia; Knez, Marija
The pea (Pisum sativum L.) is an emerging pillar in plant-based nutrition and sustainable food systems due to its high-quality proteins, diverse bioactive compounds, and agroecological benefits. This review provides an updated synthesis of the nutritional composition, health-promoting properties, and environmental relevance of peas, emphasizing recent scientific findings. Pea seeds typically contain 20%-40% protein, 45%-55% starch, and 10%-15% dietary fiber, alongside essential micronutrients such as vitamin C (40-60 mg/100 g), folate (60-70 mu g/100 g), vitamin K (30-45 mu g/100 g), iron (1.5-2.0 mg/100 g), and manganese (0.4-0.6 mg/100 g). Their storage proteins, primarily legumin and vicilin, offer high digestibility and amino acid profiles compatible with human requirements, supporting their rapidly growing use in protein isolates and meat- and dairy-alternative products. Peas represent a valuable source of phenolic acids, flavonoids, and saponins, which contribute to notable antioxidant (50-120 mu mol Trolox/g) and anti-inflammatory activities demonstrated in preclinical studies. Compared with other legumes, peas exhibit a lower glycemic index (35-45), making them suitable for metabolic health applications. Agronomically, pea cultivation enhances soil fertility through biological nitrogen fixation (up to 150 kg N/ha), supporting reduced fertilizer inputs and improved crop rotation performance, aligning with circular economy and climate-resilience strategies. Despite these advantages, global consumption and breeding innovation remain insufficient to meet the rising demand for alternative proteins. Future opportunities include improving protein extraction technologies, valorizing processing side-streams, and exploring underutilized phytochemicals to strengthen the nutritional and sustainability profile of pea-based food systems.
An investigation into the use of 3D printing technology for geogrids
(Emerald Group Publishing Ltd, 2026) Ok, Bahadir; Unverdi, Murteda
Three-dimensional (3D) printers with large-scale printing capabilities may enable engineers to print their geogrids, which would be especially useful at outlying construction sites where acquiring geogrid can be challenging or expensive. Nevertheless, for 3D geogrids to be used effectively for geotechnical applications like mechanically stabilised earth walls (MSE walls) or ground improvement, they must be able to interlock with soils in a manner similar to factory-made geogrids. The aim of this study is to comprehensively investigate the interlocking mechanisms of 3D-printed geogrids with different soils by comparing them with factory-made geogrids. To achieve this, tensile tests were conducted to determine the tensile characteristics of the 3D-printed geogrids produced in the study. In addition, large-scale direct shear tests were performed by placing 3D geogrids with soils of different grain roundness values. All test results were presented by comparing them with the test results conducted on factory-made geogrids with properties similar to the 3D-printed geogrids. The 3D-printed geogrid improved the soil shear strength; however, it was found to be more brittle and provided less interlock with soils than the factory-made geogrid. These results suggest that the production method of the geogrid was a significant factor.
Decarbonizing Yarn Production: ISO 14064-Aligned Carbon and Energy Footprint Assessment for a Sustainability-Oriented Supply Chain at Ulusoy Textile
(Taylor & Francis Inc, 2026) Gul, Berfin; Varli, Rabia Sultan Yildirim; Demirdelen, Tugce; Kadem, Fusun Doba
As global climate commitments intensify, the textile industry faces growing pressure to quantify and reduce greenhouse gas (GHG) emissions. This study presents a carbon footprint assessment of yarn manufacturing at Ulusoy Textile, following ISO 14064 and an extended five-scope approach: Scope 1 - direct emissions, Scope 2 - indirect emissions from purchased energy, Scope 3 - indirect emissions from transportation, Scope 4 - emissions from products used by the organization, and Scope 5 - emissions and removals from product use. Using 2023 data on raw material procurement, energy consumption, logistics, and facility activities, total emissions were 30,146.80 tCO2e, equivalent to 40.19 tCO2e per employee and 0.003 tCO2e per ton of yarn. Energy use and raw material sourcing were the main contributors, while efficiency measures and renewable energy reduced emissions, and product use provided balancing effects. Uncertainty was assessed via Monte Carlo simulations, and materiality analysis identified key parameters for inventory robustness and verification. These findings establish a verification-ready baseline for Ulusoy Textile's decarbonization strategy and propose a scalable ISO 14064-aligned framework for yarn producers seeking EU Green Deal and Carbon Border Adjustment Mechanism compliance, supporting innovations in circularity, energy efficiency, and net-zero pathways. (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic)(sic)(sic)(sic)ISO 14064(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)Ulusoy Textile(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic): (sic)(sic)1--(sic)(sic)(sic)(sic),(sic)(sic)2--(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)3--(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)4--(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)5--(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic)2023(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)30146.80(sic)(sic)(sic)(sic)(sic)2e, (sic)(sic)(sic)40.19(sic)(sic)(sic)(sic)(sic)2e(sic)(sic)(sic)(sic)(sic)0.003(sic)(sic)(sic)(sic)(sic)2e(sic)(sic)(sic)(sic). (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic)(sic)(sic)(sic)Ulusoy Textile(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)ISO 14064(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).
Benchmarking TabNet, NODE, and FT-Transformer for Software Defect Prediction: An Empirical Comparison and Explainability Analysis
(IEEE-Inst Electrical Electronics Engineers Inc, 2026) Asal, Burcak; Yalciner, Burcu
Software defect prediction (SDP) is essential for improving software quality and reliability. Traditional machine learning methods, while effective, often fail in capturing complex interactions among software metrics. Recently, specialized deep learning architectures designed for tabular data, including TabNet, Neural Oblivious Decision Ensembles (NODE), and FT-Transformer, have emerged, offering promising potential to enhance prediction accuracy and interpretability. This study comprehensively benchmarks the TabNet, NODE and FT-Transformer models on the challenging NASA JM1 dataset from the PROMISE repository. We address severe class imbalance using NearMiss undersampling and ensure hyperparameter optimization for fairness across comparisons. The performance of the models was evaluated using standard metrics: precision, recall, F1-score, and accuracy. In addition, the interpretability of the model was assessed using SHAP and LIME methods. The FT-Transformer and NODE models demonstrated superior performance, achieving 88% accuracy compared to the accuracy of TabNet 86%. FT-Transformer showed exceptional precision (99%) for defect detection, emphasizing its low false-positive rate. SHAP and LIME analyzes revealed unique attention patterns for each model, highlighting differences in feature importance and decision-making processes. FT-Transformer and NODE outperform TabNet in accuracy and balance between recall and precision. Interpretability analysis provides actionable insights into feature importance, enabling better decision-making in practical SDP workflows.
