Turkish Aerospace Industries (Turkey)

This pilot study investigates the performance characteristics of bedspreads produced through an upcycling-oriented design approach using the patchwork technique, compared to conventionally manufactured counterparts. Textile waste materials sourced from Zorluteks Tekstil Sanayi ve Ticaret A.Ş., certified by OEKO-TEX® STANDARD 100 and OEKO-TEX® STeP, were reprocessed into single-bed-sized bedspreads in accordance with sustainable design principles. Both upcycled and conventional bedspreads were composed of three layers, including regenerated fibre filling and 100% cotton woven fabric. Comprehensive testing was conducted to assess washing fastness, perspiration fastness (acidic and alkaline), rubbing fastness, water fastness, dimensional stability, tensile strength, and seam slippage tests in accordance with ISO standards. Exploratory statistical analysis indicated no clear differences in most fastness properties, except for alkaline perspiration fastness, where patchwork samples showed slightly reduced performance due to fabric heterogeneity. Notably, upcycled samples demonstrated higher tensile strength, attributed to increased stitching density, but exhibited greater seam slippage, likely due to multi-fabric assembly. These findings suggest that upcycled textile products, when manufactured using optimised methods, can match or exceed the mechanical and functional performance of conventional products, positioning upcycling as a viable industrial strategy for sustainable textile production.

The purpose was to assess the impact of septorhinoplasty on psychopathology and well-being and to demonstrate the effectiveness of septorhinoplasty and emphasize the importance of considering psychopathological status when selecting patients. Seventy patients scheduled for septorhinoplasty were evaluated prospectively. Following patient selection, participants were asked to complete a sociodemographic questionnaire, the Beck Anxiety Inventory (BAI), the Beck Depression Inventory, the Nasal Obstruction Symptom Evaluation Scale (NOSE), the Short Form-36 (SF-36), and the Rhinoplasty Outcome Evaluation Questionnaire (ROE) preoperatively and at 6 months postoperatively. The mean age in the study was calculated as 26.14 Å} 6.80. The postoperative NOSE and BAI scores of the patients decrease, while ROE score increases (P < .05). An increase in physical role, physical function, general health, and social functioning scores was also found in SF-36 subscales (P < .05). There was an inverse correlation between preoperative depression levels and postoperative satisfaction. Septorhinoplasty has a positive effect on quality of life and anxiety. When selecting a patient, attention should be paid to the level of depression. Cite this article as: Tekin P, Toplu Y, G.nenir Erbay L. The effect of septorhinoplasty on psychopathology and quality of life. Eurasian J Med. 2026, 58(1), 1146, doi:10.5152/eurasianjmed.2026.251146.

ABSTRACT Skin diseases, such as thermal burns and diabetic leg ulcers, can poredamage the dermal and epidermal layers, depending on injury depth and extent. Infection poses a major risk in these wounds. Wound dressings are essential in treating injured skin, as they shield open wounds from infection‐causing external factors and mechanical irritation. In this study, polyurethane (PU) foams were produced via precise casting. Their mechanical, chemical, biological, and morphological properties were characterized, and in vitro biocompatibility tests were conducted with NIH/3T3 cells. The morphological properties of the PU foams were characterized with a scanning electron microscope (SEM). Tensile test proved the mechanical strength of the foams ranging from 1.64 ± 0.66 MPa to 4.62 ± 1.22 MPa. Cell culture results detected with the XTT method showed the biocompatibility properties of the foams. XRD analysis demonstrated the amorphous structure of the PU foams. This study is anticipated to lay a foundation for further research on incorporating antibacterial agents, drugs, growth factors, vitamins, peptides, and collagen into polyurethane foam structures.

The application of machine learning (ML) in geology has gained significant momentum over the past decade. Given the importance of visual interpretation in geological tasks such as lithological classification and trace fossil identification, automating these processes through ML has offered substantial benefits to both researchers and industry professionals, including reduced human error and increased efficiency. While machine learning methodologies commonly yield highly accurate results, the opacity of decision-making processes can present challenges, as they often provide "black box" responses. To address this issue and improve user trust and interpretability, the integration of Explainable Artificial Intelligence (XAI) has emerged as a promising solution. XAI techniques provide visual explanations, typically in the form of heat maps, indicating the specific regions within an image that the model prioritized in reaching its classification. Despite its growing relevance, the application of XAI within the geosciences remains in its infancy. This study evaluates the effectiveness of XAI in the context of bioturbation intensity classification using a pre-trained deep-learning model. The model classifies core and outcrop images into three categories: (1) unbioturbated, (2) moderately bioturbated, and (3) intensely bioturbated. XAI visualizations reveal strong alignment with an experienced ichnologist's interpretations. Our findings highlight the utility of XAI in enhancing the transparency, and reliability, as well as ability to conduct a consistent, rapid assessment of images to increase efficiency of ML-based geological interpretation. In addition to its potential for improving confidence, XAI may serve as a valuable educational tool, especially in specialized domains such as ichnology, wherein expert knowledge is typically limited in academic and industry settings.

Anomaly detection is an important task in finding out the irregular patterns or behaviors in different applications such as cybersecurity, financial systems, healthcare applications, and industrial automation. Recent studies have shown that machine learning models have appeared to be promising in enhancing anomaly detection systems in terms of their accuracy and efficiency. This article aims to introduce a comparative study of the efficiency of different machine learning models in implementing anomaly detection. Different machine learning models such as support vector machines, K-Nearest Neighbor algorithms, hybrid models, and decision trees have been used in this article. Standard models such as Support Vector Machine, K-Nearest Neighbors, Decision Tree Classifier, XGBoost models have been subjected to testing through benchmarking. The models have undergone testing based on some primary factors such as accuracy rates, precision rates, recall rates, F1 Score, and efficiency. The results obtained from this study indicate that the XGBoost model and Random Forest model perform better than the existing models in terms of anomaly detection efficiency.