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Immediate Autologous Fat Transfer into the Breast for Volume Restoration After Implant Removal
No full textBackground: Autologous fat transfer to the breast is utilized for various clinical indications, including breast augmentation, reconstruction, and correction of asymmetries and deformities. This study presents the outcomes of patients who underwent simultaneous volume restoration through fat transfer following breast implant removal. Patients and Methods: Thirty-eight patients received immediate fat transfer to the breast after their implants were removed. The participants were categorized into three groups based on the type of incision used for implant removal: inframammary fold, periareolar, and mastopexy incisions. The study aimed to compare patients' ages, satisfaction levels, complication rates, and follow-up durations among the different groups. Results: The mean age of the patients was 40.94 +/- 8.73 years (range 24-61 years), and the mean follow-up period was 24.60 +/- 17.10 months (range 3-60 months). The average volume of the implants was 318.8 cc (175-480 cc). Seventy-six breasts (38 patients bilaterally) underwent fat grafting, with a mean fat graft volume of 222.8 cc (SD 63.25; range 80-350 cc; median: 200 cc). Fourteen patients reported no complications. The periareolar incision group was excluded from statistical analysis due to a small sample size (n = 3). Patient satisfaction was notably higher in the mastopexy incision group than in those who underwent the inframammary fold incision (p = 0.037; p < 0.05). No significant differences in complication rates were observed between the groups. Conclusions: Autologous fat transfer represents a safe and effective method for achieving volume restoration following implant removal, with high patient satisfaction regarding breast shape and volume restoration
Use of the BioFire® FilmArray® Pneumonia Plus Rapid Syndromic Multiplex PCR Assay in Pneumonia Patients-An Expert Opinion Report|Pnömoni Hastalarında BioFire® FilmArray® Pnömoni Plus Hızlı Sendromik Multipleks PCR Testinin Kullanımı – Bir Uzman Görüşü Raporu
No full textThis consensus report presents current expert opinions on the clinical use of the BioFire® FilmArray® Pneumonia Plus (PNplus) rapid molecular test in patients with pneumonia. A group of eight physicians with clinical experience in pneumonia identified common questions encountered in clinical practice regarding the use of PNplus for lower respiratory tract infections and developed consensus-based answers. Based on this process, a list of recommendations was compiled for the use of the PNplus rapid syndromic molecular test, which detects the most common bacteria, viruses, and resistance genes. These recommendations were supported by case examples based on fictional clinical scenarios, along with a proposed diagnostic algorithm for the management of pneumonia. © 2025 Elsevier B.V., All rights reserved
Performance Improvement of Energy Criterion Method Based on the Instantaneous Phase Characteristics in Wi-Fi Transient Detection for RF Fingerprinting
No full textTransient detection is recognized as a crucial component in Radio Frequency Fingerprinting (RFF) systems, particularly in transient-based approaches. Many existing algorithms rely on prior knowledge of signal behavior, which constrains their applicability to previously unseen devices. To address this limitation, an improved phase-based energy criterion (iEC- phi) method is proposed for accurately detecting transient start points in Wi-Fi devices without requiring any device-specific information. The method is evaluated on a diverse dataset of Wi-Fi signals collected from multiple device brands under various noise conditions. Its performance is comparatively analyzed against high-accuracy and low-complexity detection methods, and competitive results are obtained in most scenarios. Furthermore, the requirement for manual threshold tuning is eliminated. The method also demonstrates robust adaptability to diverse transient patterns, highlighting its potential for real-world RFF applications
Large-scale impact analysis on large language models for Turkish question-answering
No full textLarge language models (LLMs) have recently become popular in many natural language processing tasks. It is very important to contribute to LLMs in order to increase the use of low-level languages such as Turkish. Therefore, the success of BERT, ALBERT, DistilBERT, mDeBERTa, and mT5 LLMs was analyzed for the Turkish question-answering task in this study. The Turkish version of the benchmark dataset, SQuAD, was used as the dataset. As a result of training these LLMs by fine-tuning, mDeBERTa became the most successful model with 74.50% accuracy. In addition, the effect of the threshold value of the predicted answer probability of these models and the semantic similarity between the predicted and actual answers of the LLMs were examined. When the effect of the threshold value was analyzed, an accuracy increase of up to 0.13% was observed in the accuracy value of LLMs. Analyzing the effect of semantic similarity on LLMs showed that the accuracy value increased between 0.7% and 6.59% and the most successful model was mDeBERTa with 79.09%. The results show that analyzing LLMs' threshold value and semantic similarity had a positive effect
Thirteen-Year Experience of Complete Great Saphenous Vein Radiofrequency Ablation: Results of 4,438 Patients at Ege University
No full text[Abstract Not Available
Consumers’ attitudes towards water scarcity and eco-friendly products in Turkey: a psychological distance perspective
No full textAs the problem of water scarcity becomes increasingly acute, ways to engage people in the combat against water scarcity are gaining importance. Despite previous research has explored the role of psychological distance in attitudes toward climate change; water scarcity has not been adequately examined. To address this, the study aims to investigate the role of psychological distance in people’s willingness to act on water scarcity and pay more for eco-friendly products. Additionally, the role of perceived impact and perceived responsibility in this process will be discussed. Data were collected from a convenience sample of 392 respondents via an online questionnaire. Structural equation modelling results indicate that temporal distance and social distance, as well as perceived impact and perceived responsibility are relevant for combating water scarcity. The holistic perspective presented in the study is important for revealing the mechanisms that can be useful for involving people in solutions to water scarcity. © 2025 Elsevier B.V., All rights reserved
Ultrasonography and fine-needle aspiration cytology of thyroid nodules: assessment of malignancy using the British Thyroid Association classification
No full textBackground The widespread use of high-resolution ultrasonography (US) imaging has led to an increased detection of thyroid nodules, which are common in the general population.Purpose To evaluate the correlation between ultrasonographic and pathological findings of thyroid nodules undergoing US-guided fine-needle aspiration (FNA) and assess the contribution of US features to malignancy prediction.Material and Methods A total of 573 patients (137 men, 436 women; age range = 20-88 years) who underwent US-guided FNA were included. Nodule characteristics were recorded using the British Thyroid Association (BTA) U classification, and cytological results were assessed according to the Bethesda system. Logistic regression analysis (LRA) was performed to determine the relationship between US features and malignancy.Results The distribution of nodules in U2, U3, U4, and U5 categories was 212, 171, 84, and 36, respectively, with corresponding Bethesda (2-6) classifications of 287, 159, 18, 27, and 12. Malignancy rates (Bethesda 4-6) were 0%, 10%, 28.6%, and 44.5%, respectively. Hypoechogenicity (relative to muscle), internal vascularization, and microcalcifications were significantly associated with malignancy (P <0.05). LRA achieved an 85.5% accuracy in malignancy prediction.Conclusion US features in the BTA U classification align with pathological findings. Hypoechoic solid nodules, central vascularization, and microcalcifications should raise suspicion for malignancy in the differential diagnosis of thyroid nodules. These study findings highlight the strong association between vascularity in the BTA classification and malignancy, suggesting its potential role in risk stratification
Geo-environmental determinants of vascular plant richness in Western Anatolia: a species distribution modelling approach for Manisa Spil Mountain, Türkiye
No full textUnderstanding the spatial drivers of plant richness in mountainous regions is essential for biodiversity conservation. This study explores vascular plant richness in Spil Mountain, western Türkiye, by modelling 1831 occurrence records of 38 species using MaxEnt and stacked species distribution models (SSDMs). Environmental predictors included high-resolution topographic, climatic, and geomorphological variables. Landforms were classified with the geomorphons method, and geomorphological diversity was quantified via the Shannon diversity index. Results reveal that flat and low-slope areas harbour the highest richness, while species diversity declines in steeper, high-altitude zones. Elevation and slope were the most influential factors, showing strong negative correlations with richness, whereas climatic variables had weaker and spatially variable effects. This study demonstrates the utility of geomorphological predictors in biodiversity modelling and shows how expanding the modelling extent can address data limitations. The findings provide a spatial foundation for conservation in Spil Mountain National Park and inform future models integrating land-use, soil, and climate change variables. © 2025 Elsevier B.V., All rights reserved.Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TUBITA
Sürdürülebilir üretim için entegre risk önceliklendirmesi ve etkileşim analizi - endüstriyel bant üretiminde bir vaka çalışması
No full textGünümüz rekabetçi sanayi ortamında, işletmelerin sürdürülebilirliği ve pazar paylarını koruyabilmeleri, yüksek kalite standartlarını sağlama ve riskleri etkin şekilde yönetme yeteneklerine bağlıdır. Üretim süreçlerinde karşılaşılan hataların zamanında tespit edilip önlenmesi, hem maliyetleri azaltmakta hem de müşteri memnuniyetini artırarak işletmelerin rekabet avantajını güçlendirmektedir. Bu doğrultuda, kalite yönetimi ile entegre risk analiz yaklaşımları, işletmeler için stratejik bir gereklilik haline gelmiştir. Bu tez çalışmasında, endüstriyel yapıştırıcı bant üretim sürecinde karşılaşılan hata modlarının belirlenmesi, analiz edilmesi ve minimize edilmesi amacıyla FMEA yöntemi ile birlikte çok kriterli karar verme (ÇKKV) tekniklerinden VIKOR, DEMATEL ve ANP yöntemleri entegre bir şekilde kullanılmıştır. Hata modlarının yalnızca bağımsız olarak değil, sistem içerisindeki etkileşimleriyle birlikte değerlendirilmesi hedeflenmiştir. İlk olarak FMEA yöntemi ile üretim sürecindeki potansiyel hata türleri tanımlanmış, bu hataların oluşma olasılığı, şiddeti ve tespit edilebilirliği analiz edilmiştir. Ardından, VIKOR yöntemi ile hata modları çok kriterli değerlendirmeye tabi tutulmuş, DEMATEL yöntemi ile hata modları arasındaki neden-sonuç ilişkileri ortaya konmuş ve ANP yöntemi ile bu ilişkiler dikkate alınarak hata modları yeniden önceliklendirilmiştir. Çalışma sonucunda, en kritik hata modları belirlenmiş ve bu modların birbirleri üzerindeki etkileri sistematik biçimde analiz edilmiştir. Böylece, önleyici tedbirlerin hangi hata modlarına öncelikli olarak uygulanması gerektiği netleştirilmiştir. Elde edilen bulgular, üretim süreçlerinde kaliteyi artırmaya, maliyetleri düşürmeye ve müşteri memnuniyetini sağlamaya yönelik önemli katkılar sunmaktadır. Ayrıca, önerilen bütünleşik yöntem, endüstriyel yapıştırıcı bant sektörü dışında benzer üretim yapısına sahip diğer sektörlerde de uygulanabilecek bir model sunmaktadır.In today's highly competitive industrial environment, the sustainability and market presence of businesses depend on their ability to ensure high quality standards and effectively manage risks. Timely identification and prevention of failures in production processes not only reduce costs but also enhance customer satisfaction, thereby strengthening a company's competitive advantage. In this context, integrated approaches to quality management and risk analysis have become a strategic necessity for enterprises. This thesis presents an integrated approach using the FMEA method along with Multi-Criteria Decision Making (MCDM) methods - VIKOR, DEMATEL, and ANP - to identify, analyze, and minimize failure modes in the production process of industrial adhesive tapes. The aim is to evaluate failure modes not only independently but also by considering their interactions within the system. First, the FMEA method was used to determine potential failure modes in the production process and to analyze their severity, occurrence, and detection. Subsequently, the VIKOR method was employed for multi-criteria evaluation of the failure modes, the DEMATEL method revealed cause-effect relationships among them, and the ANP method reprioritized the failure modes based on these interdependencies. As a result of the study, the most critical failure modes were identified, and their interrelations were systematically analyzed. This enabled the determination of which failure modes should be prioritized for preventive actions. The findings contribute significantly to improving quality, reducing costs, and ensuring customer satisfaction in production processes. Moreover, the proposed integrated method offers a model that is applicable not only in the industrial adhesive tape sector but also in other industries with similar production structures
The impact of feature selection models on the accuracy of tree-based classification algorithms: Heart disease case
No full text6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024 -- -- Prague -- 207096Heart disease is a very serious illness that can result with death if it not detected in time. Early diagnosis and effective treatment of heart disease can prevent instead of the progression and improve the quality of life. In recent years, machine learning techniques are widely used to accurately predict the heart disease accurately. One of these techniques is tree-based classification algorithms that construct a tree-like structure while making predictions. Feature selection models are used to improve the performance of machine learning techniques and their ability to generalize by identifying the most informative features. However, there is limited research on how different feature selection models specifically impact the performance of tree-based classification algorithms in the context of heart disease prediction. In this study, the impact of feature selection models on the classification performance of tree-based algorithms to determine the risk of heart disease is investigated. For this purpose, five different feature selection models are applied to the dataset taken from UCI Machine Learning Repository, and the classification performances of eleven different tree-based algorithms are analysed. Classification results show that the Hoeffding Tree technique achieved the highest accuracy (0.84) on the dataset where the stability selection model is applied. © 2025 Elsevier B.V., All rights reserved