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Book Culture in the Ottoman Empire and the Western World: Book and Library Culture in the Ottoman Empire from the Perspective of European Travelers (1453-1699)
No full textThe lactate dehydrogenase-to-albumin ratio is a prognostic biomarker in extensive-stage small-cell lung cancer
No full textBackground: The lactate dehydrogenase-to-albumin ratio (LAR) is a promising prognostic marker in various malignancies. However, its clinical relevance in extensive-disease small-cell lung cancer (ED-SCLC) remains unclear. Methods: We analyzed a total of 221 patients diagnosed with ED-SCLC between January 2008 and December 2021. Patients without treatment response data (n=8), those who did not receive systemic therapy (n=37), and those who lacked baseline LDH values (n=48) were excluded. The LAR was calculated by dividing baseline serum LDH (U/L) by albumin (g/L) and, using ROC analysis, the optimal cut-off level was determined to be 5.71 (sensitivity: 81.5%, specificity: 77.8%). Kaplan–Meier and Cox regression analyses were used to evaluate both progression-free (PFS) and overall survival (OS). Results: A total of 128 patients diagnosed with ED-SCLC were included in our analysis. Patients with an LAR ≥5.71 had significantly shorter median OS (8.1 vs. 20.2 months, p<0.001) and PFS (5.9 vs. 9.4 months, p=0.003) compared to those with an LAR <5.71. In multivariate analysis, a high LAR was an independent predictor of a shorter OS (HR: 3.60; 95% CI: 1.35–9.60; p=0.010) and had a strong association with a shorter PFS (HR: 2.61; 95% CI: 0.95–7.14; p=0.063). Conclusion: The LAR is a simple, cost-effective, and independent prognostic biomarker in patients with ED-SCLC. It could assist in risk stratification and guide treatment decisions in clinical practice
The effects of healthcare workers’ technostress and change fatigue levels on their turnover intentions
No full textMultilayered Polyvinyl Alcohol-Based Nanofibrous Membranes Functionalized for Osteogenic Induction of Mesenchymal Stem Cells
No full textExploration of K-12 Teaching and Learning for Teacher Educators
No full textAlgebra is often perceived as difficult for students. To overcome these difficulties algorithms can be used. Flow Chart (FC)application, used in creating algorithms, can show complex problems with easy diagrams. The aim of this study is to examine the effect of FC on students' algebra achievement and attitudes towards algebra. The study has an explanatory mixed design. First a static group pre-test and post-test design was used. Then students' opinions on the use of FC in the algebra learning process were investigated. The sample of the study consists of 41 seventh grade students. Algebraic achievement and attitude tests were applied as pre- and post-test. It was observed that FC caused statistically significant difference in success of algebraic expressions, but there was no significant difference in attitude towards algebra. The factors affecting FC-supported algebra learning were gathered under four themes.</p
PlanetScope Uydu Görüntülerinden Derin Öğrenme Tabanlı Bölütleme ile Yanan Ormanlık Alanların Tespiti
No full textOrman yangınları, ekolojik, çevresel ve sosyo-ekonomik açıdan ciddi etkilere sahiptir. Uzaktan algılama ve derin öğrenme teknikleri, yangın sonrası hasarın tespitinde önemli avantajlar sunmaktadır. Bu çalışmada, U-Net modeli kullanılarak orman yangını sonrası yanan alanların semantik bölütlemesi gerçekleştirilmiştir. Çalışma alanı olarak Antalya ili Manavgat yangın bölgesi seçilmiş ve uydu görüntüsü olarak yangın sonrası çekilen PlanetScope uydu görüntüleri kullanılmıştır. Modelin performansı doğruluk, hassasiyet, geri çağırma ve F1- Skoru metriklerle değerlendirilmiştir. Kullanılan U-Net modeli, yanan alanların tespitinde %91 doğruluk, %95 hassasiyet ve %94 F1-Skoru ile yüksek performans sergilemiştir.Yanmamış alanlarda ise %80 hassasiyet ve %88 geri çağırma değerlerine ulaşılmıştır. Modelin genel performansını yansıtan makro ve ağırlıklı ortalama metriklerin her ikisi de %89'un üzerinde sonuç vermiştir. Bu bulgular, U-Net tabanlı yaklaşımın, yanan alanların otomatik tespiti için yüksek derecede güvenilir ve tutarlı bir çözüm sunduğunu göstermektedir. Sonuçlar, özellikle heterojen arazi yapısına sahip bölgelerde yanan alanların yüksek doğrulukla bölütlenmesinde U-Net modelinin etkinliğini göstermektedir.Forest fires have serious ecological, environmental and socio-economic impacts. Remote sensing and deep learning techniques offer significant advantages in post-fire damage detection. In this study, semantic segmentation of burned areas after forest fires is performed using the U-Net model. Manavgat fire zone in Antalya province was selected as the study area and PlanetScope satellite images taken after the fire were used as satellite imagery. The performance of the model was evaluated with accuracy, precision, sensitivity and F1-Score metrics. The U-Net model showed high performance with 91% accuracy, 95% sensitivity and 94% F1-Score in the detection of burned areas. In unburned areas, 80% accuracy and 88% sensitivity values were achieved. Both macro and weighted average metrics reflecting the overall performance of the model yielded results above 89%. These findings show that the U-Net based approach provides a highly reliable and consistent solution for automatic detection of burned areas. The results demonstrate the effectiveness of the U-Net model in segmenting burning areas with high accuracy, especially in regions with heterogeneous terrain.</p
The Relationship Between Leisure Satisfaction and Psychological Variables Before and During the Covid-19 Pandemic: A Comparative Analysis Using Structural Equation Modeling
No full textThe Covid-19 pandemic has profoundly affected individuals' daily routines and psychological well-being. Restrictions on social life and limited access to leisure activities have altered how people cope with stress and maintain mental health. This study aimed to examine the direct and indirect relationships between leisure satisfaction and psychological variables—including stress, anxiety, depression, life satisfaction, and psychological well-being—before and during the Covid-19. Data were collected from 531 adults (296 women, 235 men; M=36.63, SD=14.08) residing in Turkey at two time points: pre-pandemic and during the pandemic. Structural equation modeling was used to test the hypothesized models, and paired-samples t-tests were conducted to compare mean differences. Before the pandemic, leisure satisfaction was negatively related to stress, anxiety, and depression (β=-.13, p<.01), and positively related to life satisfaction (β=.34, p<.01) and psychological well-being (β=.21, p<.01). These relationships were also evident during the pandemic but showed stronger effects. For example, the negative association with distress variables increased (β=−.22, p<.01), while the positive effects on life satisfaction (β=.53, p<.01) and well-being (β=.39, p<.01) also intensified.Model fit indices indicated acceptable fit for the pre-pandemic model (χ²/df=3.210, RMSEA=.065, SRMR=.058, CFI=.933, NNFI=.906, TLI=.922) and superior fit during the pandemic (χ²/df=2.687, RMSEA=.056, SRMR=.051, CFI=.955, NNFI=.931, TLI=.947), suggesting robust structural validity. Mean comparisons revealed significant declines in leisure satisfaction (t(530)=13.832, p<.001), life satisfaction (t(530)=15.096, p<.001), and psychological well-being (t(530)=8.464, p<.001), along with significant increases in stress (t(530)=−12.574, p<.001), anxiety (t(530)=−13.970, p<.001), and depression (t(530)=−13.897, p<.001) during the pandemic. Eventually, it was revealed that leisure time satisfaction decreased stress, anxiety and depression of individuals both before and during the pandemic, and as a result, life satisfaction and psychological well-being of individuals increased directly and indirectly. Findings highlight that leisure satisfaction plays a protective role in mitigating psychological distress and promoting well-being, especially during crisis periods such as a pandemic.</div
Multi-objective route planning of an unmanned air vehicle in continuous terrain: An exact and an approximation algorithm
No full textUnmanned Aerial Vehicles (UAVs) are widely used for military and civilian purposes. Effective route planning is an important component of their successful missions. In this study, we address the route planning problem of a UAV tasked with collecting information from various target locations in a protected terrain. We consider multiple targets, three objectives, and time-dependent information availability. Modeling the movement of UAVs in a continuous terrain in the presence of multiple objectives is complex. Conflicting objectives typically lead to a continuum of efficient trajectory options between two targets. We formulate the routing problem as a mixed-integer programming (MIP) model that captures the movement in the continuous terrain. We demonstrate the superiority of the continuous terrain formulation over the simplified discretized terrain formulation. We also develop an approximation algorithm that reduces the computational requirements of the MIP model substantially while ensuring a desired level of precision
