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Wind power plant (WPP) feasibility: Determination of wind power potential and site selection analysis for WPP construction using the TOPSIS method
Get PDFRüzgâr çiftliklerinin başarılı bir şekilde konuşlandırılması ve işletilmesi, önemli ölçüde doğru kurulum sahasının seçilmesine bağlıdır. Bu çalışma rüzgâr enerji santrali yer seçiminde uygun araziler arasında en verimlisinin seçilmesi ile ilgilidir. Rüzgâr enerji santrali için doğru yer seçiminin çeşitli topografik durumları standarda oturtularak seçilen birkaç aday arazi arasında hangisinin daha verimli olacağı konusunda Çoklu Karar Verme Metotlarının sağladığı olanaklar ile faydalı bir çözüm yöntemi sunulmuştur. Ayrıca yeni bir kriter de sunulmuştur. Yükseltisi ve eğimi yüksek arazilerde alan kısıtlılığı söz konusudur. Hangi araziye optimize olarak daha fazla türbin kurulabileceği dolayısıyla hangi aday arazinin daha çok potansiyel kurulu güç sunacağı kriter olarak geliştirilerek çözüm metodunda gösterilmiştir. Bu çalışma RES kurulum projelerinde en uygun araziyi seçme konusunda yardımcı olması bu çalışmanın ana hedef konusudur.The successful deployment and operation of a wind farm significantly depend on the accurate selection of installation sites. This study presents a choosing method between suitable lands for wind power plants. Utilizing Multi-Criteria Decision-Making techniques, to score based identify the candidate site that would provide the most efficient outcome by standardizing various topographic conditions and quantitative data for the proper selection of a wind power plant site. A uniquely developed criterion parameter is also introduced. Installation space constraints are a major issue in areas with high elevations and steep slopes. The new criterion parameter identifies sites that can optimally accommodate more turbines, thereby offering higher achievable installed capacity. The main objective of this study is to assist in selecting the most suitable site for wind power plant installation projects
Topology optimization for manufacturing aircraft bearing brackets via laser powder bed fusion
No full textTopology optimization, a structural optimization method that allows us to obtain lighter and more performance structures with its wide design area while offering more complex geometries. With additive manufacturing technologies, complex structures that are difficult to produce with traditional methods are produced. The integrated use of additive manufacturing technologies with topology optimization is a very effective approach to produce lighter and more performance structures. In the present study, the application of topology optimization in Ansys 2019 R1 software for a bearing bracket used in the aircraft warehouse door system and its production with additive manufacturing technologies are discussed. Considering the safety factor value, topology optimization for three different materials (i.e. Ti6Al4V, 316L stainless steel and Maraging steel) on the bearing bracket, arrangements and analysis were carried out on the geometry obtained due to the optimization. As a result of the analysis, the strength properties obtained based on the optimized geometry for Maraging steel, Ti6Al4V alloy and stainless steel materials were determined as follows: the safety factor values were 1.6421, 1.3201 and 0.7606 respectively; the total deformation (mm) was 1.0643, 1.4065 and 0.8207 respectively; and the weight (kg) was reduced to 0.3637, 0.2014 and 0.3592, achieving a 58.55% reduction. After topology optimization, the original bracket volume for Maraging steel was reduced from 109.67 to 33.97 cm3. It has been observed that the geometries obtained through topology optimization of all three selected materials are suitable for production through additive manufacturing. ST-PLA, 1.75 mm was used as the printing material. Its suitability for prototype production and additive manufacturing was tested and confirmed, and it was produced using laser powder bed fusion with the Maraging steel.Scientific Project Unit of Osmaniye Korkut Ata University [OKUBAP-2021-PT3-014]This work was supported by the Scientific Project Unit of Osmaniye Korkut Ata University (Project No: OKUBAP-2021-PT3-014)
Antroposen Çağında Sosyal Darwinizm: America City Romanında İklim Değişikliğinde Hayatta Kalma
No full textChris Beckett’s America City (2017) depicts a near-future USA devastated by climate change. Set during a presidential campaign, the novel delves into themes like climate change denial, climate refugee displacement, nationalism, political manipulation, and the impact of media. This article analyses the novel from the perspective of social Darwinism in the Anthropocene, exploring how concepts of survival, adaptation, competition and elimination appear in a world influenced by human-caused climate change. This study emphasises the often-ignored connection between social Darwinism and climate change by examining how communities and nations respond to climate disasters—whether they adapt, resist, or take advantage of the crisis. In this respect, this article aims to explore the degree to which the idea of social Darwinism can be relevant in a world ravaged by anthropogenic climate change and how the concept of fitness for survival can be understood during ecological collapse.Chris Beckett’ın America Cit
The predictive power of self-esteem in decision making and decision-making styles on psychological resilience in adolescents
No full textBu araştırma, ergen bireylerin psikolojik sağlamlık düzeylerinin, karar vermede özsaygı ve karar verme stilleri bağlamında ne ölçüde yordanabildiğini incelemeyi amaçlamaktadır. Psikolojik sağlamlık; bireyin stresli yaşam olaylarına karşı direnç gösterebilme ve uyum sağlayabilme kapasitesi olarak tanımlanmakta olup, bu kapasitenin gelişiminde bireysel ve çevresel birçok faktör etkili olmaktadır. Ergenlik dönemi, bireyin karar verme becerilerinin geliştiği ve aynı zamanda psikolojik kırılganlıkların arttığı bir dönem olması nedeniyle, bu sürece ilişkin bilişsel ve duyuşsal değişkenlerin anlaşılması önem taşımaktadır. Araştırma kapsamında, karar vermede özsaygı düzeyi ile karar vermenin alt boyutları olan ihtiyatlı seçicilik, panik, umursamazlık ve sorumluluktan kaçma gibi karar verme stillerinin psikolojik sağlamlık üzerindeki etkileri incelenmiştir. Araştırmanın örneklemini, 2023–2024 eğitim-öğretim yılında Adana ili Ceyhan ilçesinde öğrenim gören 9., 10. ve 11. sınıf düzeyindeki toplam 505 öğrenci oluşturmaktadır. Katılımcıların 280'i kadın, 225'i erkektir. Veriler, "Çocuk ve Genç Psikolojik Sağlamlık Ölçeği" ile "Ergenlerde Karar Verme Ölçeği" kullanılarak toplanmıştır. Verilerin analizinde tanımlayıcı istatistiklerin yanı sıra Pearson korelasyon analizi ve çoklu doğrusal regresyon analizinden yararlanılmıştır. Karar vermede özsaygı düzeyi ve dört farklı karar verme stili (ihtiyatlı seçicilik, panik, umursamazlık, sorumluluktan kaçma) temel değişkenler olarak ele alınmıştır. Araştırma bulguları, karar vermede özsaygı ile ihtiyatlı seçicilik stilinin psikolojik sağlamlık üzerinde anlamlı ve pozitif bir yordayıcı olduğunu; panik, umursamazlık ve sorumluluktan kaçma stillerinin ise psikolojik sağlamlığı negatif yönde etkilediğini ortaya koymuştur. Elde edilen sonuçlar, karar verme süreçleri ve bireyin bu süreçlere ilişkin algısının, ergenlerin psikolojik dayanıklılığı üzerinde belirleyici bir rol oynadığını göstermektedir. Araştırma sonuçları, bireylerin psikolojik sağlamlıklarının artırılmasına yönelik geliştirilecek müdahale programlarında, sağlıklı karar verme becerilerinin desteklenmesinin kritik bir unsur olduğunu ortaya koymaktadır. Özellikle ergenlik döneminde, panik, sorumluluktan kaçma ve umursamazlık gibi olumsuz karar verme eğilimlerine yönelik farkındalık kazandıracak psiko-eğitim çalışmalarının önemli olduğu vurgulanmıştır. Ayrıca, psikolojik sağlamlık gelişiminde sadece bireysel becerilerin değil, aile ortamında sağlanan duygusal destek, güven ve sağlıklı iletişimin de belirleyici bir rol oynadığı görülmektedir. Bu doğrultuda, araştırma bulguları hem bireysel hem aile temelli hem de okul merkezli müdahale programlarının yapılandırılmasına katkı sunmakta; aynı zamanda gelecekte yapılacak çalışmalara yol gösterici nitelikte öneriler içermektedir. Anahtar kelimeler: Ergenlik, psikolojik sağlamlık, karar vermede özsaygı, karar verme stilleriThis study aims to examine the extent to which adolescents' psychological resilience can be predicted by self-esteem in decision-making and decision-making styles. Psychological resilience is defined as an individual's capacity to withstand and adapt to stressful life events, and numerous individual and environmental factors play a role in its development. Adolescence is a period when decision-making skills begin to form while psychological vulnerabilities also increase; therefore, understanding the cognitive and emotional variables associated with this process is of particular importance. Within the scope of the study, the effects of self-esteem in decision-making and the subdimensions of decision-making styles—vigilant, hypervigilant, buck-passing, and avoidant—on psychological resilience were examined. The sample consisted of a total of 505 students enrolled in the 9th, 10th, and 11th grades during the 2023–2024 academic year in the Ceyhan district of Adana, Türkiye. Of the participants, 280 were female and 225 were male. Data were collected using the "Child and Youth Resilience Measure" and the "Adolescent Decision-Making Questionnaire." For data analysis, descriptive statistics, Pearson correlation analysis, and multiple linear regression analysis were employed. Self-esteem in decision-making and four decision-making styles (vigilant, hypervigilant, buck-passing, and avoidant) were treated as the primary variables. The findings revealed that self-esteem in decision-making and the vigilant decision-making style were significant and positive predictors of psychological resilience, whereas hypervigilant, buck-passing, and avoidant styles had a negative impact. These results indicate that the individual's perception of their decision-making process plays a determining role in adolescent psychological resilience. The findings also emphasize that promoting healthy decision-making skills is a critical component in intervention programs aimed at enhancing psychological resilience. Especially during adolescence, psychoeducational practices designed to increase awareness of negative decision-making tendencies such as hypervigilance, avoidance, and buck-passing are of great importance. Moreover, not only individual skills but also emotional support, trust, and healthy communication within the family environment play a determining role in the development of psychological resilience. In this context, the findings contribute to the development of individual-, family-, and school-based intervention programs and offer recommendations for future research. Keywords: Adolescence, psychological resilience, self-esteem in decision-making, decision-making style
eHealth Literacy and Cyberchondria Among Health Sciences Students: A Comparative Analysis of First-Year and Final-Year Students
No full textBu çalışma, Sağlık Bilimleri Fakültesinde öğrenim gören birinci ve dördüncü sınıf öğrencilerinin e-sağlık okuryazarlığı ve siberkondri düzeylerini karşılaştırmayı ve bu iki değişken arasındaki ilişkiyi incelemeyi amaçlamaktadır. Bu araştırma, kesitsel ve tanımlayıcı bir çalışma olarak tasarlanmıştır. Araştırmanın örneklemini, bir devlet üniversitesinin Sağlık Bilimleri Fakültesinde öğrenim gören 466 öğrenci oluşturmaktadır. Veriler, E-Sağlık Okuryazarlığı Ölçeği ve Siberkondri Ciddiyet Ölçeği kullanılarak toplanmıştır. İstatistiksel analizlerde bağımsız örneklem t-testi, ANOVA, Pearson korelasyon analizi ve doğrusal regresyon analizi uygulanmıştır. Bulgular, dördüncü sınıf öğrencilerinin e-sağlık okuryazarlığı düzeylerinin birinci sınıf öğrencilerine göre anlamlı derecede yüksek olduğunu göstermektedir (p < 0.001). Ancak, siberkondri düzeyleri açısından sınıf düzeyleri arasında istatistiksel olarak anlamlı bir fark tespit edilmemiştir (p = 0.057). Korelasyon analizleri, e-sağlık okuryazarlığı ile siberkondri arasında sınıf düzeyine göre pozitif yönde anlamlı bir ilişki olduğunu göstermektedir (r= 0.189, p < 0.01; r = 0.259, p < 0.01). Regresyon analizi sonuçlarına göre, e-sağlık okuryazarlığının siberkondri düzeyi üzerinde istatistiksel olarak anlamlı bir etkisi bulunmaktadır (p = 0.001). Ancak, modelin açıklayıcı gücü düşük olup (R² = %3.4 - %6.7), e-sağlık okuryazarlığının siberkondriyi tek başına yeterli düzeyde açıklamadığını göstermektedir. Elde edilen bulgular, e-sağlık okuryazarlığı düzeyi daha yüksek olan öğrencilerin çevrimiçi sağlık bilgisi arama davranışlarının da daha sık olduğunu ve bunun siberkondri ile ilişkili olabileceğini göstermektedir. Ancak, bu ilişkinin doğasının nedensel olup olmadığını belirlemek için ileri araştırmalara ihtiyaç duyulmaktadır. Araştırma sonuçları, siberkondriyi daha kapsamlı bir şekilde anlamak için sağlık kaygısı, anksiyete düzeyi ve internet kullanım alışkanlıkları gibi ek değişkenlerin ilerleyen çalışmalarda modele dahil edilmesi gerektiğini ortaya koymaktadır.This study aims to compare the levels of e-health literacy and cyberchondria among first-year and fourth-year students in a faculty of health sciences and to examine the relationship between these two variables. This research was designed as a cross-sectional and descriptive study. The sample consisted of 466 students enrolled in the Faculty of Health Sciences at a public university. Data were collected using the E-Health Literacy Scale and the Cyberchondria Severity Scale. Statistical analyses included independent sample t-tests, ANOVA, Pearson correlation analysis, and linear regression analysis.The findings indicate that fourth-year students had significantly higher levels of e-health literacy compared to first-year students (p <0.001). However, no statistically significant difference was found in cyberchondria levels between the class levels (p = 0.057). Correlation analyses revealed a positive and significant relationship between e-health literacy and cyberchondria, varying by class level (r = 0.189, p <0.01; r = 0.259, p <0.01). According to the regression analysis results, e-health literacy had a statistically significant effect on cyberchondria levels (p = 0.001). However, the explanatory power of the model was low (R² = 3.4%- 6.7%), indicating that e-health literacy alone is not sufficient to fully explain cyberchondria. The findings suggest that students with higher levels of e-health literacy engage more frequently in online health information-seeking behaviors, which may be associated with cyberchondria. However, further research is needed to determine whether this relationship is causal. The results highlight the need to include additional variables such as health anxiety, anxiety levels, and internet usage habits in future studies to gain a more comprehensive understanding of cyberchondria
Prone position applied to COVID-19 patients: Systematic review-meta-analysis
No full textBackgroundPosition change and interventions to increase lung capacity should be considered in mechanically ventilated patients. The most effective of these is the prone position.AimThis systematic review and meta-analysis aimed to determine the effects of the prone position on respiratory parameters and outcomes and to guide nurses working in the intensive care unit.Study Design and MethodsThis systematic review-meta-analysis was conducted in accordance with the Preferred Reporting in Systematic Reviews and Meta-Analyses guideline. ScienceDirect, CINAHL, Academic Search Complete (EBSCOhost), MEDLINE, EMBASE, Web of Science, Cochrane and PubMed databases were searched between January 2022 and January 2023 to access studies related to prone position in COVID-19 patients.ResultsTwenty-three studies were included. This meta-analysis shows that a prone position is feasible and can achieve improvements in gas exchange. Prone position increases PaO2/FiO2 in the majority of patients followed with a diagnosis of COVID-19 and severe hypoxemic.ConclusionsThe study has shown that the prone position is effective in improving patients' respiratory function and oxygenation.Relevance to Clinical PracticeThe results presented in this article support the notion that the prone position can be an effective strategy in the clinical management of COVID-19 patients
Targeting Gut Microbiota Health in Aged Rats Through the Potent Strategy of Probiotics Supplementation During Intermittent Fasting
No full textGut health in aging populations, including animal models, is a critical area of research due to the decline in microbial diversity with age. Maintaining gut health has important implications for overall health and longevity. This study aimed to evaluate the interdependent effects of SCD Probiotics and intermittent fasting (IF) on gut microbiota (GM) in 24-month-old male Sprague-Dawley rats, a wellestablished model for aging research. The experiment involved four groups: a control, IF-only, probiotics-only, and a combination of IF and probiotics. The metagenomic analysis of cecum contents for IF and SCD Probiotics groups has shown increased Shannon and Simpson diversity of alpha index values and improved ratios for Firmicutes to Bacteroidetes. High-Performance Liquid Chromatography (HPLC) analysis of short-chain fatty acids (SCFAs) revealed significant changes in the probiotics-only and combined IF with SCD Probiotics groups, particularly with acetic and propionic acids. The results indicate that combining SCD Probiotics with IF produces interdependent benefits, improving bacterial diversity and SCFAs profiles. These findings suggest that SCD Probiotics with intermittent fasting could be a promising strategy to enhance gut health in aging populations, with potential applications in veterinary health.Muscedil; Alparslan University Scientific Research Projects Unit (BAP) [BAP-23-TBMYO-4901-01]Acknowledgements: We would like to express our gratitude to the Mu & scedil; Alparslan University Scientific Research Projects Unit (BAP) for their support under the project number BAP-23-TBMYO-4901-01
Bioactivity of Juglans regia kernel extracts optimized using response surface method and artificial neural Network-Genetic algorithm integration
No full textIn this study, the biological activities of the extracts obtained under optimum extraction conditions of the kernel part of Juglans regia L. were determined. Two different methods, Response Surface Method (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA) integration, were used for optimization. The antioxidant capacity of the extracts obtained under the extract conditions suggested by the two methods was evaluated by Rel Assay kits, DPPH and FRAP methods. Anticholinesterase activities of the optimized extracts were measured by the action of acetylcholinesterase and butyrylcholinesterase enzymes. Antiproliferative effects of the extracts were tested on A549 lung cancer cell line. Phenolic compounds were analyzed by LC-MS/MS. It was determined that both extracts exhibited strong activities against A549 lung cancer cell line depending on the concentration increase. In addition, it was determined that both extracts exhibited acetyl and butyrylcholinesterase inhibition activity close to galantamine used as a standard. In both extracts, 13 compounds including gallic acid, catechinhyrate, 4-hydroxybenzoic acid, caffeic acid, vanillic acid, syringic acid, 2-hydoxycinamic acid, resveratrol, myricetin, quercetin, kaempferol, protocatechuic acid and 2-hyroxy1,4 naphthaquinone were identified. It was determined that the extract obtained under the conditions predicted by ANN-GA exhibited higher activities in general
Experimental Evaluation and Machine Learning-Based Prediction of Laser Cutting Quality in FFF-Printed ABS Thermoplastics
No full textAdditive manufacturing, particularly Fused Filament Fabrication (FFF), provides notable advantages such as design flexibility and efficient material usage. However, components produced via FFF often exhibit suboptimal surface quality and dimensional inaccuracies. Acrylonitrile Butadiene Styrene (ABS), a widely used thermoplastic in FFF applications, commonly necessitates post-processing to enhance its surface finish and dimensional precision. This study investigates the effects of CO2 laser cutting on FFF-printed ABS plates, focusing on surface roughness, top and bottom kerf width, and bottom heat-affected zone. Forty-five experimental trials were conducted using different combinations of plate thickness, cutting speed, and laser power. Measurements were analysed statistically, and analysis of variance was applied to determine the significance of each parameter. To enhance prediction capabilities, seven machine learning models-comprising traditional (Linear Regression and Support Vector Regression), ensemble (Extreme Gradient Boosting and Random Forest), and deep learning algorithms (Long Short-Term Memory (LSTM), LSTM-Gated Recurrent Unit (LSTM-GRU), LSTM-Extreme Gradient Boosting (LSTM-XGBoost))-were developed and compared. Among these, the LSTM-GRU model achieved the highest predictive performance across all output metrics. Results show that cutting speed is the dominant factor affecting cutting quality, followed by laser power and thickness. The proposed experimental-computational approach enables accurate prediction of laser cutting outcomes, facilitating optimisation of post-processing strategies for 3D-printed ABS parts and contributing to improved precision and efficiency in polymer-based additive manufacturing
Determination of methylene violet concentration using classification algorithms
No full textThe dyestuffs used in the industry are harmful to human health and the environment. One of the most widely used of these dyestuffs is methylene violet (MV). Detection of such substances and determination of their concentrations are significant for the development of purification processes. Although there are many spectrophotometric methods and sensitive devices for the determination of dyestuff in aqueous solutions, these devices can be only used in the lab, cannot be employed beyond the laboratory, and are costly. Therefore, artificial intelligence-based systems can be preferred outside the laboratory and in case of urgent use in terms of accessibility and practicality. In this study, we photographed images of MV solutions in concentrations of 0.1–1–5–25–50 ppm. Image features are extracted using a deep learning architecture for each image found in the data set created according to different concentration values. Afterward, Linear Discriminant, Linear Support Vector Machine (SVM), Cubic SVM, Quadratic SVM, and Subspace Discriminant Ensemble classifiers were trained by employing the extracted image features. Using these trained classifiers, we designed a user-friendly Graphical User Interface (GUI) application that can predict the concentrations of MV solutions to provide advantages in terms of time savings, cost, and practicality. The results show that the most successful classifier used in the study is the Subspace Discriminant Ensemble. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024