Bülent Ecevit University

Using smart sensors and data analytics tools, predictive maintenance (PdM) aims to reduce downtime by forecasting potential faults; however, accurately distinguishing between failure and normal conditions remains a significant challenge. To address this challenge, we propose a fault classification system that transforms 1D PdM sensor signals into 2D images using a novel Predictive Maintenance Image Transformation (PdM-IT) technique, followed by feature extraction with pre-trained convolutional neural networks (CNNs). The extracted features are then combined across all CNN models. We additionally optimize the combined feature set using three nature-inspired optimization algorithms, including Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), and then evaluate four different classifier such as Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Naive Bayes (NB), and Decision Trees (DT) on the obtained data using cross-validation. In our experimental evaluation, the best-performing model, SVM with features selected via ACO, achieved 98.22% accuracy, 98.39% F1-score, 98.51% precision, and 98.26% recall. These results show the effectiveness of the proposed hybrid image-based fault classification approach and its potential to support more reliable fault diagnosis and operational planning in PdM systems.

Kısa ama derin anlamlarıyla dikkat çeken atasözleri, halk kültürünün bir anlamda damıtılmış şeklidir. Ait oldukları toplumun değer yargıları, ahlaki bakış açısı, dünya görüşü, inançları, yaşam tarzı gibi toplumsal kodlarını bünyesinde barındıran atasözleri, toplumsal bellek görevi üstlenir. Her toplum, kendi etik değerlerini, değer yargılarını dünya görüşünü sonraki nesillere aktarmak ister. Toplumun kolektif hafızasını yansıtan atasözlerinin toplumsal kodları gelecek nesillere aktarmak gibi bir misyonu vardır. Bu çalışma, adalet (ädildik) ve dürüstlük (adaldıq) gibi temel sosyal erdemlerin Kazak sözlü kültürünün önemli bir parçası olan atasözleri aracılığıyla kuşaktan kuşağa nasıl aktarıldığını incelemektedir. Çalışmada “Türkçe Açıklamalı Kazak Atasözleri (İsmail & Gümüş, 1995)”, “Qazaqtıñ maqaldarı-mätelderi (Nısanalin, 2004)” ve “Qazaqtıñ maqaldarı men mätelderi (Turmanjanov, 2007)” adlı üç atasözü kitabı taranarak adalet ve dürüstlük konulu atasözleri tespit edilmiştir. Ayrıca dijital kaynaklar da taranmıştır. Bu üç kitaptan ve dijital kaynaklardan elde edilen veriler, kendi içerisinde içerik analizi yapılarak sınıflandırılmıştır. Bulgular, Kazak halkının adalet ve dürüstlüğü ahlaki bir erdemin ötesinde, insanı insan yapan evrensel bir değer olarak gördüğünü göstermektedir. Sonuç olarak, Kazak atasözleri bireyleri eğitici bir işleve sahip olduğu gibi, toplumsal düzeni ayakta tutan erdemlerin aktarılmasında da güçlü bir araçtır.

The research gap addressed in this study is the lack of a transparent and quantitative evaluation of the governing parameters influencing deflection behavior in reinforced concrete (RC) beams reinforced with glass fiber-reinforced polymer (GFRP) bars. The objective of this study is to identify and quantify the relative importance of the key parameters controlling deflection in GFRP-reinforced RC beams, which exhibit fundamentally different behavior compared to steel-reinforced beams due to the linear-elastic response of GFRP bars until rupture. To achieve this objective, the method integrates explainable artificial intelligence (XAI) techniques, including SHapley Additive exPlanations (SHAP), Pearson correlation heatmap, scatter plot analysis, and sensitivity analysis-with experimental structural data obtained from beams with three different concrete strength classes. The main contribution of this study is the quantitative ranking and interpretation of the governing parameters affecting deflection behavior through a transparent and data-driven framework. Key parameters-including elastic modulus (Ec), compressive strength (fck), creep coefficient (φ), failure moment (Mexp), effective moment of inertia (Ieff), and applied load (P)-were evaluated. The results consistently indicate that stiffness- and capacity-related parameters dominate the deflection response. Sensitivity analysis reveals that the failure moment (Mexp) is the most influential parameter, contributing approximately 23% of the total relative influence on deflection, followed by compressive strength (fck) and cracking-related parameters. Pearson correlation heatmap and scatter plot analyses further confirm strong relationships between deflection and Ec, fck, φ, and Ieff. The proposed framework improves the interpretability of deflection prediction in GFRP-reinforced RC beams and provides a transparent basis for serviceability-based structural design and performance-oriented assessment.

Large language models (LLMs) are increasingly used to provide health-related information, yet their clinical reliability remains uncertain. This study aimed to evaluate the accuracy, comprehensiveness, and readability of AI-based LLMs in providing evidence-based guidance on early maxillary expansion for children in the primary and mixed dentition phases, assessing their potential as trustworthy resources for parental decision-making. Eight LLMs (DeepSeek V3, Gemini 2.5 Flash, Claude 4.5 Sonnet, MediSearch, Copilot, GPT-5, GPT-4o, and Grok) were tasked with responding to a total of 20 questions reflecting common parental concerns about early maxillary expansion, with 10 questions assigned to each dentition phase (primary and mixed). Responses were evaluated for accuracy and comprehensiveness, and readability was assessed using the Flesch Reading Ease Score (FRES) and Flesch-Kincaid Grade Level (FKGL). Statistical analyses included descriptive statistics and appropriate parametric and non-parametric tests based on data distribution, with significance set at p < 0.05. Significant differences were observed among the LLMs in terms of accuracy, comprehensiveness, and readability (p < 0.001). DeepSeek V3 and Grok achieved the highest scores for both accuracy and comprehensiveness across both dentition phases, with DeepSeek V3 also demonstrating the highest readability in the mixed dentition phase. Copilot, GPT-5, and GPT-4o produced the most readable outputs, as indicated by their highest FRES and lowest FKGL scores, though their content accuracy was comparatively lower. In contrast, MediSearch, Gemini 2.5 Flash, and Claude 4.5 Sonnet showed consistently weaker performance across all evaluated criteria. This study concluded that, although some LLMs can offer reliable and understandable information about early maxillary expansion, none consistently excel across all evaluated criteria. In healthcare contexts, integrating scientific accuracy with readability is crucial for supporting informed parental decision-making, enhancing overall health literacy, and strengthening patient–clinician communication. These findings highlight that AI-based LLMs should serve as supplementary tools that support, rather than replace, professional orthodontic guidance.

This case report presents a rare manifestation of cholesteatomatous chronic otitis media complicated by Bezold abscess, Citelli abscess, and sigmoid sinus thrombosis in a 35-year-old man. The patient presented with restricted neck movement, neck pain, fever, difficulty in oral intake, left mastoid tenderness, and otorrhea. Physical examination demonstrated a firm, nonfluctuant swelling extending from the left submandibular region to level III of the neck, along with purulent discharge in the left external auditory canal. Laboratory evaluation revealed markedly elevated inflammatory markers. Imaging studies, including temporal computed tomography, contrast-enhanced temporal magnetic resonance imaging, and magnetic resonance venography, confirmed a Bezold abscess extending into the parapharyngeal space, a Citelli abscess extending from the left suboccipital region toward the prevertebral space, and chronic thrombosis of the left sigmoid and transverse sinuses. Urgent surgical management was performed, including radical mastoidectomy, abscess drainage, mastoid cavity obliteration with abdominal fat, and cul-de-sac closure. Low molecular weight heparin was initiated preoperatively and continued postoperatively for three months to manage sigmoid sinus thrombosis. Postoperatively, intravenous antibiotics were administered, and inflammatory markers were monitored daily. Follow-up magnetic resonance imaging confirmed successful fat obliteration of the mastoid cavity. The patient completed a 28-day antibiotic course without complications and was discharged in stable condition; no pathological findings were identified during follow-up. This case emphasizes the importance of rapid diagnosis and timely surgical intervention in rare but potentially life-threatening complications of cholesteatomatous chronic otitis media.

Imaging techniques are widely used in dentistry to understand the 3D structure of teeth and detect diseases, but their interpretation is time-consuming and prone to error. To address this, decision support systems are increasingly utilized. This study proposes a CNN-based classification model using the UFPE dataset, which includes Cone Beam Computed Tomography (CBCT) scans. In the first scenario, both real and enhanced images were input into a CNN, yielding 68.92% accuracy for enhanced images. Due to a result, enhanced images were used in all other scenarios. In the second scenario, a newly designed CNN architecture called ToothNet, incorporating a custom activation function, was tested. It achieved 69.92% accuracy, 61.45% recall, 62.67% precision, and 68.68% F1-score, showing a 1.45% increase in accuracy. To evaluate generalizability, three more classification scenarios were examined using the same dataset. ToothNet achieved 80.14% accuracy in the “healthy vs. large lesion” and 68.73% in the “healthy vs. small lesion” classification. These results indicate that the proposed architecture not only improves accuracy but is also generalizable across different lesion sizes.

Bu çalışmada, zorlanmış sirkülasyonlu solar destekli sıcak su hazırlama sisteminin TRNSYS yazılımında zamana bağlı enerji analizi gerçekleştirilmiştir. Analiz, Ankara’da yaşayan ve günlük 200 litre sıcak kullanım suyu ihtiyacı olan bir ailenin enerji gereksiniminin solar destekli su ısıtma sistemiyle karşılanması amacıyla yapılmıştır. Simülasyon ortamında oluşturulan modelde pompa, düz plakalı güneş kolektörü ve ek elektrik ısıtıcılı sıcak su tankı yer almaktadır. Zamana göre değişen hava sıcaklığı, güneş ışınımı ve şebeke suyu sıcaklığı parametreleri ile sıcak kullanım suyunun saatlik dağılımı dikkate alınarak dinamik bir analiz gerçekleştirilmiştir. Elde edilen sonuçlara göre 34° optimum eğimli yüzeye gelen yıllık toplam ışınım değeri 1835,4 kWsa/m²-yıl olarak bulunmuş, sistem verimi %31,8 olarak hesaplanmıştır. Sisteme toplam enerji girişi 3109,7 kWsa/yıl olup, bunun 2418,3 kWsa/yıl’lık bölümü solar kolektörlerde üretilmiştir. Yıllık bazda solar katkı oranı %77,8 bulunmuştur. Yıllık bazda günlük ortalama sıcak kullanım suyu enerji ihtiyacı 6,9 kWsa/gün, tank ısı kaybı ise 1,6 kWsa/gün hesaplanmıştır. Haziran, Temmuz ve Ağustos aylarında (pompa işi hariç) sistemin enerji ihtiyacının tamamı solar kolektörler tarafından karşılanabilirken, diğer aylarda ek elektrikli ısıtıcı devreye girmiştir. Aralık ayında ek ısıtıcının enerji girdisi 166,5 kWsa/ay ile maksimuma ulaşmış ve solar katkı oranı %35,0 olarak gerçekleşmiştir. Sıcak kullanım suyu enerji ihtiyacı 251,0 kWsa/ay ile Mart ayında en yüksek seviyeye ulaşmıştır.

Ekonomik ve toplumsal değişimi etkileyen faktörler ile çevre arasındaki ilişki sürdürülebilirlik açısından önemli bir paradigma olarak karşımıza çıkmaktadır. Bu kapsamda, bu çalışmada dijitalleşme, kentleşme, finansal gelişme ve ekonomik büyümenin CO2 emisyonları üzerindeki etkisi 2003-2019 yılları arasını kapsayan dönem için AB-15 ülkeleri örnekleminde incelenmektedir. Çalışmada yatay kesit bağımlılığı, eğim homojenliği, durağanlık, eşbütünleşme, panel kantil regresyonu ve panel nedensellik testleri uygulanmıştır. Elde edilen panel kantil regresyon sonuçlarına göre dijitalleşmede, kentleşme ve finansal gelişmede meydana gelen artışların CO2 emisyonlarını azaltarak çevresel bozulmayı azalttığı, ekonomik büyüme artışının ise çevresel bozulmayı artırdığı bulgularına ulaşılmaktadır. Ayrıca panel nedensellik testi sonuçlarına göre CO2 emisyonlarının dijitalleşme ve kentleşme ile çift yönlü, finansal gelişme ve ekonomik büyüme ile ise tek yönlü bir nedensellik ilişkisi olduğu sonucuna ulaşılmaktadır. Bu sonuçlar, finansal gelişme, kentleşme ve dijitalleşmenin çevresel sürdürülebilirlik üzerindeki olumlu etkilerini göstererek literatüre önemli bir katkıda bulunmaktadır. Bu bağlamda dijitalleşme, kentleşme ve finansal gelişmenin sürdürülebilir kalkınma politikalarının desteklenmesi konusunda teşvik edilmesi gerekmektedir.

Clinical education plays a crucial role in nursing students' learning process, where they apply theoretical knowledge and develop their professional identities. However, experiences of incivility encountered during this process can negatively impact students' psychological well-being, academic performance, and professional motivation. This study explored incivility among Turkish nursing students and examined the potential consequences. A descriptive qualitative study design was used. This descriptive qualitative study was conducted between 11.03.2025 and 30.03.2025, involving 19 nursing students from the nursing program at a state university in Turkey. Data were collected through face-to-face interviews, and thematic analysis was employed to analyze the data. The data analysis yielded four primary themes and five subthemes: (I) Types of Incivility, (II) Effects of Incivility, (III) Coping Strategies, and (IV) Solutions and Suggestions. The experience of uncivil behavior during clinical practice significantly affected students' academic performance and professional career development. Beyond documenting experiences of incivility, the findings demonstrate how repeated low-intensity uncivil behaviors cumulatively undermine nursing students' professional identity development, learning engagement, and willingness to provide holistic patient care.