WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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A Comprehensive Analysis of Acoustic Emission Signals To Distinguish the Different Damage Types for Fiber-Reinforced Polymers: A Review
(Wiley, 2025) Yilmaz, Cagatay
Fiber-reinforced polymers (FRP) attract the attention of key industries, such as aerospace, wind energy, and automotive, as they can reduce the weight of structural components without compromising their mechanical properties. Due to FRP's anisotropic and non-homogeneous structure, their failure under different loading conditions and the corresponding failure mechanisms must be investigated. One method that progressively monitors the failure of FRP underload is Acoustic Emission (AE). AE can register the elastic stress waves in the form of digitized waveforms, released by the discontinuous events that occur in the FRP under load. These discontinuities can be clustered and identified as transverse cracking, fiber/matrix interface debonding, delamination, and fiber failure by analyzing the AE waveforms. Recently, numerous clustering approaches using machine learning algorithms, along with the varying features of AE waveforms, have been developed and are being used. These algorithms include supervised and unsupervised clustering, deep learning algorithms, and neural network methods, among others. While supervised algorithms require a training dataset to classify AE signals, unsupervised algorithms can perform clustering without training datasets. Deep learning and neural network algorithms can train themselves to cluster data, but they may require a significant amount of computer power when the dataset is large. This review paper provides comprehensive information on the clustering algorithm, along with the AE wave features, the range of features for different damage types, and the type of reinforcer.
A Potential Hemostatic Chitosan/Gelatin Cryogel Impregnated with Verbascum Thapsus Leaf Extract for Noncompressible Hemorrhage Management
(IOP Publishing Ltd, 2025) Uzuner, Hacernur; Yuruk, Adile; Isoglu, Ismail Alper
In this study, we prepared a series of chitosan/gelatin (CS/GEL) cryogels containing Verbascum thapsus (V. thapsus) leaf extract and identified a lead formulation for noncompressible hemorrhage (NCH). Cryogels with average pore diameters ranging from 225 to 478 mu m were fabricated through cryogelation at various CS/GEL ratios. C15 was chosen as the base scaffold due to its homogeneous pore distribution, with a pore size coefficient of variation (CV) of approximately 0.22. Extract loading was 1%, 5%, 10%, and 20% w/v. Functional porosity was reported by the relative accessible void index (RAVI). In PBS, the values relative to neat C15 were 1.00, 0.27, 0.20, 0.13, and 0.09 for concentrations of 0%, 1%, 5%, 10%, and 20% w/v, respectively. In citrated blood, the series was 1.00, 0.29, 0.12, 0.14, and 0.09. After loading, equilibrium swelling decreased and the compressive modulus increased, consistent with partial pore filling in a fixed network. The cryogels maintained an interconnected macroporous network and showed swelling from 300% to 3600% in blood and PBS. Antibacterial activity reached 89% inhibition, and cell viability remained above 80%. Hemolysis was low and within acceptance limits. Clotting improved in whole blood as the blood clotting index decreased from 11.9 to 6.5, and the clotting time was approximately 6 min. The 5% w/v group provided the optimal balance of clotting, antibacterial effects, and biocompatibility. This study presents a novel hemostatic CS/GEL cryogel containing V. thapsus leaf extract that holds strong potential for future applications in NCH management.
Advances in Natural Building and Construction Materials
(MDPI, 2025) Strzalkowski, Pawel; Sousa, Luis; Koken, Ekin
An Ultra-Low Fabric Capacitive Glove for Real-Time Motion Tracking and Human–computer Interaction
(Institute of Physics, 2025) Başıbüyük, Y.; Mutluç, M.N.; Şavur, Ö.; İçöz, K.
This study presents the development of a wearable glove system that integrates ultra-low-cost, fabric-based capacitive sensors for motion detection and human–computer interaction. The system combines touch and bend sensors fabricated from commercially available silver-coated fabric and silicone acrylic tape, enabling real-time tracking of finger movements via measurable capacitance changes. The glove translates physical gestures into digital commands, facilitating intuitive control in virtual environments. Experimental evaluation demonstrated stable operation across a wide pressure range (10–200 g, equivalent to 1.25–25 kPa), with an unnormalized sensitivity of ∼0.00504 pF g−1 (∼0.0040 pF kPa−1), corresponding to a normalized sensitivity of ∼0.0067 kPa−1 when referenced to the baseline capacitance (C0 ≈ 6 pF). The device exhibited high repeatability over 4000 loading cycles, and minimal signal variation (coefficient of variation, CV < 0.005). Integration with a Unity-based interface enabled low-latency gesture tracking in real time. Each sensor was fabricated for less than $0.05 using simple, scalable methods, without nanomaterials or cleanroom processing. Owing to its affordability, fabrication simplicity, and mechanical robustness, the proposed glove system provides a practical and scalable platform for wearable motion tracking, with strong potential in rehabilitation, assistive technologies, and interactive systems. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
Analysis of Power-Law Fin-Type Problems Using Physics Informed Neural Networks
(Sciendo, 2025) Gocer, M.; Coskun, S. B.; Atay, M. T.
This study aims to model the temperature distribution in a single fin subjected to steady one-dimensional heat conduction with nonlinear thermal behavior. For the modeling and solution of the problem, the Physics-Informed Neural Networks (PINNs) architecture was used. The temperature-dependent heat conduction problem and the nonlinear boundary conditions of this problem were formulated with a differential equation. With the help of the PINN architecture, the loss function was minimized in order to reduce the difference between the true value and the predicted value. During this minimization process, the PINN architecture was forced to be consistent with the physical laws. The results obtained after training the PINN architecture exhibit successful performance in terms of accuracy and reliability when compared with the results in the literature. These findings highlight the potential of PINNs as a powerful alternative to conventional methods for solving complex nonlinear heat conduction problems.
Assessment of the Quality of Tuffs in Central Anatolia, Turkey: A Quantitative Classification Approach
(Acad Sci Czech Republic Inst Rock Structure & Mechanics, 2025) Koken, Ekin; Ince, Ismail
The growing global demand for dimension stones necessitates efficient and accurate evaluation methods to ensure their optimal use in various industries. To assess their suitability for various dimension stone applications, this study investigates tuffs from Central Anatolia, Turkey. For this purpose, the fundamental physical and mechanical properties of the tuffs were determined in laboratory studies, and a detailed durability assessment was conducted for each rock type. The analysis results indicate that most of the examined rocks are of low quality and more suitable for non-load-bearing applications. Based on the collected data, fuzzy clustering techniques were applied to develop a new classification system, categorising the tuffs into four classes (Class A-D) according to their potential applications. Additionally, a user-friendly MATLAB-based software tool was also developed to facilitate the implementation of the proposed classification system.
Barriers in Sustainable Lean Supply Chain Management: Implementation in SMEs
(Ege Univ, Fac. Economics & Admin. Sciences, 2025) Kazancoglu, Yigit; Takcı, Ebru; Ada, Erhan
As the world undergoes significant transformations in various domains, including technology, energy supply and communication, the idea of sustainability has become a significant issue. This study investigates the barriers to Sustainable Lean Supply Chain (SLSC) management within Small and Medium-Sized Enterprises (SMEs) and explores the structural interrelationships among these barriers. A comprehensive literature review was carried out to recognize critical elements relevant to the research topic, resulting in the identification of fifteen specific elements that account for 85% of the barriers in SLSC management. The DEMATEL method was used to evaluate the significance and influence levels of these factors. Furthermore, structured in-depth interviews were conducted with ten experts representing sectors that constitute 85% of the SMEs operating in Kayseri Organized Industrial Zone (OIZ), Turkey, including metal products, furniture, plastic packaging, construction materials, textiles and food. The findings reveal that strategies represent the most significant barrier to SLSC management in SMEs. The barriers were analyzed in two dimensions: influencing and influenced factors. The primary influencing factor identified was laws, standards, regulations, and legislation while the most significant influenced factor was found supply and suppliers. The study concludes with findings and actionable recommendations for practitioners and decision-makers.
Boundaries of Belonging: the Spatial and Social Logic of Being Yilli People in Kayseri
(Sage Publications Inc, 2025) Mus Ozmen, Nihan; Asiliskender, Burak; Ozmen, Zehni
This study explores the spatial, social, and cultural dynamics of being yilli, a deeply rooted local identity in Kayseri, Turkey. Drawing on ethnographic fieldwork, oral histories, and spatial analysis, it examines how the yilli people negotiate urban transformation through selective adaptations to modernization while maintaining traditional social boundaries. The research shows that the yilli do not passively resist change but actively reinterpret modernization to reinforce status, kinship, and symbolic belonging. Spatial relocation and investment patterns reflect economic strategies and efforts to preserve cultural distinction amid urban expansion. The findings demonstrate that urban transformation in Kayseri is both a material and cultural process, shaped by layered histories of memory, hierarchy, and social imagination. Through the case of the yilli, the study contributes to broader debates in urban sociology and cultural geography, offering insights into how culture-centered societies adapt to and reshape modernization processes.
The Clinical Efficacy of Psychological Interventions for Bipolar Depression: A Systematic Review and Individual Patient Data (IPD) Meta-Analysis
(Cambridge University Press, 2025) Yilmaz, Sakir; Huguet, Anna; Kisely, Steve; Rao, Sanjay; Wang, Jianli; Price, Molly; Wright, Kim
Unlike conventional meta-analyses, individual patient data (IPD) meta-analysis assesses moderator variables at the level of each participant, which generates more precise and biased estimates. The objective of this study was to investigate whether psychological therapy reduces depression symptoms in people with Bipolar I and II disorders and examine whether baseline depression has a moderating effect on treatment outcomes. Through the use of several electronic databases, a systematic search was conducted. Eligible studies were randomized controlled trials evaluating a psychological intervention for adults diagnosed with Bipolar I or II disorder. Titles and abstracts were screened, followed by full texts. The authors of the included studies were asked to provide IPD from their trials. A multilevel model approach was used to analyze the data. From the 7552 studies found by our searches, six studies with 668 study participants were eligible. Intervention significantly reduced depression scores. There was a significant association between baseline depression and post treatment depression scores. There was no statistically significant interaction between condition allocation and baseline depression score. When IPD from the two most comparable studies were analyzed, CBT had reduced depression scores relative to the comparator condition. The study included patient data from only six studies which were heterogeneous in terms of intervention type, outcome measure, and comparators. Overall, the psychological interventions tested significantly reduced bipolar depression scores. There was no evidence of moderation by baseline depression scores.
Comprehensive Prediction of FBN1 Targeting Mirnas: A Systems Biology Approach for Marfan Syndrome
(Galenos Publishing House, 2025) Orhan, M.E.; Demirci, Y.M.; Saçar Demirci, M.D.S.
Objective: Marfan syndrome (MFS) is a genetic connective tissue disorder primarily caused by mutations in the FBN1 gene. Emerging evidence highlights the regulatory role of microRNAs (miRNAs) in modulating gene expression in MFS, but a systematic investigation into miRNAs targeting FBN1 is lacking. This study aimed to comprehensively identify miRNAs interacting with the FBN1 transcript to reveal potential molecular regulators and therapeutic targets. Methods: Human miRNA sequences were retrieved from miRBase (Release 22.1), and the canonical FBN1 transcript (RefSeq: NM_000138.5) was used for target prediction. Computational interaction analysis was conducted using the psRNATarget server with stringent parameters to detect potential miRNA binding sites. Expression profiles and disease associations of the top candidate miRNAs were further investigated through database integration and literature review. Results: Out of 2656 human mature miRNAs analyzed, 251 were predicted to bind FBN1, with the hsa-miR-181 family exhibiting the highest number of predicted interactions. Evidence from the literature highlighted dysregulation of hsa-miR-181 expression in MFS patients, suggesting a functional role in disease pathophysiology. Conclusion: This study identifies key members of the hsa-miR-181 family as post-transcriptional regulators of FBN1, offering new insights into miRNA-driven mechanisms in MFS. These findings support the potential of RNA-based diagnostics and therapeutic strategies targeting miRNA-FBN1 interactions. ©Copyright 2025 The Author.
Crashworthiness Evaluation of 3D-Printed Hybrid-Design Multi-Cell Energy Absorbers Under Lateral Compression for Unmanned Aerial Vehicles
(Springer Heidelberg, 2025) Atahan, M. Gokhan; Zeybek, Halil; Ozturk, Sezgin
Energy absorbers can be strategically integrated into critical areas of unmanned aerial vehicles to protect their structural integrity and electronic components in the event of an accident. In this study, hybrid-design multi-cell energy absorber configurations were proposed, and their crashworthiness performance and collapse mechanisms were comparatively analyzed. Hybrid energy absorbers were designed considering circular, square, hexagonal, and re-entrant unit cell geometries. The energy absorber configurations were produced via additive manufacturing. Compared to the single-cell circular energy absorber, the hybrid-design multi-cell approach resulted in a higher peak crushing force value, while offering considerable enhancements in other crashworthiness parameters. Configuration 3 is recommended for use in energy absorber applications in unmanned aerial vehicles due to its superior crashworthiness performance. Moreover, in hybrid-design multi-cell energy absorbers, the selection of layer geometries significantly influences deformation capability. Compared to the single-cell circular configuration (Configuration 1), Configuration 3 demonstrated superior crashworthiness performance by increasing the MCF, EA, and SEA values by 7.47, 4.47, and 1.41 times, respectively.
Cyber Threats to Green Hydrogen Production Within a Solar Microgrid
(Springer International Publishing AG, 2025) Bozdal, Mehmet; Pourmirza, Zoya
The transition towards sustainable energy systems depends heavily on the reliable operation of renewable energy infrastructure, which is increasingly interconnected and digitized. Therefore, ensuring cybersecurity resilience is essential for maintaining the reliability and safety of renewable energy systems in a rapidly evolving digital landscape. This paper investigates the economic implications of data integrity and system configuration attacks on a green hydrogen production system within a solar microgrid. Through a comprehensive analysis, the vulnerability of the system to cyber intrusions that manipulate relay settings, electricity prices, and hydrogen level, is examined. Drawing on a multidisciplinary framework encompassing energy economics, cybersecurity, and renewable energy technologies, a methodological approach is developed to quantify the direct economic impacts of attacks. Simulation results indicate that such attacks can decrease profits by up to 14%.
Deep-Learning Detection of Open-Apex Teeth on Panoramic Radiographs Using YOLO Models
(Springer, 2025) Edik, Merve; Celebi, Fatma; Cukurluoglu, Aykagan
ObjectivesThe use of deep learning in detecting teeth with open apices can prevent the need for additional radiographs for patients. The presented study aims to detect open-apex teeth using You Only Look Once (YOLO)-based deep learning models and compare these models.MethodsA total of 966 panoramic radiographs were included in the study. Open-apex teeth in panoramic radiographs were labeled. During the labeling process, they were divided into 6 classes in the maxilla and mandible, namely incisors, premolars, and molars. AI models YOLOv3, YOLOv4, and YOLOv5 were used. To evaluate the performance of the three detection models, both overall and separately for each class in the test dataset, precision, recall, average precision (mAP), and F1 score were calculated.ResultsYOLOv4 achieved the highest overall performance with a mean average precision (mAP) of 87.84% at IoU (Intersection over Union) 0.5 (mAP@0.5), followed by YOLOv5 with 85.6%, and YOLOv3 with 84.46%. Regarding recall, YOLOv4 also led with 90%, while both YOLOv3 and YOLOv5 reached 89%. Moreover, the F1 score was the highest for YOLOv4 (0.87), followed by YOLOv3 (0.86) and YOLOv5 (0.85).ConclusionsIn this study, YOLOv3, YOLOv4, and YOLOv5 were evaluated for the detection of open-apex teeth, and their mAP, recall, and F1 scores exceeded 84%. Deep learning-based systems can provide faster and more accurate results in the detection of open-apex teeth. This may help reduce the need for additional radiographs from patients and aid dentists by saving time.
Development and Characterization of Starch-Fatty Acid Complexes Produced with Buckwheat Starch and Capric/Stearic Acid Using Different Reaction Conditions
(Elsevier, 2025) Oskaybas-Emlek, Betul; Ozbey, Ayse; Aydemir, Levent Yurdaer; Kahraman, Kevser
The aim of present study was to investigate the impact of reaction parameters on the complex formation between buckwheat starch and capric acid (B-Capric) or stearic acid (B-Stearic). The most effective parameters on complex formation indicator (Complex index (CI) value) were found as reaction temperature (60-90 degrees C) and pH (5-8). Additionally, the effect of these parameters on physicochemical, pasting, and in-vitro digestibility properties of complex samples were evaluated. XRD and FTIR was also used in characterize the complex samples. In general, increasing pH increased the CI values of B-Stearic samples while decreasing those of B-Capric samples. Syneresis of buckwheat starch increased after complexation while paste clarity and swelling power diminished. The pasting properties of native starch significantly changed after complex formation. The FTIR results showed that starch structure changed with complex formation. XRD revealed that buckwheat starch, having an A-type pattern, converted to V-type pattern after complexation. Complex formation of buckwheat starch with capric and stearic acid significantly increased the RS content of buckwheat starch (19.01 %) by up to 36.25 % and 30.60 %, respectively. These results highlight the possibility of using buckwheat starch-capric acid/stearic acid complexes in food formulation to enhance the RS content.
Does Your Love Lift Me Higher? A Direct Replication of the Energising Role of Secure Relationships
(John Wiley & Sons Ltd, 2025) Lagap, Adar Cem; Harma, Mehmet
Previous work has revealed that priming people with significant others increases feelings of security and energy, and in turn, boosts exploration motivations. In this preregistered study, we directly replicated Luke et al.'s (2012) Study 2 (N = 281). We found similar results as the replicated study regarding increased security feelings and exploration motivations on the self-report measures after the priming. However, we did not find any support for the increased energy feelings after the attachment security priming. In addition, contrary to Luke et al.'s (2012) results, energy feelings did not mediate the relationship between security priming and exploration motivations. A discussion of null findings, along with the limitations of self-reports and potential misinterpretation of the mediational analyses, follows. We also discuss possible future implications of the current findings.
An Elementary Proof of Lucas's Theorem
(Ramanujan Mathematical Society, 2025) Cinkir, Zubeyir
Lucas's Theorem is about finding the result of a binomial coefficient modulo a prime p efficiently. The result is expressed as a product of binomial coefficients involving the base p expansions of the parameters of the original binomial coefficient. We give an elementary proof of Lucas's Theorem by deriving an analogous Vander-monde identity modulo a prime number.
Engineering a Bilayered Scaffold as a Potential Cardiac Patch: From Scaffold Design to in Vitro Assessment
(Springer Singapore Pte Ltd, 2025) Yuruk, Adile; Duzler, Ayhan; Isoglu, Sevil Dincer; Isoglu, Ismail Alper
In this study, we developed a novel bilayered scaffold consisting of a bottom layer composed of the Decellularized Bovine Pericardium (DP) coated with Polyaniline Nanoparticles (PANINPs) and a top layer made of an electrospun Poly(lactic-co-glycolic acid)/Gelatin (PLGA/Gel) membrane incorporated with Vascular Endothelial Growth Factor (VEGF) and hawthorn extract. Functionally, the DP supplies native Extracellular Matrix (ECM) components and mechanical support, while PANINPs provide conductivity. The electrospun PLGA/Gel layer mimics fibrous ECM. It incorporates bioactives, with VEGF promoting pro-angiogenic stimulation and hawthorn extract enhancing anticoagulant activity, as well as increasing surface hydrophilicity. The tissue adhesive ensures the interfacial integrity between the two layers. Decellularization efficiency was confirmed histologically using 4 ',6-diamidino-2-phenylindole (DAPI) and Hematoxylin-Eosin (H&E) staining. The DP exhibited a DNA content of 115.9 +/- 47.8 ng/mg DNA, compared to 982.88 +/- 395.42 ng/mg in Native Pericardium (NP). The PANINPs had an average particle size of 104.94 +/- 13.7 nm. The conductivity of PANINPs-coated decellularized pericardium was measured to be 9.093 +/- 8.6 x 10- 4 S/cm using the four-point probe method. PLGA/Gel membranes containing hawthorn extract (1%, 5%, 10%, and 15% w/v) and VEGF (0.1 mu g/mL, 0.5 mu g/mL, and 1 mu g/mL) were fabricated by electrospinning, resulting in fiber diameters between 850 and 1200 nm and pore sizes between 14 and 20 mu m. The anticoagulant efficiency of the membranes containing hawthorn extract reached 430 s in the Activated Partial Thromboplastin Time Assay (aPTT). Mechanical testing revealed a tensile strength of 22.70 +/- 6.33 MPa, an elongation of 53.58 +/- 10.63%, and Young's modulus of 0.67 +/- 0.10 MPa. The scaffold also exhibited over 91% cell viability and excellent cardiomyocyte adhesion. The hemolysis ratio was determined to be 0.421 +/- 0.191%, which confirms its blood compatibility. Our results indicate that the proposed bilayered scaffold can be a promising candidate for cardiac patch applications.
Enhancing Complex Disease Group Scoring with Mirgedinet: A Multi-Algorithm Machine Learning Framework Based on the GSM Approach
(IEEE, 2025) Qumsiyeh, Emma; Bakir-Gungor, Burcu; Yousef, Malik
Integrating biological prior knowledge for disease gene associations has shown significant promise in discovering new biomarkers with potential translational applications. This work investigates the application of a multi-algorithm machine learning framework based on the Grouping-Scoring-Modeling (G-S-M) approach for improving the prediction of complex diseases. The study identifies the primary gene and miRNA interactions in various complex diseases with the help of miRGediNET, which is a machine-learning based tool that integrates data from three biological databases. Traditional methods have only focused on independence between features; the G-S-M method focuses on aggregating genes based on biological interactions, pinpointing the scoring of gene groups for a disease, and modeling its predictive capability using advanced machine learning algorithms. In this research paper, seven algorithms, including Support Vector Machine, Decision Tree, and CatBoost, were applied to eight datasets extracted from the GEO database. This framework proved very robust in ranking gene clusters, thus predicting critical biomarkers while doing 100-fold randomized cross-validation within the evaluation. The results indicate this approach's high potential for refining disease and supporting research for choosing the best algorithm that can provide biological insights and computational advances.
Exploring Microbiome Signatures in Autism Spectrum Disorder via Grouping-Scoring Based Machine Learning
(IEEE, 2025) Temiz, Mustafa; Ersoz, Nur Sebnem; Yousef, Malik; Bakir-Gungor, Burcu
The rapid increase in omic data production increased the importance of machine learning (ML) methods to analze these data. In particular, the use of metagenomic data in the diagnosis, prognosis and treatment of diseases is becoming widespread. Autism Spectrum Disorder (ASD) is a neurodevelopmental disease that occurs in early childhood and continues lifelong. The aim of this study is to increase ML performance, reduce computational costs and achieve successful classification performance using a small number of metagenomic features. In addition, disease prediction is performed; ASD associated biomarkers are determined using the microBiomeGSM on metagenomic data. Classification is performed at three different taxonomic levels (genus, family and order) using the relative abundance values of species. The best performance metric (0.95 AUC) was obtained at the order taxonomic level using an average of 416 features with microBiomeGSM. The identified ASD-related taxonomic species are presented.
Citation - WoS: 2
Citation - Scopus: 2
Fine Tuning DeepSeek and Llama Large Language Models with LoRA
(IEEE, 2025) Uluirmak, Bugra Alperen; Kurban, Rifat
In this paper, Low-Rank Adaptation (LoRA) finetuning of two different large language models (DeepSeek R1 Distill 8B and Llama3.1 8B) was performed using the Turkish dataset. Training was performed on Google Colab using A100 40 GB GPU, while the testing phase was carried out on Runpod using L4 24 GB GPU. The 64.6 thousand row dataset was transformed into question-answer pairs from the fields of agriculture, education, law and sustainability. In the testing phase, 40 test questions were asked for each model via Ollama web UI and the results were supported with graphs and detailed tables. It was observed that the performance of the existing language models improved with the fine-tuning method.

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