PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collections
Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/5
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Article Thermo-Mechanical Characterization of GFRP Molded Grating Composites Exposed to Elevated Temperatures(MDPI, 2026) Özkılıç, Yasin Onuralp; Özgün, Muhammed İhsan; Madenci, Emrah; Aksoylu, CeyhunThis study comprehensively investigates the thermal and mechanical degradation behavior of molded glass-fiber-reinforced plastic (GFRP) grating composites subjected to temperatures ranging from 80 °C to 320 °C. Three types of industrially produced GFRP gratings—open-type (OG), thin closed-skin (CG), and thick closed-skin (TCG)—were evaluated using mechanical, microstructural, chemical, and crystallographic analyses. Three-point bending tests revealed that TCG-type specimens exhibited superior thermal resistance, experiencing only a 43.9% loss in strength at 320 °C, whereas OG-type specimens showed significant resin degradation, fiber–matrix decomposition, and microcrack formation at temperatures above 200 °C. Scanning Electron Microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR) analyses revealed significant resin degradation, fiber–matrix decomposition, and microcrack formation. Thermogravimetric analysis (TGA) and Differential Scanning Calorimetry (DSC) confirmed substantial mass loss and structural disintegration at temperatures above 200 °C. Dynamic Mechanical Analysis (DMA) results revealed that the glass transition temperature (Tg) occurred at approximately 115–120 °C. The second-order regression model developed to estimate flexural strength under increasing temperature provided high accuracy (R2 > 0.99) for all grating types. It should be noted that this investigation focuses on the short-term thermo-mechanical response under fundamental flexural loading to provide an accurate baseline for preliminary engineering design. The findings emphasize that the effect of temperature should be considered a critical parameter in the structural design of GFRP systems, especially in industrial environments with temperatures above 120 °C. Accordingly, tables for material selection and load-carrying capacity should be recalibrated to account for short-term temperature effects.Article Field-Scale Occurrence and Uptake of Antibiotics in Lettuce Irrigated with Reclaimed and Conventional Waters: Risk Assessment of Treated Wastewater Reuse(Springer, 2026) Aygün, Ahmet; Yakamercan, Elif; Sahin, Mehmet; Nas, BilgehanIn Konya, Türkiye-a semi-arid agricultural region where 90% of water consumption is for irrigation. Farmers in this region have relied on the de facto reuse of secondary treated wastewater for irrigation. This reclaimed water can contain residual pollutants, including antibiotics that can harm the ecosystem. Türkiye is one of the most antibiotic-consuming countries, raising growing concerns about antibiotic transfer through food chains when reclaimed water is used for irrigation. This field-scale study investigated the occurrence, fate, and uptake of antibiotics from reclaimed wastewater into lettuce irrigated with five different water sources: wastewater (WW), groundwater (GW), secondary clarified water (SCW), ultrafiltration effluent (UF), and multi-media filter with ultraviolet (MMF/UV) effluent. Antibiotic levels in irrigation water and lettuce were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS), which identified 20 antibiotics and four metabolites. The highest concentrations were observed in the WW samples, and the lowest in the UF samples. Clarithromycin (CLA, 362.4-933.8 ng/L) dominated all water types. Antibiotic accumulation was greater in leaves (651.52-1045.10 ng/g) than in roots (386.36-811.87 ng/g). Monte Carlo simulation was applied to evaluate health risks and parameter sensitivities. For children, hazard quotient (HQ) values ranged from 3.23 & times; 10-3 to 7.33 & times; 10-3 both values were lower in adults. All measured HQ values were below threshold limits. Thus, consuming lettuce irrigated with reclaimed water containing antibiotics poses negligible risk to humans under current assumption.Article A Hybrid Approach Based on Deep Feature Extraction and Machine Learning Classification for Structural Damage Detection in Concrete Structures(Nature Portfolio, 2026-04-11) Koklu, NigmetConcrete structures are a vital component of urban infrastructure, requiring regular maintenance to ensure public safety and structural integrity. A crucial element of this maintenance is the identification of surface cracks, which have traditionally relied on manual inspection methods that were frequently work-intensive, subjective, and sometimes dangerous. This work presents a hybrid methodology that integrates deep feature extraction with machine learning classification for identifying structural deterioration in concrete components. A publicly accessible dataset comprising photos of both cracked and uncracked concrete surfaces was used. Deep features were extracted using VGG16, a convolutional neural network widely recognized for its success in visual pattern recognition. Several machine learning algorithms were used for classification of these features, including Artificial Neural Network, Decision Tree, Random Forest, Support Vector Machines and k-Nearest Neighbors. The experimental results indicate that, the highest accuracy was achieved by SVM (99.883%), followed closely by ANN (99.873%), k-NN (99.598%), and DT (99.580%), while RF performed the lowest (98.050%). Although not limited to seismic applications, the proposed method has the potential to be integrated into post-earthquake structural assessment workflows as part of structural health monitoring systems. Using deep learning and machine learning methodologies to detect damage in concrete infrastructure may enhance efficiency and precision, enhancing urban resilience and risk mitigation.Article Search for Light Pseudoscalar Bosons, Pair-Produced in Higgs Boson Decays in the Four-Electron Final State in Proton-Proton Collisions at (Formula Presented)(American Physical Society, 2026-05-08) Snigirev, A.; Hayrapetyan, A.; Andrejkovic, J.W.; Benato, L.; Makarenko, V.; Tumasyan, A.; Adam, W.; Tlisova, I.A search for pairs of light neutral pseudoscalar bosons (A) resulting from the decay of a Higgs boson is performed. The search is conducted using LHC proton-proton collision data at root s = 13 TeV, collected with the CMS detector in 2016-2018 and corresponding to an integrated luminosity of 138 fb(-1). The A boson decays into a highly collimated electron-positron pair. A novel multivariate algorithm using tracks and calorimeter information is developed to identify these distinctive signatures, and events are selected with two such merged electron-positron pairs. No significant excess above the standard model background predictions is observed. Upper limits on the branching fraction for H -> AA -> 4e are set at 95% confidence level, for masses between 10 and 100 MeV and proper decay lengths below 100 mu m, reaching branching fraction sensitivities as low as 10(-5). This is the first search for Higgs boson decays to four electrons via light pseudoscalars at the LHC. It significantly improves the experimental sensitivity to axionlike particles with masses below 100 MeV.Article Robust Control of Distribution Static Compensator in Self-Excited Induction Generator-Based Wind Energy Systems Under Sensor Failures and Abnormal Load Conditions(MDPI, 2026-05-06) Özer, Ali Sait; Karaca, HulusiSelf-excited induction generators (SEIGs) used in wind energy systems suffer from poor voltage and frequency regulation due to varying active/reactive power demands of nonlinear and unbalanced loads. The distribution static compensator (DSTATCOM) provides an effective solution through reactive power support and harmonic mitigation. However, its performance strongly depends on the robustness of the control algorithm against harmonics, load imbalance, and sensor-induced measurement errors such as DC offset, which degrade reference current generation. This study proposes an Advanced Dual Fourth-Order Generalized Integrator (ADFOGI)-based control algorithm to improve voltage and frequency regulation of SEIG-DSTATCOM systems under such adverse conditions. The proposed method inherently rejects DC offset components and enables accurate reference current generation even under severe harmonic distortion, load imbalance, and transient disturbances. The effectiveness of the approach is validated on an OPAL-RT real-time platform under three scenarios: nonlinear load, unbalanced nonlinear load, and one-phase open-circuit condition, where DC offset is intentionally introduced to emulate sensor errors. Under the most severe case, where load current THD reaches 16.23%, SEIG current THD is reduced to 3.71% and voltage THD to 1.66%. In all scenarios, harmonic levels remain below the IEEE-519-2022 limit of 5%, confirming the robustness and effectiveness of the proposed control strategy.Article Ratio-Tuned Green-Synthesized Ag-Fe Bimetallic Nanoparticles Embedded in Electrospun Gelatin/Chitosan Nanofibrous Scaffolds for Antibacterial Applications(Royal Soc Chemistry, 2026) Erci, Fatih; Bayram, Fatma; Asad, Nour AlhudaWound and device-associated infections caused by drug-resistant microorganisms are a major global health problem, increasing morbidity, mortality and economic burden. Antimicrobial nanofibrous scaffolds have therefore gained attention as wound dressings capable of reducing infection risk. In this study, gelatin/chitosan (GEL/CTS) nanofibrous scaffolds incorporating silver-iron bimetallic nanoparticles (Ag-FeNPs) synthesized via a green route using Hypericum perforatum (H. perforatum) leaf extract were developed as potential antibacterial wound-dressing materials. Ag-FeNPs were produced at room temperature and characterized by different analytical techniques. Microstructural analysis confirmed that the biogenic nanoparticles were spherical, with an average size of 60.98 +/- 18.09 nm, and were uniformly distributed throughout the GEL/CTS fibers. Ag-FeNPs with varying Agspace:spaceFe ratios were subsequently embedded into the polymeric matrix to enhance scaffold wettability and antibacterial performance. FT-IR, XRD, FE-SEM, and water-contact-angle measurements demonstrated that incorporation of CTS and Ag-FeNPs reduced the fiber diameter (171.47 +/- 55.96 nm) and improved hydrophilicity, with the GEL/CTS/Ag-Fe (2space:space1) formulation exhibiting the lowest WCA value (60.72 degrees). Antibacterial assays conducted against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) revealed that the green-synthesized Ag-FeNPs were active against both strains, with stronger inhibition observed for E. coli. Among the nanofibrous scaffolds, the sample containing the highest Ag composition yielded the most pronounced reduction in bacterial colony formation. Overall, these findings indicate that GEL/CTS scaffolds reinforced with Ag-FeNPs possess favorable structural and biological properties, which highlighting their potential as effective antibacterial materials for applications such as wound dressings.Article Physics-Informed DynUNet for Brain Metastasis Segmentation(Elsevier Ireland Ltd, 2026-08-01) Güzel, Murat; Baykan, Ömer KaanBackground: In neuro-oncology, detecting, segmenting, and delineating the boundaries of small-volume brain metastatic foci remains a significant challenge. The lack of explicit biological information on metastasis growth and spread in standard deep learning architectures further limits low-volume metastatic lesions. This study investigates whether integrating physics-informed (PI) tumor growth models into segmentation architectures can overcome these size-dependent limitations. Methods: Using the BraTS-METS 2023 dataset, we integrated a physics-based growth model with DynUNet to construct PI-DynUNet and compared it with three U-Net variants under controlled conditions. All models were trained on the same data without data augmentation, using matched parameter counts, identical hyperparameters, and deterministic settings. We compared seven physics regularization weights (lambda) with 5-fold cross-validation and evaluated performance in six lesion-size categories using Dice, IoU and HD95. To assess clinical context-specific performance, we calculated scenario-weighted Dice coefficients for RANO progression assessment, radiotherapy planning, and surgical decision-making. Results: PI-DynUNet achieved effective metastasis segmentation across all BraTS regions. Relative to baseline DynUNet, it improved whole tumor (WT) Dice by 1.8 %, tumor core (TC) Dice by 2.5 %, and enhancing tumor (ET) Dice by 2.6 %. For the challenging non-enhancing tumor core (NETC), Dice increased by 5.3 %. Optimal regularization weights depended on tissue type and lesion size: lambda = 1.0 favored extensive edema and whole-tumor regions, lambda = 0.01 best served large contrast-enhancing tumors and necrotic cores. Scenario-weighted evaluation revealed context-dependent optimal models: PI-DynUNet (lambda = 0.01) excelled in enhancing-weighted scenarios (RANO: +2.6 %; RT-GTV: +2.2 % vs. baseline), while lambda = 1.0 demonstrated superior balanced accuracy (RT-CTV: +1.8 %; Surgical: +1.6 %). Conclusions: Physics-informed deep learning provides modest but measurable gains in brain metastasis segmentation, and these gains transfer across institutions: external validation on the Stanford BrainMetShare cohort (N = 105) showed that five of seven regularization weights significantly outperform the DynUNet baseline on tumor-core Dice (paired Wilcoxon p < 0.05), with the largest improvement of +10.1 % (p < 0.001) at lambda = 0.001 and a similar to 6 & times; reduction in inter-fold variance at lambda = 1.0. Optimal configuration varies by clinical application, informing context-specific deployment.Article Citation - WoS: 1Citation - Scopus: 1Radar-Based Fall Detection Using Micro-Doppler Signatures: A Comparative Analysis of YOLO Architectures(MDPI, 2026-04-24) Seflek, Ibrahim; Barstuğan, MücahidHuman lifespan is increasing in parallel with the development levels of societies. Consequently, the number of elderly individuals worldwide is also rising day by day. One of the most significant risks these individuals face is falling. In this study, fall and daily activity data were collected from different home environments using a continuous-wave (CW) radar. Micro-Doppler signatures were generated from 700 data samples obtained from 10 individuals. Furthermore, the dataset was expanded by doubling the number of spectrogram images through data augmentation. The YOLO architecture, generally used in vision-based studies for object detection and tracking, was preferred for radar-based fall and activity detection. Classifications were performed with different YOLO structures, and comparative results are presented. At this stage, binary (fall/non-fall) and multi-class (seven different classes) classifications were carried out, achieving 100% accuracy for binary classification and 88.02% for multi-class classification. Additionally, the generalizability of the proposed architecture is demonstrated using the Leave-One-Subject-Out (LOSO) approach on the collected data and through the analysis of a public dataset. These results demonstrate the applicability of YOLO architectures in radar-based fall detection studies.Article Citation - WoS: 1Citation - Scopus: 1Observation of Suppressed Charged-Particle Production in Ultrarelativistic Oxygen-Oxygen Collisions(American Physical Society, 2026) Snigirev, A.; Hayrapetyan, A.; Bergauer, T.; Benato, L.; Makarenko, V.; Tumasyan, A.; Adam, W.; Zhizhin, IA hot and dense state of nuclear matter, known as the quark-gluon plasma, is created in collisions of ultrarelativistic heavy nuclei. Highly energetic quarks and gluons, collectively referred to as partons, lose energy as they travel through this matter, leading to suppressed production of particles with large transverse momenta (p(T)). Conversely, high-pT particle suppression has not been seen in proton-lead collisions, raising questions regarding the minimum system size required to observe parton energy loss. Oxygen-oxygen (OO) collisions examine a region of effective system size that lies between these two extreme cases. The CMS detector at the CERN LHC has been used to quantify charged-particle production in inclusive OO collisions for the first time via measurements of the nuclear modification factor (R-AA). The R-AA is derived by comparing particle production to expectations based on proton-proton (pp) data and has a value of unity in the absence of nuclear effects. The data for OO and pp collisions at a nucleon-nucleon center-of-mass energy root s(NN) = 5.36 TeV correspond to integrated luminosities of 6.1 nb(-1) and 1.02 pb(-1), respectively. The R-AA is below unity with a minimum of 0.69 +/- 0.04 around p(T) = 6 GeV. The data exhibit better agreement with theoretical models incorporating parton energy loss as compared to baseline models without energy loss.Article Medical Image Segmentation Methods: A Decision-Guided Survey Covering 2D/3D CNNs, Transformers, VLMs, SAM-Based Models and Diffusion Approaches(MDPI, 2026-05-15) Sabanci, Kadir; Aslan, Muhammet Fatih; Aslan, BusraRecent advances in medical image segmentation have introduced a wide spectrum of deep learning paradigms, including 2D/3D convolutional neural networks (CNNs), transformer-based architectures, vision-language models (VLMs), prompt-driven foundation models such as Segment Anything Model (SAM), and diffusion-based approaches. Although these methods have demonstrated remarkable performance across MRI, CT, PET, ultrasound, and endoscopic imaging, the rapid proliferation of architectures has created methodological uncertainty regarding optimal model selection under varying clinical and data constraints. Existing surveys primarily focus on architectural categorization, yet provide limited guidance for decision-oriented model selection. This study presents a comprehensive and decision-guided survey that systematically analyzes segmentation paradigms across imaging modalities, task types, dataset characteristics, and evaluation protocols. Beyond taxonomy, we propose a practical model selection framework that links clinical scenarios, such as small lesion detection, multi-organ 3D segmentation, limited-data regimes, and domain shift, to appropriate segmentation strategies. Furthermore, robustness, generalization, annotation variability, and benchmarking reproducibility are critically examined. By integrating architectural taxonomy, cross-modal comparative analysis, and a structured decision framework, this work provides a clinically oriented roadmap for selecting segmentation methods and highlights future research directions toward reliable and reproducible medical AI systems.