FSM Vakıf Üniversitesi Araştırma ve Akademik Performans Sistemi
DSpace@FSM, FSM Vakıf Üniversitesi’nin bilimsel araştırma ve akademik performansını izleme, analiz etme ve raporlama süreçlerini tek çatı altında buluşturan bütünleşik bilgi sistemidir.
Güncel Gönderiler
Öğe Türü: Öğe , Effect of Swift Heavy Ion Irradiation on Zr70Ni30 Binary Metallic Glass: A Positron Annihilation Study(IOP, 2026) Boukhemkhem, Wafa; Hazem, Rafik; Izerrouken, Mahmoud; Ghidelli, Matteo; Pardoen, Thomas; Kuzeci, Saygın; Yener, Murat Yavuz; Tav, Cumali; Yahşi, UğurThe assessment of irradiation effects in metallic glasses is important considering the renewed interest for this class of material for a variety of nuclear applications. The Zr70Ni30 thin films metallic glass (MG) deposited on Si substrate by magnetron sputtering technique were exposed to 93.2MeV 129Xe23+ heavy-ion irradiation at room temperature, covering a range of ion fluences from 5×1012ionscm−2 to 8×1013ionscm−2. The evolution of the irradiation-induced defects in Zr70Ni30 MG has been investigated using Doppler broadening spectroscopy (DBS) and positron annihilation lifetime spectroscopy (PALS). Three lifetime components were distinguished, indicating the presence of different types of open-volume regions at the atomic scale in thin film. The combined results of both DBS and PALS demonstrated that ion irradiation initially increases the excess free-volume density with a homogeneous distribution up to a fluence of (≤2×1013ionscm−2). In contrast, with increasing fluence (>2×1013ionscm−2), a reduction in excess free-volume was found, which could be related to structural relaxation accompanied by modifications in atomic arrangement and defect distribution. Moreover, the correlation between the shape and wing parameters provides a basis to identify the nature of the defects, indicating that the type of defect changes at the higher fluence of 4×1013ionscm−2 and 8×1013ionscm−2, which affects the performance of Zr70Ni30 metallic glass after ion irradiation. © 2026 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.Öğe Türü: Öğe , Neural Architecture Search for 3D Biomedical Image Classification(Springer, 2026) Kuş, Zeki; Kiraz, Berna; Aydın, Musa; Kiraz, Alper3D medical image classification is crucial for improving diagnostic accuracy and treatment planning, but it encounters challenges due to the complexity and variability of volumetric data. While 3D Convolutional Neural Networks offer potential solutions, designing effective architectures is complex and resource-intensive. Neural Architecture Search automates this process, optimizing network designs for specific tasks, thereby improving model performance. This study introduces a novel extension of the PBC-NAS method for 3D medical image classification, aiming to balance prediction accuracy and model complexity. We focus on optimizing neural network architectures using Neural Architecture Search for six different 3D datasets from MedMNIST3D, including OrganMNIST3D, NoduleMNIST3D, FractureMNIST3D, AdrenalMNIST3D, VesselMNIST3D, and SynapseMNIST3D, which are derived from real-world clinical imaging datasets. We have compared our method with state-of-the-art handcrafted networks, AutoML frameworks and recent NAS studies in terms of prediction performance and model complexity. The proposed NAS methods demonstrate superior performance compared to state-ofthe- art handcrafted networks and AutoML frameworks. Our proposed model (Ours #3†) achieves the highest average Area Under the Curve (AUC) of 0.915 and accuracy (ACC) of 0.847 (best result across three independent runs), outperforming all handcrafted networks and AutoML frameworks. Compared to other NAS-based methods, all proposed models achieve higher average AUC scores, and it is important to note that they do not rely on data augmentation, pre-processing, or feature selection, unlike the competing NAS methods which do use data augmentation during training. The study also highlights significant reductions in computational complexity, with FLOPs reduced by up to 45.51 times and parameters by up to 211 times compared to ResNet models. An ablation study reveals that while fine-tuning a model optimized for one dataset can achieve competitive results on other datasets, dataset-specific NAS is crucial for optimal performance. Despite this, the ablation results still outperform ResNets and AutoML frameworks in terms of average AUC and ACC. The study concludes that the proposed NAS approach effectively optimizes neural network architectures for complex 3D medical image classification tasks, achieving state-of-the-art performance without data augmentation.Öğe Türü: Öğe , Beyond PGA: Role of Tank Geometry and Filling Level in Seismic Fragility of Atmospheric Storage Tanks-a Machine Learning Approach(Elsevier, 2026) Öztürk, SezerPeak ground acceleration (PGA) is the near-universally adopted intensity measure for seismic fragility assessment of atmospheric storage tanks, underpinning HAZUS, API 650, and Eurocode 8 provisions. Despite its practical convenience, systematic empirical evidence on whether PGA alone captures the full spectrum of damagecontrolling variables has been lacking. This study addresses that gap by assembling and analysing the largest observational damage database reported to date for vertical cylindrical atmospheric tanks, comprising 4614 records from 42 earthquake events spanning 125 years (1900–2024), supplemented with approximately 100 finite element simulation results. A gradient-boosted machine learning framework is applied to derive both PGAonly and multivariate fragility functions for five damage states (DS1–DS5), using stratified five-fold cross-validation and isotonic probability calibration throughout; noting that the reported performance metrics represent upper-bound estimates given the record-level validation strategy. Three principal findings emerge. First, incorporating height-to-diameter ratio (H/D) and filling level alongside PGA raises ROC-AUC by 0.038–0.098 across all damage states, suggesting that geometric and operational variables carry statistically significant information beyond PGA, based on upper-bound cross-validated performance estimates. Second, PGA alone cannot discriminate DS ≥ 4 from DS ≥ 5: Mann-Whitney U and Kolmogorov-Smirnov tests confirm that the PGA distributions of DS4 and DS5 records are statistically indistinguishable (p = 0.631 and p = 0.314, respectively), consistent with the interpretation that structural characteristics — rather than ground motion intensity alone — may govern the transition between extensive damage and collapse in the observational record. Third, the missingness indicator for filling level emerges as the second most influential predictor, suggesting that tanks lacking operational documentation are associated with higher damage probabilities — a statistically robust predictive signal whose physical basis remains to be established, with potential implications for risk-targeted inspection prioritization. Updated lognormal fragility parameters and multivariate ML-MCS curves are provided for all damage states.Öğe Türü: Öğe , Techno-Economic Assessment of Grid-Connected and Off-Grid Solar Water Pumping for Sugar Beet Irrigation in Konya, Türkiye(MDPI, 2026) Özcanlı, Asiye Kaymaz; Doğan, Fatma NihanAgricultural irrigation is a critical component of global food security, accounting for a substantial share of both water use and energy demand while strongly influencing production costs and market stability under volatile energy conditions. This study evaluates grid-connected and off-grid solar water pumping systems for sugar beet irrigation using real case-study data from Konya, Türkiye. Unlike conventional approaches, this work incorporates irrigation method (sprinkler vs. drip) as a core variable, linking agronomic decisions to energy demand and system sizing. The analysis is based on high-resolution real-world data, including measured hourly solar generation, crop-specific irrigation schedules, and field-based water demand. Two hydraulic conditions were evaluated: low-head (LH-45 m) and high-head (HH-80 m). The results show that grid-connected PV systems provide the most economically viable solution across conditions. While small-scale systems remain marginally unprofitable, economic viability is achieved beyond moderate farm sizes, with payback periods decreasing to 7–8 years. Although higher groundwater depth increases energy demand, it also enhances economic performance through greater energy utilization and cost savings. In contrast, off-grid PV systems with battery storage remain economically unfeasible due to high capital costs. Overall, the findings highlight that irrigation strategy, hydraulic conditions, and system scale are key determinants of solar irrigation performance.Öğe Türü: Öğe , Revealing the Influence of Hafnium on the Microstructure, Corrosion, and Wear Properties of Fe–10Cr Alloy(Springer, 2026) Erol, Mahmut; Tarakçı, Gürkan; Kısasöz, Burçin Özbay; Bayrak, Yahya; Kısasöz, AlptekinThe study aimed to reveal the influence of hafnium addition on the characteristics of Fe-10Cr alloy. Fe-10Cr alloys containing varying hafnium additions (0–3 wt%) were fabricated by plasma arc melting to investigate the effect of hafnium on microstructural evolution, mechanical properties, wear behavior, and corrosion performance. Microstructural characterization was conducted using SEM, EDS, EBSD, and XRD techniques. The results indicated that low hafnium additions (0.5–1 wt%) led to grain coarsening due to insufficient heterogeneous nucleation, whereas higher hafnium contents (≥ 2 wt%) promoted significant grain refinement through the formation of Hf-rich intermetallic phases, including Fe2Hf, FeHf2, and Cr2Hf. These intermetallic phases acted as effective heterogeneous nucleation sites and inhibited grain growth. The addition of hafnium resulted in reduced hardness and wear resistance, primarily attributed to the diminished solid-solution strengthening and increased microstructural heterogeneity caused by grain boundary segregation. In contrast, corrosion resistance improved markedly with increasing hafnium content, as demonstrated by a noble shift in corrosion potential, reduced corrosion current density, enhanced passivation behavior, and increased impedance response. The enhanced corrosion performance was attributed to the formation of stable and protective Hf-containing passive films, particularly in alloys containing 2 and 3 wt.% hafnium. Overall, the findings reveal a clear trade-off between hardness, wear performance, and corrosion resistance in Fe–Cr–Hf alloys, providing insights for tailoring alloy compositions for specific industrial applications.
