WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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  • Article
    The Effect of Layered Cover Plate Material on the Ballistic Performance of Ceramic Armors: Experimental and Numerical Study
    (Pergamon-Elsevier Science Ltd, 2026) Cellek, Seven Burcin; Tasdemirci, Alper; Cimen, Gulden; Yildiztekin, Faki Murat; Toksoy, Ahmet Kaan; Guden, Mustafa
    This study investigates the ballistic performance of silicon carbide (SiC) ceramic armor systems reinforced with single and hybrid metallic cover plates composed of Ti-6Al-4V (Ti64) and copper. Controlled ballistic experiments combined with validated LS-DYNA simulations were conducted to examine how cover-plate material, thickness, and stacking sequence influence penetration resistance, energy dissipation, and failure mechanisms. The experimental results revealed that metallic cover plates significantly enhance protection by improving projectile erosion and extending dwell time. While both Ti64 and copper single layers increased the antipenetration capability (APC) compared with bare SiC, hybrid configurations achieved the highest performance. The optimal design, consisting of a 2 mm Ti64 plate placed in front of a 1 mm copper plate, produced the greatest reduction in penetration depth and the highest APC value. Numerical analyses closely replicated the experimental trends and provided insight into stress-wave interactions, pressure evolution, and damage progression within the ceramic. The findings demonstrate that hybrid Ti64-Cu systems not only improve initial impact resistance but also redistribute energy toward the front layers, reducing stress transmission to the backing and mitigating catastrophic ceramic failure. The combined experimental and numerical results establish a clear design framework for developing lightweight, high-efficiency ceramic armor through tailored hybrid layering strategies.
  • Article
    Automated Detection and Quantification of Honey Adulteration Using Thermal Imaging and Convolutional Neural Networks
    (Pergamon-Elsevier Science Ltd, 2026) Unluturk, Mehmet S.; Berk, Berkay; Unluturk, Sevcan
    Honey is a valuable natural food rich in bioactive substances beneficial to health. Despite strict regulations prohibiting adulteration, honey remains one of the most frequently adulterated foods, often with low-cost commercial syrups. Conventional detection methods require expensive instruments, expert operators, and lengthy analysis times, limiting their practical use. This study introduces a rapid and automated method for detecting and quantifying honey adulteration using thermal image analysis combined with a tailored Convolutional Neural Network (CNN) architecture. Thirty-six pure honey samples (blossom and honeydew) from different regions of T & uuml;rkiye were adulterated with inverted sugar, maltose, and glucose syrups at varying levels (3 %-60 % weight/weight (w/w)). Samples were heated to 60 degrees C and thermal images were captured during cooling using a custom image-capturing unit. The CNN model employed a multi-layer structure, starting with a shallow network for binary classification (pure vs adulterated honey) achieving 100 % accuracy, followed by specialized deeper CNN regressors to quantify adulterant levels with mean squared errors of 0.0003, 0.001, and 0.0002 for glucose, maltose, and inverted sugar, respectively. This layered CNN approach leverages thermal patterns linked to adulteration, enabling sensitive, rapid, and non-destructive quality control. Furthermore, the method is integrated into a user-friendly hardware-software system called Compact Adulteration Testing Cabinet on Honey (CATCH), requiring no specialized expertise, demonstrating strong potential for automated honey authenticity verification in practical settings.
  • Article
    Development and Validation of Regression Model via Machine Learning to Estimate Thermal Conductivity and Heat Flow Using Igneous Rocks from the Dikili-Bergama Geothermal Region, Western Anatolia
    (Pergamon-Elsevier Science Ltd, 2026) Ayzit, Tolga; Sahin, Onur Gungor; Erol, Selcuk; Baba, Alper
    Thermal conductivity is a fundamental parameter that significantly influences the thermal regime of the lithosphere. It plays a crucial role in a variety of geological applications, including geothermal energy exploration, igneous system assessment, and tectonic modeling. In this study, a machine learning approach is used to predict the thermal conductivity of igneous rocks based on the composition of major oxides. A total of 488 samples from different regions of the world were analyzed. The thermal conductivity values ranged from 1.20 to 3.74 Wm(-1) K-1 and the mean value was 2.61 Wm(-1) K-1. The Random Forest (RF) algorithm was used, resulting in a high coefficient of determination (R-2 = 0.913 for training and R-2 = 0.794 for testing) and a root mean square error (RMSE) of 0.112 and 0.179, respectively. Significance analysis of the traits identified SiO2 (>40 %), Na2O (>15 %) and Al2O3 (>10 %) as the most influential predictors. The study presented results from the Western Anatolia region, where felsic rocks had the highest thermal conductivity (mean = 2.69 Wm(-)(1)K(-)(1)) compared to mafic (mean = 2.34 Wm(-)(1)K(-)(1)) and ultramafic rocks (mean = 2.39 Wm(-)(1)K(-)(1)). In addition, the study evaluated the predictive capabilities of machine learning models for the igneous rocks of the Dikili-Bergama region and compared the results with those of saturated models. Using these data, we calculated heat flow values of up to 400 mWm(-2) under saturated conditions in western Anatolia. These results highlight the value of integrating geochemical data with machine learning to improve geothermal resource exploration and lithospheric modeling.
  • Article
    Experimental Assessment of Alternating Magnetic Fields for Subcooled Flow Boiling Enhancement in an Annulus
    (Pergamon-Elsevier Science Ltd, 2026) Youzbashi-Zade, Saeed; Zonouzi, Sajjad Ahangar; Aminfar, Habib; Mohammadpourfard, Mousa
    The application of magnetic fields to enhance boiling heat transfer in magnetic nanofluids has emerged as a promising strategy for advanced thermal management, yet the influence of alternating magnetic fields remains largely unexplored compared to their constant counterparts. The effects of alternating and constant (steady) magnetic fields on the subcooled flow boiling of a ferrofluid in a vertically oriented annulus are thoroughly investigated experimentally in this work. The magnetic field generated by face-to-face electromagnets was systematically varied in strength (up to 0.3 T), frequency, and waveform (square, triangular, sinusoidal). The results demonstrate that magnetic fields under constant and alternating conditions substantially enhance local and average convective heat transfer coefficients and critical heat flux compared to the no-field baseline. Due to its ability to effectively disrupt the thermal boundary layer and improve bubble dynamics, the alternating square-wave magnetic field (0.3 T, 2 Hz) notably produces the greatest enhancement. Under this condition, the convective heat transfer coefficient increased by up to 21 %, and the critical heat flux improved by approximately 24 % compared to the no-field baseline. The enhancement strongly depends on mass flux and field frequency, with optimal frequencies shifting higher at increased flow rates due to shortened nanoparticle residence time in the magnetic region. At elevated mass fluxes, the benefit of alternating over constant fields diminishes as inertial effects become dominant.
  • Article
    Geogenic Determinants of Indoor Radon Exposure in Izmir (West Türkiye)
    (Pergamon-Elsevier Science Ltd, 2026) Alkan, Turkan; Simsek, Celalettin; Sac, Murat; Uzelli, Taygun; Taskin, Nurcihan
    Radon, a naturally occurring product of uranium decay, is the second leading cause of lung cancer. I(center dot)zmir Province in western T & uuml;rkiye, situated within the Aegean extensional regime, comprises complex fault-bounded basins that favor indoor radon accumulation. This study evaluates the spatial variability and geogenic controls of indoor radon to delineate radon-prone zones with public-health relevance. Indoor radon was measured in 79 dwellings distributed across major lithologies and structural settings; detectors were deployed in basements to capture soil-gas infiltration. Concentrations ranged from 12 to 366.5 Bq/m3 (mean 118 Bq/m3), exceeding the national average of 81 Bq/m3; 32 % of sites surpassed the EPA action level of 148 Bq/m3. Highest values cluster in Bornova, Buca, and Kemalpas, a, coincident with fault-controlled sedimentary basins and permeable units. Spatial mapping highlights the dominant influence of lithology and fault proximity on radon distribution and underscores the limitations of uniform, national-scale mitigation policies. We advocate targeted, geology-aware health policies and urban-planning measures for monitoring and mitigation in geogenically vulnerable districts. These findings contribute to medical geology by providing region-specific evidence of radon risk in one of T & uuml;rkiye's most seismically active metropolitan areas. These outputs provide decision-ready evidence for monitoring, mitigation, and building-code updates in seismically active metropolitan settings.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 3
    CFD-DEM Modeling of Biomass Pyrolysis in a DBD Plasma Fluidized Bed
    (Pergamon-Elsevier Science Ltd, 2025) Eslami, Ali; Kazemi, Saman; Hamidani, Golnaz; Zarghami, Reza; Mostoufi, Navid
    This study developed a CFD-DEM model to simulate biomass pyrolysis within a dielectric barrier discharge (DBD) plasma fluidized bed reactor. Biomass, as a renewable energy source, offers a promising alternative for hydrogen production through pyrolysis. The integration of non-thermal plasma technology and fluidized bed reactors is expected to enhance conversion. Key operational parameters such as inlet gas velocity, particle size, and input voltage were examined to evaluate their effects on temperature distribution, particle conversion, and hydrogen production. Results indicated that higher inlet gas velocities promote better particle mixing and more uniform temperature and conversion distribution. Smaller particle sizes significantly enhance biomass conversion by increasing the available surface area between fluid and particles. Specifically, particles with diameters of 0.85, 1.2, and 1.5 mm achieved conversions of 10.4, 8.99, and 8.57 %, respectively, at 20 s from the start of the process. Additionally, increasing the input voltage increases the mean temperatures of particles and fluid, which enhances reaction rates and conversion. Optimizing these parameters can improve the efficiency of DBD plasmaassisted biomass pyrolysis, providing valuable insights for sustainable hydrogen production.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 2
    Experimental Optimization of Alternating Magnetic Field Parameters for Convective Heat Transfer Enhancement of Ferrofluid in a Vertical Annulus
    (Pergamon-Elsevier Science Ltd, 2025) Youzbashi-Zade, Saeed; Aminfar, Habib; Mohammadpourfard, Mousa
    This study presents a detailed experimental investigation of how applying constant and alternating magnetic fields enhances the convective heat transfer of Fe3O4/water ferrofluid flowing through a vertical annulus. The setup was exposed to both constant (steady) and alternating magnetic fields with different waveforms (square, triangular, and sinusoidal), frequencies, intensities, and axial positions. Results showed that both steady and alternating fields substantially increased heat transfer within the active region, with the alternating field providing the highest enhancement. This improvement comes from stronger fluid movement under the oscillating field, which disrupts the thermal boundary layer more efficiently than the steady field. The maximum local heat transfer enhancement decreased from 54.98 % at Re = 200 to 29.43 % at Re = 1000, highlighting the reduced influence of magnetic forces at higher flow rates. The study also explored the influence of magnetic field initiation location, revealing that downstream activation yields higher peak local enhancement, while earlier activation ensures more uniform improvement along the annulus. Among the tested waveforms, the square wave resulted in the greatest convective enhancement, followed by triangular and sinusoidal forms. Results also revealed that, regardless of waveform, increasing frequency initially enhances the heat transfer coefficient, reaching an optimal value typically at 2-5 Hz depending on Reynolds number and waveform.
  • Article
    Therapeutic Targeting of Neuroinflammation in Sphingolipidosis
    (Pergamon-Elsevier Science Ltd, 2025) Ada, Ebru; Seyrantepe, Volkan
    Lysosomal storage diseases (LSDs) are a class of hereditary metabolic disorders primarily caused by lysosomal enzyme defects, leading to the accumulation of undegraded substrates. Sphingolipidoses, a subset of LSDs, are primarily associated with profound involvement of the central nervous system (CNS), characterized by progressive neurodegeneration due to massive sphingolipid accumulation. A common pathological feature among many CNS-involved LSDs is the early activation of microglia and astrocytes, which often precedes and predicts regions of subsequent neuronal loss. The extent to which neuroinflammation disrupts CNS homeostasis appears to be determined by its onset, magnitude, and duration. Although neuroinflammatory processes are increasingly recognized as critical contributors to disease progression in sphingolipidoses, the molecular mechanisms underlying glial activation and the initiation of inflammatory cascades remain incompletely understood. Therefore, mouse models of sphingolipidoses have been instrumental in elucidating these pathogenic processes and provide valuable platforms for evaluating therapeutic strategies. This review critically examines the role of neuroinflammation in sphingolipidoses, summarizes insights derived from pre-clinical models, and discusses the therapeutic potential of anti-inflammatory interventions to mitigate CNS pathology and improve clinical outcomes.
  • Article
    Knowledge-Based Training of Learning Architectures Under Input Sensitivity Constraints for Improved Explainability
    (Pergamon-Elsevier Science Ltd, 2026) Sildir, Hasan; Erturk, Emrullah; Edizer, Deniz Tuna; Deliismail, Ozgun; Durna, Yusuf Muhammed; Hamit, Bahtiyar
    The traditional machine learning (ML) training problem is unconstrained and lacks an explicit formulation of the underlying driving phenomena. Such a formulation, based solely on experimental data, does not ensure the delivery of qualitative knowledge among variables due to many theoretical issues in the optimization task. This study further tightens Artificial Neural Networks (ANNs) training by including input sensitivities as additional constraints and applies to regression and classification tasks based on literature data. In theory, such sensitivity represents the change direction of the target variable per change in measurements from indicators. The resulting nonlinear optimization problem is solved th rough a rigorous solver and includes the sensitivity expressions through algorithmic differentiation. Compared to traditional methods, with an acceptable decrease in the prediction capability, the proposed model delivers more intuitive, explainable, and experimentally verifiable predictions under input variable variations, under robustness to overfitting, while serving robust identification tasks. A classification case study includes a patient-oriented clinical decision support system development based on the impact of cancer-indicating variables. A competitive test prediction accuracy is obtained compared to commonly used algorithms despite 10 % decrease in the training. The regression case is built upon the energy load estimation to account for prominent considerations to obtain desired sensitivity patterns and proposed methodology delivers significant accuracy drop compared to some formulations to address knowledge patterns. The approach delivers a compatible pattern with practitioner expertise and is compared to widely used machine learning algorithms, whose performances are evaluated through common statistics in addition to multi-variable response graphs.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 2
    Incorporation of CuWO4 With Hollow Tubular g-C3N4: Harnessing the Potential in Photocatalytic Degradation, Hydrogen Production, and Supercapacitor Applications
    (Pergamon-Elsevier Science Ltd, 2026) Erdem, Nurseli Gorener; Caglar, Basar; Inan, Ece; Tuna, Ozlem; Ertis, Irem Firtina; Simsek, Esra Bilgin
    Driven by the urgent need for sustainable energy conversion and environmental remediation technologies, the development of multifunctional materials has gained growing interest. Herein, a bifunctional heterostructure was fabricated by depositing copper tungstate (CuWO4) spherical particles over hollow tubular graphitic carbon nitride (HTCN) using an ultrasonic-assisted thermal impregnation method. The photocatalytic activities were evaluated through tetracycline degradation and hydrogen evolution tests, while electrochemical measurements were conducted to assess the supercapacitor performance. CuWO4@HTCN composite achieved up to 83% degradation efficiency, a hydrogen evolution rate of 2538 mu mol g1 h-1, and a specific capacitance of 212 F g1, demonstrating its strong potential as a multifunctional material for solar-driven environmental and energy storage applications. The enhanced photocatalytic performance was attributed to extended visible light absorption ability, efficient charge separation, and suppressed electron-hole recombination resulting from the formation of a Z-scheme heterojunction. Furthermore, the superior capacitance behavior was ascribed to enhanced electrical conductivity and ion transport, enabled by the porous, nitrogen-rich HTCN structure. The increased HTCN content in the composite improved pore accessibility and active site availability while an excessive amount of CuWO4 reduced electrochemical performance. These results highlight the multifunctional applicability of CuWO4@HTCN composite in photocatalytic hydrogen production and supercapacitor systems, emphasizing their relevance for renewable energy technologies.