Yıldız Technical University Research Information System
Not a member yet
91324 research outputs found
Sort by
Quantitative assessment of beta radiation shielding in medical polymers based on Monte Carlo simulations
No full textThe radiation resistance and sterilization performance of polymeric materials are critical in medical and pharmaceutical technologies. This study provides a quantitative assessment of beta-particle attenuation in five medical-grade polymers (PS, PET, PVC, PVDC and PVT) using Monte Carlo (MCNP6.2) simulations. Seven beta-emitting isotopes (3H, 63Ni, 14C, 147Pm, 99Tc, 90Sr, and 90Y) were considered to cover an energy range from 5.7 keV to 2.28 MeV. The transmission-thickness relationship is described using logarithmic regression (R2 > 0.99), and the Half Absorption Thickness (HAT) is evaluated as a compact metric of shielding performance. The results reveal a strong correlation between polymer chemistry and attenuation behavior. Halogenated polymers (PVC and PVDC) exhibit the lowest HAT values and strongest attenuation at medium and high energies, whereas hydrocarbon-based PS and PVT show greater transmission and reduced energy absorption. PET demonstrates an intermediate behavior, balancing transparency with moderate attenuation capability. HAT values extend across two orders of magnitude, from ∼1.4 × 10-7 m for low-energy 3H to ∼9.2 × 10-5 m for high-energy 90Y, highlighting the strong impact of beta energy. The findings provide quantitative guidance for the selection and design of polymeric shielding layers in radiation-exposed medical components, radiopharmaceutical packaging, or sterilization systems
Production and characterization of AlCuMgMnZn high entropy alloy prepared by hot pressing
No full textHigh entropy alloys attracted attention owing to enhanced microstructural, mechanical and corrosion properties. In this study, AlCuMgMnZn alloys were produced by the hot pressing at various temperatures and duration. Microstructure, wear and corrosion properties of the produced alloys were investigated. The samples were produced at 500 °C, 550 °C, and 600 °C for 30 min and 60 min. The samples consisted of copper based matrix, intermetallics and complex phases such as MgZn2, Mg2Zn11, MgCuZn, and AlCuMg. As the production temperature increased, complex intermetallic phases as well as the manganese based phase were formed. The wear rate decreased from 8.745·10−5 mm3/Nm to 7.785·10−5 mm3/Nm via hot pressing process at 600 °C for 60 min. The formation of the manganese based phases and intermetallic structures improved wear behavior of the AlCuMgMnZn high entropy alloy. The samples produced at 600 °C exhibited significantly improved corrosion behavior. This phenomenon was attributed to microstructural transformations and the formation of a denser, less porous structure. As a result, the samples produced at high hot pressing temperatures exhibited significantly improved properties in terms of both corrosion and wear resistance
Determination of Cobalt in Tap Water by Deep Eutectic Solvent-Dithizone Assisted Liquid Phase Microextraction (LPME) Preconcentration with Flame Atomic Absorption Spectrometry (FAAS)
No full textThis study aimed to develop an analytical method for preparing samples to preconcentrate cobalt from water in advance for determination using flame atomic absorption spectrometry (FAAS). The aforementioned outcome was attained through the complexing of cobalt ions with dithizone (Co-DTZ) and subsequently extracting the resulting complex from aqueous solution using a deep eutectic solvent (DES). The DES/DTZ probe was used as a complexing and extracting agent in the developed liquid phase microextraction (LPME) procedure. The most suitable experimental conditions were determined for the highest extraction efficiency. Consequently, 8.0 mL of standard/sample solution was adjusted to pH 6.0 using 1.0 mL of phthalate buffer. 250 µL of the DES/DTZ probe (10 mL DES: 5.0 mg DTZ) was added to the aqueous solution to extract cobalt with the best efficiency. The DES/DTZ-LPME-FAAS system achieved a detection limit of 9.6 µg/L with a wide linear working range between 30 and 500 µg/L by applying the optimum procedure. The relative standard deviation of the proposed method was 6.9% for a 30 µg/L cobalt standard. Spike recovery experiments were conducted with tap water to evaluate the accuracy and feasibility of the developed method. By performing matrix matching, the results demonstrated good recoveries from 90% to 112%
The investigation into the relationship between seismic design and architectural education: a systematic literature review
No full textRecent advancements in earthquake research have not been sufficiently integrated into architectural design education. This study aims to fill this gap through identifying the focal points of research in earthquake studies and architectural design education. The study conducts a systematic literature review to explore how earthquake research findings are incorporated into curricula, lectures, studio projects, and design criteria. The qualitative and interdisciplinary features of the evaluated studies are analysed to determine the extent and depth of their contribution. A review of numerous studies reveals key insights: seismic design is underemphasized in design studios, there is a scarcity of experimental research, and existing studies primarily focus on satisfaction and usability rather than assessing student performance. These findings have significant implications, shedding light on how architectural research methodologies can be effectively integrated into educational contexts and design practices for disaster resilience. Educators and practitioners can leverage these insights to better prepare for and respond to disasters by integrating earthquake research findings into courses, lectures, and design projects
Impact Of Biaxial Loading on the Buckling Delamination Mode of the Pzt+Metal+Pzt Sandwich Rectangular Thick Plate With Embedded Interface Cracks
No full textPalmCity: An Emerging Benchmark Dataset for Semantic Segmentation of Panoramic Street View Images in Under-Represented Developing Countries
No full textStreet View Imagery (SVI) offers detailed, street-level data for urban analysis, enabling the study of green spaces, sky, buildings, and other urban elements through semantic segmentation. Techniques like Green/Sky/Building View Indexes link urban morphology, climate, socio-economic factors, and public health. However, pre-trained models such as Cityscapes and ADE20K, designed for cities in developed countries, often fail to represent the diverse architectural and land-use patterns of developing countries like Türkiye, resulting in poor segmentation performance. To address this, the PalmCity project introduces a tailored benchmark dataset for Türkiye’s unique urban characteristics. Using 360-degree action cameras, PalmCity will collect at least 5,000 panoramic SVI images from Mersin City, chosen for its representative urban typologies. The dataset aims to improve SVI semantic segmentation and support urban studies in under-represented regions. PalmCity is going to evaluate the state-of-the-art deep learning models, including FCN, PSPNet, DeepLabV3 and Transformer-based models, using ResNet as the primary backbone. Models trained on PalmCity are going to be compared to Cityscapes-trained models to assess segmentation performance. Preliminary results show that Cityscapes weights perform well for general classes like sky, road, building and trees but struggle with urban objects, vehicles, and panoramic distortions in PalmCity images, underscoring the need for dataset-specific training
Vulnerability to a just transition in coal-intensive provinces in Turkey
No full textThis study assesses the potential social vulnerabilities of Turkish provinces during the coal phase-out process. Provinces heavily reliant on coal and lignite mining activities will likely face significant socio-economic challenges during this energy transition. A vulnerability assessment indicates that coal-intensive provinces are at heightened risk, with substantial spatial interdependence among them, as neighboring provinces share similar vulnerabilities due to their reliance on coal-fired electricity generation. Without careful management, these provinces could disproportionately experience negative impacts, including unemployment and economic deterioration. The findings highlight the critical importance of inclusive strategies addressing national and regional disparities, and fostering a just and sustainable energy shift
A Microstrip Monopole Antenna Design for 5G Sub-6 GHz Applications Using Deep Learning
No full textThis study presents the design and optimization of a microstrip monopole antenna for 5G sub-6 GHz applications, employing a deep learning-based surrogate model combined with honeybee mating optimization (HBMO). The studied antenna structure employs air via arrays, intended to enhance antenna performance, including improved impedance matching and increased bandwidth. It is important to note that, unlike conventional antennas, the proposed design does not include a fully enclosed metallic cavity similar to a substrate integrated waveguide (SIW) antenna designs. A sensitivity analysis was conducted to assess the impact of these parameters, emphasizing the need for optimal tuning. To generate training and test datasets efficiently, Latin hypercube sampling (LHS) was used. A convolutional neural network (CNN) surrogate model was trained, outperforming other machine learning (ML) algorithms in predictive accuracy and generalization. The proposed CNN-HBMO framework reduced computational costs by minimizing the need for expensive electromagnetic (EM) simulations, enabling rapid design space exploration. The optimized antenna was fabricated and validated through experimental measurements, achieving 2–3 dBi gain and S11 < −10 dB across the 2.7–5.2 GHz band. Compared to existing designs, the proposed antenna offers a compact size (34 x 34 mm) with competitive performance, making it suitable for multi-band 5G applications
