Yıldız Technical University Research Information System
Not a member yet
91324 research outputs found
Sort by
Integrated assessment of mucilage impact on human health using the One Health approach: Prevalence and antimicrobial resistance profiles of Escherichia coli and Clostridium perfringens in the Marmara Sea, Türkiye
No full textThis study employed a One Health approach to assess the potential impact of mucilage on human health by characterizing the prevalence and antimicrobial resistance (AMR) profiles of Escherichia coli and Clostridium perfringens strains isolated during the 2021 mucilage event in the Marmara Sea, Türkiye. Mucilage, a gelatinous organic substance exacerbated by climate change, disrupts marine ecosystems by depleting oxygen, threatening biodiversity, and serving as a reservoir for pathogenic microorganisms. Surface and benthic mucilage samples collected from the Marmara Sea were analysed for AMR profiles using genome analysis, the BD Phoenix™ 100 automated system, and E-test methods. The study identified 13 E. coli and one C. perfringens strain, harboring 244 and six AMR genes from 21 and eight drug classes, respectively, along with multiple virulence factors (VFs). The E. coli strains exhibited four distinct serotypes (O138:H28 [Mu-3], O18:H49 [Mu-4], O128:H12 [Mu-35] and O101:H10 [Mu-125]), reported for the first time from Türkiye and mucilage. Notably, anaerobic microorganisms like C. perfringens thrived in mucilage, underscoring their ecological significance. Seasonal and climatic factors influencing mucilage formation amplify its role in transmitting antimicrobial-resistant pathogens, posing significant risks to public and environmental health. The findings highlight the urgent need for continuous monitoring and mitigation strategies for mucilage-related hazards
Understanding emotional and health indicators underlying the burnout risk of healthcare workers
No full textGAN-based text line segmentation method for challenging handwritten documents
No full textText line segmentation (TLS) is an essential step of the end-to-end document analysis systems. The main purpose of this step is to extract the individual text lines of any handwritten documents with high accuracy. Handwritten and historical documents mostly contain touching and overlapping characters, heavy diacritics, footnotes and side notes added over the years. In this work, we present a new TLS method based on generative adversarial networks (GAN). TLS problem is tackled as an image-to-image translation problem and the GAN model was trained to learn the spatial information between the individual text lines and their corresponding masks including the text lines. To evaluate the segmentation performance of the proposed GAN model, two challenging datasets, VML-AHTE and VML-MOC, were used. According to the qualitative and quantitative results, the proposed GAN model achieved the best segmentation accuracy on the VML-MOC dataset and showed competitive performance on the VML-AHTE dataset
Uncovering Communication Methods, Forms, and Dynamics in Construction Projects: A Comparative Analysis
No full textConstruction projects require multiparty engagement and communication among professionals from diverse disciplines. This study aims to investigate different communication methods, forms, and dynamics among project teams and to identify perception differences, if any, among professionals based on respondent, project, and team characteristics. A literature survey accompanied by a pilot study is conducted to determine communication factors classified into three categories (i.e. communication methods, communication forms, and communication dynamics). Data collected from 72 construction industry professionals is analyzed using various statistical techniques including mean rank analysis, relative importance index (RII), normality tests, reliability analysis, Mann–Whitney U-test and Kruskal–Wallis test. The findings indicate that choice of words, visual communication tools, and weekly project meetings are the top three factors affecting communication quality in construction projects. Furthermore, no statistically significant differences were observed in the perceptions between the teams of different sizes and respondents’ profession in any communication method, form, or dynamics. The research is a pioneering attempt that offers a holistic communication framework to improve communication quality and team performance in construction projects. The study findings are expected to enhance the project performance by bringing various aspects of communication to the project members’ attention.</p
Characteristics of Single Crystalline Rutile GeO2 Film Grown on Sapphire by Chemical Vapor Deposition with a high growth rate ∼2.2 µm/hr
No full textRutile GeO2 with ultra-wide energy bandgap (UWBG) ∼4.68 eV and theoretically ambipolar dopability has high potential as a next generation UWBG semiconductor. However, the growth of the material with either good crystal quality, high growth rate, or at a low cost has not yet been achieved at a satisfying level. We report the system design of the low-pressure chemical vapor deposition (LPCVD) growth of rutile GeO2. High purity Ge granules and GeO2 powder were used as Ge source to generate GeO precursor in the system. The r-GeO2 film was grown on an a-sapphire substrate to enable cheap and large-scale r-GeO2 production. The growth rate as high as 2.2 µm/hr was achieved. X-ray diffraction (XRD) analysis revealed single crystalline (101) r-GeO2 film with peak intensity comparable to sapphire substrate. On- and off-axis XRD rocking curve scans showed full-with at half maximum in the range of 511–806 arcsec indicating both edge and screw dislocation density at low 109 cm−2. Transmission electron microscopy (TEM) measurements indicated dislocations emerging from r-GeO2 / sapphire interface, which reduces with thickness, developing high-quality crystallinity. Atomic resolution TEM analysis unveiled the nature of highly ordered ultra-sharp r-GeO2 / sapphire interface. This study paves the way for the realization of the promising r-GeO₂ to meet various growth-related requirements using a scalable custom designed LPCVD system
TE23D: A Dataset for Earthquake Damage Assessment and Evaluation
No full textNatural disasters, especially earthquakes, require rapid and accurate damage assessment for effective response and recovery strategies. In this paper, TE23D (Turkey Earthquakes of 6 February 2023 Dataset) dataset consisting of 1183 images and 2080 polygons labelled as damaged was developed using the satellite images covering 10 cities and ∼481,72km2 taken after the earthquakes that occurred on 6 February 2023 in Turkey, and the dataset was evaluated for benchmark results using various deep learning-based object detection techniques by designing and implementing a system to detect damaged areas. Obtaining pre-earthquake images is widely recognized in the literature as beneficial for enhancing damage detection, as it allows for a more accurate assessment of changes caused by disasters. However, in many cases, including ours, it is not feasible to access recent pre-disaster images quickly enough for immediate damage analysis. Therefore, we focused solely on using postearthquake images to identify and label damaged areas, without pre-event imagery. The dataset is designed to perform damage assessment by emphasising the labelling of areas directly affected by post-disaster imagery. To evaluate the effectiveness of this approach, we trained state-of-the-art segmentation models on the dataset, including BEiT, DPT, Mask R-CNN, MobileViT, U-Net, U-Net++, and SegFormer. Among these, the SegFormer model achieved the best performance, with 91,92% Overall Pixel Accuracy (OPA) and 74,45% Intersection over Union for the damaged class (IoUD), demonstrating that labeling damaged areas directly on post-event imagery can yield effective results for damage detection. The findings emphasize the crucial role of high-quality, targeted datasets like TE23D in accelerating disaster response efforts, particularly for earthquake-related damage. By offering a focused benchmark, this dataset enables an efficient and accurate identification of areas most severely affected by earthquakes. This capability for rapid damage assessment is essential for prioritizing emergency response efforts and directing aid to the most critical locations, ultimately helping to save lives and expedite the recovery process. While TE23D is tailored to the context of the February 2023 Turkey earthquakes, its methodology can serve as a model for similar disaster scenarios, highlighting the importance of well-curated datasets in improving the effectiveness of damage detection models across different contexts
A novel controlled release system Au-CuONP/P(MMAcoMAA)/chitosan nanocomposites: Synthesis, characterization, antimicrobial activity and in silico molecular docking
No full textThe aim of the study is to synthesize antibacterial nanocomposites containing green synthesized bimetal nanoparticles, which offers a low-cost and environmentally friendly application opportunity in order to employ in drug delivery. In the study, cotoneaster (Cotoneaster horizontalis) fruits, laurel (Laurus nobilis) and sage (Salvia officinalis) leaves grown in the natural flora of Turkey were used for the synthesis of Au-CuO nanoparticles. Chitosan/nanochitosan and P(MMAcoMAA) containing nanocomposite material was synthesized with the obtained bimetallic nanoparticles. Characterization of the prepared nanocomposite was performed by FT-IR, XRD, SEM, UV–Vis and DLS. Antibacterial activities against E. coli and S. aureus and antifungal activities against A. niger were investigated. The usability of the developed nanocomposite in controlled release systems was tested in the BSA model. The binding affinities of Au-CuO nanoparticles to E. coli β-lactamase, S. aureus TMK and A. niger Fdc1 enzymes were also determined and possible antibacterial and antifungal mechanisms were simulated by molecular docking analysis. Au-CuO NPs showed absorbance peaks between 273–276 and 542–552 nm corresponding to CuO and Au, respectively in UV–Vis analysis evaluating the presence of bimetallic NPs. The peaks observed between 567 and 602 cm−1 in all samples in FT-IR analysis proves the presence of metal-oxides. In SEM images, it was seen that Au-CuO NPs are between 10 and 90 nm and nanocomposites were homogeneously distributed porous matrix structures. 2θ values in XRD patterns of Au-CuO NPs were 38.2°, 44.5°, 64.7° and 77.7° and corresponded to Au and CuO phases, and peaks from both phases represented successful nanoparticle and nanocomposite formation containing bimetal structure. All synthesized materials showed strong antimicrobial activity, close to commercial antibiotics. Inhibition zone of nanocomposite was measured as 25 mm against E. coli, 26 mm against S. aureus and 28 mm against A. niger. MIC and MBC values of nanoparticles and nanocomposites were higher against Gram (−) bacteria. Controlled drug release was studied on the BSA model for 7 days and while the release of the chitosan-containing nanocomposite was 91 % at the end of the period, the nanochitosan sample released 88 %. The binding energies of the synthesized Au-CuO NPs to β-lactamase, TMK and Fdc1 obtained in the molecular docking analysis, were −1.68, −1.60 and −2.16 kcal/mol, respectively. The results showed that a nanomaterial with controlled release capability, antibacterial and antifungal properties that can contain proteins was produced
Enhancing biogas/biohydrogen utilization in dual-fuel engines using advanced machine learning algorithms
No full textBiodiesel obtained from waste plant resources suffers from low energy density and poor cold flow properties, which affect its performance in real-world applications. Blending waste biodiesel with biogas increases the overall energy content in the combustion chamber and improves the combustion efficiency, eventually reducing carbon emissions. By lowering NOx and enhancing combustion properties, the addition of bio-hydrogen further improves the fuel's environmental profile. To analyse and optimize these blends, the study employed an Analytic Hierarchy Process (AHP) weighted k-means clustering approach. Load, compression ratio (CR), Ignition pressure (IP), biohydrogen supply and biogas flow rate are selected as operating parameters since this influence the combustion characteristics, fuel-air mixing, and emission behaviour in diesel engines. According to Pearson's r = 0.981, there is a high beneficial correlation between brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC), and there is a moderate association (r = 0.952) between CO and NOx emissions. From the priority analysis, BTE (51%) and NOx (24%) came out to be the dominant parameters as compared to CO (17%) and BSFC (8%). The k-means clustering found the optimum combination of outcomes which are BTE of 37.5%, a BSFC of 220 g/kWh, and emissions of 1.1 g/kWh for CO and 675 ppm for NOx respectively. Optimal input settings are achieved in Dataset 18 which operates at a Load of 100%, a CR of 19, and an IP of 220 bar, utilizing 12% Hydrogen and 8% Biogas Flow Rate. Henceforth, the addition of biogas-biohydrogen in biodiesel mitigates its shortcomings by improving combustion efficiency, reducing emissions, and guaranteeing sustainable engine operation. Employing green fuels will aid in reducing fossil fuel dependency and promote sustainable energy, leading to a cleaner and greener biosphere
OPTIMIZATION OF ORGANIC PHOTODETECTORS USING SCAPS-1D SIMULATION: ENHANCING PERFORMANCE OF PBDB-T-2F BASED DEVICES THROUGH LAYER CONFIGURATION AND DOPING ADJUSTMENTS
No full textIn this study, we conducted an exploration of the optimization of various parameters of a photodetector using SCAPS-1D simulation to enhance its overall performance. The photodetector structure was modified based on the structure proposed by N.I.M. Ibrahim et al. (AMPC, 14(04), 55–65 (2024) by changing the order of the hole transport layer (HTL) and electron transport layer (ETL). Through the optimization of layer thicknesses and doping concentrations, we significantly improved the photovoltaic parameters of our optimized structure (FTO/PFN/PBDB-T-2F/PEDOT/Ag). The optimized device exhibited VOC of 1.02V, JSC of 35.20 mA/cm², FF of 84.61%, and an overall efficiency of 30.40%. Additionally, the device demonstrated a high quantum efficiency (EQ) of over 99% and responsivity peaking at 0.65 A/W, covering a broad spectral region from 300 nm to 900 nm. The results indicate the critical role of meticulous optimization in developing high-performance photodetectors, providing valuable insights into the design and fabrication of devices with superior performance characteristics
Development of an integrated energy system with CO2 capture and utilization in an industrial setting for clean hydrogen and methanol
No full textThis study introduces an innovative and environmentally friendly multigeneration system designed to significantly reduce CO2 emissions in steel production while producing clean hydrogen and methanol as two critical fuels for a sustainable energy future. By utilizing advanced carbon capture technology with monoethanolamine (MEA) absorption, the system effectively removes CO2 from steel manufacturing processes and transforms it into valuable methanol. Hydrogen production, powered by wind energy through alkaline electrolysis, plays a central role in this system, showcasing the potential of renewable energy in industrial applications. Beyond its environmental benefits, the current system delivers multiple outputs, including a substantial power output of 5735 kW, a heating effect of 772.6 kW, and a hot water supply of 55 kg/h. To further improve the system performance in specific and sustainability at large, solar energy is integrated into the design, highlighting a commitment to maximizing renewable resource utilization. Furthermore, the system achieves a hydrogen generation rate of 5.75 kg/h and a methanol production rate of 97.95 kg/h. With energy and exergy efficiencies of 60.24 % and 56.58 %, respectively, this system brings a unique approach to industrial decarbonization. Its design not only demonstrates the feasibility of integrating renewable resources into energy-intensive industries but also offers a practical pathway toward cleaner, more sustainable production practices for a greener future