AGU GCRIS Premium Database (Abdullah Gül University)
Not a member yet
2706 research outputs found
Sort by
Pressure-Driven Structural Evolution of Amorphous InN
No full textThrough constant-pressure ab initio simulations, we have uncovered high-pressure phase transformations in amorphous indium nitride for the first time. Our results reveal a distinct two-step progression under compression. Initially, a polyamorphic transition occurs, where the low-density amorphous (LDA) phase transforms into a high-density amorphous (HDA) phase. This HDA structure remains stable in some pressure range and then crystallization initiates, leading to a rocksalt configuration. Upon decompression, the HDA phase reverts to an amorphous network with a slightly higher density and coordination number than the initial LDA state.Abdullah Gl University Support FoundationThe author extends gratitude to the Abdullah Guel University Support Foundation for their support. The author acknowledges the computing resources and time generously provided by TUEBITAK ULAKBIM High Performance and Grid Computing Center (TRUBA resources)
Semi Analytical Study and Calculation of Magnetic Flux Density Created by Rectangularly Shaped Planar Coils With Rounded Corners
No full textIn this paper, magnetic flux density induced by a rectangularly shaped single-turn planar coil with rounded corners is investigated. Starting from Biot-Savart law analytical derivations are obtained and contributions of straight edges and rounded corners of a coil to a flux density at a point in a three-dimensional space are formulated. In the derived formula the magnetic flux density generated by a coil with rounded corners is expressed as a function of the coil's geometrical parameters including start and end points of the roundings together with the center of the circles that contain arcs at the corners and observation point coordinates. Magnetic flux density distribution of rectangular coils with different sizes and roundings at the corners were calculated on distinct planes by using the derived formula. Also, FEM based electromagnetic simulations and measurements were done for the same coils. The results obtained in the analytical calculations, FEM calculations and measurements are in a good agreement
Future of Clean Cooking Energy Access in Emerging Economies by 2030
No full textThis study assesses the future of clean energy and technology access for cooking in emerging economic blocs—BRICS, MINT, ASEAN, and MENA—through 2030. Cooking contributes 3% of global greenhouse gas emissions, with over half of household emissions coming from cooking. Therefore, clean cooking energy is critical for sustainability and human health. The study aims to evaluate the likelihood of achieving the UN Sustainable Development Goal of universal clean cooking energy access by 2030 and the 2050 net-zero emissions target. Machine learning techniques, such as support vector regression, gradient boosting, and linear regression, alongside an ensemble approach, provide forecasts for these regions. The findings show a varied outlook. Within ASEAN, two countries are expected to reach 100% clean energy access for cooking by 2030, while two are likely to experience a decline. The MENA region shows stronger progress, with eight countries expected to meet the 2030 target. Among BRICS countries, only India is projected to reach full accessibility, while Russia faces a decline. The MINT countries face challenges, with none expected to meet the target, and Nigeria is projected to experience a decrease in clean energy access. The study concludes that the current trajectory makes achieving the 2030 Sustainable Development Goals and the 2050 net-zero emissions target unlikely for these regions. Policymakers must reassess their strategies and learn from successful countries to improve outcomes. © 2025 Elsevier B.V., All rights reserved
A Comprehensive Investigation into Strip Steel Defect Detection Using Traditional Machine Learning and Deep Learning Models
No full textThe steel manufacturing sector places great importance on guaranteeing the quality of strip steel products, which has led to a thorough investigation of defect detection approaches. This work conducts a comparative analysis of traditional machine learning and deep learning models to determine their efficacy in detecting defects in strip steel. Our analysis is based on a dataset that includes a variety of images of strip steel surfaces showing different types of defects. In this work, we adopt image preprocessing techniques to improve the quality of input images prior to the application of classification methods. We employ traditional ML algorithms including Support Vector Machine and Random Forest, and deep learning model AlexNet Convolutional Neural Networks for effective defect classification. Consequently, we present comparative evaluations that highlight the strengths and weaknesses of each approach, considering accuracy scores
Effect of Multi-Cell Approach on Crashworthiness Performance of 3D-Printed Thin-Walled Structures Under Lateral Compression Loading for Unmanned Aerial Vehicle Applications
No full textAtahan, Mithat Gokhan/0000-0002-8180-5876;Recent technological advancements in unmanned aerial vehicles have led to their use in various military and civilian applications. However, weather conditions, operator faults, and electronic or mechanical problems can result in unmanned aerial vehicle accidents. In the event of an accident, energy-absorbing structures can be placed in specific regions of vehicles to protect sensitive and costly cameras, sensors, and cargo from damage, while also preserving the vehicle's structural integrity. In this study, thin-walled energy absorbers with circular, square, hexagonal, and reentrant geometries were proposed, and the experimental investigation focused on the effect of increasing the number of cells on their crashworthiness performance and deformation mechanisms. Lateral compressive load was applied to thin-walled structures produced by fused deposition modeling technology using advanced polylactic acid filament. Experimental results showed that the triple-cell reentrant thin-walled structure demonstrated promising results for unmanned aerial vehicle applications, as it exhibited the highest mean crushing force, energy absorption, and specific energy absorption values. Thanks to the unique geometry of the reentrant structure, a gradual collapse mode was observed, and as a result, the triple-cell reentrant structure exhibited high energy absorption performance
Plant-Mediated Sustainable Nanomaterials for Biomedical and Optical Applications
No full textBitki aracılı nanomalzemeler biyomedikal ve optik çalışmalarda önemli bir ilgi görmüştür. Bu nedenle, biyomedikal ve optik uygulamalarda iki farklı bitki özütünü (Hypericum Perforatum ve Peganum Harmala) araştırdık. Bu tezin ilk bölümünde, çevre dostu yeşil sentez yöntemi ile Hypericum Perforatum kullanarak çinko oksit nanopartikülleri (ZnO NP'leri) sentezledik. Nanopartiküller UV-Vis spektroskopisi, X-ışını kırınımı (XRD), taramalı elektron mikroskobu (SEM) kullanılarak karakterize edildi. Bu nanopartiküllerin antikanser etkisi, hücre kültürü çalışmalarıyla insan karaciğer kanseri hücresinde (Hep-G2) test edildi. Son olarak, bu ZnO NP'ların anti bakteriyel aktivitesi çalışıldı. Hücre kültürü çalışmaları, hücre canlılığının nanoparçacık dozuna bağlı bir inhibisyonu olduğunu ve daha yüksek konsantrasyonlarda belirgin sitotoksik etkisi olduğunu gösterdi. Bu çalışmayla çinko oksit nanoparçacıklarının karaciğer kanseri tedavisi için terapötik olarak son derece yüksek potansiyele sahip olduğunu gösterdik. Bu tezin ikinci bölümünde, Peganum harmala özütü kullanarak kâğıt bazlı renk dönüştürücüler tasarladık. Bitki özütünün katı haldeki yüksek kuantum verimi nedeniyle, bitki özütünden elde edilen floresan biyomoleküller kristal bazlı (sükroz ve KCl kristalleri) ve selüloz elyaf bazlı (pamuklu pedler ve kurutma kağıtları) matrislere gömüldü. Optil karekterizasyonlar, lif kağıtlarının yüksek kuantum verimliliğine sahip olduğunu gösterdi. Konseptin kanıtı olarak, P. harmala özütü gömülü lif kâğıdı bir LED üzerinde renk dönüştürücü olarak kullanıldı ve 21.9 lm Welect−1 ışıma verimliliğinde mavi renkte ışıyan bir cihaz elde edildi. Sonuçlar, bu çevre dostu bitki bazlı malzemelerin, uygun maliyetli ve sürdürülebilir alternatifler olarak şu anda kullanılan renk dönüştürücülerin yerini alabileceğini gösterdi.Plant-mediated nanomaterials have gained significant attention in biomedical and optical studies because of their biological and optical properties. For that reason, we focused on two different plant extracts (Hypericum perforatum and Peganum harmala) in biomedical and optical applications. In the first part of this thesis, we synthesized zinc oxide nanoparticles (ZnO NPs) using Hypericum perforatum via an environmentally friendly green synthesis method. Nanoparticles were characterized using UV-Vis spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive X-ray (EDX) analyses. The anticancer effect of these nanoparticles was investigated against the human liver cancer cell (Hep-G2) with cell culture studies. Finally, the antibacterial activity of the ZnO NPs was studied. Cell viability studies demonstrated a NP dose-dependent inhibition of cell viability along with significant cytotoxic effects at higher concentrations. With this study, we showed that these ZnO NPs have extremely high potential as therapeutics for liver cancer treatment. In the second part of this thesis, we designed a paper-based color converter using Peganum harmala extract for solid-state lighting. Due to the high quantum yield of the plant extract in the solid state, fluorescent biomolecules from the plant extract were wrapped into crystal-based (sucrose and KCl crystals) and cellulose fiber-based (cotton pads and drying papers) matrices. Optical characterizations demonstrated high quantum efficiency in the fiber paper group. As a proof-of-concept demonstration, P. harmala extract embedded fiber paper was used as a color converter on an LED, and the device revealed a blue emission with a luminous efficiency of 21.9 lm Welect−1. The results showed that these environmentally friendly plant-based materials can replace the currently used color converters as their cost-effective and sustainable alternatives
Incorporating Worker Heterogeneity in Flexible Flow Shop Environment
No full textWe study the flexible flow shop scheduling problem with the heterogeneous worker assignment. In many real-life manufacturing systems with flow shop environments, one of the fundamental scheduling challenges that needs to be addressed is job sequences across multiple workers. In addition, the manufacturing system may require workers to have different skills at various stages during their assignment. Therefore, worker availability at each stage may vary during the scheduling horizon. Unlike traditional flexible flow shop scheduling problem, where homogeneous workers are assumed, we consider workers with different skill levels, capabilities, and capacities. We present a mixed integer linear programming model to find the optimal sequence of job assignments, guaranteeing that jobs follow their predefined operation sequence while assigning workers with various skill sets in a flexible flow shop environment. The proposed model is tested at a battery manufacturing company. By analyzing the solution, we confirm its capability to represent the problem accurately. The proposed model offers a systematic scheduling approach for a flexible flow shop environment with a heterogeneous workforce and can be implemented in other industries. © 2025 Elsevier B.V., All rights reserved
Empowering Dialogic Feedback in FLW With LLM
No full textThis doctoral study aims to address significant challenges in foreign/second language (L2) writing (FLW/SLW) instruction by leveraging artificial intelligence. The central problem this study addresses is the lack of active learner engagement and the resource-intensive nature of traditional feedback methods, which can lead to teacher burnout and ineffective student learning outcomes. Existing feedback practices often fall short in providing detailed, timely, and comprehensible feedback, which hinders students' ability to critically analyze and act upon it. The study proposes a shift from monologic to dialogic feedback, facilitated by large-language models (LLMs), to promote continuous iterations of editing and rewriting, thus enhancing linguistic and cognitive development. The goal is to reveal the potential of LLMs in facilitating effective dialogic feedback approaches in L2 writing. To achieve this, the study aims to develop a theoretical framework and design principles for AI-enabled dialogic feedback systems, create an AI-writing tool based on this framework, and test its effectiveness through experimental sessions. Ultimately, the study seeks to understand the impact of AI-enhanced feedback on L2 learners' writing progress, their perceptions and experiences, and the emerging interaction patterns during the feedback process. This research holds the potential to transform feedback practices in language learning, contributing to more effective and engaging L2 writing instruction. © 2025 Elsevier B.V., All rights reserved
AI-Assisted Optimization of Shot Peening Process and Investigation of the Effects of Secondary Processes on Hydrogen Embrittlement Resistance and Mechanical Performance of SLM-manufactured AlSi10Mg Alloy
No full textBu tez, Seçici Lazer Ergitme (SLM) yöntemiyle üretilen AlSi10Mg alaşımlarında bilyalı dövme işlemlerinin optimizasyonunu ve hidrojen gevrekliğinin azaltılmasını araştırmaktadır. Birinci bölümde, süreç optimizasyon yöntemleri (ör. Taguchi, Box-Behnken), metal katkı üretimdeki (AM) sorunlar (artık gerilme, gözeneklilik) ve hidrojen gevrekliğinin mekanizmaları ile test yöntemleri ele alınmıştır. İkinci ve üçüncü bölümler, Almen testleriyle doğrulanan yapay zeka tabanlı yaklaşımlarla bilyalı dövme yoğunluğunun optimizasyonunu ve Bell 412EP ile Piper PA-32R gibi gerçek havacılık arızalarını inceleyerek hidrojen gevrekliğinin bileşenlerdeki etkilerini analiz etmektedir. Dördüncü ve beşinci bölümler, SLM ile üretilen AlSi10Mg alaşımlarının mekanik performansına, gerinim hızı ve işlem sonrası uygulamaların (bilyalı dövme, ısıl işlem) etkilerini değerlendirmiş ve yorulma direncinde önemli iyileşmeler göstermiştir. Ayrıca hidrojen gevrekliğini önlemek için ileri düzey stratejiler önerilmiştir. Tez, artırılmış malzeme güvenilirliği ve sürdürülebilirliğin toplumsal faydalarını vurgulamakta ve yapay zeka destekli yöntemler ile üretimde gerçek zamanlı izleme sistemleri üzerine gelecekteki araştırmaları önermektedir.This thesis investigates the optimization of shot peening processes and the mitigation of hydrogen embrittlement in AlSi10Mg alloys produced via Selective Laser Melting (SLM). Chapter one reviews process optimization techniques (e.g., Taguchi, Box-Behnken), additive manufacturing (AM) challenges like residual stress and porosity, and introduces hydrogen embrittlement mechanisms and testing methods. Chapters two and three focus on optimizing shot peening intensity using AI-based approaches validated by Almen tests and analyze real-world aviation failures, such as Bell 412EP and Piper PA-32R, to highlight hydrogen embrittlement's impact on component degradation. Chapters four and five explore the effects of strain rates and post-processing treatments, including shot peening and heat treatment, on mechanical performance, demonstrating significant improvements in fatigue resistance. Advanced strategies for mitigating hydrogen embrittlement are also proposed. The thesis concludes by emphasizing the societal benefits of enhanced material reliability and sustainability, suggesting future research into AI-assisted methods and real-time monitoring systems in manufacturing
Possible Drug-Drug Interactions Between Mesalamine and Tricyclic Antidepressants Through CYP2D6 Metabolism - in Silico and in Vitro Analyses
No full textMesalamine (mesalazine, 5-aminosalicylic acid, 5-ASA) is an essential anti-inflammatory agent both used for therapy and as a remission control in patients with inflammatory bowel diseases (IBD) such as ulcerative colitis (UC). Tricyclic antidepressants (TCAs) are used to alleviate remaining symptoms in patients already receiving IBD therapy or with quiescent inflammation. The cytochrome P4502D6 enzyme is involved in the metabolism of TCAs. Hence, it is crucial to investigate the role of CYP2D6 in 5-ASA metabolism. Initially, in silico analysis involving the docking of 5-ASA to CYP2D6 and molecular dynamics simulations was conducted. Next, the rate of O-demethylation of a nonfluorescent probe 3-[2-(N,N-diethyl-N-methylammonium)-ethyl]-7-methoxy-4-methylcoumarin (AMMC) into a fluorescent metabolite AMHC (3-[2-(N,N-diethyl-N-methylammonium)ethyl]-7-hydroxy-4-methylcoumarin) was optimized with baculosomes co-expressing human CYP2D6 and human P450 oxidoreductase (hCPR) to monitor CYP2D6 activity in a microtiter plate assay. The apparent Km and Vmax were found to be 1.30 μM and 32.68 pmol/min/mg of protein for the O-demethylation of AMMC to AMHC, and the reaction was linear for 40 min. Then, nonselective inhibition of CYP2D6 activity with various concentrations of 5-ASA was detected. Finally, the conversion of AMMC to metabolites was analyzed by HPLC-ESI-MS/MS spectrometry, and none were identified. Thus, this study suggests that concurrent use of mesalamine with TCA may lead to adverse effects, and CYP2D6 genotyping should be routinely performed on these patients to eliminate possible threats. © 2025 Elsevier B.V., All rights reserved