Atılım Academic Archive (Atılım University)
Not a member yet
9347 research outputs found
Sort by
Effects of Solutionizing and Aging Conditions on the Hardness, Microstructure and Wear Resistance of Cast Fe-Mn-Al-C-Si Lightweight Steel
Get PDFBu çalışma, çözündürme ve yaşlandırma ısıl işlemlerinin östenit matrisli deneysel döküm Fe-Mn-Al-C-Si hafif çeliğinin mekanik özellikleri ve içyapısı üzerine etkilerini incelemeyi amaçlamıştır. Hedeflenen kompozisyonda ve 6,60 g/cm³ yoğunluğunda dökümler yapılmıştır. Çözündürme işlemleri 950°C-1150°C arasında 2, 4 ve 16 saat uygulanmış, ardından 400°C ile 700°C arasında 4, 16 ve 64 saat yaşlandırma işlemleri gerçekleştirilmiştir. Isıl işlemlerin etkilerini değerlendirmek amacıyla makro/nano sertlik ölçümleri, optik mikroskopi, enerji saçılım spektroskopisi ve elektron geri saçılma kırınımı ile donatılmış elektron mikroskobu, x-ışını kırınım analizleri ve aşınma testleri yapılmıştır. Elde edilen verilere göre, yaşlandırma öncesinde 1000°C'de 4 saat süreyle uygulanan çözündürme ısıl işleminin iç yapısal kararlılık ve mekanik özellikler açısından en uygun sonucu verdiği görülmüştür. 400°C–700°C sıcaklık aralığında 16 saat süreyle uygulanan yaşlandırmanın özellikle sertlik artışı açısından en uygun koşul olduğu söylenebilir. Yaşlandırma işlemleri içyapı içerisinde sertlik artışına katkıda bulunan κ-karbürlerin oluşumuyla sonuçlanmıştır. Ancak, yaşlandırmanın çok uzun süreyle yapılması mekanik özellikleri olumsuz etkilemesi nedeniyle istenmeyen tane sınırı çökeltilerinin oluşumuna yol açmıştır.This study aimed to investigate the effects of solutionizing and aging treatments on the mechanical properties and microstructure of an experimentally cast Fe-Mn-Al-C-Si lightweight steel of austenite matrix. Castings of target composition were made with a density of 6.60 g/cm³. Solutionizing treatments were applied at 950°C-1150°C for 2, 4, and 16 hours, followed by aging at temperatures ranging from 400°C to 700°C for 4, 16, and 64 hours. Macro/nano hardness measurements, optical microscopy, electron microscopy with energy-dispersive spectroscopy, electron backscatter diffraction, x-ray diffraction analyses, and wear tests were performed to evaluate the effects of heat treatments. Based on the obtained data, it was found that solution treatment at 1000°C for 4 hours prior to aging provided the optimum results in terms of microstructural stability and mechanical properties. Aging for 16 hours within the 400°C–700°C temperature range was suggested as the most promising condition, particularly for hardness improvement. Notably, aging treatments resulted in the formation of κ-carbides within the microstructure, contributing to hardness enhancement. However, aging for even longer times induced undesirable grain boundary precipitations, which adversely affected the mechanical properties
Applications of Artificial Intelligence as a Prognostic Tool in the Management of Acute Aortic Syndrome and Aneurysm: A Comprehensive Review
No full textAcute Aortic Syndromes (AAS) and Thoracic Aortic Aneurysm (TAA) remain among the most fatal cardiovascular emergencies, with mortality rising by the hour if diagnosis and treatment are delayed. Despite advances in imaging and surgical techniques, current clinical decision-making still relies heavily on population-based parameters such as maximum aortic diameter, which fail to capture the biological and biomechanical complexity underlying these conditions. In today's data-rich era, where vast clinical, imaging, and biomarker datasets are available, artificial intelligence (AI) has emerged as a powerful tool to process this complexity and enable precision risk prediction. To date, AI has been applied across multiple aspects of aortic disease management, with mortality prediction being the most widely investigated. Machine learning (ML) and deep learning (DL) models-particularly ensemble algorithms and biomarker-integrated approaches-have frequently outperformed traditional clinical tools such as EuroSCORE II and GERAADA. These models provide superior discrimination and interpretability, identifying key drivers of adverse outcomes. However, many studies remain limited by small sample sizes, single-center design, and lack of external validation, all of which constrain their generalizability. Despite these challenges, the consistently strong results highlight AI's growing potential to complement and enhance existing prognostic frameworks. Beyond mortality, AI has expanded the scope of analysis to the structural and biomechanical behavior of the aorta itself. Through integration of imaging, radiomic, and computational modeling data, AI now allows virtual representation of aortic mechanics-enabling prediction of aneurysm growth rate, remodeling after repair, and even rupture risk and location. Such models bridge data-driven learning with mechanistic understanding, creating an opportunity to simulate disease progression in a virtual environment. In addition to mortality and growth-related outcomes, morbidity prediction has become another area of rapid development. AI models have been used to assess a wide range of postoperative complications, including stroke, gastrointestinal bleeding, prolonged hospitalization, reintubation, and paraplegia-showing that predictive applications are limited only by clinical imagination. Among these, acute kidney injury (AKI) has received particular attention, with several robust studies demonstrating high accuracy in early identification of patients at risk for severe renal complications. To translate these promising results into real-world clinical use, future work must focus on large multicenter collaborations, external validation, and adherence to transparent reporting standards such as TRIPOD-AI. Integration of explainable AI frameworks and dynamic, patient-specific modeling-potentially through the development of digital twins-will be essential for achieving real-time clinical applicability. Ultimately, AI holds the potential not only to refine risk prediction but to fundamentally transform how we understand, monitor, and manage patients with AAS and TAA
Effect of Freezing Phenomenon on Gait Parameters and Pedobarographic Pressure Distribution in Patients with Parkinson's Disease
No full textBackground This study aimed to examine the gait parameters of individuals with Parkinson's Disease with and without freezing of gait (FoG) on the treadmill, where freezing difficulty decreases. Methods Our study included three groups of demographically similar individuals: 15 patients with Parkinson's disease without freezing of gait, 14 patients with Parkinson's disease with freezing of gait, and 14 healthy controls. Spatiotemporal parameters of gait and static-dynamic pedobarographic pressure distribution analysis were performed using a computerized gait evaluation system (on a treadmill). Results The step length, gait speed and gait phase duration of the freezing of gait group were lower than the other groups (p 0.05), pedobarographic pressure distribution during gait was different between groups (p < 0.05). Conclusion In patients with freezing of gait, gait speed was lower, step length and single support phase were shorter, and double support phase was longer. While the amount of load on the backfoot was lower in both Parkinson's groups, the amount of load on the forefoot was lower only in patients with freezing of gait. Even if freezing of gait did not occur, it was observed that the gait characteristics of Parkinson's disease with freezing of gait were different from those of Parkinson's disease without freezing of gait
Recent Trends on Ulam–Hyers Stability Results of Fixed Point Problems
No full textFixed point theorems have been extensively used in recent years to prove the Ulam–Hyers stability results of functional equations. In this chapter, we collect novel Ulam–Hyers stability results obtained by using well-known fixed point theorems in the setting of a complete metric space. We focus on several types of functional equations and inclusions along with some applications on the stability of the specific integral and differential equation. © 2026 by World Scientific Publishing Co. Pte. Ltd. All rights reserved
Penta-Graphene/SnS2 Heterostructures with Z-Scheme Charge Transfer for Efficient Photocatalytic Water Splitting
No full textThe present study explores the photocatalytic potential of penta-graphene (PG) and SnS2 monolayers, along with their heterostructures (PG/SnS2), using Density Functional Theory (DFT). Structural analysis confirms that the PG/SnS2 heterostructure exhibits enhanced stability, efficient charge separation, and suitable band alignment. Optimized lattice parameters (3.66 & Aring; for PG and 3.88 & Aring; for SnS2) closely matched literature values, while ab initio molecular dynamics (AIMD) confirmed thermodynamic stability at 300 K. The heterostructure's band gap of 2.75 eV (HSE method) supports visible light absorption, and the band edge positions enable hydrogen and oxygen evolution reactions across pH 0 to 6. Optical analysis reveals significant visible-light absorption with an optical band gap of 1.43 eV. Additionally, this study identifies a Z-scheme charge transfer mechanism in the PG/SnS2 heterostructure, facilitated by an internal built-in electric field that drives directional charge migration, effectively enhancing electron-hole separation and suppressing recombination losses. This Z-scheme mechanism optimizes redox reactions, making PG/SnS2 a highly efficient photocatalyst for solar-driven hydrogen production. Furthermore, the effect of water solvent is investigated, and it reveals that this heterostructure is stable under water solvent, having suitable band edges for the photocatalytic water splitting. These findings highlight the PG/SnS2 heterostructure as a promising candidate for sustainable hydrogen generation, offering a new perspective for the design of next-generation 2D photocatalytic materials
Analysis of Theb(s)→t(jp=2-) Transition in Light Cone Qcd Sum Rules
No full textThe semileptonic B & eth;s & THORN; -* T & eth;JP 1/4 2-& THORN;l & thorn;l- decays induced by flavor changing neural currents are investigated within the light cone quantum chromodynamics (QCD) sum rule method in the leading order of O & eth;as & THORN;. We apply the B meson distribution amplitudes up to twist-4 and calculate the relevant form form factors then adopted in the calculations of the corresponding widths. The present results can be used in future experiments for studying the properties of JP 1/4 2- tensor mesons.Science Citation Index Expande
Cultural Logics of Honor, Face, and Dignity as Moderators of the Relationship Between Group Process and Pro-Migrant Collective Action Intentions
No full textBesta, Tomasz/0000-0001-6209-3677Although group identification, efficacy, and injustice appraisals are well-established predictors of collective action support, contextual factors are rarely examined. We address this oversight in preregistered study by testing whether country-level norms moderate the relationships identity, anger at injustice, and efficacy have with support for pro-immigrant solidarity collective action using data from 22 countries (N = 4615). Given that cultures that emphasize honor and face prioritize harmony and social cohesion over conflict, we expected that honor codes and face orientation would attenuate the links identity, injustice, and efficacy have with collective action support. Results showed that identification, efficacy, and anger at injustice were linked to collective action intentions in most countries, but honor codes attenuated the relationship between anger and collective action intentions. We further discuss the implications and limitations of these results in light of cross-cultural studies of pro-immigrant attitudes and actions. Overall, our findings complement research on predictors of collective action and the dual-chamber model of collective action by presenting potential cultural constraints.Polish National Agency for Academic Exchange [BPN/BEK/2021/2/00001/U/00001]; HELP University Internal Research Grant Scheme (IRGS) [21-01-003]; Ministry of Science and Innovation of the Government of Spain - MCIU/AEI [PID2022-141182NB-I00]; MCIN/AEI [RYC2022-035896-I]; FSE+; National Science Centre in Poland [2021/43/B/HS6/00020]Data gathering for this research was funded by a grant from the Polish National Agency for Academic Exchange (BPN/BEK/2021/2/00001/U/00001) awarded to Tomasz Besta, data collection in Malaysia was supported by the HELP University Internal Research Grant Scheme (IRGS) 2021 (21-01-003) awarded to Associate Professor Eugene Tee Yu Jin. Work on this manuscript was also supported by Ministry of Science and Innovation of the Government of Spain by Grant PID2022-141182NB-I00, funded by MCIU/AEI/10.13039/501100011033 FEDER/UE and RYC2022-035896-I funded by MCIN/AEI/10.13039/501100011033 and FSE+, and by the National Science Centre in Poland, project no. 2021/43/B/HS6/00020 granted to Tomasz Besta.Emerging Sources Citation Inde
Financial Constraints and the ESG-Firm Performance Nexus in the Automotive Industry: Evidence From a Global Panel Study
No full textThis study examines the complex relationship between environmental, social, and governance (ESG) and financial performance in the automotive industry, with a particular focus on how financial constraints shape this relationship. Using a global data set for the period 2008 to 2023 and employing a range of panel data techniques, including those addressing endogeneity concerns, we find that higher ESG scores positively affect financial performance. Specifically, a one-point rise in ESG score corresponds to an estimated 1-1.7% increase in the market-to-book ratio, with the effect reaching approximately 1.6% for firms facing financial constraints. These findings highlight the economic significance of ESG engagement, particularly for resource-constrained companies. The novelty of this study is that it focuses on the automotive sector, an industry with limited ESG-specific research, and that it makes a theoretical contribution by linking ESG performance outcomes to financial constraints, an angle largely overlooked in prior research. The findings offer critical policy insights, emphasizing the strategic importance of ESG initiatives for value creation under varying financial conditions.Science Citation Index Expanded - Social Science Citation Inde
ISAR Imagıng of Drone Swarms Usıng mmWave Radar
No full textTeknolojide drone ve drone sürülerinin kullanımının artmasıyla birlikte, anti-drone teknolojilerinin kullanımı önemli ölçüde artmıştır. Ancak, sınırlı görüş alanına sahip senaryolarda drone ve drone sürülerinin tespiti literatürde kalıcı bir zorluk olmaya devam etmektedir. Bu tez, milimetre dalga (mmWave) frekans bantlarında yeniden oluşturulan drone sürülerinin Ters Sentetik Açıklıklı Radar (ISAR) görüntülerinin, oluşumlarına, boyutlarına ve yük yapılandırmalarına göre sınıflandırılmasına odaklanmaktadır. Drone sürülerinin ISAR görüntüleri, ANSYS Yüksek Frekanslı Yapısal Simülatör (HFSS) elektromanyetik simülasyon yazılımı kullanılarak üretilmiştir. Sürü yapıları, quadcopter dronlar kullanılarak modellenmiş ve oluşum tipleri, çizgi, çarpı, kare ve üçgen gibi temel geometrik şekillerle tasarlanmıştır. Sürülerdeki dronlar, orta, küçük ve mini olmak üzere üç boyutta kategorize edilmiştir. Ek olarak, yük dronları sürü yapılandırmalarına dahil edilmiştir. Yüksek çözünürlüklü ISAR görüntüleri elde etmek için radar ve simülasyon parametreleri optimize edilmiştir. Veri setini genişletmek için, ISAR görüntüleri çeşitli bakış açılarında (0° ila 350° arasında 10° artışlarla) oluşturulmuştur. ISAR görüntüleri kullanılarak sürü oluşumu tiplerinin belirlenmesi, görüntü tanıma aşamasında bir Evrişimsel Sinir Ağı (CNN) aracılığıyla gerçekleştirildi. Bunu takiben, nesne algılama aşamasında Sadece Bir Kez Bak (YOLO) algoritması kullanılarak drone boyutu ve yük tespiti gerçekleştirildi. Bu tezde elde edilen sonuçlar oldukça ümit vericidir. Genişletilmiş bir veri seti ve tespit algoritması sunarak, bu çalışma literatüre önemli katkıda bulunmaktadır.With the increasing use of drones and drone swarms in technology, the importance of anti-drone technologies has grown significantly. However, detecting drones and drone swarms in scenarios with limited fields of view remains a persistent challenge in the literature. This thesis focuses on the classification of Inverse Synthetic Aperture Radar (ISAR) images of drone swarms reconstructed at millimeter-wave (mmWave) frequency bands based on their formation, size, and payload configurations. ISAR images of drone swarms were generated using the ANSYS High-Frequency Simulation Software (HFSS) electromagnetic simulation software. Swarm structures were modeled using quadcopter drones, and the formation types were designed with basic geometric shapes, such as line, cross, square, and triangle. The drones in the swarms were categorized into three sizes: medium, small, and mini. Additionally, payload drones were included in the swarm configurations. Radar and simulation parameters were optimized to obtain high-resolution ISAR images. To expand the dataset, ISAR images were reconstructed at various look angles (from 0° to 350° in 10° increments). The determination of swarm formation types using ISAR images was carried out through a Convolutional Neural Network (CNN) in the image recognition phase. The detection of drone size and payload configurations was conducted using the You Only Look Once (YOLO) algorithm in the object detection phase. The results achieved in this thesis are highly promising. By presenting an extended dataset and detection algorithm, this work contributes significantly to the literature and advances the field of drone swarm detection
Chemical Mechanical Polishing as an Alternative Surface Treatment Technique for Corrosion Prevention of Carbon Steel in an Acidic Medium
No full textAli Abed Al-Timimi, Buthainah/0000-0001-5763-8396Chemical mechanical polishing (CMP) has been a standard technique in semiconductor manufacturing for achieving smooth surfaces. CMP utilizes a synergistic interplay of chemical and mechanical interactions to achieve the desired removal rates, selectivity, and ultimately planarity with different substrate materials. In this study, the impact of CMP on the surface properties of steel used in the petroleum industry was examined, with a focus on its corrosion behavior posttreatment. Steel samples were subjected to CMP with and without an oxidizer in a silica-based slurry, and their surface characteristics were compared to those of samples polished mechanically. The addition of an oxidizer to the slurry resulted in increased material removal rates and the formation of an oxide layer on the surface; this phenomenon was not observed in CMP without an oxidizer. However, in mechanical polishing, the action of silicon carbide grains on the steel surface led to an increase in the removal rate but caused a decrease in its corrosion resistance. Compared with other treatments, the oxide layer provided a good protective barrier against corrosion and improved the corrosion resistance of the steel substrate. Based on the results from the practical study, an improvement in the corrosion resistance properties was observed due to the chemical reaction of the oxidizer and the mechanical action of the silica nanoparticles; these results showed the importance of chemical mechanical polishing as an alternative method to reduce the corrosion of steel in acidic environments. Additionally, the effect of hydrogen peroxide in a silica slurry with respect to the wettability, surface roughness, and hardness of steel was examined using contact angle measurements, profilometry, scanning electron microscopy, and microhardness tests.Science Citation Index Expande