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Abrasive and Oxidative Wear Mechanisms on Additively Manufactured Ti-6Al Alloy Against Al2O3: Effect of Microstructures and Hardness
No full textThe increasing adoption of additive manufacturing (AM) in biomedical applications demands a deeper understanding of the wear behavior of AM Ti-6Al-4V alloy. The microstructures obtained from AM processes often require further tailoring through heat treatments to meet application demands. In this regard, this study investigates the dry sliding wear behavior of Ti-6Al-4V alloy produced by laser and electron beam powder bed fusion. Various microstructures were obtained through different heat treatments performed below and above the alloy (3 transus temperature. The microstructures were characterized by optical microscopy, scanning electron microscopy, and Vickers hardness testing. Wear tests were conducted under reciprocating sliding using an Al2O3 counter-body with different normal loads (1-5 N), and specific wear rates and coefficients of friction were analyzed. Results revealed different hardness values between 356.6 + 8.5 and 284.1 + 11.0 HV, associated with different microstructures, varying from a fully martensitic alpha ' structure to lamellar alpha+(3 structures with increasing heat treatment temperature. The wear mechanisms were a combination of abrasive and oxidative, with oxide debris contributing to tribolayer formation. Higher normal loads favored tribolayer formation, reducing the coefficient of friction from 0.664 + 0.021 to 0.544 + 0.004 and from 0.680 + 0.017 to 0.581 + 0.015 for the highest and lowest hardness conditions, respectively, and specific wear rate. The findings highlight that both hardness and tribolayer formation govern wear resistance, necessitating further studies on load effects in AM Ti6Al-4V alloys.Sao Paulo Research Foundation (FAPESP) [2020/05612-8, 2021/06516-51]; Ministerio de Ciencia e Innovacion (MICINN) [PID2020-112878RB-100/AEI/10.13039/501100011033]This work was supported by the Sao Paulo Research Foundation (FAPESP) [grant numbers #2020/05612-8 and #2021/06516-51; the CTI's Open Labs-Multiple Users and Shared Facilities from LAprint, CTI Renato Archer, research institution from MCTI; and partially funded by Ministerio de Ciencia e Innovacion (MICINN) , PID2020-112878RB-100/AEI/10.13039/501100011033 Development of Antimicrobial metallic surfaces. G. A. Longhitano would like to thank the Postdoctoral Researcher Program (PPPD) at FEM/UNICAMP
Erratum Medical Students’ Awareness of Infodemic and Their Infodemic Management Capacity: A Descriptive Study from Türkiye Volume 23, Issue 1 (2025), Doi: 10.20518/tjph.1525235
No full textPublished in Turkish Journal of Public Health, Volume 23, Issue 1 (2025), DOI: 10.20518/tjph.1525235-Medical students’ awareness of infodemic and their infodemic management capacity: A descriptive study from Türkiye a correction has been published for this article due to a technical problem raising from the publisher. © 2025 Elsevier B.V., All rights reserved
Faz Gürültüsünün Radar Dalga Formları Üzerindeki Etkisi ve Performans Analizi
No full textIsik UniversityThis paper presents a detailed analysis of the impact of phase noise on commonly used radar waveforms. In order to enhance range resolution and increase detection probability in pulsed radar systems, the effects of phase noise originating from oscillators on both the transmitter and receiver sides are modeled through simulations developed in the MATLAB environment. The study focuses on the generation of linear and nonlinear frequency modulated pulses and investigates how incremental increases in oscillator-induced phase noise affect these waveforms. The impact of phase noise is examined through the outputs of the matched filter at the radar receiver, with frequency spectrums and matched filter responses analyzed. Additionally, key performance metrics, such as the Peak-to-Average Noise Ratio (PANR), are extracted to assess system performance degradation due to phase noise. The findings provide valuable insights into understanding the influence of phase noise in radar systems and its implications for waveform selection and system design. © 2025 Elsevier B.V., All rights reserved
Dynamic Behavior Analysis Through Novel Windows Event Logs with Machine Learning
No full textTeknolojik gelişmelerin hız kazanmasıyla birlikte siber saldırılar ve zararlı yazılım tehditleri giderek karmaşıklaşmakta ve daha tehlikeli hale gelmektedir. Bu durum, bilgi güvenliği alanında yenilikçi yaklaşımların ve etkili çözümlerin geliştirilmesi gerekliliğini ortaya koymaktadır. Bu tez çalışması, zararlı yazılım analizinde dinamik yöntemlerin etkinliğini artırmayı amaçlayarak, özellikle Windows işletim sistemine ait olay kayıt verilerinin detaylı incelenmesine odaklanmaktadır. Çalışmanın temel hedefi, izole edilmiş sanal ortamlarda zararlı ve zararsız yazılımların çalışma anında oluşturdukları işletim sistemi kayıt verilerinden anlamlı öznitelikler çıkarıp, makine öğrenmesi teknikleri kullanılarak zararlı yazılım tespitine yönelik yenilikçi bir yaklaşım geliştirmektir. Bu amaç doğrultusunda, hem manuel hem de çevrimiçi doğrulama süreçleriyle zararlı veya zararsız olduğu belirlenen çalıştırılabilir dosyalar titizlikle toplanmıştır. Kontrollü sanal makine ortamlarında gerçekleştirilen deneysel uygulamalar sonucunda elde edilen veriler, öncelikle sayısal, bağlamsal, zaman tabanlı ve istatistiksel öznitelikler olarak sınıflandırılmıştır. Bu verilerden hangilerinin kullanılabilir olacağı belirlenirken, bazıları özellik mühendisliği teknikleriyle daha işlevsel hale getirilmiş ve model performansını artıracak kritik göstergeler oluşturulmuştur. İlgili öznitelikler, Gradient Boosting, Logistic Regression ve Destek Vektör Makineleri gibi çeşitli sınıflandırma algoritmaları kullanılarak işlenmiş; elde edilen modellerin performansı ise K-Fold çapraz doğrulama yöntemi ile titizlikle değerlendirilmiştir. Uygulanan veri normalizasyonu ve özellik mühendisliği teknikleri, modellerin genel doğruluğunu artırırken, zararlı ile zararsız yazılımlar arasındaki ayrımı daha net ortaya koymuştur. Bu kapsamlı analiz, dinamik analiz temelli yaklaşımların, geleneksel statik yöntemlere kıyasla daha yüksek doğruluk oranları ve düşük yanılma payları sağladığını göstermiştir. Ayrıca, bu çalışma literatürde sınırlı yer alan dinamik analiz yaklaşımlarına önemli katkılar sunmayı hedeflemektedir. Farklı veri setleri üzerinde gerçekleştirilen kapsamlı deneyler sonucunda, geliştirilen metodolojinin siber saldırılara karşı proaktif bir yaklaşım sağlayabileceği ortaya konulmuştur. Dinamik davranış analizi, hem eski hem de yeni zararlı yazılımların tespitinde geniş zaman aralıklarında etkili sonuçlar vermekte, böylece siber güvenlik stratejilerinin geliştirilmesinde kritik bir rol oynamaktadır. Sonuç olarak, bu tez çalışması, zararlı yazılım tespiti ve önlenmesinde dinamik analiz yöntemlerinin uygulanabilirliğini ve etkinliğini ortaya koyarak, siber güvenlik alanında yeni ufuklar açmaktadır.With the acceleration of technological developments, cyber attacks and malware threats are becoming increasingly complex and dangerous. This situation demonstrates the necessity of developing innovative approaches and effective solutions in the field of information security. This thesis aims to enhance the effectiveness of dynamic methods in malware analysis by focusing particularly on the detailed examination of Windows operating system event log data. The primary objective of the study is to extract meaningful features from the operating system log data generated during the runtime of malicious and benign software in isolated virtual environments and to develop an innovative approach for malware detection using machine learning techniques. To this end, executable files, determined to be either malicious or benign through both manual and online verification processes, have been meticulously collected. The data obtained from experimental applications conducted in controlled virtual machine environments were primarily classified as numerical, contextual, time-based, and statistical features. In determining which of these features could be utilized, some were further refined through feature engineering techniques, thereby creating critical indicators that enhance model performance. The relevant features were processed using various classification algorithms such as Gradient Boosting, Logistic Regression, and Support Vector Machines; the performance of the resulting models was rigorously evaluated using the K-Fold cross-validation method. The applied data normalization and feature engineering techniques not only increased the overall accuracy of the models but also clarified the distinction between malicious and benign software. This comprehensive analysis has demonstrated that dynamic analysis-based approaches provide higher accuracy rates and lower error margins compared to traditional static methods. Furthermore, this study aims to make a significant contribution to the field of dynamic analysis, an area that has been relatively underrepresented in the literature. Comprehensive experiments conducted on different datasets have revealed that the developed methodology can offer a proactive approach against cyber attacks. Dynamic behavior analysis yields effective results over extended time intervals in detecting both old and new malware, thereby playing a critical role in the development of cybersecurity strategies. In conclusion, this thesis illustrates the applicability and effectiveness of dynamic analysis methods in malware detection and prevention, opening new horizons in the field of cybersecurity
İş Birlikli Ana Dağıtım Üssü Ağı Tasarımı
No full textLojistik alanında iş birliği yaklaşımı, sadece maliyet tasarrufu sağlanması açısından değil aynı zamanda karbon ayak izinin azaltılması açısından da etkili bir araçtır. ADÜ ağları, akışları birleştirerek ölçek ekonomilerinden faydalanılmasını esas alır. ADÜ ağları ile üretim zincirinin aynı seviyesinde faaliyet gösteren rakip firmalar arasındaki iş birliğini ifade eden yatay iş birliği yaklaşımlarının birleştirilmesi ise, iş birliği yaklaşımının ADÜ ağlarında ölçek ekonomilerinden yararlanılmasında etkiyi arttırması gibi çeşitli ekonomik ve çevresel avantajları beraberinde getirir. Bu çalışmada, iş birlikli ADÜ ağlarına ilişkin problem iş birlikli oyun kuramı çerçevesinde ele alınmış ve söz konusu iş birlikli oyunun çekirdeğinin boş olabileceği, yani bütçe dengeli ve stabilite koşullarının sağlandığı bir maliyet dağıtımının var olmayabileceği gösterilmiştir. Söz konusu problemin iş birlikli oyun kuramı çerçevesinde ele alınarak bütçe dengeli ve stabil bir çözümün her zaman var olmayabileceğinin gösterilmesi çalışmanın temel katkıları arasında yer almaktadır. İş birliği kapsamındaki ortak maliyetlerin dağıtımı için çeşitli maliyet dağıtımı yöntemleri kullanılmıştır. Söz konusu yöntemler, iş birlikli oyunun özelliklerini ortaya koymak amacıyla kapsamlı deneysel çalışmalar gerçekleştirilerek incelenmiştir. Deneysel çalışmalar, hem rassal olarak üretilmiş veriler kullanılarak hem de literatürde yer alan ADÜ yer seçimi veri setlerinin ele alınan probleme uyarlanması ile gerçekleştirilmiştir. Ayrıca, ele alınan iş birlikli ADÜ ağı probleminin ve maliyet dağıtımı yöntemlerinin performans karşılaştırmasının detaylı gösterimi için bir vaka analizi gerçekleştirilmiştir. Son olarak, nucleolus, Shapley value ve en düşük çekirdek maliyet dağıtımı yöntemlerinin performansları, göreceli tasarruflar, stabilite kavramı ve koalisyon memnuniyet değerleri gibi farklı eşitlik ölçütleri kullanılarak değerlendirilmiştir. Bu çalışma, ADÜ ağı tasarımında iş birliğinin etkisinin ölçülmesi ve lojistik operasyonların maliyetleri ile olumsuz çevresel etkilerinin azaltılmasıyla daha verimli ve sürdürülebilir lojistik operasyonlarının oluşturulmasına katkı sağlamayı amaçlamaktadır.Collaboration in logistics is an effective tool not only for cost savings but also for reducing the carbon footprint. Hub networks take advantage of scale economies by bundling flows. Merging hub networks through horizontal collaboration unlocks further economic and environmental advantages. We consider the problem of designing a collaborative hub network as a cooperative game and show that the core of the game might be empty, meaning that a budget-balanced and stable cost allocation does not exist. Our key novelty lies in formulating this design problem as a cooperative game and demonstrating the potential absence of a fair and stable solution. Various game theoretical approaches are used for the allocation of joint costs due to the collaboration. Each approach is also tested through extensive numerical experiments to gain insight into the features and behavior of the corresponding cost allocation game. These experiments are conducted on both randomly generated and also real-world hub location instances. Achieving a stable and also fair cost allocation among collaborators is critical for the future of the organization. In addition, a case study is conducted to illustrate the collaborative hub network problem and the performance comparison of cost allocation methods. Finally, we compare the performance of the nucleolus, the Shapley value and the least core cost allocation methods based on different fairness measures such as relative savings, stability concepts and coalition satisfaction. This work ultimately paves the way for more efficient and sustainable logistics operations by measuring the value of collaboration in hub network design and minimizing the operating costs and also environmental footprint of the logistics industry
Comprehensive Analysis of Behavioral Hardware Impairments in Cell-Free Massive MIMO-OFDM Uplink: Centralized Operation
No full textIsik UniversityCell-free massive MIMO is a key 6G technology, offering superior spectral and energy efficiency. However, its dense deployment of low-cost access points (APs) makes hardware impairments unavoidable. While narrowband impairments are well-studied, their impact in wideband systems remains unexplored. This paper provides the first comprehensive analysis of hardware impairments - such as nonlinear distortion in low-noise amplifiers, phase noise, in-phase/quadrature imbalance, and low-resolution analog-to-digital converters - on uplink spectral efficiency in cell-free massive MIMO. Using an OFDM waveform and centralized processing, APs share channel state information for joint uplink combining. Leveraging Bussgang decomposition, we derive a distortion-aware combining vector that optimizes spectral efficiency by modeling distortion as independent colored noise. © 2025 Elsevier B.V., All rights reserved
The Significance of the Latent Period in the Mathematical Modeling of Airborne Diseases
No full textAlthough the latent phase affects disease transmission on a population scale, this stage is not easy to detect and trace. In this study, the explanation of latent period with classical (SEIR) and delayed compartment-based mathematical models are presented comparatively and their advantages and disadvantages are discussed. Additionally, parameter estimations and computational simulations are performed by using the data of three airborne diseases from various regions, namely, COVID-19 Omicron variant (USA, India, Brazil), Influenza A H1N1 (Mexico, USA, England), and meningococcal meningitis (South Africa, USA, Australia). Our findings indicate that, for a specific value of delay, the delayed SEIR model exhibits a lower reproduction number and a lower peak value compared to the standard SEIR model. This suggests that the delayed SEIR model may be particularly suitable for scenarios characterized by delayed disease transmission dynamics, such as diseases with longer incubation periods or significant asymptomatic periods. The results provide insight into the applicability of the delayed SEIR model and its advantages over the standard SEIR model in specific epidemiological scenarios
3 Boyutlu Evrişimsel Sinir Ağları Temelli Algoritmik Alım Satım Sistemi
No full textIsik UniversityArtificial neural networks are widely used in financial forecasting models. Although the most preferred model is LSTM, some studies based on CNN can also be found. In this study, the developed CNN model applies the convolution operation on three-dimensional data with a different approach. During data preparation, 18 different technical analysis indicators were selected. These indicators were calculated based on 20 different values, corresponding to periods ranging from 5 to 25 for each day. The resulting two-dimensional daily data was augmented with 20 days of past values, forming datasets of size 18 × 20 × 20 for each day. The data was labeled with Buy, Sell, and Hold classes. Based on the model's outputs, trading activities conducted over 750 trading days between 2022 and 2024 on Dow30 stocks and selected exchange-traded funds achieved an average annual return of 18.15% and 20.16%, respectively, outperforming the buy-and-hold strategy. © 2025 Elsevier B.V., All rights reserved
Digital Transactions, COVID-19 and Velocity of Money: Macroeconomic Insights from an Emerging Market
No full textDigital monetary transactions are revolutionizing the way economies operate by enabling faster, more efficient, and cost-effective methods of value exchange. These developments carry significant macroeconomic implications, notably for the velocity of money (VoM). Despite the growing literature on digital payments and macroeconomic variables, the interaction between digital transactions, monetary aggregates, and structural shifts such as the COVID-19 pandemic remains underexplored, especially for developing economies. This study investigates the impact of digital transactions and the COVID-19 pandemic on VoM in Turkey while accounting for a comprehensive set of macroeconomic variables. Employing an Autoregressive Distributed Lag (ARDL) model with bounds-testing, we find a stable and positive long-run relationship: a 1% increase in digital payments raises VoM by 0.3%. The COVID-19 pandemic has amplified this effect, as lockdowns and social distancing measures have accelerated the adoption of contactless transactions. Among digital banking activities, digital payments emerge as the primary drivers of increased VoM, surpassing money transfers and online credit card use. Moreover, digital banking influences the circulation of currency more than broader monetary aggregates, mainly by displacing physical cash. These results confirm that rising VoM driven by digitalization may require more dynamic and forward-looking liquidity management and closer monitoring of payment systems
Investigating Fracture Behaviour of Single-Cell Lattice Materials via XFEM: Voxel-Based Approach
No full textThis study uses the extended finite element method (XFEM) and voxel-based approach to numerically investigate additively manufactured single-cell lattice materials' fracture behavior (e.g., crack growth and fracture resistance) under tensile loading. Body-centered cubic (BCC) single-cell lattices were manufactured from Polylactic acid (PLA) with a strut diameter of 1.5 mm using the fused filament fabrication (FFF) technique. The micro-CT imaging was utilized to provide detailed information on defects (e.g., voids, gaps, and cracks) inside the lattices, improving the exactness of the models used in the numerical investigations. The 2D micro-CT images were then converted to 3D voxel models through MATLAB programming. The generated models were numerically analyzed using the XFEM technique, in which the crack initiation and propagation were modeled via maximum principal stress (MaxPS) and power-law fracture criteria, respectively. A comparison was made between the results of XFEM analyses obtained from the single-cell solid and voxel BCC lattice models. © 2025 Elsevier B.V., All rights reserved