Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651

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Numerical and Experimental Investigation of Effects of Porous Layer on Cooling of Electronic Components
(American Society of Mechanical Engineers (ASME), 2026) Kocak, E.; Türkoǧlu, H.
In this study, heat transfer and temperature distribution characteristics of an electronic component covered with a porous medium were investigated both experimentally and numerically. An experimental setup was designed and constructed to conduct the experiments. For the numerical analysis, a computational fluid dynamics (CFD) software was developed on the OPENFOAM platform. The experimental results were used to validate the mathematical model and the computer program developed. The validated computer program was used to investigate the effects of Reynolds number, porosity, Darcy number, porous layer sizes, and the channel height on the heat transfer rate from the heat dissipating elements (electronic component) to the flow in wider ranges of the parameters. Using the Nusselt number values obtained both experimentally and numerically, a correlation equation was developed, and an artificial neural network architecture was trained for the Nusselt number. Results show that the Nusselt number increases with increasing Reynolds number, porosity, and the ratio of the height of the porous layer to the channel height. It was observed that the width of the porous medium has no noticeable effect on the Nusselt number. The correlation equation developed with four independent parameters predicts the Nusselt number with an average error of 7.59%. The artificial neural network architecture developed prevails as a more accurate tool, with a maximum error of 1%, for the prediction of the Nusselt number in the range of the parameters considered. © © 2026 by ASME.
Sentiment Analysis for Arabic Using Deep Learning
(Springer Science and Business Media Deutschland GmbH, 2026) al-Hamadani, S.A.S.; Sever, H.
With the explosive growth of digital communication, understanding sentiment in online content has become increasingly critical for a wide range of applications, from customer feedback analysis to social media monitoring. However, sentiment analysis for Arabic presents unique challenges due to the language's rich morphology, diverse dialects, and complex syntactic structures. These challenges are further amplified in multimodal settings, where the fusion of textual, visual, and auditory cues is required to capture the full spectrum of human emotion. To address these issues, this paper introduces a new framework for Arabic Multimodal Sentiment Analysis (AMSA), combining multi-level deep learning approaches across text, audio, and visual modalities. Our approach utilizes state-of-the-art transformer-based architecturees, including Multimodal Transformer (MulT) and Early Fusion models, to tackle both linguistic complexity and multimodal alignment. Specifically, we leverage DeBERTa for extracting rich textual features, ViT (Vision Transformer) for visual cues, and Whisper for capturing nuanced audio signals, creating robust and contextualized representations. Experimental results on a curated Arabic multimodal dataset demonstrate the effectiveness of this approach, with our proposed MulT model achieving an F1 score of 72.73%, reflecting a substantial improvement of 13.98% in F1 score and 14.6% in accuracy over existing baselines. These findings highlight the power of cross-modal attention mechanisms and early fusion strategies in accurately capturing subtle sentiments across multiple modalities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
The Art of Being: Haruki Murakami’s Killing Commendatore and Kierkegaardian Existentialism
(Springer Science+Business Media, 2025) Rundholz, A.; Kirca, M.
The protagonist of Killing Commendatore retreats to deal with the trauma of divorce. Pivotal to the protagonist’s journey is his discovery of a painting. Depicting a scene from Mozart’s opera, Don Giovanni, the painting marks the protagonist’s departure to finding meaning in a complex world. His self-discovery hinges on the arts, leading the protagonist to grasp his essence and place in an indifferent and absurd universe. Fantastic and surreal events in the novel can be seen as an adaptation of Kierkegaard’s existentialism, a reinterpretation of the philosopher’s tenets to fit the twenty-first century. © 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
A Generalization of Fixed Point Result of Nonlinear Cirić Type Contraction on Suprametric Spaces
(University of Nis, 2025) Yalçin, C.; Bilazeroglu, S.
In this study, the nonlinear technique: (ψ,ϕ)-weak contraction, created by Dutta and Choudhury [6], is used to make the Ćirić type contraction nonlinear. Moreover, it is demonstrated that there is unique fixed point in suprametric space for this nonlinear Ćirić type contraction. © 2025, University of Nis. All rights reserved.
Enhancement of Brazing Performance of Inconel 718 By Electroless Cobalt Coated Nickel-Based Brazing Alloys
(Elsevier, 2025) Goynuk, Tansu; Esen, Ziya; Karakaya, Ishak
Effect of electroless cobalt-coated BNi-2 on the brazing performance of Inconel 718 was investigated in this study. A new method for modifying the microstructure and thermal properties of brazing alloys by incorporating cobalt through electroless deposition was introduced. This approach offers a more controlled and uniform alloy modification compared to conventional mechanical mixing techniques, enhancing the performance of the brazed joints. The introduction of cobalt into the filler material influences the microstructural evolution and refining the joint structure by reducing brittle precipitates. Microstructural analysis confirms that the Co-coated BNi-2 results in a more homogeneous joint with improved phase distribution. Mechanical characterization indicated that the shear strength increased nearly 4.5 times, while fracture strain improved approximately fourfold. Moreover, the cobalt addition raised the solidus temperature of the filler alloy by 25-30 degrees C, contributing to better high-temperature stability. These findings highlight the effectiveness of electroless cobalt coating in optimizing brazing alloys for demanding aerospace and high-temperature applications.
Introduction
(Springer, 2008) Aydogan, N.
Intelligent and Energy-Aware Task Scheduling in Cloud Systems
(Springer Science and Business Media Deutschland GmbH, 2025) Böke, K.N.; Qadri, S.S.S.M.; Kabarcik, A.
The rapid advancement of information technologies has significantly reshaped industrial operations and daily life, leading to a growing demand for responsive and scalable digital services. Among the technologies addressing this growing need, cloud computing has emerged as a foundational infrastructure for delivering on-demand computing resources over the internet. However, its increasing adoption presents complex challenges such as managing dynamic workloads and minimizing virtual machine (VM) usage costs. Therefore, cloud service providers aim to optimize performance and reduce the operational costs of VMs by integrating intelligent scheduling algorithms. In response to this need, the present study explores the use of algorithms, particularly focusing on machine learning driven approaches, to enhance the sustainability and efficiency of cloud systems. Specifically, the study investigates the effectiveness of reinforcement learning through Q-learning for optimizing task scheduling against the traditional Round Robin (RR) scheduling algorithm. The primary objective is to evaluate their performance in minimizing VM usage costs within dynamic and continuously evolving cloud environments. Experimental results indicate that in reducing costs, Q-learning outperforms RR with a 33.14% improvement, demonstrating its superior adaptability and cost efficiency under varying conditions. These insights highlight the potential of reinforcement learning to enable intelligent and cost-aware scheduling strategies in modern cloud computing systems. © 2025 Elsevier B.V., All rights reserved.
Design and Implementation of a Custom ERP Framework for a Drilling Equipment Manufacturer
(Springer Science and Business Media Deutschland GmbH, 2025) Torunoğlu, D.; Erkoç, E.C.; Abay, Z.E.; Qadri, S.S.S.M.; Gök, E.C.; Karataş, D.; Güçlüer, G.
This study presents the design and implementation of a web-based Enterprise Resource Planning (ERP) system tailored for a small-to-medium-sized enterprise (SME) operating in the manufacturing sector. With a focus on GEO Sondaj Makine İmalat LTD. ŞTİ, the system was developed to digitize and streamline core operational workflows, including sales order processing, production scheduling, inventory management, procurement, and coordination between customers and suppliers. Built using the Django web framework, the ERP platform provides modular functionality with real-time data integration across departments. Unlike generic ERP packages, this custom-built solution mirrors the company’s actual business processes and addresses typical challenges faced by SMEs, such as limited IT infrastructure, absence of digital records, and resistance to organizational change. The internally developed modules led to enhanced traceability, operational efficiency, and data-driven decision-making. The system also includes a simulation module to support production visualization and planning, although advanced features like bottleneck identification and dynamic queue tracking remain under development. The findings demonstrate that a cost-effective, scalable ERP system can be successfully deployed in resource-constrained environments when grounded in business-specific needs. The system was evaluated based on internal testing, interdepartmental workflow validation, and observed improvements in operational efficiency and traceability. This project offers a practical reference for other SMEs seeking to modernize their operations through digital integration. © 2025 Elsevier B.V., All rights reserved.
The Impact of Technology on Economic Growth in Turkey
(Inderscience Publishers, 2025) Ercan, M.; Temiz, D.; Gökmen, A.
The Turkish economy has been suffering from trade imbalance for a long time. Exporting high value-added products will diminish Turkey’s dependence on foreign resources for capital and imported products. At the same time, it may be possible to divert more resources from gross domestic product (GDP) to R&D funds. Appropriate and efficient usage of technology will help companies innovate and find new areas of employment. As a result, the Turkish economy may have a better chance of obtaining a sustainable economic growth for the longer term. This study concludes that increased R&D expenditures leads to a rise in technology and this in turn contributes positively to economic growth. The results obtained from the study show that technology affects Turkey’s economic growth. Therefore, Turkey needs to work harder in the field of technology in order to achieve sustainable growth. Improving the situation and quality of research and development activities in Turkey, encouraging research and development investments by both the government and the business sector should be priority reform movements for Turkey. Policy makers should support science and technology, make institutional arrangements for intellectual property rights and raise the level of education, and make arrangements to increase R&D spending. © 2025 Elsevier B.V., All rights reserved.
Cognitive and Social Antecedents of Brand Loyalty in Over-The Streaming Services
(Science Publishing Corporation Inc., 2025) Ôzsaçmaci, B.
This study examines cognitive (brand image, perceived quality, brand identity) and social (eWOM) antecedents of brand loyalty in Over-the-Top (OTT) streaming using a cross-sectional survey (N=418) and covariance-based SEM. All four antecedents significantly predict loyalty, with brand image exerting the strongest effect, followed by eWOM, then perceived quality and brand identity. Group comparisons show that evaluations of perceived quality, but not eWOM, identity, image, or loyalty, differ by usage frequency and subscription breadth. The findings substantiate a dual cognitive–social route to loyalty, and suggest that, in content-rich, multi-homing markets, quality functions as a hygiene factor while brand image carries the differentiating signal. We discuss managerial implications for sequencing investments (image, eWOM, quality thresholds) and outline finance-relevant links to CLV, LTV: CAC, and revenue stability. © 2025 Elsevier B.V., All rights reserved.
Stylometric Analysis of Sustainable Central Bank Communications: Revealing Authorial Signatures in Monetary Policy Statements
(MDPI, 2025) Emekci, Hakan; Ozkan, Ibrahim
Sustainable economic development requires transparent and consistent institutional communication from monetary authorities to maintain long-term financial stability and public trust. This study investigates the latent authorial structure and stylistic heterogeneity of central bank communications by applying stylometric analysis and unsupervised machine learning to official announcements of the Central Bank of the Republic of Turkey (CBRT). Using a dataset of 557 press releases from 2006 to 2017, we extract a range of linguistic features at both sentence and document levels-including sentence length, punctuation density, word length, and type-token ratios. These features are reduced using Principal Component Analysis (PCA) and clustered via Hierarchical Clustering on Principal Components (HCPC), revealing three distinct authorial groups within the CBRT's communications. The robustness of these clusters is validated using multidimensional scaling (MDS) on character-level and word-level n-gram distances. The analysis finds consistent stylistic differences between clusters, with implications for authorship attribution, tone variation, and communication strategy. Notably, sentiment analysis indicates that one authorial cluster tends to exhibit more negative tonal features, suggesting potential bias or divergence in internal communication style. These findings challenge the conventional assumption of institutional homogeneity and highlight the presence of distinct communicative voices within the central bank. Furthermore, the results suggest that stylistic variation-though often subtle-may convey unintended policy signals to markets, especially in contexts where linguistic shifts are closely scrutinized. This research contributes to the emerging intersection of natural language processing, monetary economics, and institutional transparency. It demonstrates the efficacy of stylometric techniques in revealing the hidden structure of policy discourse and suggests that linguistic analytics can offer valuable insights into the internal dynamics, credibility, and effectiveness of monetary authorities. These findings contribute to sustainable financial governance by demonstrating how AI-driven analysis can enhance institutional transparency, promote consistent policy communication, and support long-term economic stability-key pillars of sustainable development.
Citation - WoS: 2
Citation - Scopus: 1
An Investigation of the Performance of Equal Channel Angular Pressed Copper Electrodes in Electric Discharge Machining
(MDPI, 2025) Simsek, Ulke; Cogun, Can
This study examines the mechanical, thermal, and electrical properties of copper tool electrodes processed via Equal Channel Angular Pressing (ECAP), with a specific focus on their performance in Electrical Discharge Machining (EDM) applications. A novel Crystal Plasticity Finite Element Method (CPFEM) framework is employed to model anisotropic slip behavior and microscale deformation mechanisms. The primary objective is to elucidate how initial crystallographic orientation influences hardness, thermal conductivity, and electrical conductivity. Simulations are performed on single-crystal copper for three representative Face Centered Cubic (FCC) orientations. Using an explicit CPFEM model, the study examines texture evolution and deformation heterogeneity during the ECAP process of single-crystal copper. The results indicate that the <100> single-crystal orientation exhibits the highest Taylor factor and the most homogeneous distribution of plastic equivalent strain (PEEQ), suggesting enhanced resistance to plastic flow. In contrast, the <111> single-crystal orientation displays localized deformation and reduced hardening. A decreasing Taylor factor correlates with more uniform slip, which improves both electrical and thermal conductivity, as well as machinability, by minimizing dislocation-related resistance. These findings make a novel contribution to the field by highlighting the critical role of crystallographic orientation in governing slip activity and deformation pathways, which directly impact thermal wear resistance and the fabrication efficiency of ECAP-processed copper electrodes in EDM.
The Implementation of a Successive Cancellation Polar Decoder on Xilinx System Generator
(Institute of Electrical and Electronics Engineers Inc., 2017) Arli, A.Ç.; Colak, A.; Gazi, O.
Polar coding is the first kind of the capacity achieving codes which are defined for binary-input discrete memoryless channels initially. Parallel processing property of the FPGA allows to decode faster with a margin of complexity. Xilinx System Generator as a practical tool to construct decoding designs in shorter time is a fact. In this study, FPGA implementation of decoding polar codes through Xilinx System Generator is shown. © 2023 Elsevier B.V., All rights reserved.
Covariance Features for Trajectory Analysis
(Institute of Electrical and Electronics Engineers Inc., 2016) Karadeniz, T.; Maras, H.H.
In this work, we aimed to demonstrate that covariance estimation methods can be used for trajectory classification. We have shown that, features obtained via shrunk covariance estimation are suitable for describing trajectories. We have arrived to the conclusion that, when compared to Dynamic Time Warping, the explained technique is faster and may yield more accurate results. © 2017 Elsevier B.V., All rights reserved.
Citation - Scopus: 4
Parallel Data Reduction Techniques for Big Datasets
(IGI Global, 2016) Yıldırım, A.A.; Özdoǧan, C.; Watson, D.
Data reduction is perhaps the most critical component in retrieving information from big data (i.e., petascale-sized data) in many data-mining processes. The central issue of these data reduction techniques is to save time and bandwidth in enabling the user to deal with larger datasets even in minimal resource environments, such as in desktop or small cluster systems. In this chapter, the authors examine the motivations behind why these reduction techniques are important in the analysis of big datasets. Then they present several basic reduction techniques in detail, stressing the advantages and disadvantages of each. The authors also consider signal processing techniques for mining big data by the use of discrete wavelet transformation and server-side data reduction techniques. Lastly, they include a general discussion on parallel algorithms for data reduction, with special emphasis given to parallel waveletbased multi-resolution data reduction techniques on distributed memory systems using MPI and shared memory architectures on GPUs along with a demonstration of the improvement of performance and scalability for one case study. © 2025 Elsevier B.V., All rights reserved.
Citation - Scopus: 12
Computation of Supervisors for Fault-Recovery and Repair for Discrete Event Systems
(IFAC Secretariat, 2014) Sülek, A.N.; Schmidt, K.W.
In this paper, we study the fault-recovery and repair of discrete event systems (DES). To this end, we first develop a new method for the fault-recovery of DES. In particular, we compute a fault-recovery supervisor that follows the specified nominal system behavior until a fault-occurrence, that continues its operation according to a degraded specification after a fault and that finally converges to a desired behavior after fault. We next show that our method is also applicable to system repair and we propose an iterative procedure that determines a supervisor for an arbitrary number of fault occurrences and system repairs. We demonstrate our method with a manufacturing system example. © 2021 Elsevier B.V., All rights reserved.
Citation - Scopus: 1
Enterprise Architecture for Personalization of E-Government Services: Reflections From Turkey
(IGI Global, 2012) Erdem, A.; Medeni, İ.T.; Medeni, T.D.
As there has not yet been enough work on enterprise architectures for fully integrated knowledge-based, highly-sophisticated (citizen-oriented) personalized services, this chapter aims to articulate a perspective to design architectures for the development and provision of sophisticated, personalized services. Doing so, the authors benefit from their knowledge and experience in the Turkish e-Government Gateway (eGG) and general e-Government services development and provision. First providing an introduction and background information, the chapter discusses the development of eGG services in Turkey, and then provides a visionary suggestion for knowledge-based personalized, citizen-centric e-Government. Among the suggested perspectives, an E-Citizen Decision Support System, and Entity-Utility and Information Flow Model could be useful for eGG development in Turkey and elsewhere. © 2025 Elsevier B.V., All rights reserved.
Influencers in Luxury Brand Communication: An Evaluation of the Relationship Between Source Credibility, Persuasive Message, and Parasocial Engagement
(Sage Publications Inc, 2025) Alkan, Zeynep; Dolunay, Ayhan; Ulas, Sevilay
This study examines persuasive messages and source credibility in luxury brand influencers' content within their brand collaborations. It also explores how influencers establish parasocial engagement with followers through their posts. Conducted as an online survey between February and July 2023, the study targeted 400 individuals in Northern Cyprus who follow influencers. Findings indicate that increased parasocial engagement enhances the perceived persuasiveness of messages. A positive relationship between parasocial engagement and source credibility was observed, demonstrating that as influencers' credibility rises, so does the persuasiveness of their messages. Additionally, the study analyzed relationships between parasocial engagement, persuasive messages, source credibility and demographic factors. While no significant differences were found between parasocial engagement and most demographic characteristics, education level stood out. Individuals with a primary education level showed a greater tendency toward parasocial engagement. A significant difference was noted between source credibility and monthly income, with the highest perceived credibility reported in the 7,000 to 8,999 TL income group. Similarly, persuasive messages were most effective among individuals in this income range. In conclusion, this study highlights the importance of considering demographic differences and parasocial engagement in influencer brand collaborations. It underscores that source credibility and persuasive messages play a crucial role in influencer communication, influencing how audiences perceive and engage with branded content. It has been concluded that strong parasocial bonds in influencer and brand collaborations play a strategic role in establishing effective interaction with the target audience.
Indoor Soundscape Intervention (ISI) Criteria for Architectural Practice: A Systematic Review With Grounded Theory Analysis
(MDPI, 2025) Ercakmak Osma, Ugur Beyza; Dokmeci Yorukoglu, Papatya Nur
Indoor soundscape is a relatively new and developing field compared to urban soundscape in practice. To address this gap, this study aims to identify the key influencing factors as a first step of the indoor soundscape intervention approach. The study employed a two-phase methodology. Phase one involved a Systematic Review (SR) of the literature, conducted through the PRISMA 2020 guidelines, to collate data on the influencing factors and intervention criteria of the indoor soundscape approach. Searching was conducted using two databases, Web of Science and Scopus. As a result of the search, a total of 29 studies were included in the review. The review included studies addressing the soundscape influencing factors and theoretical frameworks. Studies that did not address these criteria were excluded. Phase two comprised the application of the Grounded Theory (GT) coding process to organize, categorize, and merge the data collected in phase one. As a result of the coding process, three levels of categories were achieved; L1: key concept, L2: overarching category, L3: core category. Four core categories were identified as 'Sound', 'People', 'Building', and 'Environment' by proposing the Indoor Soundscape Intervention (ISI) criteria. The repeatable and updatable nature of the proposed method allows it to be adapted to further studies and different contexts/cases.
Self-Supervised Learning With BYOL for Non-Alcoholic Fatty Liver Disease Diagnosis Using Ultrasound Imaging
(Springer London Ltd, 2025) Buktash, Ali; Gorur, Abdul Kadir
Purpose:The study aims to evaluate the effectiveness of Bootstrap Your Own Latent (BYOL), a self-supervised learning method for diagnosing NAFLD from ultrasound images using limited labeled data, which represents a novel approach in this domain. Self-supervised learning provides an alternative approach to traditional supervised learning by learning useful representations from unlabeled data, thereby reducing the time and cost required by radiologists to annotate images.Methods:The pre-trained ResNet-50 and ResNet-101 on the labeled ImageNet dataset were used for BYOL pre-training on ultrasound images without relying on labels. The training was conducted using default and custom augmentation, as well as balanced and imbalanced class distribution protocols. The model was then evaluated using linear and fine-tuning protocols with varying percentages of labeled data. The model was trained using three shuffled subsets, each trained 10 times. The custom augmentation set was derived by testing various augmentation settings using 100% and 1% of the labels to enhance feature learning.Results:BYOL with ResNet-101 and using the proposed custom augmentation set achieved average accuracies of 93.44%, 92.29%, and 88.49% using 100%, 10%, and 1% of the training labels across three shuffled datasets. In addition, our proposed method attained an average accuracy of 96.9% using patient-specific leave-one-out cross-validation (LOOCV).Conclusion:BYOL, with the proposed custom augmentation set, can learn effective image representations without relying on a large amount of labeled data, thereby enhancing scalability since unlabeled images are easier to acquire. It surpasses BYOL with default augmentation and training under supervised learning, especially with a low-labeled data regime.

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