Scopus İndeksli Yayınlar Koleksiyonu

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

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Quantum Implementation of S-Boxes: A Memory Optimized Approach
(Institute of Electrical and Electronics Engineers Inc., 2025) Tilki, Ozcan; Saran, A.N.; Cildiroglu, H.O.; Yayla, O.
Substitution boxes (S-boxes) serve as fundamental non-linear components in symmetric cryptography, and their quantum circuit implementation is critical for quantum security. This work addresses the dual challenges of quantum circuit depth optimization and computational intractability in S-box synthesis. We introduce memory-optimized data structures, a pointer-efficient RandomAccessSet and a dynamic devector, that reduce memory overhead by 12 times per element, thereby mitigating the computational complexity associated with Pauli representation. Our enhanced Meet-in-the-Middle framework achieves exhaustive depth optimization for standardized S-boxes, demonstrating up to 8.5% depth reduction over DORCIS baselines at equivalent T-depth. The approach scales to 5-8-bit primitives, establishing memory efficiency as an independent resource dimension in quantum circuit synthesis. Comparative analysis under varied cost parameters provides new insights for resource-efficient cryptographic implementations on quantum hardware. © 2025 IEEE.
New Insights Into the Correlation Between Secondary Traumatic Stress and Cognitive Flexibility in Mental Health Therapists and Counselors
(Florida Gulf Coast University, 2025) Durak, H.; Çelik, E.G.; Çelik, B.
Mental health professionals regularly witness their clients' challenging life events, which they must cope with throughout their careers. This study examines the association between secondary traumatic stress (STS) and cognitive flexibility among counselors and therapists and explores their variations by demographic and professional factors. The participants included 536 professionals (psychiatrists, psychologists, social workers, and child development specialists). Secondary traumatic stress and cognitive flexibility were assessed using the Secondary Traumatic Stress Scale and the Cognitive Control and Flexibility Scale, respectively. Demographic data were collected using the Personal Data Form. SPSS 21.0 software was used for data analysis. Pearson correlation analysis revealed a moderate negative association between the participants' STS and cognitive flexibility levels. The findings indicated a higher STS risk among child development specialists, younger professionals, those who are single or do not have children, those who have lower income or fewer years of experience, and those without trauma-related training or supervision. Conversely, older age, marriage, children, higher income, higher level of education, longer experience, and trauma-related training were identified as protective factors for cognitive flexibility. Several recommendations were provided to strengthen mental health professionals’ flexibility and reduce the impact of STS on them. First, supportive measures in protection, prevention, and treatment for mental health professionals who work in the field of trauma should be taken. Second, priority should be given to extending certified training programs that will improve the professional skills of mental health workers. Third, counselors should be supported by peer, individual, and group supervision. Finally, examining gender-specific risks is essential to increase women's cognitive flexibility and improve their physical and emotional well-being. © 2025, Florida Gulf Coast University. All rights reserved.
Perspectives on Audit Opinions and Key Audit Matters in the Global Airline Industry and the COVID-19 Pandemic
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Dansık, Umutcan; Öztürk, C.
The present study investigates whether the COVID-19 pandemic had a negative effect on audit opinion and led to differences in the composition of key audit matters (KAMs) observed in the airline industry. This study uses a sample of 55 airlines whose financial statements are based on International Financial Reporting Standards (IFRSs) and whose financial statement audit follows National or International Standards on Auditing (ISAs) for audit opinion, as well as a sample of 42 airlines whose financial statements are based on IFRSs and whose financial statement audit follows ISAs for the composition of KAMs. A textual analysis, a content analysis, a frequency distribution, and a chi-square test were conducted for the periods before, during, and after the COVID-19 pandemic. The findings reveal that the COVID-19 pandemic had no significant effect on audit opinion, except for one airline whose audit report declared a disclaimer of opinion. In contrast, the impairment of goodwill and intangible assets (as an industry-specific KAM) and going concern (as a KAM specific to the COVID-19 pandemic) were the two KAMs that were typically observed during the COVID-19 pandemic due to increased uncertainty. This was found to be the case, even though the main KAMs in the airline industry are usually revenue recognition; lease accounting; property, plant, and equipment (PPE); and hedge accounting. This study contributes to the debate on the effect of the COVID-19 pandemic on audit opinions and KAMs by offering evidence from the underexplored airline industry. © 2025 by the authors.
CFD and DEM Analysis of Cyclone Separator Performance: Implications of Cylinder-to Ratios for Sustainable Engineering
(Springer Heidelberg, 2025) Ayli, Ece; Kocak, Eyup
This research addresses a common industrial challenge: efficiently separating particles from gas using cyclone separators, a critical component for various applications in sustainable engineering. While several studies have focused on airflow within these separators, this research introduces a novel approach by combining two advanced simulation methods (CFD and DEM) to analyze how different cone heights in a cyclone separator impact its performance. This combined methodology enables the examination of particle movement within the separator, a critical aspect often overlooked in previous studies. By visualizing particle dynamics and analyzing them with DEM, the research underscores the importance of considering particle behavior for obtaining accurate results. Overall, this study enhances our understanding of cyclone separators through state-of-the-art simulations and empirical testing. By elucidating the complex airflow and the influence of geometric design on performance, practical recommendations are provided for the development of more efficient cyclone separators. These improvements can lead to enhanced particle separation and reduced energy consumption, offering significant benefits across multiple industries. The findings reveal that as the conical height-to-total height ratio (h/hc) increases, indicating a more pointed cone, there is a substantial increase in efficiency alongside a minimal and tolerable rise in pressure drop. For instance, at a velocity of 25 m/s, increasing the h/hc ratio from 0.33 to 3 results in a 0.7% reduction in pressure drop and a 14% efficiency increase, contributing to more sustainable operational practices.
Improved Arithmetic Efficiency in TFHE Through Gate-Level Optimizations
(Springer, 2025) Tasel, Faris Serdar; Saran, Ayse Nurdan
Fully homomorphic encryption (FHE) enables computations to be performed directly on encrypted data without decryption, offering a promising solution for privacy-preserving applications, such as secure cloud computing, confidential machine learning, and encrypted analytics. However, one major drawback of FHE is the high computational cost of homomorphic operations, which slows down real-world implementations, making them impractical. This paper explores the implementation of arithmetic operations within the framework of Torus FHE (TFHE) and demonstrates the construction of gate-level optimization for fundamental operations such as addition, subtraction, negation, comparison, and multiplication on fixed-point numbers. Our work emphasizes optimizing arithmetic logic to reduce the number of bootstrapping operations, a critical factor in improving computational efficiency. Furthermore, we investigate the error rates associated with the proposed operations, providing valuable insight into their accuracy and practical applicability. This study contributes to developing more efficient and reliable arithmetic logic for privacy-preserving computations in FHE systems. The experimental results indicate that the proposed optimizations yield speedups of up to 2.27x for addition/subtraction, 3.55x for comparison, and 1.80x for multiplication operations.
An Investigation of Discontinuities in Time-Dependent 2D and 3D Parabolic Partial Differential Equations Utilizing Collocation Methods: A Comparative Analysis of Complex Interface Problems
(Springer Heidelberg, 2025) Faheem, Muhammad; Asif, Muhammad; Amin, Rohul; Haider, Nadeem; Jarad, Fahd
Parabolic double interface problems have many applications in the fields such as materials science, fluid dynamics, and heat transfer. This paper presents a comparison of the Haar wavelet-based collocation method and two variants of radial basis function (RBF) method for solving 2D and 3D, linear as well as nonlinear, parabolic double interface problems. The two variants of RBF methods are the multiquadric RBF method and the integrated RBF method. For linear problems, the system of equations obtained from the integrated RBF method is solved using Moore-Penrose pseudoinverse. Error analysis is performed using L infinity\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_\infty $$\end{document} norm error and root mean square error, and the findings are discussed in detail. The methods are compared based on their accuracy and efficiency in solving different benchmark problems. The results show that both the Haar wavelet collocation method and the integrated RBF method perform better than the conventional RBF method in terms of accuracy.
Process Simulation of Pseudo-Static Seismic Loading Effects on Buried Pipelines: Finite Element Insights Using RS2 and RS3
(MDPI, 2025) Alrubaye, Maryam; Sengor, Mahmut; Almusawi, Ali
Buried pipelines represent critical lifeline infrastructure whose seismic performance is governed by complex soil-structure interaction mechanisms. In this study, a process-based numerical framework is developed to evaluate the pseudo-static seismic response of buried steel pipelines installed within a trench. A comprehensive parametric analysis is conducted using the finite-element software Rocscience RS2 (version 11.027) to examine the influence of burial depth, pipeline diameter, slope angle, groundwater level, soil type, and permanent ground deformation. The seismic loading was represented using a pseudo-static horizontal acceleration, which approximates permanent ground deformation rather than full dynamic wave propagation. Therefore, the results represent simplified lateral seismic demand and not the complete dynamic soil-structure interaction response. To verify the reliability of the 2D plane-strain formulation, a representative configuration is re-simulated using the fully three-dimensional platform Rocscience RS3. The comparison demonstrates excellent agreement in shear forces, horizontal displacements, and cross-sectional distortion patterns, confirming that RS2 accurately reproduces the dominant load-transfer and deformation mechanisms observed in three-dimensional (3D) models. Results show that deeper burial and stiffer soils increase shear demand, while higher groundwater levels and larger permanent ground deformation intensify lateral displacement and cross-sectional distortion. The combined 2D-3D evaluation establishes a validated computational process for predicting the behavior of buried pipelines under a pseudo-static lateral load and provides a robust basis for engineering design and hazard mitigation. The findings contribute to improving the seismic resilience of lifeline infrastructure and offer a validated framework for future numerical investigations of soil-pipeline interaction.
Mitigation of Laser Beam Fluctuation and Performance of Probability of Fade in Weak Ocean Turbulence
(Pergamon-Elsevier Science Ltd, 2026) Gercekcioglu, Hamza; Baykal, Yahya
Utilizing the Rytov method in weakly turbulent oceanic medium, minimum scintillation index of sinusoidal Gaussian (SG) laser beams, named as the optimum beam (OB), is investigated for the underwater wireless optical communication (UWOC). Horizontal link between any underwater vehicles is considered. The formulation of the on-axis scintillation index of these beams is derived analytically, and the minimum scintillation index is determined with suitable adjustment of the complex displacement parameters. The complex displacement parameters are identified and tabulated for the selected propagation distance and source size. Obtained scintillation index results are drawn against the propagation length and source size. When compared with the plane, spherical, collimated, focused Gaussian, cos-Gaussian and cosh-Gaussian beams, OB is found to possess essential advantage. Additionally, with the obtained scintillation index values, probabilities of fade of these beams are calculated and their behaviors are also presented. OB also has a significant advantage when considering the fade probability.
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
(Univ Nis, Fac Sci Math, 2025) Yalcin, Ceylan; Bilazeroglu, Seyma
In this study, the nonlinear technique: (psi,phi)-weak contraction, created by Dutta and Choudhury [6], is used to make the Ciric type contraction nonlinear. Moreover, it is demonstrated that there is unique fixed point in suprametric space for this nonlinear Ciric type contraction.
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.
Forecasting the Methane Yield of a Commercial-Scale Anaerobic Digestor Based on the Biomethane Potential of Feedstocks
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Türker Bayrak, Ö.T.; Uludaǧ-Demirer, S.; Xu, M.; Liao, W.
With rising energy demand and the need for sustainable waste treatment, anaerobic digestion (AD) has emerged as a key technology for converting organic residues into renewable energy. However, predicting methane yield in full-scale facilities remains challenging due to the complexity of AD processes, the variability of feedstocks, and the impracticality of frequent biochemical methane potential (BMP) testing. In this study, we developed a simple, data-driven approach to forecast methane production in a commercial-scale digester co-digesting manure and food waste. The model employs weekly cumulative BMP of feedstock mixtures, calculated from literature values, as the explanatory variable. The model achieved an R2 of 0.70 and a forecast mean absolute percentage error (MAPE) of 7.4, indicating its potential for full-scale AD prediction. Importantly, the analysis revealed a long-run equilibrium between BMP and methane yield, with deviations corrected within roughly one month—closely matching the system’s hydraulic retention time. These findings demonstrate that literature-based BMP values can be used to reliably predict methane yield in operating AD systems, offering a low-cost and scalable tool to support decision-making in waste management and biogas plant operations. © 2025 by the authors.
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.

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