Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12469/1248

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Preface
(Taylor and Francis, 2024) Mair, J.; Aktas, G.; Kozak, M.; Advertising; 04. Faculty of Communication; 01. Kadir Has University
Citation - Scopus: 1
Resisting Through Images: Video Activism in the Gezi Park Movement
(Bloomsbury Publishing Plc., 2021) Şener, G.; Emre, P.Ö.; 01. Kadir Has University
Citation - Scopus: 3
Politics of News Reception and Circulation in Turkish News Culture
(Bloomsbury Publishing Plc., 2021) Koçer, S.; Public Relations and Information; 04. Faculty of Communication; 01. Kadir Has University
Communication as Political Action: Gezi Park and Online Content Producers
(Bloomsbury Publishing Plc., 2021) Yanardaǧoǧlu, E.; 01. Kadir Has University; 04. Faculty of Communication; New Media
Citation - Scopus: 3
The Transatlantic Link in Turkey’s Middle-Power Identity
(Bloomsbury Publishing Plc., 2021) Aksu-Ereker, Fulya; Akgül-Açıkmeşe, Sinem; 01. Kadir Has University; International Relations; 03. Faculty of Economics, Administrative and Social Sciences
An Integrated Decision-Making Framework to Evaluate the Route Alternatives in Overweight/Oversize Transportation
(Elsevier Ltd, 2026) Faruk Görçün, Ö.F.; Kundu, P.; Kucukonder, H.; Doğan, G.; Tirkolaee, E.B.; 01. Kadir Has University; Business Administration
Overweight and oversized transport (O&OT) has become one of the most critical elements of project logistics, driven by advancements in transportation and lifting technologies that now allow high-volume loads to be moved across long distances. This type of transportation operation, also called abnormal transportation, is greatly affected by technical factors such as the weight and geometry of the load, road surface, axle load limitations, slope, and ground strength, as well as external variables such as weather conditions, traffic density, and legal regulations. In planning and operational processes, Decision-Makers (DMs) and practitioners who plan and execute operations without adequately considering these factors and variables can lead to delays in operations, serious risks, and loss of productivity. This research proposes a flexible decision support model that integrates Step-wise Weight Assessment Ratio Analysis (SWARA) and Logarithmic Percentage Change-driven Objective Weighting (LOPCOW), and a ranking technique; i.e., Mixed Aggregation by Comprehensive Normalization Technique (MACONT) techniques to address the decision problems related to route selection, one of the most critical problems in transporting heavy and bulky loads, and to produce reasonable solutions. The proposed model significantly reduces information losses by processing subjective and objective information and integrating subjective (SWARA) and objective (LOPCOW) methods. Unlike traditional ranking approaches, the MACONT method combines three different normalization techniques to determine the ranking performance of alternatives. In this way, it provides more reliable and accurate results by reducing the deviations of the results provided by the single normalization technique. In addition, it shows each alternative's good and bad performance compared to the others and is more convincing about the results obtained. According to the results obtained by applying the proposed model, fuel consumption (0.096) is determined as the most effective and critical factor in selecting the route on which heavy and bulky loads will be transported. In this context, choosing routes that allow lower fuel consumption can contribute to reducing carbon emissions and external costs arising from transportation. The extensive robustness and validation check to test the proposed model prove that the proposed model is a reliable, robust, and practical decision-making tool for making reasonable and rational decisions in O&OT. © 2025 Elsevier B.V., All rights reserved.
Measuring the Semantic Priming Effect Across Many Languages
(Nature Research, 2025) Buchanan, E.M.; Cuccolo, K.; Heyman, T.; Van Berkel, N.; Coles, N.A.; Iyer, A.; Lewis, S.C.; 01. Kadir Has University
Semantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. Although previous studies provide insight into the cognitive underpinnings of semantic representations, they have suffered from small sample sizes and a lack of linguistic and cultural diversity. In this Registered Report, we measured the size and the variability of the semantic priming effect across 19 languages (n = 25,163 participants analysed) by creating the largest available database of semantic priming values using an adaptive sampling procedure. We found evidence for semantic priming in terms of differences in response latencies between related word-pair conditions and unrelated word-pair conditions. Model comparisons showed that the inclusion of a random intercept for language improved model fit, providing support for variability in semantic priming across languages. This study highlights the robustness and variability of semantic priming across languages and provides a rich, linguistically diverse dataset for further analysis. The Stage 1 protocol for this Registered Report was accepted in principle on 15 July 2022. The protocol, as accepted by the journal, can be found at https://osf.io/u5bp6 (registration) or https://osf.io/q4fjy (preprint version 6, 31 May 2022). © 2025 Elsevier B.V., All rights reserved.
Relationships Between Student Engagement in Higher Education and Academic Success and Desire to Attend University
(STAR Scholars Network, 2025) Bilir-Koca, B.; Karadaǧ, E.; 01. Kadir Has University
The purpose of this research is to examine the relationships between student engagement in a higher education system and the desire to attend university and academic success. A causal design was used in the research. The research sample consisted of 3,093 undergraduate students in Turkey, selected using the stratified sampling method. NSSE was used as a data collection tool in the study. Pearson product-moment correlation analysis, simple linear regression analysis, and multilevel logistic regression analysis were used to analyze the data. Within the scope of the research, it was determined that student engagement statistically significantly predicted academic success and the desire to attend university. As students' level of engagement increases, their academic success and desire to attend their universities also increase. In this context, universities should establish institutional policies to enhance student engagement and regularly evaluate their progress by measuring the level of student engagement within their institutions. © 2025 Elsevier B.V., All rights reserved.
Ranking Circularity Levels in Industrial Parks: A Holistic Approach Incorporating Environmental, Economic and Social Indicators
(Springer, 2025) Berk, İ.; Ediger, V.Ş.; Öztürk, E.B.; Uctug, F.G.; Kucuker, M.A.; Inan, A.; Aktuna, G.B.; Industrial Engineering; 05. Faculty of Engineering and Natural Sciences; 01. Kadir Has University
This study introduces a circularity ranking system at the meso-level, specifically targeting industrial parks, through the development of the Circular Economy Sustainability Index (CESI). The index comprises five economic-environmental indicators: energy intensity, emission intensity, water intensity, waste intensity, and recycling ratio, as well as a social indicator as a sixth dimension. We utilize CESI to evaluate the circular economy performance of 22 manufacturing firms in the Adana Hacı Sabancı Organized Industrial Zone (AOSB). AOSB, one of the most prominent industrial parks in Türkiye, serves as an excellent case study to assess companies’ circularity performance and identify areas for improvement in the country’s green industrial transformation endeavor. Our findings reveal that waste and recycling indicators are pivotal in determining circularity, contributing 34.6% to the overall score, while the social indicator adds another 16.3%. These results underscore the significance of effective waste management and social responsibility in enhancing circularity. © 2025 Elsevier B.V., All rights reserved.
Neurogenesis in the Zebrafish Olfactory Epithelium in Response to Neurotoxicity and Injury
(CRC Press, 2025) Kocagoz, Y.; Fuss, S.H.; 01. Kadir Has University
Solar Photovoltaic Development in West Africa Will Face Million-Ton Waste Challenges, and Off-Grid Systems Will Dominate
(2025) Dong, D.; Emem, O.; Liu, L.; Sen, B.; Rasmussen, K.; Edomah, N.; Liu, G.; 01. Kadir Has University
Solar photovoltaic (PV), especially off-grid systems, is a low-hanging fruit option among various renewable energy technology choices to address universal energy access, energy security, and climate challenges for vulnerable regions like West Africa. West Africa dominates in the uptake of solar PV solutions, while little attention has been paid to the potential PV waste generation. In this study, we developed a technology-specific, prospective material flow analysis model to investigate material stocks and flows of both on-grid and off-grid solar PV systems for 15 West African countries up to 2050. We show that the cumulative solar PV waste generation ranges from 2.3 to 7.8 million tons by 2050 in West Africa under different scenarios, around 70% of which comes from off-grid PV systems. The potential secondary materials supply ranges from 213 to 704 kilotons, which have potential economic value amounting to 143-475 million dollars or material equivalent to produce 6-19 GW of solar PV capacity. These results call for urgent policy attention, technology development, and infrastructure investment for future PV waste management and highlight the significance of addressing off-grid PV waste in Africa. This record is sourced from MEDLINE/PubMed, a database of the U.S. National Library of Medicine
A Nano-Design of Image Masking and Steganography Structure Based on Quantum Technology
(Elsevier Ltd, 2025) Salahov, H.; Ahmadpour, S.S.; Navimipour, N.J.; Das, J.; Rasmi, H.; Computer Engineering; 05. Faculty of Engineering and Natural Sciences; 01. Kadir Has University
Secure image storage and transmission require sound encryption methods that resist key exposure while maintaining high image quality. Various encryption approaches have been developed to protect image content and its transmission from unauthorized access. One such method is image masking, where a special mask is generated to conceal information within the original image. Instead of hiding the image visually, the mask creates an intermediate layer that obfuscates the encryption key, eliminating the need to transmit it directly. However, implementing such masking techniques efficiently at the hardware level poses particular challenges. Traditional Complementary Metal-Oxide-Semiconductor (CMOS)-based Very-Large-Scale-Integration (VLSI) systems face scalability issues, excessive heat, and high-power consumption. To overcome these challenges, this study utilizes a nano-scale image masking architecture based on Quantum-dot Cellular Automata (QCA), offering reduced area, lower power dissipation, and faster processing. The core operations utilize a three-input XOR gate, designed as a single-layer QCA structure without rotated cells. While QCA-based approaches improve hardware efficiency, most existing implementations focus only on grayscale images, leaving a gap in colorful image encryption. To address this, the work presents a QCA-based encryption and masking architecture for colored images. The method encrypts an image using a random key to generate a cipher image, which is then XORed with the original image to produce a mask. This process, applied independently to each RGB channel, produces three cipher-mask pairs, embedding steganographic property by concealing key information within the image. The keys are generated using a true random number generator (TRNG) based on cross-coupled loops and cross-oriented structures, ensuring high entropy. The design was modeled in QCADesigner 2.0.3, with the encryption/decryption algorithms implemented in Python. Experimental results demonstrated a meaningful reduction in cell count and consumed area compared to the prior designs. Image quality and security analysis confirmed visual fidelity and improved robustness. © 2025 Elsevier B.V., All rights reserved.
Evaluation of the Financial Performance of the Textile and Apparel Industry in Interval Type-2 Fuzzy Environment
(Elsevier Ltd, 2025) Faruk Görçün, Ö.F.; Shabir, M.; Çalık, A.; Isik, Ö.; 01. Kadir Has University; Business Administration
The Turkish textile and apparel sector plays a crucial role in the national economy through employment, exports, and investment. The financial performance of companies is a key determinant of their sustainability and competitiveness, especially in global markets. The Turkish textile and apparel sector is one of the essential industries in terms of macro-economic indicators such as net foreign exchange inflow, employment and investment. This sector is also one of the critical actors in world trade. A robust performance evaluation model is essential for stakeholders such as investors, creditors, and managers. However, the assessment of firms is a very critical decision involving uncertainty due to various conflicting criteria based on judgements. In this study, an integrated multi-criteria decision-making (MCDM) model including interval type-2 fuzzy hierarchy process (IT2FAHP) and Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) approaches are proposed to assess the financial performance of Turkish textile and clothing firms that are traded in Borsa İstanbul (BİST) in the period from 2006 to 2020. In line with the determined purpose, the arithmetic average of the determined financial ratios during the analysis period covering 15 years is computed to obtain long-term performance indicators. The importance weights of the selected financial criteria for the performance evaluation model are identified by employing the IT2FAHP approach. Then, the firms are ranked according to their financial performances with the CRADIS method. In addition, the results from the sensitivity analysis validate the proposed approach and prove that it is practical. Moreover, practical and managerial implications are discussed based on the results. The results offer valuable insights for strategic decision-making and can support efforts to enhance financial stability in the textile and apparel sector. According to the results, “LUKSK” had the highest long-term financial performance among the 11 companies discussed. This company is followed by BOSSA, YATAS, and ATEKS companies. The alternatives confirm the robustness of the proposed model in maintaining its place in the ranking in 190 scenarios. In addition, the comparative analysis confirms the consistency of the proposed ranking framework. © 2025 Elsevier B.V., All rights reserved.
A Low-Latency and Area-Efficient QCA-Based Quantum-Dot Design for Next-Generation Digital Sustainable Systems
(Elsevier Inc., 2025) Zohaib, M.; Ahmadpour, S.S.; Rasmi, H.; Khan, A.; Navimipour, N.J.; Computer Engineering; 05. Faculty of Engineering and Natural Sciences; 01. Kadir Has University
Digital sustainable system plays a vital role in the advancement of dynamic industries, including agriculture, healthcare, smart cities, Edge Artificial Intelligence (AI), and the Internet of Things (IoT), by facilitating high-speed, low-power, and highly compressed processing. These systems are based on the capabilities of real-time execution, processing, and analysis of large-scale information with extreme power and area limitations. However, traditional Arithmetic Logic Units (ALUs) based on complementary metal-oxide semiconductors (CMOS) are becoming challenging in terms of scalability, power consumption, space demand, and nanoscale fabrication. The ALU is one of the most important parts of such systems and has a direct effect on the overall computing performance, but current implementations cannot sustain the requirements of next-generation applications. To overcome these shortcomings, this paper offers an area-efficient and low-latency ALU that can be designed with the quantum-dot cellular automata (QCA) technology, with the advantage of employing area-efficient layout and simple cell design. The proposed QCA-based ALU has high performance, less delay, and less energy consumption, which makes it properly suitable for the next generation of digital sustainable systems applications. The outcome of the simulation indicates that there are considerable performance gains, such as an 82.37% decrease in energy consumption, and a 9.21% decrease in area relative to current available design. These enhancements emphasize the power of QCA technology as a scalable and low-energy consumption alternative to CMOS in the realization of critical computing components in sustainable digital systems. © 2025 Elsevier B.V., All rights reserved.
A New Design of Arithmetic and Logic Unit for Enhancing the Security of Future Internet of Things Devices Using Quantum-Dot Technology
(Elsevier Ltd, 2025) Zaker, M.; Ahmadpour, S.S.; Navimipour, N.J.; Zohaib, M.; Misra, N.K.; Kassa, S.; Alsaleh, O.I.; Computer Engineering; 05. Faculty of Engineering and Natural Sciences; 01. Kadir Has University
The Internet of Things (IoT) is a network of interconnected devices that collect, monitor, analyze, and exchange data. This technology plays a crucial role in the smart city infrastructure by seamlessly interconnecting various nodes. The extensive application and recognition of IoT across multiple city domains, such as healthcare, transportation, energy, education, and agriculture, bring significant challenges, with security among the most pressing. Traditional hardware technologies like Complementary Metal Oxide Semiconductor (CMOS) and Very Large Scale Integration (VLSI) suffer from limitations such as high power consumption and insufficient scalability, which hinder secure and sustainable IoT deployment. Such limitations have prompted the need to seek other technologies that would serve the dual purpose of providing security as well as energy. Quantum-based technologies can become adequate candidates offering promising solutions to make IoT devices and sustainable systems more secured. Quantum-dot Cellular Automata (QCA) has been proposed as a nanotechnology with the potential of consuming ultra-low powers, less area, and high-speed operation. QCA enhances security through sustainable computing objectives by minimizing energy usage. To improve the future security and efficiency of IoT hardware, this paper suggests a QCA-based Arithmetic Logic Unit (ALU). This ALU can generate more than 12 logical and arithmetic operations. Designed together with the majority gates, XOR gates, multiplexers, and full adders, the ALU is simulated using the QCA-Designer 2.0.3. Simulated results indicate improvements in the number of cells and reduced occupied area relative to the earlier designs. These results indicate the potential of QCA technology in enabling secure, energy-efficient, and compact computing architecture applicable in the future IoT. © 2025 Elsevier B.V., All rights reserved.
Overview of Personal Privacy Risks and Consequences in Smart Homes
(Institute of Electrical and Electronics Engineers Inc., 2025) Haider, U.; Imelda, M.T.; Nachevski, N.; 01. Kadir Has University
The Internet of Things (IoT) has transformed the world into an interesting place. Smart homes, multiplied by IoT, revolutionized human comfort and living. On the one hand, smart homes brought immense ease to lives, but on the other hand, this advancement brought many privacy concerns. Smart homes contain critical security vulnerabilities that are most often overlooked, like weak authentication, default login credential usage, lack of regular security updates, etc. Since smart appliances collect and use personal data, the security of smart homes should be the first and foremost priority. In this work, we collected a sample of real-world incidents and presented a new dataset. A case study approach was utilized to discuss common security concerns in smart home environments and the potential consequences of personal data privacy breaches. This paper focuses on financial loss, personal profiling, surveillance, targeted social engineering, and identity theft & fraud risks that can result after a personal data privacy breach. © 2025 Elsevier B.V., All rights reserved.
Markov Decision Processes: Monotonicity of Optimal Policy in Exponential and Quasi-Hyperbolic Discounting Parameters
(Elsevier B.V., 2025) Kılıç, H.; Canbolat, P.G.; Gunes, E.D.; 01. Kadir Has University
Intertemporal preferences of decision makers, i.e., the way they discount delayed utilities, impact their decisions. Empirical evidence suggests that individuals commonly have hyperbolic discounting preferences. This can result in time-inconsistent behavior, e.g., procrastination, which may be a barrier to adopting preventive behavior such as machine maintenance and patient adherence to treatment. In this paper, we theoretically compare the actions of individuals based on their discounting characteristics. We consider the Hyperbolic Discounting (HD) model, which is more representative of individual behavior than Exponential Discounting (ED). We formulate a discrete-time finite-horizon Markov decision process with Quasi-Hyperbolic Discounting (QHD), an analytically tractable function representing HD and present sufficient conditions that ensure the monotonicity of the optimal policy in the discounting parameters. We consider submodular maximization or supermodular maximization problems. Our paper is the first to investigate the monotonicity of the optimal policy in QHD parameters for these problems. Moreover, we compare the optimal actions under ED and QHD. We apply our results to the settings of machine maintenance, individual health behavior and inventory control. We provide numerical examples that show there might not be monotonicity if our sufficient conditions are not met. Also, we explore the discrepancy between the expected total exponentially-discounted rewards of the actions obtained from QHD and of the actions that are optimal under ED, and observe that this discrepancy is affected mainly by the present bias. © 2025 Elsevier B.V., All rights reserved.
Training and Evaluation of a Variational Autoencoder for Seismic Station Data Analysis
(Institute of Electrical and Electronics Engineers Inc., 2025) Gurkan, D.B.; Tileylioglu, S.; Akagündüz, E.; 01. Kadir Has University; 05. Faculty of Engineering and Natural Sciences; Civil Engineering
In this study, a variational autoencoder model is proposed to encode the geological, geophysical, and geographical data of seismic stations in Turkey. The model encodes various station-specific data, such as site frequency, surface lithology, and latitude-longitude, into a low-dimensional latent space, aiming to disentangle the generative factors of these data and improve their representation. This approach effectively represents the regional characteristics surrounding the station, enabling the disentanglement of station-related effects. Evaluations based on disentanglement and completeness scores indicate that the model successfully distinguishes station characteristics, such as the average shear wave velocity in the top 30 meters of the surface. The resulting station data encoder can provide additional information to deep learning models processing acceleration records, contributing to a better understanding and modeling of station effects in seismic analysis. © 2025 Elsevier B.V., All rights reserved.
Variational Autoencoders for P-Wave Detection in Strong Motion Earthquake Records
(Institute of Electrical and Electronics Engineers Inc., 2025) Ispak, T.S.; Tileylioglu, S.; Akagündüz, E.; 01. Kadir Has University; 05. Faculty of Engineering and Natural Sciences; Civil Engineering
Earthquake early warning systems rely on accurate detection of Primary waves before the destructive Secondary waves arrive. However, identifying P-wave onsets in strong-motion accelerograms is challenging due to high noise, limited labeled data, and complex waveforms. This paper proposes a Variational Autoencoder framework for self-supervised P-wave detection in strong-motion data. A Convolutional VAE is trained to reconstruct P-wave segments while rejecting noise and non-P-wave inputs. We employ a sliding window method, combining reconstruction loss and normalized cross-correlation, to locate P-wave arrivals. Experimental results on 1, 2, and 3 second segments show robust performance with area-under-the-curve up to 0.97, demonstrating improved accuracy for longer segments and reduced computational cost for shorter segments. © 2025 Elsevier B.V., All rights reserved.
Risk Perceptions and Financial Decision Making
(Borsa Istanbul Anonim Sirketi, 2025) Togan, A.; Tiniç, M.; Giray, T.C.; International Trade and Finance; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
We examine whether training individuals about the riskiness of financial products changes their risk perception in making financial decisions. Conducting a nationwide survey in Turkey, we first map individuals’ use of regulated and unregulated financial products in borrowing, saving, and investing. We next train a randomly selected sample of people in three regions where use of unregulated or risky products is high and test their financial preferences by asking them to take the survey after the training. With controls for observable characteristics, our results suggest that training on the riskiness of financial products helps improve individuals' risk perception, and this improvement seems to motivate them to prefer regulated financial products and to seeking professional advice about borrowing, saving, and investment. © 2025 Elsevier B.V., All rights reserved.

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