WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12469/4465

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Governing Migrant Mobilities in the Aegean Sea From Moral Rhetoric to Blatant Use of Violence
(SUNY-State Univ New York Press, 2024) Karadag, Sibel; Political Science and International Relations; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
In Silico Identification and Evaluation of Antibiofilm Agents to Overcome Drug Resistance in Hepatocellular Carcinoma
(Wiley, 2025) Baltaci, N.; Alisarli, S.; Guven, E. Bilget; 01. Kadir Has University
Potential Allosteric Sites Investigated in Hexokinase of Plasmodium Vivax and Humans, to Identify Possible Species-Specific Drugs
(Wiley, 2025) Vural, L. S.; 01. Kadir Has University
Efficient QCA-Based Circuits for Low-Power Medical IoT System
(Elsevier, 2025) Ajitha, D.; Zohaib, Muhammad; Ahmad, Firdous; Zaman, Khalid; Prabin, S. M.; 01. Kadir Has University
The Internet of Things (IoT) plays a vital role in the recent healthcare industry by providing precise diagnostic and treatment capabilities. There is a growing interest in medical IoT incorporated into healthcare systems. The processing unit of all medical IoT comprises complementary metal-oxide semiconductor (CMOS) technology. However, CMOS Medical IoT technology has become integrated into biomedical hardware systems at the nanoscale regime. Due to regulatory, ethical, and technological challenges, including slow processing speeds, high power consumption, and slow switching frequencies, particularly in the gigahertz (GHz) range. On the other hand, compared to traditional computers, quantum technology will accelerate processing by an order of magnitude and affect all artificial and medical (AI) and medical IoT processing applications. Quantum-dot cellular automata (QCA) present a promising alternative digital hardware system in medical IoT. QCA technology makes an optimal choice for establishing circuit design frameworks for AI in medical IoT applications, where low-cost, real-time, energy-efficient performance is crucial. Moreever, encryption and decryption circuits have been used in medical IoT operations to protect sensitive patient data while it is being transmitted and stored. The essential arithmetic and logic unit (ALU) is proposed in this context, which is the foundation for processing and computational units for medical IoT systems at the nanoscale devices. A systematic approach is involved in integrating adders, multiplexers, an ALU, and a logic unit to enhance processor drive and privacy via encryption and decryption in medical IoT. The experimental outcomes reveal that the recommended design overtakes the previous design by 15.48 % in terms of cells and 16.07 % in terms of area. The designs are accurately simulated using the QCADesigner-E 2.0.3 software tool.
Rethinking Performance Measurement in Esports: How Games Shape Coaching Priorities for Amateur Player Development
(Sage Publications Ltd, 2025) El Oraiby, Maryam; Kiygi-Calli, Meltem; Business Administration; 01. Kadir Has University
With the rising popularity of esports, the demand for effective performance assessment methods has become critical for coaching practices. Esports performance evaluation remains a complex task due to the unique demands of different games and the dynamic nature of the esports ecosystem. Additionally, the effectiveness of performance indicators in evaluating amateur players in the early stages of their careers remains unclear. To investigate this, we conduct expert interviews with head coaches of three distinct team games, Valorant, PUBG Mobile, and League of Legends, to uncover critical factors that coaches consider in evaluating amateur player performance. Our findings reveal fundamental challenges that question the validity of standardized indicators for performance measurement. Coaches highlighted that while quantitative metrics, such as in-game statistics, are commonly used in tracking player progress, they must be carefully contextualized within the unique demands of each game and the team dynamics. Moreover, coaches underscore that soft skills are as determinant as individual technical skills for fostering future career success in esports, particularly in team-based games where communication and stress management play a crucial role. These findings highlight the need for a more nuanced, coach-driven approach to performance assessment for amateur players. This exploratory study suggests that performance measurement frameworks need to account for context-dependent competencies and player career development in esports.
Impacts of the Changes in Agriproduction on Rural Heritage in the Case of Müşküle, Iznik, Turkey
(Emerald Group Publishing Ltd, 2025) Ulu, Damla Pilevne; Erkan, Yonca; Alkan Reis, Amine Seyhun; Guvenc, Himmet Murat; Cavur, Mahmut; Management Information Systems; Architecture; 03. Faculty of Economics, Administrative and Social Sciences; 06. Faculty of Art and Design; 01. Kadir Has University
PurposeAgriculture is both a constituent and an integral part of rural culture. Therefore, agricultural planning is essential to the conservation of rural heritage. However, this relationship has received limited scholarly attention. Focusing on the rural settlement of M & uuml;sk & uuml;le in Turkey, this research paper reveals the vital role of agricultural planning in sustaining rural architectural heritage.Design/methodology/approachWe examine the settlement's history of agricultural production in relation to national agricultural policies and practices from the early 20th century to the present, analyzing how these shifts have affected the built heritage. The research is a combination of literature review, fieldwork and face-to-face interviews. Aerial images, on-site architectural surveys and interviews were used to identify the features of the built environment. These were followed by in-depth thematic analysis.FindingsWe find that the lack of agricultural planning has led to economic decline among rural households, resulting in the neglect of architectural heritage, abandonment of traditional dwellings and increased rural outmigration. As specialized agricultural products shape the character of rural architecture, changes in production can lead to the removal of heritage-valued building elements, degradation of traditional architectural features and loss of traditional knowledge.Originality/valueThis paper demonstrates the strong link between rural production (i.e. agriproduction) and architectural heritage. It shows how agriproduction shapes rural fabric, plan typologies and building elements and underscores the decisive role of agricultural planning in rural heritage conservation.
Multi-Omics Profiling Uncovers LINC00486-Associated lncRNA Regulation in Human Traumatic Brain Injury
(Springer, 2025) Al-Rubaye, Tala; Isa, Zenab; Erenkol, Doga; Tarahomi, Elham; Erdogan, Nuray Sogunmez; 01. Kadir Has University
BackgroundTraumatic brain injury (TBI) induces broad molecular changes in the human brain, altering gene expression in diverse neural and glial cells. While the transcriptional effects of TBI on protein-coding genes are well characterized, the roles of long noncoding RNAs (lncRNAs), key regulators of gene expression and chromatin, remain largely unknown.ObjectiveOur objective was to identify lncRNAs altered in TBI and explore their potential regulatory functions.MethodsWe applied an integrative multi-omics approach combining single-nucleus RNA sequencing (snRNA-seq), isoform-level transcriptomics, transposable element (TE) annotation, and RNA-binding protein (RBP) interaction analyses. Public snRNA-seq datasets from cortical tissues of 12 TBI patients and 5 controls were analyzed to resolve injury-driven transcriptional signatures. We have performed differential expression analysis on 12,801 human lncRNAs, examined isoform-specific expression with TE content, and explored RBP-lncRNA interactions using CLIP-seq data.ResultsCell-type diversity decreased in TBI, and reactive and progenitor-like states were expanded. We identified 190 upregulated lncRNAs, mainly in glial cells. Among these, LINC00486 emerged as a brain-enriched lncRNA consistently increased after TBI. Isoform analysis showed its dominant brain isoform contains LINEs and LTRs, linking it to regulatory networks associated with endogenous retroelement activation. Functional enrichment connected LINC00486 to neurodevelopment, serotonin metabolism, and neuroinflammatory pathways. CLIP-seq data confirmed its interactions with stress-responsive RBPs such as AGO2 and TARDBP.ConclusionsOur multi-omics analysis identifies LINC00486 as a potential regulator of transcriptional plasticity in TBI. Its TE content and RBP interactions suggest a role in lncRNA-mediated regulatory networks during injury, highlighting possible therapeutic targets in neurotrauma.
Veterinary Ethics in Practice: Euthanasia Decision Making for Companion and Street Dogs in Istanbul
(MDPI, 2025) Yildirim, Mine; 01. Kadir Has University
This article examines how veterinarians in Istanbul experience and navigate the ethical, emotional, and institutional complexities of performing euthanasia on dogs, with particular attention to the differences between companion and street dogs. Drawing on 29 in-depth interviews with private practice veterinarians in Istanbul, this study employs qualitative analysis using the NVivo 12 Plus software and reflexive thematic analysis to identify key patterns in moral reasoning, emotional labor, and clinical decision making. The findings indicate that euthanasia of companion dogs is often framed through shared decision making with guardians, emotional preparation, and post-procedural grief rituals. While still emotionally taxing, these cases are supported by relational presence and mutual acknowledgment. In contrast, euthanasia of street dogs frequently occurs in the absence of legal ownership, institutional accountability, or consistent caregiving, leaving veterinarians to bear the full moral and emotional weight of the decision. Participants described these cases as ethically distinct, marked by relational solitude, clinical ambiguity, and heightened moral distress. Six key themes that reveal how euthanasia becomes a site of both care and conflict when structural support is lacking are identified in this study, including emotional burden, ethical strain, and resistance to routinized killing. By foregrounding the roles of institutional absence and relational asymmetry in shaping end-of-life decisions, this study contributes to empirical veterinary ethics and calls for more contextually attuned ethical frameworks, particularly in urban settings with large populations of street dogs and culturally entrenched practices of collective guardianship and caregiving.
Detection of Early School Drop Out in Vocational and Technical High Schools in Turkey
(Sage Publications inc, 2025) Korkmaz, Ozgur; Aydin, Mehmet Nafiz; Management Information Systems; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
This study investigates the factors contributing to early school dropout in vocational and technical high schools in Turkey, utilizing machine learning techniques to analyze a dataset of personal, socio-economic, familial, and academic variables. The data was collected via a detailed survey administered to students at one of the largest Vocational and Technical High School in Istanbul, capturing 35 features (factors) relevant to dropout rates. Various classifiers, including Decision Trees and Random Forest, were employed to identify at-risk students with high accuracy. The Decision Tree model, enhanced by the Synthetic Minority Over-sampling Technique (SMOTE), demonstrated the best results for identifying potential dropouts, indicating its effectiveness in educational settings where early intervention is critical. By feature importance analysis this research reveals that parental education levels, family structure, and financial hardships are significant predictors of dropout likelihood. Despite the study's limitations, such as a small dataset and some features with zero-filled columns, the results underscore the importance of data-driven approaches in developing targeted interventions to reduce dropout rates. This research not only enhances the understanding of dropout phenomena in Turkish vocational education but also provides practical insights for policymakers and educators to improve student retention through early and informed interventions. The findings highlight the potential of machine learning to enhance educational support systems, ensuring that every student can succeed.
Exploring Challenges in Deep Learning of Single-Station Ground Motion Records
(Springer Heidelberg, 2025) Caglar, Umit Mert; Yilmaz, Baris; Turkmen, Melek; Akagunduz, Erdem; Tileylioglu, Salih; Civil Engineering; 05. Faculty of Engineering and Natural Sciences; 01. Kadir Has University
Contemporary deep learning models have demonstrated promising results across various applications within seismology and earthquake engineering. These models rely primarily on utilizing ground motion records for tasks such as earthquake event classification, localization, earthquake early warning systems, and structural health monitoring. However, the extent to which these models truly extract "deep" patterns from these complex time-series signals remains underexplored. In this study, our objective is to evaluate the degree to which auxiliary information, such as seismic phase arrival times or seismic station distribution within a network, dominates the process of deep learning from ground motion records, potentially hindering its effectiveness. Our experimental results reveal a strong dependence on the highly correlated Primary (P) and Secondary (S) phase arrival times. These findings expose a critical gap in the current research landscape, highlighting the lack of robust methodologies for deep learning from single-station ground motion recordings that do not rely on auxiliary inputs.
Citation - Scopus: 1
Reforming External Debt Governance in Turkey to Reach External Debt Sustainability
(Elsevier, 2025) Togan, Asli; Togan, Subidey; 01. Kadir Has University
The paper argues that the attainment and maintenance of external debt sustainability is challenging, and that it is not a choice. A country whose government fails to respect external debt sustainability would eventually default on its external debt. But in the case of default the penalty is the inability to borrow in international markets, and hence the cost of defaulting could be extremely high. The paper emphasizes the importance of having a functioning external debt governance system that will reduce the probability of explosive debt trajectories over time requiring solutions to the following three issues. First, in policy circles minds should be clear about the importance of achieving sustainability of external debt. Second, policy makers have to agree on the way to attain external debt sustainability. Based on empirical analysis, the paper recommends implementing legal reforms, reducing inflation, and devaluing when necessary the real exchange rate. Finally, the country needs to find a way to translate the concept of external debt sustainability into policy technicality. In particular, such a translation requires the development of an institution that when established will enable the country to avoid facing external debt problems over time. The paper proposes the creation of an independent public advisory body, the External Debt Council, equipped with adequate resources to ensure sustainable debt management, and building and sustaining social consensus in the society on the achievement of external debt sustainability that will bind not only the officials in the present government but also the officials in future governments.
Citation - WoS: 1
Citation - Scopus: 1
Epistemic Norm Differences Matter
(Routledge Journals, Taylor & Francis Ltd, 2025) Yilmaz, Onurcan; Isler, Ozan; Psychology; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
Sarcasm Detection on News Headlines Using Transformers
(Springer, 2025) Gumuscekicci, Gizem; Dehkharghani, Rahim; Computer Engineering; 05. Faculty of Engineering and Natural Sciences; 01. Kadir Has University
Sarcasm poses a linguistic challenge due to its figurative nature, where intended meaning contradicts literal interpretation. Sarcasm is prevalent in human communication, affecting interactions in literature, social media, news, e-commerce, etc. Identifying the true intent behind sarcasm is challenging but essential for applications in sentiment analysis. Detecting sarcasm in written text, as a challenging task, has attracted many researchers in recent years. This paper attempts to detect sarcasm in news headlines. Journalists prefer using sarcastic news headlines as they seem much more interesting to the readers. In the proposed methodology, we experimented with Transformers, namely the BERT model, and several Machine and Deep Learning models with different word and sentence embedding methods. The proposed approach inherently requires high-performance resources due to the use of large-scale pre-trained language models such as BERT. We also extended an existing news headlines dataset for sarcasm detection using augmentation techniques and annotating it with hand-crafted features. The proposed methodology could outperform almost all existing sarcasm detection approaches with a 98.86% F1-score when applied to the extended news headlines dataset, which we made publicly available on GitHub.
Rewilding-Based Planning Against Ecocide: A Strategic Response to Infrastructure-Led Ecological Degradation in Northern Istanbul (2006-2025)
(Routledge Journals, Taylor & Francis Ltd, 2025) Demirtas, Emre; Gunes, Gulsah; 01. Kadir Has University
This study critically analyses the ecological damage caused by mega-projects and uncontrolled urbanization in northern Istanbul between 2006 and 2025. It develops a strategic framework based on rewilding principles to address these issues. The research comprehensively examines the ecological changes triggered by infrastructure projects such as the Third Airport, the Northern Marmara Highway, and the Istanbul Canal, supported by spatial data analysis. The study examines how urban development affects water systems, forests, and wildlife connections. It combines urban political ecology, post-humanist ideas, and a planning approach focusing on rewilding. One of the main contributions of this study is that it introduces the concept of rewilding as a spatial planning approach adapted to Istanbul's unique ecological context. The idea is shown through a design proposal for a national architecture competition about the K & uuml;& ccedil;& uuml;k & ccedil;ekmece Lagoon Basin. The conclusion highlights how this proposal could help restore Istanbul's ecological systems, boost biodiversity, and improve climate resilience. It also acknowledges the challenges from institutions and laws, stressing the need to reconnect city residents with nature. Positioning rewilding as an ecological and socio-spatial strategy, this research offers a critical perspective on how cities like Istanbul can resist environmental degradation and build a more sustainable urban future.
Beyond the Disaster: Vulnerability of Governance in the Turkey-Syria Earthquake
(Palgrave Macmillan, 2024) Yilmaz, Elif Ebru; Tomaka, Deniz Halman; 01. Kadir Has University; Core Program; 07. Core Program
Data-Driven Modeling of Traffic Flow in Macroscopic Network Systems
(AIP Publishing, 2025) Firat, Toprak; Eroglu, Deniz; Molecular Biology and Genetics; 05. Faculty of Engineering and Natural Sciences; 01. Kadir Has University
Urban traffic modeling is essential for understanding and mitigating congestion, yet existing approaches face a trade-off between realism and scalability. Microscopic agent-based simulators capture individual vehicle behavior but are computationally intensive and hard to calibrate at scale. Macroscopic models, while more efficient, often rely on strong assumptions, such as fixed origin-destination flows, or oversimplify network dynamics. In this work, we propose a data-driven macroscopic model that simulates traffic as a discrete-time load-exchange process over flow networks. The model captures key phenomena such as bottlenecks, spillbacks, and adaptive load redistribution using only road-type attributes, network structure, and observed traffic density. Parameter learning is performed via evolutionary optimization, allowing the model to adapt to both synthetic and real-world conditions without assuming latent travel demand. We evaluate the framework on synthetic grid-like networks and on real traffic data from London, Istanbul, and New York. The resulting framework provides a scalable and interpretable alternative for urban traffic forecasting, balancing predictive accuracy with computational efficiency across diverse network conditions.
Addressing Social Vulnerabilities Resulting From Low-Carbon Energy Transition Policies in EU-27 Countries: A Systematic Survey of the Literature
(Pergamon-Elsevier Science Ltd, 2026) Albulut, Koray; 01. Kadir Has University
Low-carbon transition research has experienced exponential growth in recent years, driven by the urgent need to mitigate climate change and achieve sustainability goals. The disruption of traditional industries, increased energy costs, and changes in land use are inevitable consequences of the low-carbon turn, often adversely impacting the least equipped to handle it. Vulnerable groups often face the greatest risks from climate change and the side effects of the policies designed to combat it. This study conducts a systematic literature review following the PRISMA 2020 guidelines, covering publications from the Web of Science and Scopus databases. Data were extracted into spreadsheets for descriptive analytics, and trends in publication years, countries, and policy tools were visualized with Python-generated heatmaps and summary tables. The findings reveal that despite best efforts to unburden vulnerable groups, many unaddressed concerns remain in the European 27 countries, where one might least expect them. The analysis highlights how one-size-fits-all policies ignore regional and social differences, disproportionately burdening vulnerable groups while favoring wealthier segments through subsidies and incentives. The mixed effectiveness of countermeasures-such as social tariffs, subsidies, and the Just Transition Mechanism-highlights ongoing challenges, including misrecognition, elite capture, and institutional constraints, while also underscoring notable successes like participatory community energy projects and locally tailored retrofitting initiatives. This research underscores the necessity of moving beyond uniform solutions, advocating for locally sensitive, equitable approaches that address affected communities' diverse needs and aspirations while ensuring social and environmental justice in the transition to a lowcarbon economy.
Verbal Harassment Detection in Online Games Using Machine Learning Methods
(Elsevier Sci Ltd, 2025) Hibatullah, Helmi; Balli, Tugce; Yetkin, E. Fatih; Business Administration; Management Information Systems; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
Video games have been an inseparable aspect for many throughout their upbringing. The widespread adoption of the internet in the early 2000s has brought video games from the traditional offline media to the online environment. Consequently, people from different parts of the world can play together and communicate in-game with each other. Nowadays, most massively multiplayer online games (MMOs) incorporate voice communication features. Playing video games online with a certain degree of anonymity, along with the ability to verbally communicate with each other, has proven to be a dangerous combination that can breed toxic and abusive behaviors if left unmoderated. This paper proposes a new approach to integrating Whisper, a pre-trained automatic speech recognition (ASR) model, with the well-researched topic of text-based abusive behavior detection. Our proposed verbal harassment detection pipelines yielded an average F-score of 0.899 for all variants tested.
Integrated Lighting and Solar Shading Strategies for Energy Efficiency, Daylighting and User Comfort in a Library Design Proposal
(MDPI, 2025) Kaymaz, Egemen; Manav, Banu; Interior Architecture and Environmental Design; 06. Faculty of Art and Design; 01. Kadir Has University
This research proposes an integrated lighting and solar shading strategy to improve energy efficiency and user comfort in a retrofit project in a temperate-humid climate. The study examines a future library addition to an existing faculty building in Bursa, featuring highly glazed fa & ccedil;ades (77% southwest, 81% northeast window-to-wall ratio), an open-plan layout, and situated within an unobstructed low-rise campus environment. Trade-offs between daylight availability, heating, cooling, lighting energy use, and visual and thermal comfort are evaluated through integrated lighting (DIALux Evo), climate-based daylight (CBDM), and energy simulations (DesignBuilder, EnergyPlus, Radiance). Fifteen solar shading configurations-including brise soleil, overhangs, side fins, egg crates, and louvres-are evaluated alongside a daylight-responsive LED lighting system that meets BS EN 12464-1:2021. Compared to the reference case's unshaded glazing, optimal design significantly improves building performance: a brise soleil with 0.4 m slats at 30 degrees reduces annual primary energy use by 28.3% and operational carbon emissions by 29.1% and maintains thermal comfort per ASHRAE 55:2023 Category II (+/- 0.7 PMV; PPD < 15%). Daylight performance achieves 91.5% UDI and 2.1% aSE, with integrated photovoltaics offsetting 129.7 kWh/m2 of grid energy. This integrated strategy elevates the building's energy class under national benchmarks while addressing glare and overheating in the original design.
Diversity as a Core Feature of Language Acquisition: A Commentary on Scaff et al. (2025)
(Wiley, 2025) Goksun, Tilbe; Aktan-Erciyes, Asli; 01. Kadir Has University; Psychology; 03. Faculty of Economics, Administrative and Social Sciences
This commentary builds on Scaff et al.'s (2025) systematic review of the CHILDES database, highlighting persistent biases in child language corpora and research. We expand the discussion, emphasizing three key areas: (1) the need to diversify naturalistic data across languages to strengthen language acquisition theories; (2) the importance of including diverse child and parent demographics within specific language environments; and (3) the underrepresentation of bilingual samples from non-WEIRD, non-Indo-European contexts. We argue that these limitations not only hinder generalizability but also shape prevalent theoretical assumptions. Promoting inclusive, globally representative corpora is important for advancing a fair and accurate understanding of child language acquisition.

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