WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Widening the Moral Circle: Perceived Responsibility and Guilt as Mediators of Moral Expansiveness and pro-Environmental Behavior(Springer, 2026) Sari, Erkin; Coskun, Muhammet; Cingoz-ulu, Banu; Candanoglu, InciPrevious research has shown that moral constructs, such as personal norms, play a significant role in shaping individuals' pro-environmental engagement. However, the specific influence of moral expansiveness on such behaviors has received limited attention in the existing literature. This study aims to investigate the relationship between moral expansiveness and pro-environmental engagement within the Turkish context, which is particularly vulnerable to the impacts of climate change. It specifically examines the mediating roles of perceived environmental responsibility and environmental guilt to better understand the mechanisms underlying this association. In total, 244 undergraduate students (167 women, 76 men, 1 did not state; Mage = 21.95, SDage = 2.58) participated in the current study in exchange for course credit. Findings indicated that moral expansiveness indirectly predicted pro-environmental behaviors through both perceived environmental responsibility and environmental guilt, although its direct effect was not significant. Perceived environmental responsibility and environmental guilt both significantly predicted pro-environmental behaviors. Additionally, individuals with more left-leaning political views reported higher levels of pro-environmental behavior. This study highlights how broader moral concern can support sustainability, while contributing to the inclusion of non-Western contexts in environmental psychology.Article Predicting Respiratory Infection and Symptoms Development Using Gene Set Enrichment Scores and Machine Learning(Elsevier Sci Ltd, 2026) Aydin, Zafer; Isik, Yunus EmreRecent advancements in precision medicine enable personalized predictions grounded in individual-level genetic data. However, relying solely on a single type of data can decrease prediction accuracy and limit the biological interpretability of the resulting models. Incorporating predefined genetic knowledge, such as derived gene sets, can improve performance and provide deeper biological insights for complex diseases, including respiratory infections. This study aimed to evaluate the usability of enrichment scores (ES), calculated using gene sets from the Molecular Signatures Database (MSigDB), as a feature representation for machine learning models to predict respiratory viral infections and symptom development. In addition, the proposed feature representation approach was extensively compared with the de facto gene-level expression representation. A total of 36,834 predefined gene sets were compiled from the MSigDB, and their ES values were calculated. Experiments used the GSE73072 dataset from Gene Expression Omnibus, containing gene expression profiles before and after virus exposure. Various machine learning and feature selection algorithms were applied to ES-based and probe-level feature sets. The results showed that both feature representation approaches achieved an area under the precision-recall curve (AUPRC) value greater than 0.90 for all tasks. Compared with the Respiratory Viral DREAM Challenge leaderboard phase, our models showed a 14.8% improvement in pre-exposure predictions (T0) and a 17.4% improvement in symptom classification. Using enrichment scores as a feature representation generally resulted in better performance than probe-level representation when predicting respiratory infections and symptom development. Identifying key gene sets through feature selection and comparing them with essential genes for respiratory viruses enabled a more comprehensive analysis, providing deeper insights into the pathways that contribute to these predictions.Article Driving Sustainability and Sustainable Developmental Goals in Hospitality: The Influence of Green HRM, Environmental CSR and Shared Green Vision on Pro-Environmental Behaviour(Wiley, 2026) Rafiq, Nosheen; Bulbul, Yagiz Efe; Cobanoglu, Cihan; Shah, Syed Haider Ali; Raza, GulzaibEnvironmental sustainability is highly dependent on the employees' pro-environmental behaviour (PEB) particularly in the hospitality industry context, yet there is still a gap of explanation on mechanisms that systematically improve employees' PEB. Based on Social Identity Theory, this study attempts to examine how green human resource management (GHRM) practices influence employees' PEB in two paths, directly and indirectly, through dual mediating mechanisms: environmental corporate social responsibility (ECSR) and green shared vision (GSV). Using a quantitative design, the study data were collected from 449 hotel managers through a structured questionnaire and analyzed with SmartPLS (SEM). The results show that GHRM has a significant positive effect on PEB and also through indirect effects of both ECSR and GSV. Further, the model explains the substantial amount of variance in PEB. The effect size reveals that indirect pathways are stronger mechanisms than the direct path from GHRM to PEB. Through bringing together the collective cognitive and identity based aspects into one framework, this study extends the theoretical understanding of mechanisms of how GHRM transform sustainability strategies into employee level environmental engagement, which is also aligned with the Sustainable Development Goals (SDGs). The study empirically broadens the GHRM research in the hospitality sector within developing country context and also presents multiple practical insights for hotel policymakers and managers to synchronize environmental strategies with employee conduct.Article Time Distributed Classification of Alzheimer’s Disease on MRI Scans(John Wiley and Sons Ltd, 2026) Dundar, Mehmet Sait; Yilmaz, BulentThe diagnosis of Alzheimer's disease (AD) has progressively depended on sophisticated neuroimaging methods alongside cognitive assessments. This study combines volumetric feature analysis with computational modeling techniques, focusing on spatial and temporal analysis, to categorize individuals as cognitively normal (CN), mild cognitive impairment (MCI), or AD using magnetic resonance imaging (MRI) data. In the initial phase, volumetric changes, comprising cortical thickness, white matter, grey matter, cerebrospinal fluid, and total intracranial volume, were derived from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset utilizing the CAT12 toolbox in statistical parametric mapping (SPM). Linear regression was utilized on these variables over time to create slopes that reflect volumetric change rates, which then served as inputs for machine learning classifiers. The slopes of cortical thickness exhibited the greatest classification accuracy, reaching 82.5% with a random forest model for differentiating AD from CN individuals. During the second phase, a deep learning methodology was utilized, relying solely on the MRI scans and excluding the outcomes from the first phase. A pre-trained 3D ResNet-101 convolutional neural network (CNN) model extracted spatial characteristics from MRI volumes, whereas long short-term memory (LSTM) networks recorded temporal dynamics across subsequent annual scans. This hybrid CNN-LSTM design markedly improved classification performance, attaining 96.7% accuracy for AD against CN and enhancing the distinction of MCI cases. Nonetheless, discrepancies in MCI categorization were chiefly ascribed to the restricted access to annual MRI data and the model's pre-training on CN and AD cohorts. These findings highlight the potential of integrating volumetric statistical analysis with deep learning for automated AD categorization. This work enhances neuroimaging diagnostic methods by utilizing both spatial and temporal MRI data, enabling early diagnosis and better evaluation of disease development.Article Threat Landscape of Edge-AI-Assisted Connected Autonomous Vehicles (CAV)(Elsevier B.V., 2026) He, Ligang; Maple, Carsten; Atmaca, Ugur Ilker; Kasyap, Harsh; Nezhad, Mahshid Mehr; Atmacaa, Ugur Ilker; Nezhada, Mahshid Mehr; Kasyapa, HarshConnected Autonomous Vehicles (CAVs) powered by Edge Artificial Intelligence (Edge-AI) are revolutionising intelligent transportation systems through real-time data processing for obstacle avoidance, adaptive learning of dynamic traffic patterns, and improved decision-making in safety-critical tasks like traffic sign recognition. However, deploying machine learning (ML) models directly at the vehicle level introduces new security vulnerabilities, making CAVs susceptible to adversarial attacks that can compromise system integrity, reliability, and safety. These attacks primarily target onboard ML systems, highlighting the need for a structured threat modelling approach to identify and mitigate risks. This paper investigates the threat landscape of Edge-AI-assisted CAVs, focusing on the CAV layer as a critical attack surface within the hierarchical system architecture. Employing the STRIDE framework, we analyse vulnerabilities across four stages of the ML lifecycle: input data, model training, aggregation and inference. We present adversarial concept drift as a case study of an evolving threat in which malicious actors introduce subtle data manipulations over time to degrade ML model performance while avoiding detection. Through attack tree analysis and experimental evaluation, we show how adversarial concept drift propagates through the system, ultimately affecting its reliability. To the best of our knowledge, this is the first study to illustrate the impact of gradual adversarial concept drift on robustness. Our results reveal that gradual drifts are harder to detect than sudden ones, even when using Byzantine-robust defenses. These findings underscore the limitations of existing security mechanisms and emphasise the urgent need for adaptive, resilient countermeasures to address the dynamic, evolving threat landscape facing Edge-AI-enabled CAV systems.Article System-Level Design and Experimental Demonstration of a Laser Power Transfer System(Elsevier Sci Ltd, 2026) Boyekin, Tahsin; Sari, A. Yigit; Boynuegri, Ali Rifat; Yigit, Hayri; Herdem, Yusuf; Kalebasi, M. Talha; Kumru, Celal F.This study presents the design and experimental validation of a laser-based wireless power transfer (LPT) system that integrates electrical, optical, and control subsystems within a unified framework. The proposed setup consists of a silicon carbide (SiC)-based LLC resonant converter driving a high-power laser diode, an optical transmission link of 20 m, and a photovoltaic (PV) receiver equipped with a maximum power point tracking (MPPT) unit. The system was designed to evaluate the feasibility of stable, continuous optical power transfer under realistic alignment and thermal conditions. Experimental results demonstrate that the laser driver achieves an electrical efficiency of 95.96%, while the overall end-to-end LPT efficiency reaches 12.4% at a delivered power level of approximately 99 W. The findings confirm that coordinated design of the driver, optical path, and receiver sub systems can significantly improve operational stability and energy conversion consistency, offering a practical reference for the development of medium-range optical power transfer systems.Article Spatial Proximity and Accessibility Patterns of X-Minute Cities(Elsevier Ltd, 2026) Östh, John; Türk, Umut; Kourtit, Karima; Nijkamp, PeterThe 15-minute city concept has gained prominence in urban planning as a framework linking proximity, mobility, and quality of life at the intra-urban scale. An open question is whether this concept remains meaningful when applied to spatially connected urban regions or national urban systems. To address this issue, the new X-minute city concept allows proximity thresholds to vary with spatial context, mobility conditions, and service distribution. This paper develops an operational framework with the aim to examine and highlight spatial accessibility patterns from an X-minute city perspective, using the relatively urbanized area of the Netherlands as a national-scale case study. Accessibility is analyzed at the building level using OpenStreetMap data and a combination of object-based k-nearest-neighbor measures, distance-decay functions, inequality metrics, Average Nearest Neighbor analysis, and quantile regression. Concepts from central place theory are employed as an interpretive lens to understand service clustering and accessibility patterns, rather than as a formal model to be tested. The results show pronounced spatial disparities in accessibility between urban and rural areas, as well as substantial variation within cities. Major urban centers such as Amsterdam, Rotterdam, and Utrecht exhibit high accessibility levels due to dense service provision, while peripheral and rural areas face systematically lower accessibility. Next accessibility outcomes also differ sharply across population groups. Elderly residents and groups reliant on walking experience the highest levels of inequality, with accessibility distributions remaining highly uneven at short-distance thresholds. Cycling expands the range of effective activity spaces and reduces inequality, but does not offset structural disadvantages in areas with sparse service provision. The findings point to the limits of uniform proximity targets and reveal the need for context-sensitive accessibility planning that explicitly accounts for service distribution, mobility constraints, and population heterogeneity within national urban systems.Article SDG-Oriented Compulsory Courses in Higher Education: An Exploratory Case from Turkey(Emerald Group Publishing Ltd, 2026) Gover, Ibrahim Hakan; Ayten, Asim MustafaPurpose This study aims to examine students' perceptions of a set of compulsory Sustainable Development Goal (SDG)-oriented courses implemented across undergraduate programmes at a newly-established Turkish state university. Rather than evaluating individual courses, the study explores how a compulsory, SDG-framed curricular approach is associated with students' reported sustainability awareness, civic orientation and social engagement within a specific institutional context.Design/methodology/approach Data were collected from 325 undergraduate students enrolled in four compulsory SDG-oriented courses during the 2024-2025 academic year using a non-probability convenience sampling approach. A 19-item structured survey was administered to capture students' self-reported awareness and attitudes related to sustainability and social responsibility. Descriptive and inferential analyses (including t-tests and ANOVA after verifying normality assumptions) were conducted using SPSS and R.Findings The findings, derived from a composite impact scale, suggest that participation in SDG-oriented compulsory courses is associated with higher levels of sustainability awareness and positive orientations towards civic responsibility and social engagement. Differences were observed across demographic groups, with female students and those reporting prior familiarity with sustainability concepts indicating stronger perceived impacts. Given the exploratory design and reliance on self-reported data, the results should be interpreted as indicative rather than conclusive.Research limitations/implications The study is limited by its cross-sectional design, convenience sampling and reliance on self-reported perceptual data, which restricts causal interpretation and generalisability. The findings capture the initial, formative stages of learning rather than long-term transformation. As the data were collected from students enrolled in the earlier and intermediate stages of a set of compulsory SDG-based courses, the findings do not capture longer-term educational effects associated with the full curricular sequence. Future research employing longitudinal and comparative designs could provide deeper insight into how SDG-oriented curricular approaches develop over time and across institutional contexts.Practical implications The study provides empirical insight into how compulsory, SDG-oriented courses may contribute to students' sustainability-related awareness and civic orientations within higher education curricula. For curriculum developers and policymakers, the findings highlight both the potential and the limitations of using dedicated courses as one possible approach to embedding education for sustainable development.Social implications By foregrounding students' perspectives, the study contributes to ongoing discussions on the role of higher education in supporting sustainability-oriented values and social responsibility.Originality/value This study contributes exploratory, context-specific evidence on students' perceptions of SDG-oriented compulsory courses in Turkish higher education, offering a cautious empirical basis for further discussion and research on education for sustainable development at the institutional level.Article Institutions and Bank Intermediation: The Joint Role of State Capacity and Civil Liberties(Wiley, 2026) Raz, Arisyi F.; Gokmen, SeyitWe examine the effects of state capacity and civil liberties on bank intermediation, measured by banks' ability to generate liquidity in the economy. Theory suggests that a strong state that upholds civil liberties can create institutions that promote economic activity, including the development of its banking sector. We investigate this hypothesis by testing a possible channel: confidence in the banking system. Democracies tend to increase trust in the banking system by reforming institutions, while autocracies frequently rely on cronyism. Over the long run, trust in the banking system promotes banking development and intermediation in democracies, whereas cronyism and the risk of expropriation by autocrats undermine the potential for banking sector advancement in autocracies, despite having a trustworthy banking system. Our findings provide evidence in support of this channel, offering new insights into the role of political institutions in the banking sector.Article Functional Characterization of Loss of RNF43 Reveals Neuronal Defects in a Caenorhabditis Elegans Model(MDPI, 2026) Kazan, Hasan Huseyin; Turkyilmaz, Zafer; Ekim, Burcu; Kaya, Cem; Ergun, Mehmet Ali; Sonmez, Kaan; Güzel, SinemRing finger protein 43 (RNF43) encodes a transmembrane E3 ubiquitin ligase that negatively regulates canonical Wnt signaling and is classically associated with serrated polyposis syndrome and colorectal cancer. In this study, regarding a homozygous truncating RNF43 variant (NM_001305545.1:c.1906C>T; p.Gln636Ter) in a patient segregating with a severe neurodevelopmental phenotype characterized by developmental delay, neonatal hypotonia, recurrent seizures, progressive microcephaly, and bilateral optic atrophy, the loss of polarity defective 1 (plr-1), an ortholog of RNF43, was modeled in Caenorhabditis elegans and the phenotype was primarily characterized. The results demonstrated that loss of the plr-1 disrupted gamma aminobutyric acid (GABA)ergic axon organization, reduced locomotor speed calculated from 60 s recordings, and altered developmental growth. These findings expand the phenotypic spectrum of RNF43 and support a dosage-dependent developmental role.