Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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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 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 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 Citation - WoS: 1Citation - Scopus: 1Exergy-Based Evaluation of High-CO2 Biogas/Diesel RCCI Combustion Heat Flow for Enhanced Mixture Distribution, Power Output, and Fuel-Energy Performance(Pergamon-Elsevier Science Ltd, 2026) Dalha, Ibrahim B.; El-Adawy, Mohammed; Wong, Nur Leena W. S.; Man, Hafsalina C.; Said, Mior A.; Koca, Kemal; Abdulsalam, MuhammedUtilising high-CO2 biogas in compression-ignition engines poses significant challenges due to poor mixture reactivity, inefficient combustion, and increased energy degradation. This work addresses these difficulties by conducting experimental research on a port-injection at the valve reactivity-controlled compression ignition (PIVE-RCCI) strategy. This study addresses these concerns by conducting experiments on a PIVE-RCCI technique to improve mixture distribution and combustion efficiency in biogas-diesel engines. The engine was modified to provide biogas through the inlet valve, allowing for controlled variations of biogas injection pressure (BIP: 1-4 bar) and port swirl ratio (PSR: 0-80%) at 1600 rpm and 4.9-5.7 bar IMEP. Energy and exergy analyses were used to determine the effect of intake flow dynamics on temperature uniformity, heat transfer, and power generation during combustion. The results reveal that normal airflow conditions minimise accounted heat loss, indicating higher thermal efficiency (ITE) and increased output power across all BIPs. In contrast, introducing a strong intake swirl dramatically improves combustion performance. The 80% PSR configuration resulted in the lowest exergy destruction and the maximum energy recovery potential, with an ITE of 26.54% at 4 bar BIP. Increasing BIP increased power output, whereas the optimal combustion work was found at 1 bar BIP and 40% PSR. The optimal working conditions were 1 bar BIP, 80% PSR, and 5.45 bar IMEP, which resulted in 26.00% exergy destruction, 39.38% destruction-to-released exergy ratio, 86.00% exergy-energy ratio of heat transfer, and 63.78% exhaust exergy-energy ratio. This work's novelty lies in integrating biogas injection, intake swirl control, and exergy-based evaluation to measure mixture distribution and energy recovery in high-COQ biogas RCCI combustion. The findings offer useful operational guidance for increasing energy efficiency and advancing the commercialization of renewable gaseous fuels in RCCI engines. As a result, operating the engine at half load, 80% PSR, and atmospheric air pressure (1 bar) conditions significantly enhanced the combustion efficiency and energy utilisation.Article Buffalo’s Captured Imaginary: Atmosphere, Absurdity and the Depoliticized Imagination of the Rust Belt(SAGE Publications Inc, 2026) Dincer, Evren MUsing Buffalo, New York, as a key case study, this article examines the cultural grammar through which the American Rust Belt is represented as a site of terminal decline and absurdity. Analyzing films, novels, and memoirs, it argues that a captured imaginary has taken hold, one that converts the systemic crises of deindustrialization into mood, tone, and atmosphere. Narratives of the region frequently deploy irony and absurdity as aesthetic strategies that depoliticize decline, stylizing it as an ambient condition rather than a structural problem to be contested. This representational pattern, often centered on white protagonists, displaces political critique and renders local agency incoherent. By framing these cities as uniquely dysfunctional and incapable of self-renewal, these cultural texts create the ideological conditions for external, technocratic intervention. The article concludes that this aestheticization of collapse is a political act that forecloses democratic possibility and captures the urban imaginary for outside management.Article AI-Driven Drug Repositioning: A Diffusion Model Approach on Knowledge Graphs(Elsevier, 2026) Erkantarci, Betul; Şen, Tarık Üveys; Bakal, GokhanDrug repositioning - discovering new therapeutic applications for existing drugs - offers a promising pathway to accelerate cancer treatment development. This study proposes a diffusion model-driven framework that leverages biomedical knowledge graphs and graph-based learning to enhance drug repositioning predictions. The framework integrates data from the Semantic MEDLINE Database (SemMedDB), the Unified Medical Language System (UMLS), and the Repurposing Drugs Database (RepoDB) to construct a comprehensive therapeutic knowledge graph. Drug embeddings are generated using a one-layer Relational Graph Convolutional Network (R-GCN) incorporating semantic type-guided structural perturbations. These embeddings are refined through a flow-matching algorithm to denoise and reconstruct biologically meaningful representations. To evaluate the model's effectiveness, we apply a consensus strategy using Cosine Similarity, Euclidean Distance, and Manhattan Distance as proximity metrics. The model successfully identified, on average, 74 candidate drugs for repositioning in the context of leukemia. Qualitative analysis using t-distributed stochastic neighbor embedding (t-SNE) revealed enhanced clustering of pharmacologically relevant drugs in the denoised embedding space. Trastuzumab, in particular, emerged as a strong repositioning candidate for leukemia, supported by 156 co-mentions in PubMed. These findings demonstrate that the proposed framework improves embedding robustness and semantic fidelity, offering a powerful artificial intelligence (AI)-driven approach for precision oncology. Integrating structural noise modeling with diffusion-based denoising advances the discovery of novel drug-disease associations and holds potential for translational research and clinical hypothesis generation in drug repurposing.Article Citation - WoS: 5Citation - Scopus: 5Targeting Cholinergic Dysfunction and Neuroinflammation through Rationally Designed Thieno[3,2-d]Pyrimidine Hybrids(Academic Press Inc Elsevier Science, 2026-07) Acar, Ozden Ozgun; Acar, Busra; Senol, Halil; Tokali, Feyzi Sinan; Sen, Alaattin; Demir, Yeliz; Cakir, FurkanNeurodegenerative diseases involve the convergence of cholinergic dysfunction, neuronal loss, and sustained neuroinflammatory responses, necessitating the development of multifunctional therapeutic agents. In this study, a series of novel thieno[3,2-d]pyrimidine-phenolic Mannich base hybrids were rationally designed, synthesized, and evaluated as dual cholinesterase inhibitors with neuroprotective and anti-neuroinflammatory potential. The synthesized compounds exhibited potent inhibition against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), with inhibition constants in the low nanomolar range. Among them, compounds 5 and 9 emerged as the most active derivatives, displaying Ki values of 8.79 and 14.11 nM for AChE and 7.04 and 11.75 nM for BChE, surpassing the reference inhibitors tacrine and donepezil. Molecular docking and molecular dynamics simulations supported the experimental findings, and Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) binding free energy calculations further confirmed their superior binding affinities compared with donepezil. Cytotoxicity profiling in SH-SY5Y neuronal cells and RAW 264.7 and THP-1 immune cells identified a narrow sub-cytotoxic concentration window (EC05-EC10 = 1.2-2.1 mu M), ensuring biological effects independent of nonspecific cell damage. Within this range, both compounds exerted pronounced antineuroinflammatory activity. Notably, compound 9 significantly downregulated pro-inflammatory mediators, reducing IL-1 beta, IL-6, and NF-kappa B1 gene expression by up to 2.78-, 3.37-, and 4.84-fold, respectively. Consistently, it suppressed nitric oxide production in LPS-stimulated macrophages to levels comparable with ascorbic acid and markedly decreased Iba1 expression in activated THP-1 cells. This integrated enzymatic, computational, and cellular investigation identifies compounds 5 and 9 as promising multifunctional lead combining dual cholinesterase inhibition with robust anti-neuroinflammatory activity. The results provide a strong foundation for future in vivo studies and further optimization toward disease-modifying agents for neurodegenerative disorders.Article Pressure-Induced Polyamorphic Transition and Stepwise Ordering to Superhard B-Doped Diamond-like BC3(Elsevier Science SA, 2026-04) Durandurdu, MuratWe employ constant-pressure ab initio molecular dynamics simulations to investigate the pressure-induced phase transformations of amorphous BC3, which initially possesses a graphite-like layered structure. Our simulations reveal a first-order polyamorphic transition marked by a significant volume collapse and an increase in atomic coordination from a predominantly sp(2) network to a dense, tetrahedrally coordinated sp(3) network. Subsequent thermal annealing of the high-pressure phase uncovers a multi-step ordering process involving a metastable paracrystalline intermediate that bridges the high-density amorphous state and a thermally induced boron-doped diamond-like phase. All high-pressure phases are quenchable to ambient conditions, importantly retaining their semiconducting electronic structures across these transformations. Mechanical characterization demonstrates substantial stiffening, with bulk moduli ranging similar to 252 to 323 GPa. These findings illuminate novel and accessible routes to superhard semiconducting BC3 phases stabilized by pressure and temperature, with the boron-doped diamond-like phase identified as a metastable superhard semiconductor that is thermodynamically favored over the amorphous precursor but kinetically accessible only via the stepwise pathway described. This offers promising directions for advanced material design under extreme conditions.