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 Unsettled Grounds, Enduring Bonds: A Literature Review on Employee Engagement during Organizational Change(Routledge Journals, Taylor & Francis Ltd, 2026) Abbas, Alhamzah F.; Ahmed, Ayesha; Usman, Muhammad; Shah, Syed Haider AliThis systematic review critically examines how employee engagement is conceptualized and operationalized within the context of organizational change, aiming to uncover prevailing themes, highlight conceptual inconsistencies, and identify gaps in the literature to advance understanding in evolving organizational settings. Guided by the the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA framework, the study analyzes 29 peer-reviewed journal articles published between 2006 and 2025, retrieved from the Scopus database, employing thematic synthesis and citation mapping to explore both organizational and individual-level influences on engagement during change. The findings reveal a dual-level framework: at the organizational level, leadership, organizational support, communication, and cultural and contextual dynamics play a central role, while at the individual level, psychological and emotional factors, behavioral patterns, attitudes toward change, and opportunities for skill development and career progression are key drivers. This review contributes by clarifying conceptual boundaries, identifying underexplored dimensions, and providing actionable insights for leaders and HR practitioners to sustain and enhance employee engagement during periods of organizational transition.Article Surface Integrity and Tool Wear in S2-GFRP Milling: Experimental and Statistical Evaluation of Cutting Parameters and DLC and TiAlN Tool Coatings(Springer London Ltd, 2026) Danisman, Sengul; Yilmaz, Cagatay; Ersoy, Emin; Kesriklioglu, SinanIn this study, the surface integrity of S2-glass fiber-reinforced polymer (S2-GFRP) composites during milling with carbide cutters was investigated using a two-phase experimental design, focusing on surface roughness (Ra), burr area, and tool wear (VB). In the first phase, using a Taguchi L9 design, the effects of coating type (uncoated, TiAlN, DLC), spindle speed (2000-6000 rpm), and feed rate (0,15-0,25 mm/rev) on Ra and burr area were evaluated. In this short machining range where tool wear was negligible, the optimal combination yielding the lowest Ra (approximate to 0.97 mu m) and the minimum burr area (approximate to 231 mm & sup2;) was determined to be 4000 rpm, 0,15 mm/rev, and the DLC-coated tool. Regarding the effect of tool material on Ra, the DLC-coated tool provided approximately 4 and 2 times better results than the uncoated and TiAlN-coated tools, respectively. In the second phase, experiments extended up to 130 passes with this optimal parameter set showed that Ra increased significantly with increasing VB, and the burr area exhibited threshold-like behavior. In particular, a sudden increase in Ra and the burr area was observed when the VB approximate to threshold of approximately 100 mu m was exceeded; partial regression analyses confirmed different burr-formation tendencies in the low- and high-wear regimes. The results reveal that DLC coating initially provides superior performance in S2-GFRP milling, but surface degradation accelerates after the critical VB threshold.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 From Agility to Advocacy: Exploring How Gender Moderates the Impact of Social Media Agility on Brand Trust and Influencing Behavior(Palgrave Macmillan Ltd, 2026) Bozkurt, Sıddık; Gligor, David; Hollebeek, Linda D.; Sarp, SerapThis research investigates the effect of perceived social media agility (P-SMA) on customers' influencing behavior, a theoretical subset of engagement behavior, with brand trust acting as a mediator and gender (male/female) as a moderator. It also explores how these relationships differ across age groups. Two online surveys were conducted: Study 1 with 165 younger participants and Study 2 with 178 adults. Using PROCESS Macro Models 4 and 7, we tested direct, mediated, and moderated mediation effects. Both studies found that P-SMA enhances brand trust, which subsequently mediates its influence on customer behavior. Study 2 confirmed these results, showing that gender moderates the relationship between P-SMA and both brand trust and influencing behavior, with stronger effects observed among females (vs. males) in both samples. The findings highlight the importance of tailoring social media strategies to gender-based engagement patterns. Crafting content and interaction styles that foster trust can improve influence and advocacy, especially when agility is a key element of the brand's digital presence. This research integrates P-SMA, brand trust, and customer influence behavior within a moderated mediation model. Using two age-diverse samples, it shows gender shapes reactions to brand agility, while age differences remain non-significant across both datasets.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 Dissecting the Strain and Sex Specific Connectome Signatures of Unanesthetized C57BL/6J and DBA/2J Mice Using Magnetic Resonance Imaging(Wiley, 2026) Neuberger, Thomas; Kamens, Helen M.; Zhang, Nanyin; Ünsal, Hayreddin Said; Arefin, Tanzil M.Mouse models are an essential tool for understanding behavior and disease states in neuroscience research. While genetic and sex-specific effects have been reported in many neurodegenerative and psychiatric illnesses, these factors may also alter baseline neuroanatomical features of mice. This raises the question of whether the observed changes are related to the disease being studied (i.e., pathological differences) or if there are baseline strain or sex differences that may predispose animals to different responses. Over the past decade, tremendous effort has been made to map neural architecture at various scales; however, the complex relationships, including identifying genetic and sex-specific differences in brain structure and function, remain understudied. To bridge this gap, we used C57BL/6J and DBA/2J mice, two of the most widely used inbred mouse strains in neuroscience research, to investigate strain and sex-specific features of the brain connectome in awake animals using magnetic resonance imaging (MRI). By combining resting-state functional MRI and diffusion MRI, we found that the motor, sensory, limbic, and salience networks exhibit significant differences in both functional and structural domains between C57BL/6J and DBA/2J mice. Further, functional and structural properties of the brain were significantly correlated in both strains. Our results underscore the importance of considering these baseline differences when interpreting brain-behavior interactions in mouse models of human disorders.Article Activation and Avoidance Mediate the Relationship Between Depression and Life Satisfaction: Insights from Behavioral Activation Therapy(Springer, 2026) Koşan, Yavuz; Kaya, M. SiyabendBehavioral activation therapy has been increasingly used in depression intervention in recent years. It aims to increase the activation level and decrease the avoidance level. In this way, the individual's life satisfaction is expected to increase while trying to reduce depression. However, the relationship between depression and life satisfaction in behavioral activation therapy-based interventions has not been sufficiently revealed. Furthermore, the effects of activation and avoidance, which are the most basic concepts of behavioral activation therapy, and their underlying mechanisms are not well understood. Therefore, this study focused on this gap and aimed to explore the mediating roles of activation and avoidance variables in the relationship between depression and life satisfaction. Participants (N = 440) from a non-clinical sample group completed various self-report scales on depression, behavioral activation, and life satisfaction. Our results showed that depression and life satisfaction negatively predicted each other, and that activation and avoidance variables mediated this relationship in parallel. Accordingly, increasing activation and decreasing avoidance in young people with depressive symptoms may be an effective way to improve life satisfaction and reduce depression. Effective prevention efforts that reduce depression can increase the life satisfaction levels of young adults. In summary, behavioral activation and avoidance can be used to prevent depression and increase life satisfaction. The current study provides more information about the applicability of activation and avoidance, two important elements of behavioral activation therapy, in depression intervention.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.