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.Conference Object Web-Based Comparative Analysis Framework for Metaheuristic Optimization Algorithms: A Unified Educational Approach(Institute of Electrical and Electronics Engineers Inc., 2026) Durmus, Ali; Eryasar, Selin; Kurban, RifatArticle 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.Conference Object Unified Control: Managing Multiple PLCs Through a Single HMI Interface via MODBUS RTU Protocol(Institute of Electrical and Electronics Engineers Inc., 2026) Aykut, Ercan; Yavuz, Izzet; Erdogan, Kubra; Benli, Sena Nur; Mumcu, M. CihatConference Object Sustainable Approach to Fabricating Flat Micro Ribbon Electric Wires Directly from Turning Process Chips(Elsevier B.V., 2026) Kesriklioglu, Sinan; Sivesoglu, AbdurrahmanArticle 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 Structural Optimization for Robotic Concrete Construction: A Systematic Review(MDPI, 2026) Gürer, Ethem; Pektaş, Ömer Korkut; Alaçam, Sema; Güzelci, Orkan Zeynel; Çevik, Ayşe Nesligül; Türel, Ahmet; Özdemir, SalihConcrete construction is associated with high environmental impact and geometric limitations imposed by conventional formwork, which has led to growing interest in combining structural optimization with robotic fabrication. In this study, structural optimization refers to computational methods such as topology optimization, shape optimization, and form finding that aim to improve material efficiency and load-bearing performance by modifying the geometry of structural elements. This systematic review investigates how these optimization approaches are translated into fabrication-aware design workflows for robotic concrete construction. Following a PRISMA-based methodology, 90 peer-reviewed studies published between 2015 and 2025 were analyzed. The review focuses on fabrication routes including (i) 3D concrete printing, (ii) 3D-printed formwork, (iii) shotcrete-based additive manufacturing, and (iv) controlled casting systems, and examines how each route constrains geometry representation, design decisions, toolpath generation, and robotic execution. The review analyzes design-to-fabrication workflows that link optimized structural geometry to production logic and process control. Key findings indicate that incorporating fabrication constraints at early design stages can support buildability and potential material efficiency, while reinforcement integration and quality control remain critical challenges for structural reliability. The review also highlights the increasing role of in situ sensing and feedback-driven automation in improving process stability. Overall, the study clarifies current practices, limitations, and emerging directions for integrating structural optimization with robotic concrete fabrication.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 Probing Adversarial Robustness of Protein Language Models: A Reproducible Case Study of ESM-2 Under Substitution-Based Attacks(AIUB Office of Research and Publication, 2026) Aydin, Zafer; Niloy, Md. Robiul Islam; Moazzam, Md. GolamConference Object Retrieval-Augmented Generation for Technical Intelligence in Aerospace and Defense Systems(Institute of Electrical and Electronics Engineers Inc., 2026) Yener, Fehmi; Duman, Merve; Bakal, Gokhan