Welcome to the Koç University Institutional Academic Repository
The Koç University Institutional Academic Repository (KUHub Research) is a state-of-the-art academic repository that consolidates all academic outputs—such as articles, conference proceedings, theses, research data, and more—produced by the university since its establishment in 1993.
KUHub Research adheres to the principles of Open Access and FAIR (Findable, Accessible, Interoperable, Reusable), providing access to the bibliographic records, research data, and full texts of research outputs. Equipped with innovative features such as Author Profile pages and enhanced tools, KUHub Research aims to enhance the visibility of researchers both nationally and internationally. KUHub Research aims to preserve and maintain Koç University's valuable academic legacy for future generations while also serving the needs of today’s researchers.
For more information or inquiries please contact openaccess@ku.edu.tr
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Recent Submissions
Fine tools for fine work. The form and function of ‘Small Craft Tools’ from Bronze Age Kaymakçı (Türkiye)
(Univerzita Karlova, Filozofická fakulta, 2025-08-26) Roosevelt, Christopher; Kaner, Tunç; Schaupp, Kathleen C.; Pieniążek, Magda; ANAMED (Koç University Research Center for Anatolian Civilizations); Research Center; ANAMED
Tools have always played an extremely important, defining role in human life. A closer look at Small Craft Tools (SCTs), such as drills, awls, punches, and small chisels, not only allows us an insight into the exact production techniques of artefacts but can also provide information on regional exchanges of goods and craft specializations in individual settlements. Nevertheless, these artefacts have been largely neglected in previous studies, and there seems to be no agreement among the few authors who have described such small bronze tools in more detail. The large number of middle and late Bronze Age SCTs found at Kaymakçı (Türkiye) was used as an opportunity to study their typology, characteristics, and use -wear. A total of 31 SCTs were found in only six years of excavation (2014–2021). In relation to the years of excavation, this is comparable to the number of SCTs found at Hattusa. This indicates the importance of Kaymakçı as a potential leather/textile production center. This study describes and characterizes the SCTs from Kaymakçı in detail. The authors are able to identify distinct differences between these tools and make suggestions for a more unified classification, underlining that such items are worthy of closer consideration in future studies.
Drinking with animals: investigating zoomorphic decorations on second-millennium BCE Western Anatolian pottery
(Consiglio Nazionale delle Ricerche, 2025-08-25) Roosevelt, Christopher; Kaner, Tunç; Bobik, Ján; Pavúk, Peter; Department of Archaeology and History of Art; ANAMED (Koç University Research Center for Anatolian Civilizations); College of Social Sciences and Humanities; Research Center
This study provides the first comprehensive analysis of zoomorphic plastic decorations on pottery from second-millennium BCE western Anatolia. Despite early observations by Heinrich Schliemann at Troy, these decorations have not been systematically studied until now. This research addresses a gap in figural iconographic material from western Anatolia, contrasted with the rich iconography of neighboring regions. Over 300 zoomorphic decorations of the second millennium BCE from western Anatolia are categorized and described. The chronological and geographical situation and changes over time of seven types are discussed across four recognized phases. Moreover, the article highlights similarities and possible links with central Anatolia, the Aegean, and Italy. It underscores the cultural and symbolic significance of these decorations, suggesting their role in ritual and economic contexts. Finally, the article also contributes to deeper understandings of western Anatolian material culture and its interactions with neighboring regions.
The transformative role of machine learning in advancing MOF membranes for gas separations
(AIP Publishing, 2025-08-25) Sezgin, Pelin; Keskin, Seda; Department of Chemical and Biological Engineering; College of Engineering
Metal-organic frameworks (MOFs) have been widely recognized for their potential as gas separation membranes thanks to their unique structural properties and high performance to selectively separate different types of gas molecules. MOF membranes offer great potential to replace conventional membrane materials in addressing environmental challenges like carbon capture. Experimental fabrication and testing of a single MOF membrane, even for a single type of gas separation, requires significant resources and time. Therefore, computational modeling of MOF membranes, more specifically high-throughput molecular simulations of MOFs, for various types of gas separations has been very useful in accelerating the discovery of novel MOF membranes. With the ever-increasing number of synthesized and hypothetical MOFs, reaching up to several million material candidates, brute-force molecular simulations are no longer sufficient to comprehensively explore the vast MOF space. Integration of machine learning (ML) approaches with molecular simulations has very recently accelerated materials discovery in the field of MOF membranes. ML has been very useful not only for predicting the key membrane properties of MOF membranes such as gas permeability and selectivity but also for uncovering the hidden structure-performance correlations. Compared to experimental methods and classical molecular simulations, ML offers similar accuracy at a fraction of the cost for the design and discovery of high-performing MOF membranes. This perspective focuses on the state-of-the-art ML applications in the field of MOF membranes, discusses the recent advances in this emerging field, and addresses current challenges and future directions.
PBL4TEA Module Enhancement Plans Book of Abstracts
(PBL4COLLABTT, 2025-07-27) to be filled manually; Lima, Rui M.; Mesquita, Diana; Zeybekoğlu, Zuhal; O'Mahony, Tom; Uždanavičius, Andrius; to be filled manually; to be filled manually
A machine learning approach for marginal fulfillment cost estimation in last mile delivery
(Elsevier, 2025-07-01) Nalbant, Ali; Yıldız, Barış; Graduate School of Sciences and Engineering; Department of Industrial Engineering; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING; College of Engineering
Determining marginal fulfillment costs (MFC) is crucial for effective decision-making in online grocery retail, a sector struggling with small profit margins and arduous service requirements of attended home deliveries. Paramount to improving operational efficiency, e-grocers need accurate real-time MFC estimations to optimize their service offers and prices for online customers. Traditional methods for estimating MFC are either too slow for online decision-making or inaccurate. This paper introduces a novel machine learning (ML) approach that provides fast and accurate MFC estimations with the help of carefully engineered features (predictors) that can capture complex routing dynamics. Experiments with real-world data demonstrate the superiority of the proposed approach over state-of-the-art MFC estimation methods. Our analysis of more than 2000 potential predictors, from which 20 are curated for practical applicability, reveals critical insights into the use of network-level, neighborhood-based, and node-level features in capturing complex VRP dynamics to develop ML-based approaches to address problems that arise in different transportation applications.