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
To learn how to deposit your publications in your KUHub Research profile, watch our video guide: “How to Deposit Publications into KUHub Research?”
Recent Submissions
Mechanisms of fast CO2 fixation reaction by enoyl-CoA carboxylases/reductase
(European Synchrotron Radiation Facility, 2028-01-01) Chretien, Anaïs; Ertem Kuzucu, Fatma Betul; Summers, Jacob; Wranik, Maximilian; 0000-0001-8480-1443; 0000-0002-2144-989x; 0000-0003-3113-0353; 0000-0002-2482-0164
Carbon dioxide (CO2) is an atmospheric greenhouse gas that feeds all life, plays a critical role in global warming, and could constitute an inexpensive carbon source for future sustainable industries. While synthetic chemistry lacks suitable catalysts to functionalize carbon dioxide in mild reaction conditions, autotrophs do it constantly, and thus there is increasing interest in exploiting the CO2-fixation mechanisms offered by nature. In this exchange proposal, we propose fast time-resolved structural-dynamics studies of one of the fastest CO2-fixation enzymes, enoyl-CoA carboxylase/reductase (ECR), using ambient temperature serial X-ray crystallography on Beamline ID29, ESRF, which achieves 10μs resolution. This study will reveal details of the enzyme subunit coupling as well as the enzyme-substrate interactions to correlate the structural and functional states of the enzyme during fixation and pave the way for faster biomolecule productions using engineered C-cycling enzymes.
Mechanisms of fast CO2 fixation reaction by enoyl-CoA carboxylases/reductase
(European Synchrotron Radiation Facility, 2027-01-01) Summers, Jacob; Sanctis, Daniele; Vlahakis, Niko; Knight, Victoria; Ertem Kuzucu, Fatma Betul; Chretien, Anaïs; Nurizzo, Didier; 0000-0003-3113-0353; 0000-0003-0391-8290; 0000-0002-5092-0265; 0000-0002-2144-989x; 0000-0001-8480-1443; 0000-0002-7367-5098
Carbon dioxide (CO2) is an atmospheric greenhouse gas that feeds all life, plays a critical role in global warming, and could constitute an inexpensive carbon source for future sustainable industries. While synthetic chemistry lacks suitable catalysts to functionalize carbon dioxide in mild reaction conditions, autotrophs do it constantly, and thus there is increasing interest in exploiting the CO2-fixation mechanisms offered by nature. In this exchange proposal, we propose fast time-resolved structural-dynamics studies of one of the fastest CO2-fixation enzymes, enoyl-CoA carboxylase/reductase (ECR), using ambient temperature serial X-ray crystallography on Beamline ID29, ESRF, which achieves 10μs resolution. This study will reveal details of the enzyme subunit coupling as well as the enzyme-substrate interactions to correlate the structural and functional states of the enzyme during fixation and pave the way for faster biomolecule productions using engineered C-cycling enzymes.
Updatable private Set Intersection and beyond: efficient constructions via circuit PSI
(Springer, 2027) Alborch, F.; Chauvier, T.; Faonio, A.; Fontaine, A.; Karakoç, F.; Küpçü, A.; Malek, C.; Önen, M.
Private Set Intersection (PSI) has been widely studied, deployed, and demonstrated on static datasets. In this work, we investigate the problem of designing efficient and secure updatable PSIs in the honest-but-curious model by adopting the approach of executing a small number of PSIs over smaller sets instead of one PSI over the entire updated sets. We first identify that existing constructions suffer from privacy leakages and further propose to mitigate them thanks to the use of circuit PSIs, which are variants of PSI protocols that output the secret shares of the intersection instead of outputting the resulting intersection, combined with secure shuffling when needed. We construct a generic framework for PSI over updated sets and show that this framework can easily be extended to a protocol that outputs the cardinality of the intersection instead of the intersection itself.
“looking at artifacts”: the development of visual aesthetic preferences
(Elsevier BV, 2027) Göksun, T.; Coskun, A.; Chatterjee, A.
Developmental aesthetics is an underexamined yet growing area of aesthetics research. Very few studies have investigated infants’ visual preferences for artifacts. Would a young child have visual preferences for artifacts, given their limited exposure to the world and with a developing brain? Revisiting our framework (Göksun et al., 2014), which was built on the aesthetic triad of Chatterjee and Vartanian (2014), we provide an overview of the current state of the field on early visual aesthetic preferences with a focus on art and architecture. After presenting evidence for perceptual primitives that may guide visual aesthetic preferences, we center on early artifact appreciation with infants and young children. We conclude the paper by charting new research directions required to examine early aesthetic appreciation.
Sok: a taxonomy of attacks and defenses in split Learning
(Springer, 2027) Shabbir, A.; Kanpak, H. I.; Küpçü, A.; Sav, S.
Split Learning (SL) has emerged as a promising paradigm for distributed deep learning, allowing resource-constrained clients to offload portions of their model computation to servers while maintaining collaborative learning. However, recent research has demonstrated that SL remains vulnerable to a range of privacy and security threats, including information leakage, model inversion, and adversarial attacks. While various defense mechanisms have been proposed, a systematic understanding of the attack landscape and corresponding countermeasures is still lacking. In this study, we present a comprehensive taxonomy of attacks and defenses in SL, categorizing them along three key dimensions: employed strategies, constraints, and effectiveness. Furthermore, we identify key open challenges and research gaps in SL based on our systematization, highlighting potential future directions.