Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12469/1248

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Hands-On Docking With Molegro Virtual Docker
(Humana Press Inc., 2026) Dere, D.; Pehlivan, S.N.; da Silva, A.D.; de Azevedo Junior, W.F.
Molegro Virtual Docker (MVD) integrates state-of-the-art search algorithms and scoring functions dedicated to protein-ligand docking simulations. It implements differential evolution as a search engine and MolDock and Plants scores to calculate binding affinity. In this work, we describe a workflow focused on how to build regression models to predict the inhibition of cyclin-dependent kinase 2 (CDK2). We employ available structural and binding data to construct machine learning models to calculate CDK2 inhibition based on the atomic coordinates obtained through docking simulations performed with MVD. We present a hands-on approach to show how to integrate docking results and machine learning methods available at Scikit-Learn to build targeted scoring functions. Our regression models show superior predictive performance compared with classical scoring functions. All CDK2 datasets and Jupyter Notebooks discussed in this work are available at GitHub: https://github.com/azevedolab/docking#readme. We made the source code of the program SAnDReS 2.0 available at https://github.com/azevedolab/sandres. © 2025 Elsevier B.V., All rights reserved.
Exploring Isis’ Takfir Discourse: A BERT-Based Entity Level Sentiment Analysis Approach
(Springer, 2025) Dehkharghani, R.; Aydin, M.N.; Yıldırım, Ş.
The Islamic State of Iraq and Syria (ISIS) significantly influenced the lives of many people during and after the Syrian civil conflict, especially civilians. Analyzing social media discussions about ISIS can provide valuable insights into the group’s beliefs and attitudes. In this paper, we examine ISIS’s takfir discourse—their practice of labeling other Muslims as unbelievers to justify exclusion or violence—in Telegram groups. We collected 14,500 Telegram messages (2015–2017) using snowball sampling, API-based crawling, language filtering, and time-window selection. We then integrated a BERT-based Named Entity Recognition (NER) model with two layers of the Span ASTE (Aspect-Based Sentiment Analysis) model. We also used the Span ASTE as an end-to-end baseline for comparison. Based on Precision, Recall, and F1-scores, our hybrid model outperformed the baselines, demonstrating its effectiveness in sentiment analysis of extracted named entities. © 2025 Elsevier B.V., All rights reserved.
Respiratory System-Based in Vitro Antiviral Drug Repurposing Strategies for Sars-Cov
(Bentham Science Publishers, 2025) Genc, D.; Kati, A.; Mandal, A.K.; Ghorai, S.; Salami, H.; ElHefnawi, S.N.K.; Altuntaş, S.
To date, no known drug therapy is available for COVID-19. Further, the complicated vaccination processes like limited infrastructure, insufficient know-how, and regulatory restrictions on vaccines caused this pandemic episode more badly. Due to the lack of ready-to-use vaccination, millions of people have been severely infected by SARS-CoV-2. Additionally, the increasing contagion risk of the SARS-CoV-2 variants makes drug repurposing studies more critical. Conventionally, antiviral drug repurposing has been conducted on two-dimensional (2D) cell culture systems or in vivo-based experimental setups. Recently, In vitro three-dimensional (3D) cell culture techniques have proven more coherent in mimicking host-pathogen interactions and exploring or repurposing drugs than other 2D cell culture methods. 3D culture techniques like organoids, bioprinting, and microfluidics/organ-on-a-chip have just been started to mimic the natural microenvironment respiratory system infected with SARS-CoV-2. These techniques avoid the need for animals in agreement with the 3R principles (Replacement, Reduction, and Refinement) to enhance animal welfare. Herein, SARS-CoV-2-host interaction and 3D cell culture techniques have been. © 2025 Elsevier B.V., All rights reserved.
A Grey Wolf Optimization Based Approach to Provide Ancillary Services for Battery Owners
(University of Kragujevac Faculty of Technical Sciences in Cacak, 2025) Çakir, M.T.; Esen, I.S.; Ceylan, O.; Zehir, M.A.; Zanaj, E.
As is known, batteries have started to be used increasingly in both power distribution and transmission networks. This study develops a near-optimal approach for ancillary services in power networks from the perspective of the battery owner. We first model the optimization algorithm for the battery owner, then utilize a grey wolf optimization approach, where near-optimal actions are selected daily from available services. We use real data of frequency, voltage magnitude, combined home and Photovoltaic system, and transformer load to perform the simulations. The simulation results show that battery owners may profit from these services and help the system operators solve the issues such as over-voltage, under-voltage, frequency, and similar. © 2025 Elsevier B.V., All rights reserved.
High- and Low-Frequency Cooperation Based Resource Allocation in Vehicular Edge Computing Via Deep Reinforcement Learning
(Institute of Electrical and Electronics Engineers Inc., 2025) Luo, Q.; Ou, Y.; Zheng, D.; Zhang, J.; Ma, Z.; Panayirci, E.
In vehicular edge computing (VEC) environment, the increasing task offloading requirements from diverse vehicular applications pose significant challenges to the limited and single communication resources. High- and low-frequency cooperation (HL-FC) has the advantages of large capacity, low latency, large coverage capability, and stable communication link during task offloading. However, how to efficiently allocate communication resources for task offloading in the presence of high- and low-frequency communication resources is a challenge. Furthermore, coupled with the allocation of computing resources and the offloading-decision making, the allocation of high- and low-frequency communication resources is even more complex and challenging. To cope with these challenges, in this paper, we investigate the resource allocation scheme under the high- and low-frequency cooperation in VEC. Specifically, to facilitate the processing of latency-sensitive and computation-intensive tasks, a multi-queue model for task caching is first designed to prioritize latency-sensitive workloads, enabling efficient data buffering and processing. Considering vehicle mobility, we then develop the communication model, task migration model, and the computing model. After that, we formulate a long-term average cost optimization problem that jointly optimizes resource expenditure and latency, which is a NP-hard problem. To obtain the optimal strategy, we leverage the Markov decision process (MDP) to model the optimization problem, which is then solved by our proposed twin delayed deep deterministic policy gradient (TD3)-based two-phase resource allocation scheme (TTRAS). Finally, extensive simulations are conducted to assess and validate the effectiveness of the TTRAS. © 2025 Elsevier B.V., All rights reserved.
Haptic-Assisted Soldering Training Protocol in Virtual Reality: The Impact of Scaffolded Guidance
(Institute of Electrical and Electronics Engineers Inc., 2025) Yilmaz, M.; Batmaz, A.U.; Sarac, M.
In this paper, we present a virtual training platform for soldering based on immersive visual feedback (i.e., a Virtual Reality (VR) headset) and scaffolded guidance (i.e., disappearing throughout the training) provided through a haptic device (Phantom Omni). We conducted a between-subject user study experiment with four conditions (2D monitor with no guidance, VR with no guidance, VR with constant, active guidance, and VR with scaffolded guidance) to evaluate their performance in terms of procedural memory, motor skills in VR, and skill transfer to real life. Our results showed that the scaffolded guidance offers the most effective transitioning from the virtual training to the real-life task — even though the VR with no guidance group has the best performance during the virtual training. These findings are critical for the industry and academy looking for safer and more effective training techniques, leading to better learning outcomes in real-life implementations. Furthermore, this work offers new insights into further haptic research in skill transfer and learning approaches while offering information on the possibilities of haptic-assisted VR training for complex skills, such as welding and medical stitching. © 2025 Elsevier B.V., All rights reserved.
Analyzing the Structure of Groupoids of Order 3, 4, and 5 Using PCA
(Springer Science and Business Media Deutschland GmbH, 2025) Mollakuqe, E.; Daǧ, H.; Mollakuqe, V.; Dimitrova, V.
Groupoids are algebraic structures, which generalize groups by allowing partial symmetries, and are useful in various fields, including topology, category theory, and algebraic geometry. Understanding the variance explained by Principal Component Analysis (PCA) components and the correlations among variables within groupoids can provide valuable insights into their structures and relationships. This study aims to explore the use of PCA as a dimensionality reduction technique to understand the variance explained by different components in the context of groupoids. Additionally, we examine the interrelationships among variables through a color-coded correlation matrix, facilitating insights into the structure and dependencies within groupoid datasets. The findings contribute to the broader understanding of data representation and analysis in mathematical and computational frameworks. © 2025 Elsevier B.V., All rights reserved.
Restorative: Improving Accessibility to Cultural Heritage With AI-Assisted Virtual Reality
(Bulgarian Academy of Sciences, Institute of Mathematics and Informatics, 2025) Balli, T.; Peker, H.; Piskin, Ş.; Fatih Yetkin, E.F.
Digitalization of the cultural heritage can be considered from multiple perspectives. In this work, we present a case study based on the ancient city of Karkemish to propose a structured pipeline for developing an Artificial Intelligence (AI)-assisted Virtual Reality (VR) system. The framework outlines a roadmap for creating a user-friendly and gamified VR interface, incorporating qualitative and quantitative evaluation methods before deployment. Qualitative assessments focus on User Interface/User Experience (UI/UX) design, while quantitative evaluations utilize electroencephalogram (EEG) data to monitor cognitive and emotional responses, aiming to promote a positive user experience. Moreover, we introduce a privacy-preserving approach to ensure the user's privacy during the system interaction. The study's aim is twofold: a) preservation and dissemination of endangered cultural heritages, and b) improving the quality of life for individuals with limited mobility (handicapped, elderly, heritage site restrictions, poverty) by enabling virtual access to cultural heritages. © 2025 Elsevier B.V., All rights reserved.
Evaluating Cognitive and Emotional Engagement in AI-Assisted Virtual Reality Through EEG
(Bulgarian Academy of Sciences, Institute of Mathematics and Informatics, 2025) Balli, T.; Fatih Yetkin, E.F.
This study proposes an EEG-based evaluation pipeline for an AI-assisted VR platform designed to deliver immersive cultural heritage experiences for elderly people. EEG data is used to evaluate emotional and cognitive responses while performing real-world versus virtual tasks, offering a reusable evaluation framework for future immersive heritage applications. © 2025 Elsevier B.V., All rights reserved.
Evaluation of Energy Harvesting Methods for Lighting Urban Roads With Single-Valued Neutrosophic EDAS Method
(Institute of Electrical and Electronics Engineers Inc., 2025) Faruk Görçün, Ö.F.; Kucukonder, H.
Lighting the roads, especially in big cities, is critical for safety and risk reduction. Meeting this need with renewable energy sources instead of traditional methods contributes to environmental sustainability by reducing carbon emissions. However, choosing the most appropriate one among the many energy harvesting methods requires balancing uncertainty and contradictions. Therefore, this study proposed the single-valued neutrosophic EDAS method to determine the most suitable energy harvesting method for road lighting. The model provides a flexible and balanced analysis by separately evaluating the degrees of accuracy, uncertainty, and falsity. It also addresses many criteria with a sustainability-oriented approach. According to the results, the most effective criterion is energy efficiency (T1), and photovoltaic energy harvesting has been determined as the most priority alternative for urban lighting. The robustness check supports the reliability and validity of the model. © 2025 Elsevier B.V., All rights reserved.
A Nano-Scale Quantum-Dot Multiplexer Architecture for Logic Units in Internet of Things Healthcare Systems
(Elsevier, 2025) Safoev, Nuriddin; Karimov, Madjit; Ahmadpour, Seyed-Sajad; Zohaib, Muhammad; Tashev, Komil; Ahmed, Suhaib
The Internet of Things (IoT) is a propelling technological shift that enables seamless networking between billions of physical devices across healthcare sectors, agriculture, smart cities, and industrial production lines. By integrating embedded sensors, actuators, and communication modules, IoT systems can gather real-time data, leading to better operational decisions and improved efficiency in healthcare systems. The rapid growth of IoT devices creates three main operational challenges related to power usage, efficiency, and thermal management requirements. The demand for more efficient, compact, high-speed, and energy-efficient devices poses significant challenges for these systems. Traditional complementary metal-oxide-semiconductor (CMOS)-based architectures struggle to meet these demanding requirements, representing a major barrier to the development of reliable and scalable next-generation IoT systems. This research demonstrates Quantum-Dot Cellular Automata (QCA) nanotechnology as an alternative solution because it performs logical operations through electron positioning rather than conventional current flow. This paper proposes a modified version of a QCA-based multiplexer design (MUX) since digital logic systems require these signal routing elements for operation. The fundamental 2:1 MUX is established using QCA cell-interaction principles, and then 4:1 and 8:1 QCA MUXs are designed through hierarchical expansion. The suggested modified MUX devices operate on a compact scale with minimal cells to reduce the occupied area compared to current MUX designs. The research outcomes demonstrate that QCA circuits hold promising potential for creating energy-saving, powerful, and scalable computational platforms for future IoT healthcare systems.
Heritage Geopolitics: Hegemonic Meaning-Making, International Orders, and the Heritagisation of Traditional Archery in Turkey and Beyond
(Cambridge Univ Press, 2025) Hisarlioglu, Fulya; Yanik, Lerna K.
This piece argues that to understand how cultural heritage functions as a form of power at the international level, it is essential to deconstruct the 'productive politics' that surround and shape the material and symbolic spatial formations of heritage and heritagisation. To this aim, by integrating critical accounts on heritage politics, geopolitics, and biopolitics, this piece deconstructs the dynamics of Turkey's heritagisation of traditional Turkish archery (TTA) in Turkey and beyond. We introduce heritage geopolitics as a novel analytical framework to unpack the role of these multiple intertwined scales of spaces in heritagisation and the 'productive politics' behind it. Heritage geopolitics, explained through the heritagisation of TTA, helps to illustrate how heritagisation becomes a multiscalar hegemonic process that shapes various features of the domestic and international orders, from the biopolitical to the geopolitical, attempting to challenge existing narratives of power and moral authority. We demonstrate that heritage geopolitics differs from other uses of heritage in world politics (such as cultural diplomacy, heritage diplomacy, or soft power) by foregrounding the domestic and embodied moral foundations of biopolitical and geopolitical imaginations embedded in the heritagisation processes.
Engineering of Geobacillus Kaustophilus Lipase for Enhanced Catalytic Efficiency and Methanol Tolerance in Biodiesel Production from Sunflower Oil
(Elsevier, 2025) Tulek, Ahmet; Poyraz, Yagmur; Sukur, Gozde; Pacal, Nurettin; Ozdemir, F. Inci; Yildirim, Deniz; Essiz, Sebnem
Lipase-mediated biodiesel production offers a sustainable and environmentally friendly alternative to conventional chemical methods. However, enzyme limitations such as low activity, poor thermal stability, and limited solvent tolerance remain challenges. In this study, a lipase from Geobacillus kaustophilus (Gklip) was engineered for improved biodiesel production using molecular docking, molecular dynamics (MD) simulations, and molecular mechanics/generalized born surface area (MM/GBSA) free energy calculations. Five mutants (Y29S, Q114T, F289D, Q184M, and Q114F) were generated via site-directed mutagenesis and expressed in Escherichia coli. Biochemical characterization revealed that all mutants retained the wild-type's optimal temperature (50 degrees C) and pH (8.0), while showing varying pH ranges, with the broadest observed in Q184M. Thermal stability increased significantly in Q184M (32.86-fold) and Q114F (5.93-fold). Catalytic efficiencies improved by 2.07-, 2.05-, and 2.63-fold in Q184M, F289D, and Y29S, respectively, compared to the wild-type (0.57). In the presence of 60 % methanol, the wild-type retained only 30.4 % activity, while Q184M maintained 67.5 %, highlighting superior solvent tolerance. Biodiesel conversion assays using sunflower oil showed no product formation by the wild-type, whereas Q184M, Q114F, and F289D achieved yields of 58.7 %, 56.3 %, and 49.2 %, respectively. These findings identify Q184M and Q114F as promising enzyme candidates for enzymatic biodiesel production.
Steady-State Entanglement Generation Via Casimir-Polder Interactions
(Nature Portfolio, 2025) Izadyari, Mohsen; Pusuluk, Onur; Sinha, Kanu; Mustecaplioglu, Ozgur E.
We investigate the generation of steady-state entanglement between two atoms resulting from the fluctuation-mediated Casimir-Polder (CP) interactions near a surface. Starting with an initially separable state of the atoms, we analyze the atom-atom entanglement dynamics for atoms placed at distances in the range of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim 25$$\end{document} nm away from a planar medium, examining the effect of medium properties and geometrical configuration of the atomic dipoles. We show that perfectly conducting and superconducting surfaces yield an optimal steady-state concurrence value of approximately 0.5. Furthermore, although the generated entanglement decreases with medium losses for a metal surface, we identify an optimal distance from the metal surface that assists in entanglement generation by the surface. While fluctuation-mediated interactions are typically considered detrimental to the coherence of quantum systems at nanoscales, our results demonstrate a mechanism for leveraging such interactions for entanglement generation.
Riemannian Manifold Approach for RIS/IOS-Assisted Wireless Networks Design
(IEEE-Inst Electrical Electronics Engineers Inc, 2025) Li, Bin; Guo, Ning; Hu, Yulin; Panayirci, Erdal; Dong, Zhicheng
Reconfigurable Intelligent Surfaces (RIS) and Intelligent Omni-Surfaces (IOS) have emerged as transformative technologies in wireless communications, offering enhanced energy and spectral efficiency. However, the inherent characteristics of RIS/IOS also bring new challenges to the resource allocation design of RIS/IOS-assisted wireless networks, such as the nonconvexity caused by the constant modulus constraint of RIS/IOS phase shift elements and the complexity of jointly designing RIS/IOS phase shifts and base station beamforming. Although existing approaches, such as the max-min algorithm and the alternating optimization algorithm, can address these challenges, they suffer from high computational complexity. This paper systematically analyzes the challenges in the design of RIS/IOS-assisted wireless networks and briefly introduces the principles of Riemannian manifold optimization. To address these complex design challenges, we introduce four pivotal manifolds: the complex circle manifold, sphere manifold, Cholesky manifold, and product manifold, each providing unique solutions for enhancing network performance. Finally, we discuss future research directions for Riemannian manifold methods in the design of RIS/IOS-assisted networks.
Sociospatial Dynamics of Workplace Discrimination Against LGBTI Plus Employees in Turkey: Systemic Implications, Discursive Patterns, and Legal Considerations
(Springer, 2025) Selen, Eser; O'Neil, Mary Lou; Ergun, Reyda
IntroductionDiscrimination against LGBTI+ employees in Turkey is widespread and structurally embedded in the spatial and social organization of the workplace. In this study, we investigate the pervasive discrimination faced by LGBTI+ employees in Turkey's workplaces, focusing on how sociospatial dynamics shape these experiences. We draw from Henri Lefebvre's spatial triad-which conceptualizes space as comprising perceived (physical), conceived (institutional), and lived (experiential) dimensions-we examine how workplace environments reproduce and sustain cisnormativity and heteronormativity.MethodsWe conducted a critical interpretive content analysis of open-ended survey responses from 2695 LGBTI+ employees collected between 2015 and 2020 to uncover multifaceted discrimination across employment stages. This qualitative approach enabled the identification of recurring patterns of discrimination across different stages of employment. Inductive coding revealed three central domains: systemic implications, discursive patterns, and legal considerations.ResultsParticipants reported discrimination throughout all stages of employment, from recruitment to dismissal. Many felt pressure to deploy their identity strategically, often negatively impacting their mental health and job satisfaction. While concealment was a common coping strategy, it often failed to protect individuals from structurally embedded discrimination. The findings show how institutional norms, biased language, and legal shortcomings reinforce systemic exclusion. These dynamics demonstrate how perceived, conceived, and lived spaces converge to create hostile work environments for LGBTI+ individuals.ConclusionsThrough the sociospatial analysis, the study reveals how workplace discrimination against LGBTI+ employees in Turkey is deeply embedded through institutional norms, discriminatory discourses, and legal shortcomings that systematically reinforce cisnormative and heteronormative exclusion. The sociospatial organization of these workplaces creates a paradox where LGBTI+ employees become hypervisible targets of bias while remaining invisible in terms of legal protection, demonstrating how spatial dynamics perpetuate structural discrimination that current legal frameworks cannot adequately address.Policy ImplicationsLegal and institutional reforms are urgently needed to challenge heteronormative and cisnormative workplace structures. Explicit legal protections and inclusive organizational practices must be adopted to ensure equity and safety for LGBTI+ employees.
Flattery and the Misanthrope
(Wiley, 2025) Diken, Bulent; Akcali, Elif; Tuzun, Defne
Molière's Alceste is often discussed with reference to his misanthropic personality, but what he aspires to doing, truth-telling, has received relatively less attention. This is curious especially if we consider that Alceste defines flattery, the opposite of truth-telling, as his main adversary. Indeed, it is Alceste's hatred of flattery that explains his misanthropy, not the other way around. We will first discuss the significance of flattery. Then, we trace the consequences of this idea in the play drawing on Aristophanes, Plato, and Aristotle where they define flattery as a relation to untruth and in opposition to friendship. In Plato's Gorgias, however, a second sense of flattery transpires: distorting ideas and practices through instrumental use. We ask what a reflection on flattery in these two interrelated senses can contribute to our understanding of Molière's comedy. What frames our discussion is the relation between Alceste and Philinte (as a stand-in for the social), on the one hand, and the relation between Alceste and Célimène (as a stand-in for seduction) on the other. Alceste cuts an abject figure in relation to both Philinte and Célimène. We end with a discussion of how Alceste can, for all his abjection, continue to fascinate us.
6-Point Tripled Ashkin-Teller Global Phase Diagrams in Two and Three Dimensions
(Elsevier, 2025) Zeynioglu, Deniz Ipek; Berker, A. Nihat
The tripled Ashkin-Teller model including 6-point interactions is solved in d = 2 and 3 by renormalization-group theory that is exact on the hierarchical lattice and approximate on the recently first/second-order-transition improved Migdal-Kadanoff procedure. Five different ordered phases occur in the dimensionally distinct global phase diagrams. 16 different phase diagram cross-sections in the 2-point and 4-point interaction space are obtained, with first-and second-order phase transitions, multiple tricritical points and critical endpoints.
Kant on the Ontological Argument for the Existence of God: Why Conceivability Does Not Entail Real Possibility
(MDPI, 2025) Thorpe, Lucas; Thorpe, Zubeyde Karadag
In the ontological argument for the existence of God, Descartes famously argues that the idea of God is the idea of a perfect being. As such, the idea of God must combine all of the perfections. Now, as (necessary) existence is a perfection, God must exist. Leibniz criticized Descartes' argument, pointing out that it rests upon the hidden assumption that God is possible. Leibniz argues, however, that God is really possible because realities cannot oppose one another, and so there could be no real opposition between the perfections. So, at least in the case of God, conceivability entails real possibility. Kant rejects this assumption and insists that the non-contradictoriness of an idea is not an adequate criterion for the real possibility of the object of the idea, for although predicates may be combined in thought to form a concept, this does not entail the properties they indicate may be so combined in reality. For this reason, Kant believes that it is impossible to prove the real possibility of God, and so the ontological argument is not sound. In this paper, I examine Kant's reasons for reaching this conclusion. I pay particular attention to Kant's argument in the Amphiboly, which deals with the concepts of agreement and opposition, and where Kant stresses the importance of the distinction between logical and real opposition. I will argue that this distinction plays a crucial role in Kant's rejection of the ontological argument and rationalist Leibnizian-Wolffian metaphysics in general. I also show how Kant's rejection of the possibility of what he calls the complete determination of a concept in the Ideal of Pure Reason, plays a role in his rejection of the conceivability entails real possibility principle.
Forecasting Critical Economic & Political Events Via Electricity Consumption Patterns in the United States of America and Turkey
(Springernature, 2025) Ozdes, Celik; Ediger, Volkan S.; Eroglu, Deniz
Impacts from natural disasters, government decisions and public's reactions can significantly alter societal daily routines. These effects resonate in systems where individual contributions, such as energy consumption, serve as indirect indicators of societal welfare and living standards. Preparedness for unforeseen events is crucial to enhancing societal well-being. Thus, analysing historical data for unexpected critical transitions and forecasting future occurrences is paramount. Recurrence properties of gross monthly electricity consumption in the United States of America and Turkey are examined, revealing coinciding critical periods with extreme regimes identified by a determinism time series. An ensemble of neural network proxies is then employed to forecast critical periods within a limited time frame, enabling the anticipation of similar occurrences. Validation of this approach demonstrates high predictive performance when measured quantities adequately reflect underlying system dynamics. Predictions based on electricity consumption data suggest potential systemic and socioeconomic crises for both nations within one year, with probabilities, 85% for the US and 32% for Turkey.

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