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Enhancing the performance of 3D-printed fiber-Reinforced mortar: synergistic effects of clays and bacterial cells as viscosity modifying agents
No full textThe absence of standardized 3D concrete printing mixtures drives ongoing material exploration. This study investigates mineral and bio-based viscosity modifying agents (VMAs) application in fiber-reinforced cementitious mortar. Nano-montmorillonite, sepiolite, and bacterial cells function as supplementary materials, partially substituting cement with fly ash for sustainability assessment and compatibility evaluation in our study. Mechanical tests encompass compressive, flexural strength, and interlayer bonding in 3D-printed samples. Based on the results, incorporating 0.150% Polyamide (PA) fiber positively affected interlayer bonding strength and deceleration of the flow loss rate. Additionally, adding clays decreased workability and compressive strength but improved interlayer bonding and flexural properties. On the other hand, substituting 20% of cement with FA improved the flowability of the mixture and showed great compatibility with other materials during printing. Applying a cement paste layer was evaluated as a practical method for bond enhancement at the interface. Bacteria incorporation improved flexural strength but weakened interlayer bonding. However, the combination of bacteria and clays resulted in a superior improvement in mechanical properties. This study demonstrates clays and bacteria's potential in enhancing rheological and mechanical features in cementitious fiber-reinforced mortar. Notably, this study introduces bacteria cells and sepiolite as rheology modifiers in 3D concrete printing for the first time. Additionally, it presents a novel dog-bone-shaped structure test for assessing interlayer bond performance.TÜBİTA
Reaping what was sown: Ontological insecurity and the far-right consequences of anti-communism in Turkish-German cold war relations
No full textThis article explores how ontological insecurity shaped Cold War collaboration between the Federal Republic of Germany (FRG) and Turkey, and how their shared anti-communist anxiety produced lasting far-right consequences. Drawing on newly examined archival documents, it argues that communism was not merely a geopolitical or ideological threat but an existential danger to the state's self in both countries. In response, the FRG and Turkey built a security partnership that extended into diaspora governance and intelligence coordination, often empowering far-right nationalist networks as bulwarks against leftist mobilization. These covert strategies - particularly the cultivation of far-right Turkish actors within Germany - were rationalized at the time as necessary countermeasures but ultimately contributed to long-term radicalization and blowback. By applying the framework of ontological security, the article reinterprets Cold War alliance dynamics as driven as much by existential anxieties as by strategic calculations. It concludes that contemporary German efforts to confront Turkish far-right extremism - such as the designation of the Grey Wolves as a security threat - risk obscuring this deeper legacy, producing a form of selective amnesia that externalizes a problem the FRG helped create.Publisher versio
Bi2O3 additive effects on gamma radiation shielding properties of 45SiO2-20B2O3-23Na2O3-9CaO-3Al2O3 glass
No full textRadiation poses a significant threat to living organisms, necessitating the development of effective shielding materials. This study investigates the properties of glass materials with varying proportions of SiO2 and B2O3, selecting 45SiO(2)-20B(2)O(3)-23Na(2)O-9CaO-3Al(2)O(3) (mol%) as the base composition. X-ray diffraction analysis confirmed the amorphous nature of the prepared glasses, while differential thermal analysis (DTA) determined glass transition temperatures (Tg). Precise density measurements were conducted to validate the structural findings. Monte Carlo N-Particle (MCNP) simulations were employed to assess the photon energy absorbance characteristics of the glasses, revealing that adding Bi2O3, up to 15 mol% in the GSB2-15 glass, significantly enhances gamma-ray shielding properties. Key results show that increasing Bi2O3 content from 0 to 15 mol% improves photon energy absorption, with the GSB2-15 glass achieving a density of 2.691 g/cm(3) and a Tg of 533.19 degrees C, both superior to those of other compositions. Fourier Transform Infrared (FTIR) and Raman spectroscopy further demonstrated that GSB2-15 maintains structural stability under gamma radiation doses up to 3.98 Gy, with minimal bond disruption. These findings establish the GSB2-15 glass, with 15 mol% Bi2O3, as a highly effective candidate for radiation shielding applications.Publisher versio
The tenth international symposium on radiative transfer (RAD-23)
No full textN/AInternational Centre for Heat ; Symposium by Elsevie
Driving digital transformation: marketing solutions for servitization challenges a paper written with ChatGPT
No full textThis report examines the critical role of marketing strategies in enabling the successful transition of manufacturing firms from product-centric models to integrated product-service systems—a transformation known as servitization. Drawing on qualitative analysis of case studies and published interviews, this study identifies key gaps in marketing approaches, including the challenges of communicating value, balancing customization with scalability, leveraging digital tools, and adapting strategies to diverse market needs. By addressing these gaps, the report proposes actionable solutions informed by theoretical frameworks such as Service-Dominant Logic (SDL), Relationship Marketing, and Integrated Marketing Communications (IMC). Findings highlight the importance of clear value propositions, modular service systems, and personalized marketing campaigns supported by digital technologies like IoT and predictive analytics. Case studies from industry leaders such as Rolls-Royce, Siemens, and Caterpillar illustrate successful applications, while examples from Kodak and IBM underscore the consequences of inadequate marketing in servitization efforts. The discussion further emphasizes the interconnectedness of identified gaps and proposes an integrated approach to align marketing strategies with operational capabilities and customer expectations. This research contributes to academic discourse by bridging theoretical insights with practical applications, offering a nuanced understanding of servitization marketing. It provides actionable recommendations for manufacturers to enhance customer engagement, foster adoption, and sustain competitive advantage. Limitations and future research directions are discussed, highlighting opportunities for quantitative validation and cross-industry exploration. This study underscores marketing’s pivotal role in driving the digital transformation of servitized business models, positioning it as a cornerstone for sustainable growth in a rapidly evolving global landscape
Physics-informed learning for economic dispatch in sustainable power systems
No full textThe variable and intermittent nature of renewable energy sources and the uncertain behavior of electrical loads pose a critical challenge for real-time economic dispatch (ED) in modern power grids. ED aims to optimize the dispatch of generation units to minimize production costs while satisfying operational constraints. However, solving this problem in real time is computationally demanding, especially in renewable-dominated power systems. This paper examines two approaches to address the ED problem: conventional solvers and artificial intelligence-based methods. Conventional solvers provide accurate and reliable solutions but are often too slow for real-time applications. Traditional learning techniques improve computational efficiency but may compromise accuracy and fail to obey physical system constraints. A physics-informed neural network (PINN) can address these shortcomings. The proposed PINN method in this paper has achieved a 3.88x speedup in computation compared to traditional solvers, reducing execution time from 1.215 seconds to 0.313 seconds on average. This AI-based model also produced results with minimal constraint violations, with 3.36 percent overall violations across the test data
Dawn: A robust tone mapping operator for multi-illuminant and low-light scenarios
No full textWe introduce Dawn, a novel Tone Mapping Operator (TMO) designed to address the limitations of state-of-the-art TMOs such as Flash and Storm, particularly in challenging lighting conditions. While existing methods perform well in stable, well-lit, single-illuminant environments, they struggle with multi-illuminant and low-light scenarios, often leading to artifacts, amplified noise, and color shifts due to the additional step to adjust overall scene brightness. Dawn solves these issues by adaptively inferring the scaling parameter for the Naka-Rushton Equation through a weighted combination of luminance mean and variance. This dynamic approach allows Dawn to handle varying illuminant conditions, reducing artifacts and improving image quality without requiring additional adjustments to scene brightness. Our experiments show that Dawn matches the performance of current state-of-the-art TMOs on HDR datasets and outperforms them in low-light conditions, providing superior visual results.Publisher versio
Embracing the unknown and the novel: Can personality traits shape a teacher's approach to ChatGPT?
No full textThis study, conducted in 2024 at a foundation university in Istanbul, Türkiye, examined the relationship between English for Academic Purposes instructors’ personality traits and their integration of ChatGPT into teaching practices. A convergent concurrent mixed-methods design was employed, allowing for the simultaneous collection and separate analysis of quantitative and qualitative data before merging the results for interpretation. In the quantitative phase, 111 English for Academic Purposes instructors were recruited through convenience sampling to complete an online survey measuring their attitudes toward ChatGPT use and personality traits such as Openness to Experience and Ambiguity Tolerance. In the qualitative phase, 8 instructors were selected via purposeful sampling for semi-structured interviews, ensuring variation in personality profiles relevant to the study focus. Quantitative findings indicated that openness, conscientiousness, and ambiguity tolerance significantly influenced instructors’ willingness to integrate ChatGPT into their teaching. Qualitative insights further revealed that while instructors appreciated ChatGPT's support in lesson planning and language instruction, many expressed ethical and pedagogical concerns. These included fears of diminishing student creativity, the risk of overreliance, and the challenge of aligning Artificial Intelligence tools with learning outcomes. The integration of both data sets enabled a richer understanding of how individual personality traits shape educators’ perceptions and use of Artificial Intelligence technologies. The study contributes to ongoing discussions on digital pedagogy by offering practical insights for all stakeholders of education, suggesting that personality-aware strategies may support more thoughtful and effective adoption of Artificial Intelligence in academic contexts. © 2025, Duzce University, Faculty of Education. All rights reserved.Publisher versio
Sociospatial dynamics of workplace discrimination against LGBTI+ Employees in Turkey: Systemic implications, discursive patterns, and legal considerations
No full textIntroduction: Discrimination 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. Methods: We 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. Results: Participants 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. Conclusions: Through 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 Implications: Legal 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.Friedrich Naumann Stiftung ; Friedrich Naumann Foundatio
Analysis of diplomatic texts with LLMs: A hybrid approach in computational social sciences
No full textThis paper presents the practical application of big language models in text mining and topic modeling processes using texts shared online by the Ministry of Foreign Affairs of the Republic of Türkiye. The research describes an iterative and hybrid algorithm in which domain experts and large language models work together. The findings indicate that well-defined themes play a critical role in model success. It is emphasized that in social sciences, topic modeling processes should be handled with a "tailor-made"approach that includes the contextual interpretations of the researcher in the data evaluation processes, rather than being evaluated only through numerical outputs. In conclusion, the proposed algorithm offers a comprehensive and flexible analysis opportunity in social scientific research by generating topic labels suitable for qualitative or quantitative analysis of texts