University of Kyrenia (Turkish: Girne Üniversitesi) is the first university of Northern Cyprus specializing on maritime studies was established in 2013 in Kyrenia. There are now 12 faculties, 3 vocational schools, 3 graduate schools and an academy, in total 58 programs.
High entropy alloys (HEAs) have become the focus of research and industrial attention as a new class of advanced engineering materials due to their exceptional properties and performance. Generally, HEAs exhibit superior performance compared to traditional alloys, especially when they form FCC or BCC single-phase solid solutions. However, achieving such systems is beyond simple mixing of elements and is not always straightforward. It strongly depends on element selection, deliberate design of compositions, processing strategies, and thermodynamic control. Given the vast compositional space of HEAs, the use of predictive tools is Inevitable. Phase formation rules, as an empirical approach, have become effective and valuable tools for accelerating the screening of single-phase FCC or BCC alloys and minimizing trial-and-error efforts due to their low cost, time-saving nature, and simplicity. In this regard, the present review elaborated quantitatively and conceptually the thermodynamic parameters such as Gibbs free energy of mixing (ΔGmix), configurational entropy (ΔSmix), enthalpy of mixing (ΔHmix), and the prediction parameter for solid solution formation (Ω), along with Hume-Rothery criteria, including atomic size difference (δ), valence electron concentration (VEC), and electronegativity difference (Δχ). In addition, the processing and manufacturing routes, economic aspects, and applications of HEAs are discussed. This review provides a comprehensive overview of HEA with a practical framework from element selection, design, and fabrication techniques to the relationship between phase, properties, and performance. The present work bridges the gap between HEAs design, manufacturing processes, practical implementation, and application. Additionally, an overview of cost and manufacturing considerations has highlighted future research directions in targeted design, scalable production, and the industrial application of HEAs.
Ultra-high-temperature ceramics (UHTCs), particularly titanium diboride (TiB2), are critical for demanding applications such as thermal protection systems, wear-resistant components, and high-temperature applications. However, their strong covalent bonding and poor sinterability pose significant fabrication challenges. This study investigates the incorporation of graphitic carbon nitride (g-C3N4) as a sintering aid and reinforcing phase in TiB2 composites processed via spark plasma sintering (SPS). A TiB2-10 wt
In this paper, we introduced bivariate Chlodowsky variant of BernsteinSchurer operators based on (p,q)-integers. We also studied the estimates of moments of these operators, the weighted approximation theorem, and Korovkin-type approximation theorems. Besides these, the error of approximation using full modulus of continuity and partial modulus of continuity, the rate of convergence, and a convergence theorem for Lipschitz continuous functions were also presented. Furthermore, we generalised the Chlodowsky variant of Bernstein-Schurer operators based on (p,q)integers. Lastly, the numerical results for the defined operators are comparatively illustrated to show the minimised error of approximation to some functions.
Soft tissue sarcomas (STS) are rare, heterogeneous mesenchymal malignancies with variable clinical behavior and prognosis. Identifying prognostic factors is crucial for informing treatment strategies and enhancing patient outcomes. This retrospective, single-center cohort study reviewed the medical records of 223 patients diagnosed with STS between November 2002 and January 2014. Demographic, clinical, pathological, and treatment-related variables were also recorded. Recurrence, metastasis, and survival outcomes were analyzed using Kaplan–Meier curves and Cox proportional hazard models. Of the 223 patients, 113 (50.7
Recent advancements in artificial intelligence (AI) have significantly permeated educational systems, reshaping pedagogical approaches, student engagement, and support mechanisms. This study addresses this research void by evaluating the role of Al in advancing students' emotional intelligence. This study employs bibliometric analysis, to synthesises prevailing literature and delineate emerging patterns, technological implementations, and unresolved obstacles at the Al-El nexus. In addition to this, it examines the role of Al in facilitating students' emotional intelligence development in educational contexts. Bibliometric analysis to establish the existing body of knowledge and self- reflection data from 260 students of the university's Faculty of Education were used. The self-reflections were analysed using the content analysis method. As a result of the research, it was determined that the number of studies conducted after 2021 increased and that the studies most frequently contributed to SDG 3 (Good Health and Well-being). In line with the participants' opinions, it was concluded that artificial intelligence should be developed in perceiving emotions, and that artificial intelligence can provide personalised, empathy-oriented and guiding support while developing students' emotional intelligence. Moreover, the study underscores the significance of integrating Al-supported emotional intelligence development within the framework of sustainable education. By fostering students' social-emotional competencies, Al not only enhances individual well-being but also contributes to the broader agenda of sustainable development, particularly in promoting inclusive, equitable, and quality education (SDG 4) and ensuring good health and well-being (SDG 3). These findings highlight the potential of Al-driven emotional intelligence support systems to strengthen the resilience of future generations, thereby aligning with the goals of environmental sustainability and sustainable societal development.
