Gümüşhane University

The study sheds light on the subtle interactions among financial development, foreign direct investment (FDI), and the quality of regulatory frameworks, with particular reference to their deep influence on renewable energy use and carbon emissions across 22 developed countries from 2002–2021. The results show an interesting tendency: Financial development and FDI will reduce reliance on renewable energy, whereas a significant increase in GDP per capita will increase reliance. Secondly, carbon emissions have a negative association with the adoption of renewable energy and financial development, though both reduce environmental quality; there is a positive relation between real gross domestic product (GDP) and energy depletion in terms of these toxic emissions. The significant role of regulatory quality as a moderator in this process is particularly striking. There is a direct correlation between financial stability and more robust regulation, resulting in reduced financial liquidity available for investing in renewable projects and restricting the free flow of clean FDI. Crucially, the paper argues that when combined with strong regulation, FDI is more likely to contribute to reductions in emissions, while FYGD, nevertheless regulated at a high level of quality, should raise emissions. Winding up, the result indicates that neither financial depth nor institutional quality, in isolation, is sufficient to deliver significant environmental improvement. Thus, it is urgent to adopt sound green finance policies and to formulate focused regulatory systems that integrate financial development and foreign direct investment with a broader sustainability agenda.

This work derives analytical solutions to fractional partial differential equations utilizing the Hilfer fractional derivative through the use of the Laplace transform method. The suggested method offers a cohesive solution framework that concurrently retrieves the Liouville-Caputo and Riemann-Liouville formulations as particular instances for designated selections of the Hilfer parameters. Exact novel solution representations are derived for the fractional KdV, modified KdV, K(2,2), and Klein-Gordon equations under appropriate initial conditions, emphasizing the impact of fractional order and type parameters on wave propagation characteristics. The approach circumvents linearization or perturbation assumptions and demonstrates a rapid convergence rate. Numerical simulations and graphical representations produced with the Maple program corroborate the analytical findings and illustrate the efficacy and universality of the proposed method.

Abstract Ductile irons (spheroidal graphite cast irons, SGI) are widely used in automotive and energy systems due to their high strength, ductility, and excellent machinability. The casting quality of these alloys largely depends on the flow and filling behavior of the molten metal, namely its fluidity. In this study, the effects of pouring temperature, filling time, and metallurgical quality on the fluidity of GGG40 ductile iron were investigated through both experimental and numerical approaches. Experimental trials were conducted at pouring temperatures of 1350 °C, 1400 °C, and 1450 °C; metallurgical quality levels of 10%, 50%, and 90%; and filling times of 5 s and 10 s. The fluidity lengths were measured and statistically evaluated. The results revealed that increasing the pouring temperature and metallurgical quality significantly improved fluidity, while shorter filling times allowed the molten metal to travel longer distances before solidification. Modeling studies performed using a commercial casting simulation software showed strong correlation with the experimental data. Microstructural examinations indicated that higher pouring temperatures and improved metallurgical quality led to a more homogeneous distribution of graphite nodules and an increased ferrite fraction in the matrix. The findings provide valuable insights into the influence of process parameters on the fluidity behavior of ductile iron and offer critical design data for enhancing casting quality and productivity in industrial foundry applications.

Objective: The aim of this study is to determine the symptoms experienced by hematological cancer patients receiving high-dose chemotherapy. Materials and Methods: This descriptive cross-sectional study was conducted with 158 patients receiving treatment at the hematology clinic of a university hospital. Data were collected using a personal information form and the “Edmonton Symptom Assessment System”. Results: The mean age of the patients included in the study was 45.37±13.24 years and 51.9% were female. It was determined that the patients experienced fatigue (76.6%), anorexia (66.5%), pain (48.7%) and sadness (48.7%) as the most common symptoms, respectively. In particular, the first week mean scores of many symptoms (pain, fatigue, nausea, insomnia, loss of appetite, feeling of well-being, shortness of breath, changes in skin and nails, stomatite or sore mouth) were found to be significantly higher than those before treatment and/or in the second week (p<0.05). Conclusion: In conclusion, fatigue, anorexia, pain and sadness were the most common symptoms in patients receiving high-dose chemotherapy, and the severity of many symptoms increased especially in the first week. Based on these findings, it is recommended that nurses closely assess symptoms in the early phase of treatment, implement proactive symptom management strategies, and develop individualized care plans.

Bio-nanocomposites, produced from renewable resources, stand out as one of the promising next-generation material classes for both biomedical and environmental applications due to their biocompatible and biodegradable structures. However, the vast and complex design space of these materials, which depends on numerous variables including the type of biopolymer matrix and the type and concentration of the nanostructured filler, makes their optimization quite challenging with traditional “trial-and-error” based research methods. This study provides a general overview of an artificial intelligence (AI)-based methodology that holds the potential to overcome this complexity. In this context, the limitations of the traditional “forward screening” approach are discussed, and the “inverse design” paradigm, based on the idea of “designing a material with desired properties,” is proposed as a solution. The study outlines the potential roles of various AI tools in this field, from supervised learning models to deep learning approaches and optimisation algorithms. This theoretical framework is substantiated by practical case studies that range from drug delivery to sustainable packaging. The case studies demonstrate how AI not only enables the prediction of properties but also facilitates strategically developing materials for a particular function, thereby accelerating the discovery process. Finally, current challenges, such as data scarcity and the interpretability of models, are addressed, and future visions to overcome these obstacles, including physics-informed AI, autonomous discovery platforms, and human-AI partnerships, are discussed. It is concluded that this synergy has the capacity to stimulate a new era of innovation in sustainable materials science.

ABSTRACT The primary perspective of this study is the global tourism industry (GTI) as an interconnected supply chain network, encompassing both tourism firms (e.g., hotels, airlines, and operators) and destinations (e.g., local communities and ecosystems). This industry‐level focus allows for analyzing resilience indicators that bridge firm‐specific capabilities with destination‐wide vulnerabilities, rather than isolating one over the other. Using a mixed‐method approach, this research conceptualizes resilience through various management theories. First, it identifies critical resilience indicators across institutional, societal, economic, and ecological dimensions. Second, these indicators are analyzed with a novel Spherical Fuzzy Interpretive Structural Modeling and Cross‐Impact Matrix Multiplication Applied to Classification framework, utilizing expert opinions to uncover complex interdependencies among indicators. The findings of this study highlight that effective government policies, robust incentive mechanisms, advanced digital infrastructure, dynamic capabilities, and ecologically sustainable tourism practices are key drivers of resilience. However, socioeconomic impacts, tourist satisfaction, and price competitiveness emerge as highly dependent outcomes influenced by these drivers. This study also introduces the Adaptive Global–Local Interaction Theory, a new framework that integrates global influences with local realities to enhance tourism resilience. By offering a comprehensive framework and practical guidelines, this research equips policymakers and stakeholders with actionable strategies to bolster the industry's capacity to anticipate, adapt to, and recover from crises, promoting sustainable development amidst volatile global conditions.