DSpace Arşivi :: Ana Sayfa

İstanbul Gelişim Üniversitesi Kurumsal Açık Erişim Arşivi

DSpace@Gelişim, İstanbul Gelişim Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve yayınların etkisini artırmak için telif haklarına uygun olarak Açık Erişime sunar.

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Twitter Analysis of Collective Action of OECD Countries Against Climate Crises
(SAGE PUBLICATIONS INC, 2455 TELLER RD, THOUSAND OAKS, CA 91320, 2025) Tuncay, Esra; Savaş, Sezgin
A crisis is conceptually defined as an unexpected situation requiring an urgent solution. The global climate crisis is especially significant, threatening the stability and livability of the world. Building on this conceptual framework, this study aims to explore the dimensions of stakeholder interactions to highlight the need for collective efforts to combat the climate crisis. Twitter (now known as X) interactions between climate and environment ministries in OECD countries were analyzed in R using graph theory. The study found that the United States ministry was the most followed. As the largest network node, Canada acts as both a gateway and hub. As a central player, Canada receives and disseminates information and possesses a high potential for interaction. France is connected to influential nodes and is therefore a leader in the chain of influence. Germany is a strong center of stability and acts as a leader/bridge node. Norway, Spain, and Greece are considered to be peripheral nodes. Furthermore, the network had a medium-strength community structure divided into four parts. These results highlight that interactions among OECD countries are insufficient and that climate and environment ministries can facilitate global cooperation to combat the climate crisis. Strengthening nodal connections is a recommended policy step to translate these results to real-life and industry-specific contexts.
From Sophora japonica to Smart Nanomedicine: Molecular Docking Simulations and Multifaceted Applications of CaO Nanoparticles
(2025) Baykal Alpaslan, Ecem; Attar, Azade; Aktaş, Emre; Altikatoğlu Yapaoz, Melda
The growing demand for multifunctional nanomaterials in biomedical and environmental applications has driven the need for sustainable synthesis methods and comprehensive performance evaluations. In this study, calcium oxide nanoparticles (CaONPs) were synthesized using Sophora japonica extract via a green route, comprehensively characterized, and evaluated for biomedical and environmental applications. UV−vis spectroscopy confirmed the formation of CaONPs with a characteristic absorption peak at 321 nm. SEM showed spherical morphology with an average size of 30−70 nm, and FT-IR analysis confirmed the removal of organic residues postcalcination. X-ray diffraction analysis revealed sharp peaks corresponding to crystalline CaO with an average crystallite size of 53.45 nm. Molecular docking simulations were performed to evaluate the binding potential of synthesized CaONPs against selected bacterial outer membrane proteins (7NG9, 1BY3, 1FEB, 2HDF, and 4C4V) and the FDPS enzyme. The results revealed that CaO exhibited strong and stable binding interactions, comparable to or exceeding those of reference drugs, suggesting its promise as a dual-function bioactive agent. The calcinated CaONPs exhibited notable antibacterial and antifungal activity, with inhibition zones up to 18 mm, which enhanced up to 27 mm in combination with antibiotics/antifungals. In drug delivery studies, Zoledronic acid-loaded CaONPs showed pH-responsive behavior, releasing 92% of the drug at 250 h at pH 5.0, suggesting targeted delivery potential in acidic tumor environments. CaONPs showed no toxicity to Saos-2 osteosarcoma cells with 82% cell viability at 500 μg/mL and 78% cell viability at 1000 μg/mL. Furthermore, CaONPs achieved 93% removal efficiency of Congo red at 50 °C and pH 5.0 in 24 h, highlighting their potential in wastewater treatment. Synthesized CaONPs exhibited antimicrobial, drug delivery, and dye degradation properties while maintaining biocompatibility. Their pH-dependent drug release performance and strong synergistic antimicrobial effects highlight their applicability in antibiotic resistance, cancer therapy, and wastewater treatment.
Assessing food label literacy: development and validation of a psychometric scale for adults
(EMERALD GROUP PUBLISHING LTD, Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE LS1 4DL, ENGLAND, 2025) Yıldırım, Güldane; Çakır, Muhammet Ali
Purpose – Food labels are essential tools that communicate critical nutritional and health information to consumers, guiding healthier choices. This study aims to develop and validate the Food Label Literacy Scale (FLLS) for adults. Design/methodology/approach – The scale evaluates four core competencies: accessing, understanding, appraising and applying food label information. The study followed a three-stage process: (1) item generation through literature review, exploratory study and expert opinions, (2) exploratory factor analysis with 240 adults aged 18–65 and (3) confirmatory factor analysis (CFA) with a separate sample of 354 adults. The scale’s reliability and validity were assessed through internal consistency analysis, criterion validity testing and test-retest reliability. Findings – The final FLLS consists of 32 items across five factors. CFA results demonstrated good model fit (χ²/df = 2.255, RMSEA = 0.060, CFI = 0.956, GFI = 0.946, NFI = 0.944, SRMR = 0.066). Internal consistency was high (Cronbach’s alpha = 0.945). The scale also showed acceptable test-retest reliability and positive correlations with existing food literacy measures. Originality/value – The effectiveness of food labels depends on consumers’ ability to interpret and use food label information. However, to the authors’ knowledge, no validated tool exist to measure the ability to use food labeling with a health literacy approach in adults.
Advanced fractional soliton solutions of the Joseph–Egri equation via Tanh–Coth and Jacobi function methods
(NATURE PORTFOLIO, HEIDELBERGER PLATZ 3, BERLIN 14197, GERMANY, 2025) Shakeel, Khadija; Baleanu, Dumitru; Abbas, Muhammad; Yousif, Majeed Ahmad; Mohammed, Pshtiwan Othman; Abdullah, Farah Aini; Abdeljawad, Thabet
This study introduces new exact soliton solutions of the time-fractional Joseph–Egri equation by employing the Tanh–Coth and Jacobi Elliptic Function methods. Using Jumarie’s modified Riemann– Liouville derivative, a wide variety of soliton structures—such as periodic, bell-shaped, W-shaped, kink, and anti-bell-shaped waves—are obtained and expressed through hyperbolic, trigonometric, and Jacobi functions. The analysis reveals the significant impact of fractional-order derivatives on soliton dynamics, with graphical illustrations highlighting their physical relevance. This work expands the known solution space of the fractional Joseph–Egri equation, demonstrates the effectiveness of advanced analytical techniques, and provides fresh insights into the behavior of fractional nonlinear waves, with potential applications in physics and engineering.
Machine learning framework for forecasting air pollution: Evaluating seasonal and climatic influences in Istanbul, Turkey
(PUBLIC LIBRARY SCIENCE, 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111, 2025) Al-Rousan, Nadia; Al-Najjar, Hazem; Elhaty, Ismail A. M.
Air pollution, driven by seasonal and meteorological variations, poses a significant threat to public health and urban sustainability. Despite numerous forecasting approaches, the influence of seasonal patterns on air pollutant levels remains underexplored. This study presents a computational framework utilizing the Nonlinear Autoregressive network with Exogenous inputs (NARX) model to predict concentrations of key pollutants SO₂, PM₁₀, NO, NOX, and O₃ in Esenyurt, one of the most industrialized districts in Istanbul, Turkey. Through systematic feature selection techniques, the study determines the most influential seasonal factors for each pollutant, reducing model complexity while improving predictive accuracy. The developed framework exhibits substantial improvements in predictive performance, with the optimal models achieving high determination coefficients (up to R²=0.965 for O₃) and low error metrics across training and validation datasets. Particularly, the inclusion of seasonal variables considerably improved prediction accuracy for NO, NO₂, and PM₁₀, while SO₂ predictions performed best when utilizing comprehensive seasonal indicators. These results demonstrate that seasonal dynamics play a crucial role in governing pollutant behavior and highlight the importance of incorporating such variables in forecasting models. This research contributes significantly to the field by advancing methodological approaches in air quality prediction while providing an adaptable model for policymakers and environmental agencies to implement in proactive pollution management strategies. Through examination of seasonal dependencies in air pollutant patterns, the study delivers a practical tool for urban planning and public health applications in rapidly expanding metropolitan regions.