Final International University

The increasing burden of infectious diseases and antimicrobial resistance underscores the urgent need for alternatives to conventional therapeutics. Although Eucalyptus essential oils (EOs) are well recognized for their broad-spectrum antimicrobial and antibiofilm properties, their relatively high volatility and limited physicochemical stability restrict their practical applications. In this study, a systematic, design-driven comparative approach was employed to develop nanoemulsions containing Eucalyptus globulus, E. citriodora, and E. radiata EOs, with the aim of improving their stability and biological efficacy. Initially, gas chromatography-mass spectrometry was performed to identify species-specific chemotypes, which guided rational formulation design. A Box-Behnken design enabled the precise optimization of critical colloidal parameters, resulting in nanoemulsions with droplet sizes below 200 nm, polydispersity indices below 0.3, and ζ-potentials of approximately -22 mV. Comprehensive structural characterization by Fourier transform infrared spectroscopy and scanning electron microscopy, together with stress testing, confirmed the robust physical stability of the formulations. In-vitro antimicrobial evaluations against Staphylococcus aureus, Enterococcus faecalis, and Klebsiella pneumoniae revealed up to a 4-fold enhancement in antimicrobial activity and up to 80% inhibition of biofilm compared to the corresponding unformulated essential oils. The enhancements are most likely attributable to enhanced dispersion, increased interaction with greater microbial interfaces, and nanoscale stabilization of volatile bioactive constituents. Collectively, the optimized Eucalyptus nanoemulsions constitute reproducible, chemically well-defined nanocarrier systems with markedly improved antibacterial and antibiofilm performance, highlighting their promise as next-generation delivery systems for phytopharmaceutical applications. Further comprehensive biological and toxicological investigations are required to support the safe and effective translation of essential oil-containing nanocapsulations into practical use.

The present research analyzes the interaction between policy uncertainties, as captured by Climate Policy Uncertainty (CPU) and Global Economic Policy Uncertainty (GEPU), and key sustainable finance indices such as Global Green Bond Index (GB), S&P Global 1200 ESG Index (ESG), S&P 500 Green Global Index (SPC), S&P Global 1200 Carbon Efficient Index (CF), and S&P Global 1200 Fossil Fuel-Free Index (FF). Using the Wavelet Coherence Analysis technique, this research has managed to identify that each of these sustainable finance indices reacts to uncertainty in different ways, with varying levels of volatility between 2010 and 2023. These findings clearly indicate that while CPU impact is random and acute around key climate change policy deadlines such as COP21, the Green Deal in Europe, and COVID recovery policies for sustainable development, the impact of GEPU is consistent and widespread in all sustainable indices analyzed, thus firmly establishing macroeconomic uncertainty's key and overarching impact on market sustainability and performance in sustainable finance instruments and markets. Phase difference analysis also clearly confirms and supports that sustainable finance markets tend to react and respond to uncertainty signals in general and, more specifically, to those of GEPU, thus clearly and conclusively determining that macroeconomic policies are again and again key and leading determinants of sustainable performance in green and ESG assets. These findings and research conclusions clearly indicate that sustainable markets are certainly not and do not remain insulated or unaffected by macroeconomic conditions in general in today's interdependent and interconnected world; rather, all sustainable markets react and respond to uncertainty in different and varying ways depending on key uncertainties in varying and different instances and circumstances. This research study has provided significant research insights and added substantially to the emerging and growing research and literature on sustainable markets during and in conditions of economic and financial instabilities. This particular research study has also helped and supported portfolio and sustainability managers, as well as climate change and sustainability professionals and policymakers, to better and properly recognize and factor in key uncertainties in sustainable assets during long-term sustainable and sustainable finance investments and transactions.

The development of modern technologies such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI) resulted in a new era of industrial automation and data interchange, which is known as Industry 4.0. AI-based decision support systems (DSS) play a crucial role in this paradigm by enhancing the integration and processing of IoT and sensor data to optimize operations, improve productivity, and enable predictive maintenance. Machine learning models analyze production data and visual inspections to identify defects and ensure product quality. This review paper explores the transformative role of AI in enhancing DSS within Industry 4.0, highlighting key technologies including machine learning, deep learning, and natural language processing. It explores a number of applications, including supply chain optimization, energy management, predictive maintenance, quality control, and production planning, showing how AI-driven DSS can significantly boost operational dependability, cut costs, and improve efficiency. The article also discusses the AI-based DSS's architecture and implementation, with a focus on data management, user interface design, and IoT integration. Furthermore, it examines the challenges related to data quality, technical integration, and human factors, offering potential solutions and strategies for effective deployment. The study highlights the continued development of AI technologies and their potential to support autonomous decision-making in industrial settings by identifying new trends and areas for further research. This comprehensive review aims to provide valuable insights for researchers and practitioners, fostering a deeper understanding of the capabilities and future potential of AI-based DSS in Industry 4.0.

In 2023, Turkey was hit by a devastating doublet of earthquakes, measuring 7.7 and 7.6 in magnitude, resulting in widespread destruction of residential and public infrastructure. In the aftermath, the Turkish Republic of Northern Cyprus initiated a project aimed at rehabilitating educational facilities. This paper investigates the rehabilitation efforts carried out at Atatürk Technical High School in Nicosia. To assess the building's seismic resilience, a nonlinear time-history analysis was performed using 11 mainshock-aftershock sequences to evaluate its lateral load resistance and potential plastic deformations. As part of the rehabilitation, shear walls and steel X-braced frames were added to the lateral load-resisting system, with their effectiveness being examined. Post-retrofit results demonstrate substantial improvements: maximum roof displacement and drift ratios decreased by 70 to more than 90% for different blocks and directions, and plastic hinge formation shifted predominantly to the Immediate Occupancy state. Although added stiffness led to moderate increases in floor acceleration, the retrofit strategy dramatically reduced human-related losses, with fatality costs decreasing by over 90%. The rehabilitation cost of all blocks was investigated and found to be less than 15% of rebuilding costs. While non-structural and downtime-related losses increased, overall safety- related economic outcomes were significantly enhanced.

This study utilizes the Breast Ultrasound Image (BUSI) dataset to present a deep learning technique for breast tumor segmentation based on a modified UNet architecture. To improve segmentation accuracy, the model integrates attention mechanisms, such as the Convolutional Block Attention Module (CBAM) and Non-Local Attention, with advanced encoder architectures, including ResNet, DenseNet, and EfficientNet. These attention mechanisms enable the model to focus more effectively on relevant tumor areas, resulting in significant performance improvements. Models incorporating attention mechanisms outperformed those without, as reflected in superior evaluation metrics. The effects of Dice Loss and Binary Cross-Entropy (BCE) Loss on the model’s performance were also analyzed. Dice Loss maximized the overlap between predicted and actual segmentation masks, leading to more precise boundary delineation, while BCE Loss achieved higher recall, improving the detection of tumor areas. Grad-CAM visualizations further demonstrated that attention-based models enhanced interpretability by accurately highlighting tumor areas. The findings denote that combining advanced encoder architectures, attention mechanisms, and the UNet framework can yield more reliable and accurate breast tumor segmentation. Future research will explore the use of multi-modal imaging, real-time deployment for clinical applications, and more advanced attention mechanisms to further improve segmentation performance.

This study explores how uncertainties in domestic financial markets, including stocks, bonds, and exchange rates, as well as global crises like the subprime mortgage and European sovereign debt crises, affect the dynamic correlation between the Australian stock market index (ASX 300) and foreign exchange rates. Using quantile regression estimation and analyzing high-frequency data from 1999 to 2021, we uncover distinct relationships influenced by currency-specific uncertainties. Our findings reveal that Australian stock market concerns impact co-movements with financial instruments, showing different effects on the correlations with the Euro, British Pound, US Dollar, Japanese Yen, and Chinese Yuan. Surprisingly, uncertainties in the Australian bond market have a negative impact on co-movement with the Euro and British Pound but a positive impact with the US Dollar, Japanese Yen, and Chinese Yuan. Additionally, we observe that volatility in the Australian currency’s exchange rate with various currencies positively influences dynamic co-movements. However, the strength of this connection varies based on the volatility of the Australian dollar against the Japanese Yen. Global financial crises, especially the subprime mortgage crisis, significantly impact dynamic co-movements, supporting both Flow and Stock-oriented theories. In summary, our research sheds light on the diverse impacts of domestic financial market uncertainties on co-movement, providing valuable insights for portfolio managers and foreign investors aiming to understand the intricate relationship between the Australian stock market and exchange rates. JEL Classification: C32, F31, G01, G15

Ekonomi literatüründe piyasa başarısızlıklarında ve konjonktürel dalgalanmalar sonucu oluşan problemlerde bazı temel araçların uzunca bir süredir kullanılageldiği görülebilmektedir. Ortodoks ve heteredoks iktisadi okulların tercihleri farklı olmakla beraber ağırlıklı olarak tercih edilen Ortodoks yaklaşımda, enflasyonist bir dönemde talebi daraltıcı etki oluşturacak para ve maliye politikaları uygulamada bulunurken, tam tersi olarak deflasyonist bir dönemde ise genişletici etkiler içeren politikalara yönelim artmaktadır. Bunun dışında bir de bağımsız merkez bankaları ile para politikası araçları kullanılarak da ekonomiye çeşitli nedenlerden dolayı müdahalede bulunulabilmektedir. Bu çalışma 1985-2022 yılları arasında Türkiye'de Katma Değer Vergisi (KDV) gelirleri, mevduat faiz oranları ve enflasyon oranları arasındaki ilişkiyi incelenmiştir. Johansen eşbütünleşme ve VECM modeli kullanılarak analiz edilen verilerin uzun dönemde durağan ve eşbütünleşmeye olduğu gözlemlenmiştir. VECM sonuçlarına göre, enflasyon oranlarındaki bir değişimin 2,5 yıl içinde uzun dönem dengesine ulaşabildiğini, KDV gelirlerindeki bir değişimin yaklaşık 1,3 yıl sürdüğünü ve mevduat faiz oranlarındaki bir değişimin dengeye ulaşmasının yaklaşık 10 ay sürdüğünü göstermektedir. Uzun dönem denklemi, enflasyon oranlarındaki %1'lik bir düşüşün KDV gelirlerini %0,003 ve mevduat faiz oranlarını %0,49 oranında etkilediğini göstermektedir. KDV gelirlerindeki %1'lik bir düşüş, enflasyon oranı ve mevduat faiz oranları üzerinde negatif bir etkiye sahiptir. Öte yandan, enflasyon ile KDV gelirleri ve mevduat faiz oranları arasında ve KDV gelirlerinden enflasyon ve mevduat faiz oranlarına doğru çift yönlü nedensellik olduğu saptanmıştır.

In mathematical measure theory, the “Ham-Sandwich” theorem states that any n objects in an n-dimensional Euclidean space can be simultaneously divided in half with a single cut by an (n-1)-dimensional hyperplane. While it guarantees its existence, the theorem does not provide a way of finding this halving hyperplane, as it is only an existence result. In this paper, we look at the problem in dimension 2, more in the style of Euclid and the antique Greeks, that is from a constructible point of view, with straight edge and compass. For two arbitrary regions in the plane, there is certainly no hope for constructing the halving line by straight edge and compass. At the opposite end, for two circles the problem is very easy, as the halving line is simply the line which passes through the centers of both circles. It turns out that for the case of two equilateral triangles the problem is already interesting and challenging from a constructible point of view, and this is the main goal of our paper. We will investigate the “deltoid” of a triangle, that is, three hyperbolas which form an “envelope” of all halving lines of a given triangle, and how to construct halving lines with certain given properties.