Since 2022, global geopolitical conflicts have intensified, and there has been a notable increase in the international community’s demand for currency diversification. This has created a new opportunity for the internationalization of the Renminbi (RMB). This paper examines the factors influencing the internationalization of the RMB, with a particular focus on its role as a unit of account, medium of exchange and store of value. These functions are considered in conjunction with the digital technological innovation represented by e-CNY. The methodology employed is based on the vector autoregression (VAR) model, Granger causality test and variance decomposition analysis. The Granger causality test indicates that digital technology innovation is not the primary driver of RMB internationalization at this juncture. The impulse response analysis and variance decomposition analysis revealed that the impact and direction of influence exerted by the various factors on RMB internationalization exhibit considerable discrepancies.

The present study focuses on improving Cognitive Radio Networks (CRNs) based on applying machine learning to spectrum sensing in remote learning scenarios. Remote education requires connection dependability and continuity that can be affected by the scarcity of the amount of usable spectrum and suboptimal spectrum usage. The solution for the proposed problem utilizes deep learning approaches, namely CNN and LSTM networks, to enhance the spectrum detection probability (92% detection accuracy) and consequently reduce the number of false alarms (5% false alarm rate) to maximize spectrum utilization efficiency. By developing the cooperative spectrum sensing where many users share their data, the system makes detection more reliable and energy-saving (achieving 92% energy efficiency) which is crucial for sustaining stable connections in educational scenarios. This approach addresses critical challenges in remote education by ensuring scalability across diverse network conditions and maintaining performance on resource-constrained devices like tablets and IoT sensors. Combining CRNs with new technologies like IoT and 5G improves their capabilities and allows these networks to meet the constantly changing loads of distant educational systems. This approach presents another prospect to spectrum management dilemmas in that education delivery needs are met optimally from any STI irrespective of the availability of resources in the locale. The results show that together with machine learning, CRNs can be considered a viable path to improving the networks’ performance in the context of remote learning and advancing the future of education in the digital environment. This work also focuses on how machine learning has enabled the enhancement of CRNs for education and provides robust solutions that can meet the increasing needs of online learning.

This paper investigates the factors influencing credit growth in Kosovo, focusing on the relationship between credit activity and key economic variables, including GDP, FDI, CPI, and interest rates. Its analysis targets loans issued to businesses and households in Kosovo, employing a VAR model integrated into a VEC model to investigate the determinants of credit growth. The findings were validated using OLS regression. Additionally, the study includes a normality test, a model stability test (Inverse Roots AR Characteristic Polynomial), a Granger causality test for short-term relationships, and variance decomposition to analyze variable shocks over time. This research demonstrates that loan growth is primarily driven by its historical values. The VEC model shows that, in the long run, economic growth in Kosovo leads to less credit growth, showing a negative link between it and GDP. Higher interest rates also reduce credit growth, showing another negative link. On the other hand, more foreign direct investment (FDI) increases credit demand, showing a positive link between credit growth and FDI. The results show that loans and inflation (CPI) are positively linked, meaning higher inflation leads to more credit growth. Similarly, more foreign direct investment (FDI) increases credit demand, showing a positive link between FDI and credit growth. In the long term, higher inflation is connected to greater credit growth. In the short term, the VAR model suggests that GDP has a small to moderate effect on loans, while FDI has a slightly negative effect. In the VAR model, interest rates have a mixed effect: one coefficient is positive and the other negative, showing a delayed negative impact on loan growth. CPI has a small and negative effect, indicating little short-term influence on credit growth. The OLS regression supports the VAR results, finding no effect of GDP on loans, a small negative effect from FDI, a strong negative effect from interest rates, and no effect from CPI. This study provides a detailed analysis and adds to the research by showing how macroeconomic factors affect credit growth in Kosovo. The findings offer useful insights for policymakers and researchers about the relationship between these factors and credit activity.

A numerical investigation utilizing water as the working fluid was conducted on a 2D closed loop pulsating heat pipe (CLPHP) using the CFD software AnsysFluent19.0. This computational fluid dynamics (CFD) investigation explores three instances where there is a consistent input of heat flux in the evaporator region, but the temperatures in the condenser region differ across the cases. In each case, the condenser temperatures are set at 10 ℃, 20 ℃, and 30 ℃ respectively. The transient simulation is conducted with uniform time steps of 10 s. Generally, the heat rejection medium operated at a lower temperature performs better than at a higher temperature. In this CFD study the thermal resistances gets decreased with the decreasing value of condenser temperatures and the deviation of 35.31% of thermal resistance gets decreased with the condenser region operated at the temperature of 10 ℃.