This study investigates the relationship between hydrological processes, watershed management, and road infrastructure resilience, focusing on the impact of flooding on roads intersecting with streams in River Nile State, Sudan. Situated between 16.5° N to 18.5° N latitude and 33° E to 34° E longitude, this region faces significant flooding challenges that threaten its ecological and economic stability. Using precise Digital Elevation Models (DEMs) and advanced hydrological modeling, the research aims to identify optimal flood mitigation solutions, such as overpass bridges. The study quantifies the total road length in the area at 3572.279 km, with stream orders distributed as follows: First Order at 2276.79 km (50.7%), Second Order at 521.48 km (11.6%), Third Order at 331.26 km (7.4%), and Fourth Order at 1359.92 km (30.3%). Approximately 27% (12 out of 45) of the identified road flooding points were situated within third- and fourth-order streams, mainly along the Atbara-Shendi Road and near Al-Abidiya and Merowe. Blockages varied in distance, with the longest at 256 m in Al-Abidiya, and included additional measurements of 88, 49, 112, 106, 66, 500, and 142 m. Some locations experienced partial flood damage despite having water culverts at 7 of these points, indicating possible design flaws or insufficient hydrological analysis during construction. The findings suggest that enhanced scrutiny, potentially using high-resolution DEMs, is essential for better vulnerability assessment and management. The study proposes tailored solutions to protect infrastructure, promoting sustainability and environmental stewardship.

This study delves into the complex flow dynamics of magnetized bioconvective Ellis nanofluids, highlighting the critical roles of viscous dissipation and activation energy. By employing a MATLAB solver to tackle the boundary value problem, the research offers a thorough exploration of how these factors, along with oxytactic microorganism’s mobility, shape fluid behavior in magnetized systems. Our findings demonstrate that an increase in the magnetization factor  leads to a decrease in both velocity and temperature due to enhanced interparticle resistance from the Lorentz force. Additionally, streamline analysis reveals that higher mixed convection parameters  intensify flow concentration near surfaces, while increased slip parameters reduce shear stress and boundary layer thickness. Although isotherm analysis shows that higher Ellis fluid parameters enhance heat conduction, with greater porosity values promoting efficient thermal dissipation. These insights significantly advance our understanding of nanofluid dynamics, with promising implications for bioengineering and materials science, setting the stage for future research in this field.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

This study investigates the evolution of monetary policy in Ghana and explores the potential of Central Bank Digital Currencies (CBDCs), specifically the e-Cedi, as a tool to enhance financial inclusion and modernize the country’s financial system. Ghana’s monetary policy framework has undergone significant transformations since the establishment of the Bank of Ghana in 1957, with notable achievements in stabilizing the economy and managing inflation. However, large segments of the population, particularly in rural areas, remain unbanked or underbanked, highlighting the limitations of traditional monetary tools. The introduction of the e-Cedi presents an opportunity to bridge these gaps by providing secure, efficient, and accessible financial services to underserved communities. The study employs a qualitative research design, integrating historical analysis, case studies, and thematic analysis to assess the potential benefits and challenges of CBDCs in Ghana. Key findings indicate that while the e-Cedi could significantly enhance financial inclusion, challenges related to technological infrastructure, cybersecurity, and public trust must be addressed. The study concludes that a balanced approach, which prioritizes digital infrastructure development, strong cybersecurity measures, and collaboration with financial institutions, is essential for maximizing the potential of CBDCs in Ghana. Recommendations for future research include a deeper exploration of the impact of CBDCs on financial stability and further analysis of rural adoption barriers.