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.

Despite noticeable research interest, the labor-intensive Readymade Garments (RMG) industry has rarely been studied from the perspective of workers’ productivity. Additionally, previous studies already generalized that rewards and organizational commitment lead to employee productivity. However, extant research focused on the RMG industry of Bangladesh, which consists of a different socio-cultural, economic, and political environment, as well as profusion dependency on unskilled labor with an abundance supply of it, hardly considered job satisfaction as a factor that may affect the dynamics of compensations or rewards, commitment, and employee productivity. To address this research gap, this study analyzes the spillover effect of compensation, organizational commitment, and job satisfaction on work productivity in Bangladesh’s readymade garments (RMG) industry. Besides, it delves into the analysis of job satisfaction as a mediator among these relationships. We examined the proposed model by analysing cross-sectional survey data from 475 respondents using the partial least squares-structural equation model in Smart PLS 4.0. The findings show that higher compensation and organizational commitment levels lead to higher levels of job satisfaction, leading to greater productivity. This research also discovered that job satisfaction is a mediator between compensation and productivity and commitment and productivity, respectively. Results further show that increased organizational commitment and competitive wages are the two keyways to boost job satisfaction and productivity in the RMG industry. Relying on the findings, this study outlines pathways for organizational policymakers to improve employee productivity in the labor-intensive industry in developing countries.

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.

Conversion of the ocean’s vertical thermal energy gradient to electricity via OTEC has been demonstrated at small scales over the past century. It represents one of the planet’s most significant (and growing) potential energy sources. As described here, all living organisms need to derive energy from their environment, which heretofore has been given scant serious consideration. A 7th Law of Thermodynamics would complete the suite of thermodynamic laws, unifying them into a universal solution for climate change. 90% of the warming heat going into the oceans is a reasonably recoverable reserve accessible with existing technology and existing economic circumstances. The stratified heat of the ocean’s tropical surface invites work production in accordance with the second law of thermodynamics with minimal environmental disruption. TG is the OTEC improvement that allows for producing two and a half times more energy. It is an endothermic energy reserve that obtains energy from the environment, thereby negating the production of waste heat. This likewise reduces the cost of energy and everything that relies on its consumption. The oceans have a wealth of dissolved minerals and metals that can be sourced for a renewable energy transition and for energy carriers that can deliver ocean-derived power to the land. At scale, 31,000 one-gigawatt (1-GW) TG plants are estimated to displace about 0.9 W/m² of average global surface heat into deep water, from where, at a depth of 1000 m, unconverted heat diffuses back to the surface and is available for recycling.