Fitzgerald is one of the most important American writers in the 20^th century. The publication of The Great Gatsby shows his great talent in American literature. Eliot, a famous poet and literary critic, once said that it was the first step in the development of the art of the novel since the age of Henry James. Fitzgerald's masterpiece, The Great Gatsby, focuses on Jazz Age New York in the 20th century, depicting the people in the different classes of the society. Besides, love is an eternal theme in literature. And the reality and humanity shown in The Great Gatsby are also of great significance to today's society.

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.

The advent of Artificial Intelligence (AI) has transformed Learning Management Systems (LMSs), enabled personalized adaptation and facilitated distance education. This study employs a bibliometric analysis based on PRISMA-2020 to examine the integration of AI in LMSs from an educational perspective. Despite the rapid progress observed in this field, the literature reveals gaps in the effectiveness and acceptance of virtual assistants in educational contexts. Therefore, the objective of this study is to examine research trends on the use of AI in LMSs. The results indicate a quadratic polynomial growth of 99.42%, with the years 2021 and 2015 representing the most significant growth. Thematic references include authors such as Li J and Cavus N, the journal Lecture Notes in Computer Science, and countries such as China and India. The thematic evolution can be observed from topics such as regression analysis to LMS and e-learning. The terms e-learning, ontology, and ant colony optimization are highlighted in the thematic clusters. A temporal analysis reveals that suggestions such as a Cartesian plane and a league table offer a detailed view of the evolution of key terms. This analysis reveals that emerging and growing words such as Learning Style and Learning Management Systems are worthy of further investigation. The development of a future research agenda emerges as a key need to address gaps.

The rise of internet-based pharmacies has transformed the healthcare sector, giving patients access to medications, information, and direct interaction with pharmacists. While online pharmacies have become popular around the world, there are challenges hindering their widespread use in developing countries due to a limited understanding of the factors affecting their acceptance and usage. To bridge this knowledge gap, a study utilized a model combining the unified theory of acceptance and use of technology (UTAUT 2) with the technology acceptance model (TAM) to explore the drivers behind online pharmacy usage in Oman. Through this framework, twelve hypotheses were. A survey involving 378 individuals familiar with online pharmacies was conducted. Structural equation modeling (SEM) was applied to analyze the data and test these hypotheses. The results indicate that factors such as perceived expectancy effort expectancy and facilitating conditions hedonic motivation, habit perceived risk, technology trust, and technology awareness play roles in influencing the adoption of online pharmacies in Oman. The findings suggest that personal innovation plays a moderating role in the connection between perceived risk and behavioral intention, while it has a negative moderating influence on the relationship between technology trust and behavioral intention. Word of mouth was identified as a moderator in enhancing the correlation between behavioral intention and online pharmacy adoption. This research emphasizes the moderating relationship of personal innovation and word of mouth on shaping consumer attitudes towards online pharmacies and their acceptance. In summary, these results add to the existing knowledge on pharmacy adoption and in developed areas such as provide practical insights for online pharmacy providers to improve their offerings and attract a larger customer base.