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The global impact of COVID-19 has been significant and several vaccines have been developed to combat this virus. However, these vaccines have varying levels of efficacy and effectiveness in preventing illness and providing immunity. As the world continues to grapple with the ongoing pandemic, the development and distribution of effective vaccines remains a top priority, making monitoring prevention strategies mandatory and necessary to mitigate the spread of the disease. These vaccines have raised a huge debate on social networks and in the media about their effectiveness and secondary effects. This has generated big data, requiring intelligent tools capable of analyzing these data in depth and extracting the underlying knowledge and feelings. There is a scarcity of works that analyze feelings and the prediction of these feelings based on their estimated polarities at the same time. In this work, first, we use big data and Natural Language Processing (NLP) tools to extract the entities expressed in tweets about AstraZeneca and Pfizer and estimate their polarities; second, we use a Long Short-Term Memory (LSTM) neural network to predict the polarities of these two vaccines in the future. To ensure parallel data treatment for large-scale processing via clustered systems, we use the Apache Spark Framework (ASF) which enables the treatment of massive amounts of data in a distributed way. Results showed that the Pfizer vaccine is more popular and trustworthy than AstraZeneca. Additionally, according to the predictions generated by Long Short-Term Memory (LSTM) model, it is likely that Pfizer will continue to maintain its strong market position in the foreseeable future. These predictive analytics, which uses advanced machine learning techniques, have proven to be accurate in forecasting trends and identifying patterns in data. As such, we have confidence in the LSTM's prediction of Pfizer's ongoing dominance in the industry.

The discovery of quasi-crystals has led to a great debate about their unusual structure. The big surprise is that these structures were found in Islamic art several centuries ago. This latest discovery drew the attention of scientists to propose several approaches for the comprehension of these structures by analyzing several quasi-periodic patterns spread around the Islamic world. In this article, we propose a systematic method for generating new quasi-periodic patterns inspired by existing Islamic historical patterns. The method builds Islamic quasi-periodic patterns based on a quasi-periodic tiling and a few intuitive parameters. Given a quasi-periodic tiling, the method divides its tiles (rhombs) into symmetric right triangles and constructs their template motifs. The construction of these template motifs is achieved by a systematic and well-organized process. The content of the tiles is obtained by applying mirror reflections to the constructed template motifs. Finally, the pattern is drawn by putting the content of the constructed tiles in the tiling. To show the effectiveness of this generative method, examples of new quasi-periodic patterns will be presented.

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