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In today's competitive higher education sector, institutions increasingly rely on international rankings to secure financial resources, attract top-tier talent, and elevate their global reputation. Simultaneously, these universities have expanded their presence on social media, utilizing sophisticated posting strategies to disseminate information and boost recognition and engagement. This study examines the relationship between higher education institutions' (HEIs') rankings and their social media posting strategies. We gathered and analyzed publications from 18 HEIs featured in a consolidated ranking system, examining various features of their social media posts. To better understand these strategies, we categorized the posts into five predefined topics-engagement, research, image, society, and education. This categorization, combined with Long Short-Term Memory (LSTM) and a Random Forest (RF) algorithm, was utilized to predict social media output in the last five days of each month, achieving successful results. This paper further explores how variations in these social media strategies correlate with the rankings of HEIs. Our findings suggest a nuanced interaction between social media engagement and the perceived prestige of HEIs.

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To address the challenges of self-care in oncology, gamification emerges as an innovative strategy to enhance health literacy and self-care among individuals with oncological disease. This study aims to explore and map how gamification can promote health literacy for self-care of oncological diseases. A scoping review was conducted following the Joanna Briggs Institute guidelines and the PRISMA-ScR Checklist developed for scoping reviews. A comprehensive search strategy was employed across MEDLINE (R), CINAHL (R), Scopus (R), and Web of Science (R) databases, with keywords focusing on oncological patients and gamification tools applied to self-management, from inception to December 2023. Thirty studies published between 2011 and 2023 were included, with a total of 1,118 reported participants. Most interventions (n = 21) focused on the development of mobile applications. The most frequent gamification elements included customizable avatars, rewards, social interaction, quizzes, and personalized feedback. The interventions primarily targeted health literacy and patient education, symptom monitoring, management of side effects, pain control, and adherence to medication and nutrition regimens. The integration of gamification elements into digital health solutions for oncology is expanding and holds promises for supporting health literacy and self-care. Further studies, preferably longitudinal, are needed to assess the effectiveness and impact of these interventions across different oncological populations and clinical settings.

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General-purpose AI-assisted tools, such as ChatGPT, have recently gained much attention from the media and the general public. That raised questions about in which tasks we can apply such a tool. A good code design is essential for agile software development to keep it ready for change. In this context, identifying which design pattern can be appropriate for a given scenario can be considered an advanced skill that requires a high degree of abstraction and a good knowledge of object orientation. This paper aims to perform an exploratory study investigating the effectiveness of an AI-assisted tool in assisting developers in choosing a design pattern to solve design scenarios. To reach this goal, we gathered 56 existing questions used by teachers and public tenders that provide a concrete context and ask which design pattern would be suitable. We submitted these questions to ChatGPT and analyzed the answers. We found that 93% of the questions were answered correctly with a good level of detail, demonstrating the potential of such a tool as a valuable resource to help developers to apply design patterns and make design decisions.

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We present parametric probabilistic models for numerical distributional variables. The proposed models are based on the representation of each distribution by a location measure and inter-quantile ranges, for given quantiles, thereby characterizing the underlying empirical distributions in a flexible way. Multivariate Normal distributions are assumed for the whole set of indicators, considering alternative structures of the variance-covariance matrix. For all cases, maximum likelihood estimators of the corresponding parameters are derived. This modelling allows for hypothesis testing and multivariate parametric analysis. The proposed framework is applied to Analysis of Variance and parametric Discriminant Analysis of distributional data. A simulation study examines the performance of the proposed models in classification problems under different data conditions. Applications to Internet traffic data and Portuguese official data illustrate the relevance of the proposed approach.

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