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The power transformer is one of the key components of any electrical grid, and, as such, modern day industrialization activities require constant usage of the asset. This increases the possibility of failures and can potentially diminish the lifespan of a power transformer. Dissolved gas analysis (DGA) is a technique developed to quantify the existence of hydrocarbon gases in the content of the power transformer oil, which in turn can indicate the presence of faults. Since this process requires different chemical analysis for each type of gas, the overall cost of the operation increases with number of gases. Thus said, a machine learning methodology was defined to meet two simultaneous objectives, identify gas subsets, and predict the remaining gases, thus restoring them. Two subsets of equal or smaller size to those used by traditional methods (Duval's triangle, Roger's ratio, IEC table) were identified, while showing potentially superior performance. The models restored the discarded gases, and the restored set was compared with the original set in a variety of validation tasks.

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Societies throughout today’s global village are increasingly aware of the social injustices that minorities face, and immigrants are no exception. Combined with the lack of adaptation resources and the prejudice of non-migrant residents, immigrants may feel powerless in foreign places as they try to find comfort and security in new and unfamiliar environments. It is increasingly urgent to address immigrant issues, considering the crucial role of enhancing diversity, combating prejudice, and raising awareness of minority experiences. This systematic literature review investigates the innovative use of gamification in exploring and addressing the experiences and issues immigrants face. The review follows the PRISMA statement guidelines and checklist. Scopus, CINAHL, and Medline databases were searched, resulting in 17 relevant articles that were carefully analyzed. This research highlights the diverse applications of gamification in studying immigrant experiences via role-playing, interactive storytelling, and empathy-building simulations. This work explores the potential of gamified interventions in addressing pressing issues immigrants face and assesses their effectiveness in fostering empathy and intercultural communication. It also identifies gaps in the existing information sciences literature and proposes directions for future research. In conclusion, this review sheds light on the emerging field of gamification in immigration studies and games studies in the information sciences, providing valuable insights for scholars, policymakers, and practitioners working with immigrant communities worldwide.

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Reading, adapting, and maintaining complex software can be a daunting task. We might need to refactor it to streamline the process and make the code cleaner and self-explanatory. Traditional refactoring tools guide developers to achieve better-quality code. However, the feedback and assistance they provide can take considerable time. To tackle this issue, we explored the concept of Live Refactoring. This approach focuses on delivering real-time, visually-driven refactoring suggestions. That way, we prototyped a Live Refactoring Environment that visually identifies, recommends, and applies several refactorings in real-time. To validate its effectiveness, we conducted a set of experiments. Those showed that our approach significantly improved various code quality metrics and outperformed the results obtained from manually refactoring code. © 2024 IEEE Computer Society. All rights reserved.

Abstract
Background: Many programming environments include automated feedback in the form of hints to help novices learn autonomously. Some experimental studies investigated the impact of automated hints in the immediate performance and learning retention in that context. Automated feedback is also becoming a popular research topic in the context of formal specification languages, but so far no experimental studies have been conducted to assess its impact while learning such languages. Objective: We aim to investigate the impact of different types of automated hints while learning a formal specification language, not only in terms of immediate performance and learning retention, but also in the emotional response of the students. Method: We conducted a simple one-factor randomised experiment in 2 sessions involving 85 BSc students majoring in CSE. In the 1st session students were divided in 1 control group and 3 experimental groups, each receiving a different type of hint while learning to specify simple requirements with the Alloy formal specification language. To assess the impact of hints on learning retention, in the 2nd session, 1 week later, students had no hints while formalising requirements. Before and after each session the students answered a standard self-reporting emotional survey to assess their emotional response to the experiment. Results: Of the 3 types of hints considered, only those pointing to the precise location of an error had a positive impact on the immediate performance and none had significant impact in learning retention. Hint availability also causes a significant impact on the emotional response, but no significant emotional impact exists once hints are no longer available (i.e. no deprivation effects were detected). Conclusion: Although none of the evaluated hints had an impact on learning retention, learning a formal specification language with an environment that provides hints with precise error locations seems to contribute to a better overall experience without apparent drawbacks. Further studies are needed to investigate if other kind of feedback, namely hints combined with some sort of self-explanation prompts, can have a positive impact in learning retention. © 2024 Copyright held by the owner/author(s).