Abstract
Palaeoclimate time series, reflecting the state of Earth's climate in the distant past, occasionally display very large and rapid shifts showing abrupt climate variability. The identification and characterisation of these abrupt transitions in palaeoclimate records is of particular interest as this allows for understanding of millennial climate variability and the identification of potential tipping points in the context of current climate change. Methods that are able to characterise these events in an objective and automatic way, in a single time series, or across two proxy records are therefore of particular interest. In our study the matrix profile approach is used to describe Dansgaard-Oeschger (DO) events, abrupt warmings detected in the Greenland ice core, and Northern Hemisphere marine and continental records. The results indicate that canonical events DO-19 and DO-20, occurring at around 72 and 76 ka, are the most similar events over the past 110 000 years. These transitions are characterised by matching transitions corresponding to events DO-1, DO-8, and DO-12. They are abrupt, resulting in a rapid shift to warmer conditions, followed by a gradual return to cold conditions. The joint analysis of the delta 18O and Ca2+ time series indicates that the transition corresponding to the DO-19 event is the most similar event across the two time series.

Abstract
Ensuring the reproducibility of computational scientific experiments is crucial for advancing research and fostering scientific integrity. However, achieving reproducibility poses significant challenges, particularly in the absence of appropriate software tools to help. This paper addresses this issue by comparing existing tools designed to assist researchers across various fields in achieving reproducibility in their work. We were able to successfully run eight tools and execute them to reproduce three existing experiments from different domains. Our findings show the critical role of technical choices in shaping the capabilities of these tools for reproducibility efforts. By evaluating these tools for replicating experiments, we contribute insights into the current landscape of reproducibility support in scientific research. Our analysis offers guidance for researchers seeking appropriate tools to enhance the reproducibility of their experiments, highlighting the importance of informed technical decisions in facilitating reproducibility across diverse domains.

Abstract
This article addresses the implementation of Business Intelligence (BI) systems in Higher Education Institutions (HEIs), focusing on developing an appropriate data architecture that meets the specificities and requirements of this sector. With the rapid advance of information technologies, HEIs face the growing challenge of managing a considerable volume of data, making it essential to implement BI systems that support informed and efficient decision-making. Using the Design Science Research methodology, this study proposes a BI architecture model that aligns technologies with HEIs' academic and administrative needs and facilitates their integration and ongoing maintenance. The model is designed to be flexible and scalable, allowing adaptations as institutional needs evolve. The article describes the architecture development process, from initial planning to implementation, and discusses how this framework can significantly improve data management and the quality of decision-making processes in educational institutions. The research offers practical and theoretical insights for academics and managers seeking to optimize the use of BI in educational contexts.

Abstract
Background: The Internet of Things (IoT) has improved many aspects that have impacted the industry and the people's daily lives. To begin with, the IoT allows communication to be made across a wide range of devices, from household appliances to industrial machinery. This connectivity allows for a better integration of the pervasive computing, making devices smart and capable of interacting with each other and with the corresponding users in a sublime way. However, the widespread adoption of IoT devices has introduced some security challenges, because these devices usually run in environments that have limited resources. As IoT technology becomes more integrated into critical infrastructure and daily life, the need for stronger security measures will increase. These devices are exposed to a variety of cyber-attacks. This literature review synthesizes the current research of artificial intelligence (AI) technologies to improve IoT security. This review addresses key research questions, including: (1) What are the primary challenges and threats that IoT devices face?; (2) How can AI be used to improve IoT security?; (3) What AI techniques are currently being used for this purpose?; and (4) How does applying AI to IoT security differ from traditional methods? Methods: We included a total of 33 peer-reviewed studies published between 2020 and 2024, specifically in journal and conference papers written in English. Studies irrelevant to the use of AI for IoT security, duplicate studies, and articles without full-text access were excluded. The literature search was conducted using scientific databases, including MDPI, ScienceDirect, IEEE Xplore, and SpringerLink. Results were synthesized through a narrative synthesis approach, with the help of the Parsifal tool to organize and visualize key themes and trends. Results: We focus on the use of machine learning, deep learning, and federated learning, which are used for anomaly detection to identify and mitigate the security threats inherent to these devices. AI-driven technologies offer promising solutions for attack detection and predictive analysis, reducing the need for human intervention more significantly. This review acknowledges limitations such as the rapidly evolving nature of IoT technologies, the early-stage development or proprietary nature of many AI techniques, the variable performance of AI models in real-world applications, and potential biases in the search and selection of articles. The risk of bias in this systematic review is moderate. While the study selection and data collection processes are robust, the reliance on narrative synthesis and the limited exploration of potential biases in the selection process introduce some risk. Transparency in funding and conflict of interest reporting reduces bias in those areas. Discussion: The effectiveness of these AI-based approaches can vary depending on the performance of the model and the computational efficiency. In this article, we provide a comprehensive overview of existing AI models applied to IoT security, including machine learning (ML), deep learning (DL), and hybrid approaches. We also examine their role in enhancing the detection accuracy. Despite all the advances, challenges still remain in terms of data privacy and the scalability of AI solutions in IoT security. Conclusion: This review provides a comprehensive overview of ML applications to enhance IoT security. We also discuss and outline future directions, emphasizing the need for collaboration between interested parties and ongoing innovation to address the evolving threat landscape in IoT security.

Abstract
Nowadays, Higher Education Institutions (HEIs) are faced with the crucial challenge of establishing and supervising strategies and policies that are essential for decisions in various areas and at various levels. Within this context, the importance of Business Intelligence (BI) has increased significantly, emerging as an essential tool for analysing and managing data. This BI capability enables HEIs to make more informed choices in line with their global strategies. This research focuses on developing a roadmap for the effective implementation of BI systems in HEIs. Using a Design Science Research (DSR) methodology, this work proposes a structured and adaptable roadmap that covers the key factors from the design to the implementation of BI systems in HEIs. This roadmap includes not only a reference architecture for BI systems but also a set of dashboards. The roadmap was validated through a case study at the University of Tras-os-Montes e Alto Douro (UTAD), involving exploratory analysis and feedback from experts. This study stands out for its practical and theoretical approach, offering a strategic and practical guide for the adoption of BI systems in HEIs, thus responding to a need identified in the academic literature.

Abstract
Many companies have been trying to take advantage of the entertainment boom on the internet and are searching for the bestways to make money from content creators. Among these are Virtual YouTubers, who have been growing in English-speaking spaces. This paper compares their popularity and influence in Portugal and the USA, while reflecting on their impact on viewers. To accomplish this, we created a survey, which obtained 102 answers, 46 being from Portuguese people and 44 being from U.S. citizens. We noted that only 37% of Portuguese people had heard of VTubers before and only 13% watch them on a weekly basis. On the other hand, every respondent from the USA was familiar with VTubers, with 86.4% watching them regularly. When asked about how VTubers affected the respondents' personal lives, some stated that it was merely a form of entertainment, however, many U.S. citizens reported that they watch VTubers to fulfill their need for social interaction or cope with loneliness. Thus, it is evident that these creators are much more popular in the USA. Lastly, we conclude that VTubers can have a positive impact on people's lives, but they also serve as an unhealthy escape from reality at times.