Abstract
The purpose of this study is to ascertain the hierarchical importance of a patent's characteristics to licensing. This research has a causal-exploratory purpose, in that it sought to establish relationships between variables. This research aims to identify which characteristics are influential in the licensing of Brazilian academic patents in the biotechnology and pharmaceutical technology fields, based on the mining of data contained in licensed and unlicensed patent documents. Which characteristics of Brazilian academic patents are most influential in their licensing potential? An analysis through Random Forest was performed. To the best of our knowledge, there are no studies in Brazil using machine learning to identify which characteristics are influential in licensing a particular academic patent, especially given the difficulty of gathering this information. We found that regardless of the measure used, the three most critical licensing characteristics for the Biotechnology and Pharmaceutical patents analysed are Patent Scope, Life Cycle, and Claims. At the same time, the least important is the Patent Cooperation Treaty. The relevance of this research is based on the fact that after identifying which intrinsic characteristics influence the final value and licensing probabilities of a given patent, it will be possible to develop mathematical models that provide accurate information for establishing technology transfer agreements. In practical terms, the results suggest that greater patent versatility, combined with lifecycle management and a technical effort to build strong claims, increases the licensing potential of academic biopharmaceutical patents.

Abstract
Many companies have been trying to take advantage of the entertainment boom on the internet and are searching for the best ways 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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

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
This scoping review systematizes the current research related to the use of both blockchain and machine learning techniques in medical imaging applications. A systematic electronic search was performed, and twenty-five studies were included in the review. These studies aimed to use blockchain and machine learning techniques to provide (i) efficient security mechanisms to support the communication of medical imaging data, (ii) aggregation of distributed medical imaging data to train machine learning algorithms, and (iii) machine learning algorithms based on federated learning strategies. Among the ten machine learning techniques identified in the included studies, Convolutional Neural Network was the most representative (i.e., 44% of the studies). Moreover, Artificial Neural Network, Capsule Network, Deep Neural Network, Gated Recurrent Units, and Neural Network were machine learning techniques used by more than one study. Although the included studies developed algorithms with potential impact in clinical practice, it must be noted that they did not discuss the generalizability of their algorithms in real-world clinical conditions.

Abstract
The scoping review reported by this paper aimed to analyze and synthesize state-of-the-art studies focused on the application of machine learning methods to enhance the cyber resilience of cyber-physical systems. An electronic search was conducted, and 24 studies were included in this review after the selection process. The most representative application domains were computer networks and power systems, while in terms of cyber resilience functions, risk identification, risk mitigation or protection, and detection of anomalous situations were the most implemented functions. Moreover, the results of this scoping review show that the interest in the topic of cyber resilience and machine learning is quite recent, which justifies the heterogeneity of the included studies in terms of machine learning methods and datasets being used for the experimental validations, as well as in terms of outcomes being measured. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
This article aimed to analyse state-of-the-art empirical evidence of randomized controlled trials designed to assess preventive cognitive training interventions based on virtual reality for older adults without cognitive impairment, by identifying virtual reality setups and tasks, clinical outcomes and respective measurement instruments, and positive effects on outcome parameters. A systematic electronic search was performed, and six randomized controlled trials were included in the systematic review. In terms of results, the included studies pointed to significant positive impact of virtual reality-based cognitive training interventions on global cognition, memory, attention, information processing speed, walking variability, balance, muscle strength, and falls. However, further research is required to evaluate the adequacy of the virtual reality setups and tasks, to study the impact of the interventions’ duration and intensity, to understand how to tailor the interventions to the characteristics and needs of the individuals, and to compare face-to-face to remote interventions. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.