Abstract

Abstract
Promoting active modes of transport, such as walking and cycling, has a positive impact on environmental sustainability and the health and well-being of citizens. This study explores the elderly population’s perception of comfort, safety and security when using active modes of transport. It begins with a systematic review of the literature considering research works that relate to active travel, the elderly population, and random forest. Then a questionnaire was applied to 653 participants and the results were analyzed. This analysis consisted of using statistics to evaluate the socio-demographic profile, the preferences regarding the use of active modes of this population, and the importance given to each dimension: comfort, safety, distance, and time, comparing these indicators through the Wilcoxon Rank Sum test and the Random Forest algorithm. The results showed that people over 56 years old walk as much as younger people. Furthermore, the importance given by this group of people to indicators referring to active modes is related to safety and security, distance, time, and comfort. The statistical results of the Wilcoxon Rank Sum test indicate the most important indicators: Adequate Travel Distance & Time and Existence of Commercial Areas by age group [0–55], and Absence of Allergenics and Existence of Green Areas by age group [56+]. Finally, the Random Forest algorithm provides the relative importance for both age groups, [0–55] and [56+], where the indicators that stand out in the [56+] age group, which is the focus of our study, are air quality, adequate travel distance & time, adequate crowd density, adequate thermal sensation, absence of allergenic, good street illumination level, adequate traffic volume, and adequate noise level. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract

Abstract
This paper evidences the ability of a VSM-based grid-forming to mitigate stability problems of different classes, raising a special concern towards the importance of its location in systems with large converter-interfaced renewable energy systems. Within this context, a multi-class stability assessment, that pillars on the simulation of different nature disturbances and in the subsequent evaluation of a 4 index set, was performed. Such analysis was carried out on a modified version of the IEEE39 Test System, using DigSILENT Power Factory as the simulation engine.

Abstract
Selective thinning is a crucial operation to reduce forest ignitable material, to control the eucalyptus species and maximise its profitability. The selection and removal of less vigorous stems allows the remaining stems to grow healthier and without competition for water, sunlight and nutrients. This operation is traditionally performed by a human operator and is time-intensive. This work simplifies selective thinning by removing the stem selection part from the human operator's side using a computer vision algorithm. For this, two distinct datasets of eucalyptus stems (with and without foliage) were built and manually annotated, and three Deep Learning object detectors (YOLOv5, YOLOv7 and YOLOv8) were tested on real context images to perform instance segmentation. YOLOv8 was the best at this task, achieving an Average Precision of 74% and 66% on non-leafy and leafy test datasets, respectively. A computer vision algorithm for automatic stem selection was developed based on the YOLOv8 segmentation output. The algorithm managed to get a Precision above 97% and a 81% Recall. The findings of this work can have a positive impact in future developments for automatising selective thinning in forested contexts.

Abstract
Cybersecurity has a major impact on the healthcare sector, mainly due to the sensitive data and vital medical devices that, when an attack occurs, may compromise the life, safety, and well-being of the patients. However, those institutions fail on implementing correct system protection policies and providing adequate programs for cybersecurity training and raising cybersecurity awareness. Healthcare professionals develop their academic courses focusing on providing the best care for the patients, studying guidelines, treatment protocols, and diagnostic criteria. However, there are insufficient subjects dedicated to the development of digital literacy to match the requisites of the daily challenges of those professionals, with human error being the main cause of data breaches worldwide. So, developing training programs to face the cybersecurity day-to-day threats is mandatory. Broadly speaking, traditional training programs seem to fail on retaining students' motivation, engagement, and long-term knowledge acquisition, being time-consuming and challenging in scheduling and planning. To face this situation, new techniques, such as gamification, have emerged, with promising results on motivation and engagement, allowing the users to be the center of the training programs, matching the strategy to their levels of knowledge and preferences. This paper aims to identify the existing gamified approaches available, review the state-of-the-art related to gamification and cybersecurity training, and elaborates on how they can be successfully applied to training programs for healthcare professionals. © 2024 Elsevier B.V.. All rights reserved.