Abstract
Gliomas are a common and aggressive kind of brain tumour that is difficult to diagnose due to their infiltrative development, variable clinical presentation, and complex behaviour, making them an important focus in neuro-oncology. Segmentation of brain tumour images is critical for improving diagnosis, prognosis, and treatment options. Manually segmenting brain tumours is time-consuming and challenging. Automatic segmentation algorithms can significantly improve the accuracy and efficiency of tumour identification, thus improving treatment planning and outcomes. Deep learning-based segmentation tumours have shown significant advances in the last few years. This study evaluates the impact of four denoising filters, namely median, Gaussian, anisotropic diffusion, and bilateral, on tumour detection and segmentation. The U-Net architecture is applied for the segmentation of 3064 contrast-enhanced magnetic resonance images from 233 patients diagnosed with meningiomas, gliomas, and pituitary tumours. The results of this work demonstrate that bilateral filtering yields superior outcomes, proving to be a robust and computationally efficient approach in brain tumour segmentation. This method reduces the processing time by 12 epochs, which in turn contributes to lowering greenhouse gas emissions by optimizing computational resources and minimizing energy consumption.

Abstract
STEM graduates play a key role in the society's sustainable development, contributing to the productivity, competitiveness and progress of the economy. However, the number of students enrolled in STEM courses is low, despite the high demand and valuation by the labour market. This reality is even more striking when analysing the numbers related to the presence of women in STEM fields, particularly in engineering courses. The under-representation of women is a problem arising from multiple factors that need to be overcome through educational policies and specific initiatives. This article describes the iProg, Scratch on Road, and Scratch4All projects that aimed at the development of competences in technological areas, namely in programming and robotics, among students from basic schools. The positive impacts of the mentioned projects encouraged the authors to create the Make a Lab project, a partnership between Coimbra Institute of Engineering (ISEC) and CASPAE, a Private Social Solidarity Institution. The central goal of this project is to respond to the critical gender gap in STEM subjects, having as a goal that 70% of the students involved in the program activities should be women. This project aims to promote interest in science and technology in students from secondary schools through the use of creative, experimental and exploratory approaches. The development of technological products adopt project based learning methodologies, mainly implemented in a laboratory located at ISEC campus. The co-definition and co-creation of these products is based on a mentoring process carried out by ISEC students, preferably women. One-off initiatives will also be carried out, as for instance hands-on experiences and thematic workshops. An itinerant exhibition will be organized using the technological products developed over the Make a Lab project. The exhibition will be available to basic and secondary schools, after the terminus of the project.

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This work focuses on the motivation levels of introductory programming students and their relationship with their learning performance. The study involved students enrolled in the Introduction to Programming (IP) course included in two slightly different Informatics Engineering degrees at the same institution. The motivation section of the Motivated Strategies for Learning Questionnaire (MSLQ) instrument includes several scales and subscales used to analyse different motivational factors.Four research questions guided the study. The first is comparing the results of the two groups of students. The second considered the student's previous programming experience and tried correlating it with motivational factors. The third is similar but separates students following IP for the first time and those who had failed it in previous years. Finally, the fourth research question examined the influence of motivational factors on students' learning performance measured by their final grades.This paper provides a detailed study description and presents and discusses its results. © 2023 IEEE.

Abstract
The 'Scratch4All' project aims to reduce school dropout rates by encouraging and motivating students in the 1st, 2nd, and 3rd cycles of elementary schools to achieve academic success. In addition, the project promotes true equality of opportunity in terms of educational resources. A learning environment that contributes positively to the improvement of educational results is created through the use of new technologies, specifically programming in the Scratch language and robotics. Impact indicators were developed and used as project evaluation tools based on Theory of Change. This paper presents and analyzes the changes observed from the perspective of the project's teachers' stakeholders during the 2020/2021 school year. This group of beneficiaries is critical to the project's success and continuation, not only because of the special relationship they have with the students, but also with their families and the school. The assessment of impact is very positive in terms of indicators such as school success and motivation, particularly in Mathematics and Portuguese Language, as well as in the reduction of inequalities, access to new technologies, and contribution to gender equality. © 2023 ITMA.