Abstract
Este estudo testa a multidimensionalidade de Universidade Empreendedora (UE) e visa aferir o respetivo contributo para a competitividade regional. Com base em 619 respostas de estudantes, professores e outros colaboradores de dez universidades públicas portuguesas, foi efetuada uma análise fatorial confirmatória e uma estimação de regressão linear múltipla. O construto da UE é confirmado, comprovando a adequação das escalas para o contexto destas universidades. Os resultados mostram que os cinco fatores associados à UE - processos internos, medidas de apoio ao empreendedorismo, colaboração internacional, estratégia de financiamento e estrutura organizacional - contribuem positivamente para a perceção de competitividade regional, acentuando a função das universidades públicas enquanto polos de transferência de conhecimento dinamizadores da competitividade regional.

Abstract
Learning software programming is challenging for software engineering students. In this paper, students' engagement in learning software engineering programming is considered under the SimProgramming approach using the OC2-RD2 narrative technique to create an immersive learning context. The objectives of this paper are twofold: presenting a narrative-driven immersive learning approach to introduce software engineering concepts and coding techniques to online undergraduate students; and analyzing the students' feedback on this approach. Thematic analysis of the metacognitive tasks was performed on the students' fortnightly reflections about their learning progress. Content analysis was based on interest categories, students' perceptions, metacognitive challenges, narratives, examples and aspects to be kept or to be improved. Data from the content analysis were organized into categories, subcategories, indicators, and recording units and their categorization was peer-reviewed. The narratives were considered by the students as interesting, appealing, akin to professional reality and promoting interaction. Most students thought the approach was helpful for learning software programming.

Abstract
The need of lockdown, due to COVID-19, led many manufacturing companies to accelerate the adoption of digital technologies. Manufacturing companies were strongly affected by workforce shortages associated with the spread of COVID-19 and the lockdown, as well by connectivity losses among business partners. Therefore, these companies are reviewing their strategies to increase productivity, mainly embracing digital manufacturing technologies. Here the adoption of digital technologies aims to improve efficiency and flexibility in their processes, also improving connectivity among business partners. This study investigates how collaborative academia-industry R&D cases accelerated the adoption of digital technologies by manufacturing companies, given the current COVID-19 pandemic situation. Based on multiple case studies, this article reports the challenges and the strategies of three ongoing collaborative industry-academia R&D projects developed during the COVID pandemic situation. The results are presented in four different perspectives derived from industry 4.0 readiness maturity models: interpersonal communication, personal competencies and skills, systems integration, and technological strategy. It highlights the importance of manufacturing companies to have a well-designed digitalization strategy, need of continuous training and development of their workforce, and the support of Research & Technology Organizations (RTO) to bring more maturity to the efforts required during a turbulent situation. The results of this paper can provide relevant decision support for manufacturing companies, and its stakeholders, in face of challenges of the actual pandemic and post-pandemic scenario.

Abstract
Cancer is a leading cause of death worldwide. The detection and diagnosis of most cancers are confirmed by a tissue biopsy that is analyzed via the optic microscope. These samples are then scanned to giga-pixel sized images for further digital processing by pathologists. An automated method to segment the malignant regions of these images could be of great interest to detect cancer earlier and increase the agreement between specialists. However, annotating these giga-pixel images is very expensive, time-consuming and error-prone. We evaluate 4 existing annotation efficient methods, including transfer learning and self-supervised learning approaches. The best performing approach was to pretrain a model to colourize a grayscale histopathological image and then finetune that model on a dataset with manually annotated examples. This method was able to improve the Intersection over Union from 0.2702 to 0.3702.

Abstract
In the technology communication era, the use of the Internet of Things (IoT) has become popular among other digital solutions, since it offers the integration of information from several organisms and at several sources. By means of this, we can access data from distant locations and at any time. In the specific case of water monitoring, the conventional outdated measurement methods can lead to low efficiency and complexity issues. Hence, Smart systems rise as a solution for a broad of cases. Smart River is a smart system platform developed to optimize the resources and monitoring the quality of water parameters of the Fervença river. The central solution is based at Centro Ciência Viva de Bragança (CCVB), one of the 21 science centers in Portugal that aims to promote the preservation and environmental awareness for the population. By using the IoT technologies, the system allows real-time data collection with low cost and low energy consumption, being a complement of existing projects that are being developed to promote the ecological importance of natural resources. This paper covers sensor module selection for data collection inside the river and data storage. The parameters of the river are visualized using a program developed in Unity engine to present data averages and comparison between weeks, months, and years.

Abstract
In recent years, the energy sector has undergone major changes, particularly in Portugal, where there is complete liberalization of the electricity sector. Like in other European countries, a market agent has been created to facilitate trading relations between producer and trader. The Virtual Power Plant (VPP) agent aims to minimize the costs to the trader and maximizes the profits of producers. In this work, five renewable power plants, which are contractually linked with a VPP, are analyzed to verify the profitability of these contracts for both parties. Using this framework, an analysis is carried out examining the differences between actual renewable production and the planned (forecasted) production. In some instances, there are significant deviations between actual and forecast production and this results in higher costs. Consequently, the greater the deviations, the greater the expenses and, therefore, the lower the profit of each party. Thus, new bidding strategies that result in the reduction of these differences are sought. The bidding strategies proposed in this paper involve markets and various types of contracts to deliver the optimal solution that results in higher profits for both parties. The results show an increase in VPP profit on average of 32%.