Abstract
Missing data can pose severe consequences in critical contexts, such as clinical research based on routinely collected healthcare data. This issue is usually handled with imputation strategies, but these tend to produce poor and biased results under the Missing Not At Random (MNAR) mechanism. A recent trend that has been showing promising results for MNAR is the use of generative models, particularly Variational Autoencoders. However, they have a limitation: the imputed values are the result of a single sample, which can be biased. To tackle it, an extension to the Variational Autoencoder that uses a partial multiple imputation procedure is introduced in this work. The proposed method was compared to 8 state-of-the-art imputation strategies, in an experimental setup with 34 datasets from the medical context, injected with the MNAR mechanism (10% to 80% rates). The results were evaluated through the Mean Absolute Error, with the new method being the overall best in 71% of the datasets, significantly outperforming the remaining ones, particularly for high missing rates. Finally, a case study of a classification task with heart failure data was also conducted, where this method induced improvements in 50% of the classifiers.

Abstract
This paper investigates the contribution of hydrogen electrolysers (HEs) as highly controllable loads in the context of the Frequency Containment Reserve (FCR), in future operation scenarios on the Iberian Peninsula (IP). The research question is whether HEs can mitigate system insecurity regarding frequency or Rate of Change of Frequency (RoCoF) in critical periods of high renewable energy penetration (i.e. low system inertia), due to the fact that these periods will coincide with high volume of green hydrogen production. The proposed simulation platform for analysis consists of a simplified dynamic model developed in MATLAB/Simulink. The results obtained illustrate how HEs can outperform conventional generators on the provision of FCR. It is seen that the reference incident of 1GW loss in the IP in a 2040 low inertia scenario does not lead to insecure values of either frequency or Rate of Change of Frequency (RoCoF). On the other hand, an instantaneous loss of inverter-based resources (IBR) generation following a short-circuit may result in RoCoF violating security thresholds. The obtained results suggest that the HEs expected to be installed in the IP in 2040 may contribute to reduce RoCoF in this case, although this mitigation may be insufficient. The existing FCR mechanism does not fully exploit the fast-ramping capability of HEs; reducing measurement acquisiton delay would not improve results.

Abstract
This work aims to identify the critical production costs, related to raw materials and labor, of ordered inflatable-based products without standardization in order to develop a quantitative model to predict these costs accurately in the early project stage, within the budget step. In order to achieve this goal, it was necessary to understand the production processes and the raw materials, as well as to study the principal theoretical aspects related to cost estimating techniques and methods, cost estimating models, model selection, and validation. Therefore, it is intended to develop a multiple linear regression model, applied to historical quantitative data, to estimate each critical variable concerning the quantity of the main raw material and the labor times for critical processes. Six models were analyzed, in which two models are identified for each critical variable such as the linear meters value of the main raw material used in the product, the main raw material cut time involved in the product and the sew time required by the product. The models were evaluated, selected, and validated, defining the best model for each critical variable. The model parameters were obtained using a train dataset and, afterwards, the results of the selected models were validated using a test dataset. The obtained results, through the proposed methodology, were evaluated and proved to be reliable for use in the early stage of product development within the budget step.

Abstract
Open innovation (OI) has been implemented to develop competitive advantages based on the management of innovation with external players. As such, it is expected that the generalized adoption of OI practices needs to be nurtured by governmental public policies in order to enhance OI-based ecosystems. The role of open innovation ecosystems is known by the importance of multiple synergies among players/stakeholders, which are expected to be supported by regulations and funding to consolidate firms’ innovation results. This paper analyzes the role of regulations and funding on firms’ innovation performance using the double-hurdle estimation procedure. The results show that, in the first tier, inbound knowledge flows positively affect performance, and, in the second tier, public funds further reinforce innovation performance and fiscal and security regulations. In contrast, as regulations are perceived as barriers, they fail to impact innovation performance. With this paper, we manage to shed light on the importance of public policy funds in the support of thriving OI-based ecosystems as enhancers of firms’ innovation performance.

Abstract
The aim of this paper is to analyse the relationship between academic inbreeding in Portugal and research collaboration, using co-authored publications as proxies. As previous research has shown that inbreeding is detrimental for research collaborations, it is hypothesised that academic inbreeding will lead to smaller research networks and, consequently, to fewer co-authored publications outside the institution of affiliation. Relying on a large data set which merged information on academics, their inbreeding status and their publications, binomial negative and fractional models were estimated to test the hypothesis. Findings show that inbred academics have smaller research networks; while they publish most co-authored papers, the relative weight of publications written in collaboration with institutional colleagues is the highest. In contrast, non-inbred academics with foreign PhDs have larger co-authorship networks. However, they publish most single-authored papers and the weight of their international co-authorships is heaviest.

Abstract
This paper arises from experience by the authors in teaching software engineering courses. It discusses the need for adequate coverage of Human-Computer Interaction topics in these courses and the challenges faced when addressing them. Three courses, at both licentiate and master's levels, are used as triggers for the discussion. The paper argues that the lack of relevant Human-Computer Interaction concepts creates challenges when teaching and learning requirements analysis, design, and implementation of software systems. The approaches adopted to address these challenges are described.