Abstract
In order to address one of the most challenging problems in hospital management - patients' absenteeism without prior notice - this study analyses the risk factors associated with this event. To this end, through real data from a hospital located in the North of Portugal, a prediction model previously validated in the literature is used to infer absenteeism risk factors, and an explainable model is proposed, based on a modified CART algorithm. The latter intends to generate a human-interpretable explanation for patient absenteeism, and its implementation is described in detail. Furthermore, given the significant impact, the COVID-19 pandemic had on hospital management, a comparison between patients' profiles upon absenteeism before and during the COVID-19 pandemic situation is performed. Results obtained differ between hospital specialities and time periods meaning that patient profiles on absenteeism change during pandemic periods and within specialities.

Abstract

Abstract
Reinforcement Learning (RL) is a well-suited paradigm to train robots since it does not require any previous information or database to train an agent. This paper explores using Deep Reinforcement Learning (DRL) to train a robot to navigate in maps containing different sorts of obstacles and which emulate hallways. Training and testing were performed using the Flatland 2D simulator and a Deep Q-Network (DQN) provided by OpenAI gym. Different sets of maps were used for training and testing. The experiments illustrate how well the robot is able to navigate in maps distinct from the ones used for training by learning new behaviours (namely following walls) and highlight the key challenges when solving this task using DRL, including the appropriate definition of the state space and reward function, as well as of the stopping criteria during training.

Abstract
Disentangling innovation from growth is unrealistic in the present times. Also, anticipating the future behavior of innovative firms is relevant to the entire innovation ecosystem; and assessing the persistence of innovation and appraising the role of factors affecting ongoing innovation activities in firms is essential. This chapter discusses a very important subject related to the concept of innovation persistence in relation to structural innovation characteristics of firms, with a focus on technological regimes, to better understand if there is change in innivation continuity accordingly to the technological intensity embedded in the sector. The empirical research is based on data from CIS database, comprising 3237 firms which present in the 2014 and 2018 waves. We analyze the innovative persistence behavior of these firms regarding proxies like firm dimension, innovation activities, types of innovation, government funding, and more importantly, technological regimes. To do this, we applied binary logistic regression for developing a model which can forecast the drivers of innovation persistency propensity. The presented study shows that some very important results are achieved. Besides demonstrating innovative persistency in 75% of science-based firms, the findings confirm that firms in high-tech and science-based industries are more prone to continue innovating and, as a result, this consistency in innovation will generate virtuous cycles of innovation. Furthermore, our data shows that complex innovators are more likely to persist than single innovators, proving the existence of complementarities among the innovation types.

Abstract
This paper presents an analysis of the distribution system harmonic behavior when a photovoltaic (PV) system is connected at the low-voltage network. Through harmonic power flow simulations in the Open Distribution System Simulator (OpenDSS), harmonic impact at the point common coupling is studied. To reduce the harmonic analysis, simulations are carried out using a simplified equivalent for utility and customer. In order to model a more realistic distribution system, it is necessary to calibrate the harmonic sources before connecting the PV system. The harmonic state estimation method implemented in combination with the simulator is used to calibrate the utility harmonic voltage sources. After calibration, the harmonic levels at medium-voltage are verified. Results for a case study using quality measurements performed by Ecuadorian CENTROSUR electric utility show that the simulated harmonic levels are similar to those measured. It also shows that a PV system does not produce power quality problems.

Abstract
Despite major advances in recent years, the field of Machine Learning continues to face research and technical challenges. Mostly, these stem from big data and streaming data, which require models to be frequently updated or re-trained, at the expense of significant computational resources. One solution is the use of distributed learning algorithms, which can learn in a distributed manner, from distributed datasets. In this paper, we describe CEDEs-a distributed learning system in which models are heterogeneous distributed Ensembles, i.e., complex models constituted by different base models, trained with different and distributed subsets of data. Specifically, we address the issue of predicting the training time of a given model, given its characteristics and the characteristics of the data. Given that the creation of an Ensemble may imply the training of hundreds of base models, information about the predicted duration of each of these individual tasks is paramount for an efficient management of the cluster's computational resources and for minimizing makespan, i.e., the time it takes to train the whole Ensemble. Results show that the proposed approach is able to predict the training time of Decision Trees with an average error of 0.103 s, and the training time of Neural Networks with an average error of 21.263 s. We also show how results depend significantly on the hyperparameters of the model and on the characteristics of the input data.