Abstract
This work presents a novel methodology for Sub-Optimal Excitation Signal Generation and Optimal Parameter Estimation of constrained nonlinear systems. It is proposed that the evaluation of each signal must also account for the difference between real and estimated system parameters. However, this metric is not directly obtained once the real parameter values are not known. The alternative presented here is to adopt the hypothesis that, if a system can be approximated by a white box model, this model can be used as a benchmark to indicate the impact of a signal over the parametric estimation. In this way, the proposed method uses a dual layer optimization methodology: (i) Inner Level; For a given excitation signal a nonlinear optimization method searches for the optimal set of parameters that minimizes the error between the outputs of the optimized and benchmark models. (ii) At the outer level, a metaheuristic optimization method is responsible for constructing the best excitation signal, considering the fitness coming from the inner level, the quadratic difference between its parameters and the cost related to the time and space required to execute the experiment.

Abstract
An early detection of neurodegenerative diseases, such as Parkinson’s disease, can improve therapy effectiveness and, by consequence, the patient’s quality of life. This paper proposes a new methodology for automatic classification of voice samples regarding the presence of acoustic patterns of Parkinson’s disease, using a deep structured neural network. This is a low cost non-invasive approach that can raise alerts in a pre-clinical stage. Aiming to a higher diagnostic detail, it is also an objective to accurately estimate the stage of evolution of the disease allowing to understand in what extent the symptoms have developed. Therefore, two types of classification problems are explored: binary classification and multiclass classification. For binary classification, a deep structured neural network was developed, capable of correctly diagnosing 93.4% of cases. For the multiclass classification scenario, in addition to the deep neural network, a K-nearest neighbour algorithm was also used to establish a reference for comparison purposes, while using a common database. In both cases the original feature set was optimized using principal component analysis and the results showed that the proposed deep structure neural network was able to provide more accurate estimations about the disease’s stage, reaching a score of 84.7%. The obtained results are promising and create the motivation to further explore the model’s flexibility and to pursue better results.

Abstract
In this work we propose a regression approach based on separability maximization (RASMa) for modeling a continuous-valued estimate of the stress level (we called it stress index) using some features extracted from electrocardiogram (ECG) data. Since no objective measure of the actual stress level (output) is available, finding the stress index cannot be addressed as a classical regression problem. Instead, the proposed approach finds the linear combination of features that maximizes the separability of stress index values for non-stress and stress events. In short, RASMa combines linear discriminant analysis with the Bhattacharyya distance, embedded in a leave-one-subject-out cross-validation scheme. A 26-case pilot study using 17 heart rate variability (HRV) features was conducted as a proof of concept. A near real-time application tool for monitoring stress level over time was also implemented based on the model obtained from the pilot study.

Abstract
In a medical appointment, patient information, including past exams, is analyzed in order to define a diagnosis. This process is prone to errors, since there may be many possible diagnoses. This analysis is very dependent on the experience of the doctor. Even with the correct diagnosis, prescribing medicines can be a problem, because there are multiple drugs for each disease and some may not be used due to allergies or high cost. Therefore, it would be helpful, if the doctors were able to use a system that, for each diagnosis, provided a list of the most suitable medicines. Our approach is to support the physician in this process. Rather than trying to predict the medicine, we aim to, given the available information, predict the set of the most likely drugs. The prescription problem may be solved as a Multi-Label classification problem since, for each diagnosis, multiple drugs may be prescribed at the same time. Due to its complexity, some simplifications were performed for the problem to be treatable. So, multiple approaches were done with different assumptions. The data supplied was also complex, with important problems in its quality, that led to a strong investment in data preparation, in particular, feature engineering. Overall, the results in each scenario are good with performances almost twice the baseline, especially using Binary Relevance as transformation approach.

Abstract
The use of metaheuristics to solve real-life problems has increased in recent years since they are easy to implement, and the problems become easy to model when applying metaheuristic approaches. However, arguably the most important aspect is the simulation time since results can be obtained from metaheuristic methods in a much smaller time, and with a good approximation to the results obtained with exact methods. In this work, the Genetic Algorithm (GA) metaheuristic is adapted and applied to solve the optimization of electricity markets participation portfolios. This work considers a multiobjective model that incorporates the calculation of the profit and the risk incurred in the electricity negotiations. Results of the proposed approach are compared to those achieved with an exact method, and it can be concluded that the proposed GA model can achieve very close results to those of the deterministic approach, in much quicker simulation time.

Abstract
The Portuguese footwear industry registered a strong performance over the last few years and the openness to adopt new technologies was a key factor. Among these, the implementation of innovative logistic systems for the transport and assignment of work-in-process in manufacturing processes was a key technology. By means of case research it was analyzed an internal logistic system, deployed in a Portuguese large footwear producer, which reveals some weaknesses at the physical and cyber levels. A lack of managing applications was detected at the factory and enterprise levels, which contribute to lower levels of visibility of production and productivity. This article presents the development of a Cyber-Physical Production System, in the context of the Horizon 2020 research and innovation program BEinCPPS, that comprises four application experiments. These experiments were successful and the results demonstrated a relevant increase of production efficiency and decrease of maintenance costs.