Abstract
In the spring of 2020, six undergraduate students from diverse countries and engineering fields decided to design together a solution to monitor the elderly. This project was performed as part of the European Project Semester (EPS) programme at Instituto Superior de Engenharia do Porto (ISEP). The EM-BRACE solution encompasses two interconnected devices (a home station and a bracelet) and mobile/Web twin applications. The bracelet measures and transmits vital user data (pulse, temperature and impacts) to the home station, whereas the latter measures home environment parameters (temperature, humidity and pressure) and sends local and bracelet data to an Internet of Things (IoT) platform. This way, these data become accessible via the mobile/Web application. Thereby, EM-BRACE monitors the health and environment of the elderly and timely notifies caregivers about problems, contributing to the well-being of the elderly and their families.

Abstract
An original graph-based model and algorithm for optimal industrial task scheduling is proposed in this article. The innovative algorithm designed, dubbed "Dijkstra optimal tasking" (DOT), is suitable for fully distributed task scheduling of autonomous industrial agents for optimal resource allocation, including energy use. The algorithm was designed starting from graph theory fundamentals, from the ground up, to guarantee a generic nature, making it applicable on a plethora of tasking problems and not case-specific. For any industrial setting in which mobile agents are responsible for accomplishing tasks across a site, the objective is to determine the optimal task schedule for each agent, which maximizes the speed of task achievement while minimizing the movement, thereby minimizing energy consumption cost. The DOT algorithm is presented in detail in this manuscript, starting from the conceptualization to the mathematical formulation based on graph theory, having a thorough computational implementation and a detailed algorithm benchmarking analysis. The choice of Dijkstra as opposed to other shortest path methods (namely, A* Search and Bellman-Ford) in the proposed graph-based model and algorithm was investigated and justified. An example of a real-world application based on a refinery site is modeled and simulated and the proposed algorithm's effectiveness and computational efficiency is duly evaluated. A dynamic obstacle course was incorporated to effectively demonstrate the proposed algorithm's applicability to real-world applications.

Abstract
The present research aims to formulate competition in a retail energy market in the presence of an Integrated Demand Response (IDR) program to reduce prosumer costs and increase retailer profits. This gives prosumers more degrees of freedom to reduce their energy costs. The retail energy market includes retailers and prosumers equipped with an energy hub containing a boiler for producing heat and combined heat and power (CHP). Retailers aim to maximize profit, whereas prosumers seek to minimize their costs. Hence, a multi-leader-follower game with a bi-level program emerges in which the upper level deals with the profit maximization of each retailer while the lower level considers the cost minimization of each prosumer. The strategic behaviour of each retailer is modelled as a Mathematical Program with Equilibrium Constraints (MPEC) problem. Simultaneously solving all MPECs, which leads to an Equilibrium Problem with Equilibrium Constraints (EPEC), determines the market equilibrium point. The equilibrium point is achieved using mathematical, analytical methods and linearization of nonlinear constraints by accurate techniques. Two different case studies are developed to investigate how the number of retailers influences the market equilibrium point. The first case includes two retailers, while the second case considers an increase in the number of retailers. The results demonstrate that with an increase in retailers' number, their competition increases, causing the prosumers costs to reduce. Furthermore, our results suggest the IDR impact on reduced prosumers cost and increased retailers profit.

Abstract
Due to recent advances in power electronic systems, direct current (DC) microgrid (MG) topology is considered as a promising solution to unite pollution-free renewable energy sources and DC loads. This paper investigates the issue of finite-time robust adaptive stability and tracking issue of a nonlinear direct current (DC) microgrid (MG) comprising a buck converter, linear resistive loads, and nonlinear constant power loads (CPLs). The developed approach is based on a sliding mode controller (SMC) and a nonlinear and nonsingular sliding surface. It is proved that the tracking error converges to zero in a finite-time in the presence of matched disturbance input and uncertainties. The novel controller manipulates the buck converter of the source side to regulate the DC bus voltage by counteracting the destabilizing effect of CPLs and disturbances. Further, the finite value of the convergence time is presented and the effects of the SMC parameter on the stability and transient performance are evaluated. Lastly, numerical simulations are conducted to illustrate the merits of the developed control approach in the viewpoints of fast reference tracking and robust stability.

Abstract
In recent years data stream mining and learning from imbalanced data have been active research areas. Even though solutions exist to tackle these two problems, most of them are not designed to handle challenges inherited from both problems. As far as we are aware, the few approaches in the area of learning from imbalanced data streams fall in the context of classification, and no efforts on the regression domain have been reported yet. This paper proposes a technique that uses sampling strategies to cope with imbalanced data streams in a regression setting, where the most important cases have rare and extreme target values. Specifically, we employ under-sampling and over-sampling strategies that resort to Chebyshev's inequality value as a heuristic to disclose the type of incoming cases (i.e. frequent or rare). We have evaluated our proposal by applying it in the training of models by four well-known regression algorithms over fourteen benchmark data sets. We conducted a series of experiments with different setups on both synthetic and real-world data sets. The experimental results confirm our approach's effectiveness by showing the models' superior performance trained by each of the sampling strategies compared with their baseline pairs.

Abstract
Knowledge Engineering has become essential in the fields of Medical and Health Care with emphasis for helping citizens to improve their health and quality of life. This includes individual methods and techniques in health-related knowledge acquisition and representation and their application in the construction of intelligent systems capable of using the acquired information to improve the patients' health and/or quality of life. Haemato-oncological diseases can provide significant disability and suffering, with severe symptoms and psychological distress. They can create difficulties in fulfilling professional, family and social roles, affecting an individual's quality of life. Health related quality of life (HRQoL) is a subjective concept but there is also an objective component related to physiological indicators. Some of these physiological indicators can be easily assessed by wearable technology such heart rate variability (HRV). This paper introduces an intelligent system to assess, in real-time, potential HRV indices, that can predict HRQoL in lymphoma patients throughout chemotherapy treatment and to account the individuals' variability. The system is based on wearable technology and intelligent processing of the patients' biometric information to assess some quality of life related parameters. A longitudinal study was conducted among 16 lymphoma patients using this intelligent system. Mixed-effect regression models were performed to investigate predictors for and time effects on HRQoL. There were no significant changes in all HRQoL domains over time. Some quality of life domains revealed similar time trends as HRV indices. These HRV indices also have a significant effect on the domains of quality of life.