Abstract
Green energy has become a media issue due to climate changes, and consequently, the population has become more aware of pollution. Wind farms are an essential energy production alternative to fossil energy. The incentive to produce wind energy was a government policy some decades ago to decrease carbon emissions. In recent decades, wind farms were formed by a substation and a couple of turbines. Nowadays, wind farms are designed with hundreds of turbines requiring more than one substation. This paper formulates an integer linear programming model to design wind farms' cable layout with several turbines. The proposed model obtains the optimal solution considering different cable types, infrastructure costs, and energy losses. An additional constraint was considered to limit the number of cables that cross a walkway, i.e., the number of connections between a set of wind turbines and the remaining wind farm. Furthermore, considering a discrete set of possible turbine locations, the model allows identifying those that should be present in the optimal solution, thereby addressing the optimal location of the substation(s) in the wind farm. The paper illustrates solutions and the associated costs of two wind farms, with up to 102 turbines and three substations in the optimal solution, selected among sixteen possible places. The optimal solutions are obtained in a short time.

Abstract
The buildings' energy consumption increasing requires solutions to improve their energy efficiency, thus reducing the electricity bill's associated costs. This paper aims to study the load profiles of a service building and its optimization to reduce the costs related to electricity consumption. The electrical load profiles are analyzed, and the electrical equipment and its consumption are characterized. Moreover, to increase energy efficiency and reduce energy costs, a renewable energy system based on photovoltaic panels is sized and integrated into the building. The analysis of the building's consumption profiles allowed the PV system's dimensioning to eliminate power peaks, enabling a reduction in the contracted power. The results demonstrate the effectiveness of the proposed solution, resulting in a reduction of the electricity bill.

Abstract
Multiple traveling salesman problem (mTSP) is a variant of the famous and standard traveling salesman problem, an NP-hard problem in combinatorial optimization. This kind of problem can be solved using exact methods but usually results in high exponential computational complexities. Heuristics and metaheuristics are required to overcome this shortcoming. This study proposes a hybrid method based on the Genetic Algorithm, Ant Colony Optimization, and 2-opt to improve the solution. Computational results with some benchmark instances are provided and compared with other published studies. In three instances, the proposed technique provides better results than the best-known solutions reported in the literature.

Abstract
Falls, especially in the elderly, are one of the main factors of hospitalization. Time-consuming intervention can be fatal or cause irreversible damages to the victims. On the other hand, there is currently a significant amount of smart clothing equipped with various sensors, particularly gyroscopes and accelerometers, which can be used to detect accidents. The creation of a tool that automatically detects eventual falls allows helping the victims as soon as possible. This works focuses in the automatic fall detection from sensors signals using long short-term memory networks. To train and test this approach, the Sisfall dataset is used, which considers the simulation of 23 adults and 15 older people. These simulations are based on everyday activities and the falls that may result from their execution. The results indicate that the procedure provides an accuracy score of 97.1% on the test set.

Abstract
In Portugal, the dropout rate of university courses is around 29%. Understanding the reasons behind such a high desertion rate can drastically improve the success of students and universities. This work applies existing data mining techniques to predict the academic dropout mainly using the academic grades. Four different machine learning techniques are presented and analyzed. The dataset consists of 331 students who were previously enrolled in the Computer Engineering degree at the Universidade de Tras-os-Montes e Alto Douro (UTAD). The study aims to detect students who may prematurely drop out using existing methods. The most relevant data features were identified using the Permutation Feature Importance technique. In the second phase, several methods to predict the dropouts were applied. Then, each machine learning technique's results were displayed and compared to select the best approach to predict academic dropout. The methods used achieved good results, reaching an Fl-Score of 81% in the final test set, concluding that students' marks somehow incorporate their living conditions.

Abstract
Wastewater treatment systems are major consumers of electricity being responsible for 3 to 5% of global energy consumption, and 56% of greenhouse gas emissions into the atmosphere in the water treatment sector. Climate change currently imposes the definition of a new pattern of human behavior in the defense and sharing of a common space that is the planet, so the optimization of water treatment models plays a crucial role in the definition of sustainability strategies as part of the challenges for decarbonization by 2050. The physical-chemical characteristics of the influent, the treatment techniques and associated technologies and the unpredictability of external phenomena of inefficiency transform wastewater treatment plants (WWTPs) into complex systems, sometimes difficult to understand. The study of energy efficiency plays an important role in the emergence of a standard behavior model, which allows the correction of unbalanced situations in the expected energy consumption. Given the importance of the topic, the present review aims to study energy auditing techniques and benchmarking tools developed for the wastewater treatment sector to reduce the current electricity consumption, which could represent up to 90% of total energy consumption. The result of the research was organized according to the criteria defined for the characterization of auditing techniques and benchmarking tools. A review was conducted from 51 scientific papers from different reference research platforms published in the last 20 years according to the keywords. This literature review has shown that there are, in the classification of consumption reduction, energy auditing and benchmarking tools; energy management techniques and methods directed to the energy efficiency of the treatment stages and specific equipment; and, finally, decision support tools. According to the methodology followed, it was possible to conclude that although the concern is not recent, there are techniques and tools for assessing energy performance more suitable for the wastewater sector. However, the authors recognize that associated with the complexity of wastewater treatment systems, inefficiency phenomena still strongly impact energy efficiency assessment, so the contributions for their identification and quantification may represent an added value for data analysis, systematization, and optimization methodologies.