Abstract
O trabalho desenvolvido tem como objetivos identificar e compreender que estratégias de autorregulação e corregulação das aprendizagens foram adotadas pelos estudantes de programação de computadores no ensino a distância na transição entre iniciantes e proficientes. Este estudo decorreu na Unidade Curricular “Laboratório de Desenvolvimento de Software” (LDS) do 2.º semestre do 2.º ano da Licenciatura de Engenharia Informática da Universidade Aberta, com alunos heterogéneos em idade, residência, habilitações académicas e profissão. Adotou-se uma metodologia de investigação qualitativa por análise de conteúdo (Amado, 2014) de reflexões quinzenais solicitadas aos estudantes, sobre o processo de aprendizagem. A construção das matrizes de análise de conteúdo baseou-se no modelo teórico de Zimmerman (2013). Os resultados apontam para uma maior incidência na adoção de estratégias de autorregulação de aprendizagem de organização e planeamento. As menos frequentes foram a procura de ajuda e a consciência das dificuldades. Nas de corregulação de aprendizagem, as mais evidenciadas foram as de gestão do trabalho em equipa e planeamento. As menos evidentes foram a consciência das dificuldades e a procura de ajuda.

Abstract

Abstract
In this paper, we discuss the design considerations and goals of an interactive digital musical instrument (DMI) for novice pianists. It aims to promote the practice of melodic improvisation by guiding pianists in the selection of notes. The DMI includes two major components: a generative jazz model (software) and an illuminating keyboard controller (hardware). Visual feedback in the form of illuminated keyboard controller keys guides notes selection. Illuminated keys correspond to scale degrees that comply with a harmonic structure generated on-the-fly. The generative nature of the engine allows high degrees of novelty while guaranteeing a structurally-coherent harmonic structure anchored in the blues/jazz idiom. Preliminary experimental results inform critical directions for future design iterations of the proposed system.

Abstract
An efficient unit commitment planning must consider frequency regulation capacity in the model. Such models are more complicated under a high penetration level of renewable energy because of renewable ramping and uncertainty. This paper addresses these issues in the unit commitment. The proposed model for unit commitment considers uncertainty and ramping of wind power, frequency regulation capacity, spinning reserve, demand response, and pumped-storage hydroelectricity. Two reserve capacities including primary frequency regulation and spinning reserve are designed to handle the intermittency and ramping of renewable energies. In order to optimize the costs, the pumped-storage hydroelectricity and demand response program are also included to deal with ramping and uncertainty. The numerical results specify that the arrangement of frequency regulation capacity, pumped-storage system and demand response can effectively tackle both the ramping and uncertainty. The system includes 10-generator with total power equal to 1070 MW and one wind generator with 300 MW power. The initial wind integration level is about 28%. It is verified that decreasing the frequency regulation capacity by 10% reduces wind integration level by 94%. The demand response and pumped-storage increase wind integration level by 10% and 16%; while both together increase wind integration by 25% compared to the initial level. The wind integration level without large wind ramping can be increased up to 200%.

Abstract
Traditional management models reveal issues with the introduction of unnecessary hierarchies, slow decision making, among other obstacles, which left a large number of workers out of the process of seeking efficiency and growth. In this sense, the third generation of management models focused on agility and collaboration emerged. Instead of focusing on hierarchies and functions, there is a priority on the way people behave and relate. In this sense, this study aims to analyze this phenomenon and to understand the various dimensions of Management 3.0. Furthermore, it intends to explore and understand the practices and challenges that are posed in its business implementation, focusing on the role of people and information and communication technologies.

Abstract
The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that the UAVs may have and the appropriate action. Moreover, in many missions, the vehicle will not return to its original location. If it fails to arrive at the landing spot, it needs to have the onboard capability to estimate the best area to safely land. This paper addresses the scenario of detecting a safe landing spot during operation. The algorithm classifies the incoming Light Detection and Ranging (LiDAR) data and store the location of suitable areas. The developed method analyses geometric features on point cloud data and detects potential right spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point cloud clusters. The areas that have a slope less than a threshold are considered potential landing spots. These spots are evaluated regarding ground and vehicle conditions such as the distance to the UAV, the presence of obstacles, the area's roughness, and the spot's slope. Finally, the output of the algorithm is the optimum spot to land and can vary during operation. The proposed approach evaluates the algorithm in simulated scenarios and an experimental dataset presenting suitability to be applied in real-time operations.