Abstract
Sectorization refers to partitioning a large territory, network, or area into smaller parts or sectors considering one or more objectives. Sectorization problems appear in diverse realities and applications. For instance, political districting, waste collection, maintenance operations, forest planning, health or school districting are only some of the application fields. Commonly, sectorization problems respect a set of features necessary to be preserved to evaluate the solutions. These features change for different sectorization applications. Thus, it is important to conceive the needs and the preferences of the decision-makers about the solutions. In the current paper, we solve sectorization problems using the Genetic Algorithm by considering three objectives: equilibrium, compactness, and contiguity. These objectives are collected within a single composite objective function to evaluate the solutions over generations. Moreover, the Analytical Hierarchy Process, a powerful method to perceive the relative importance of several objectives regarding decision makers' preferences, is used to construct the weights. We observe the changes in the solutions by considering different sectorization problems that prioritize various objectives. The results show that the solutions' progress changed accurately to the given importance of each objective over generations.

Abstract
This project proposes a machine learning (ML) pipeline for inferring office employee's well-being, from heterogeneous sources of contextual data (cf. physiological, social and workplace environment), which brings several demanding issues. In this paper we focus specifically in raw data collection problems and pre-processing challenges. To start with, context data was collected in real environments, during weeks, in several office organizations and involving employees along theirs daily working routines. Moreover, data collection resort to a wide range of sources (e.g. sensors, questionnaires, apps, etc.) that were subject to potential interferences and noisy conditions. Given the influence of data quality in ML algorithms results and considering the number of instruments used, it was essential to implement a pre-processing stage to automate and improve the quality of collected data. Hence, the usefulness of the proposed DQVA tool, which computes several common statistical measures and provides also graphical and tabular visual insights about the data. For example, it allows to: i) compare data sources from different participants and organizations, on a per sensor/data source basis (through data tables, data distribution histograms, and visualizations); iii) check and pinspot the existence of outliers; iv) visually spot signal gaps; etc. Therefore, we argue that the proposed DQVA tool allows to evaluate, per sensor and per individual, raw data quality, on the integration stage of our classification pipeline. It proved to be an agile, useful and simple to re-use tool for detecting raw data irregularities, thus increasing data quality assurances for the next steps of our classification pipeline.

Abstract

Abstract
We present VR Designer, a tool for expediting the creation 3D scenes inside VR. It uses controllers and voice commands to create and manipulate primitives and objects imported from openly available repositories. We use modifiers to accelerate repetitive tasks, resorting to procedural content creation techniques to automate the workflow. The tool allows non-expert users to quickly create scenes for contexts such as training or education. We also conducted a user study to validate VR Designer.

Abstract
Forests assume a great socioeconomic and environmental importance, requiring good management decisions to value and care for these natural resources. In Portugal, forest land use accounts for 34.5% of the continental area. The softwood species with the highest representation is maritime pine (Pinus pinaster Ait.). Traditionally, the species is managed as pure and even-aged stands for timber production, with a rotation age of 45 to 50 years. Depending on the initial stand density, the stands are thinned 2 to 4 times during the rotation period. Disturbances associated with forest fires have a negative impact on the age structure of stands over time, as they result in a narrow range of stand ages. This age homogenization over large forest areas increases with the recurrence and size of forest fires, bringing new challenges to forest management, namely the difficulty in ensuring the long-term sustainability of the wood supply. The problem aggravates with the increasing demand pressure on pine wood. This article aims to suggest a framework of DSS for Pinus pinaster that can effectively support the management of forest areas under these circumstances, i.e., narrow age ranges and high demand of harvested timber volume. A communal woodland area in the Northern region of Portugal affected by forest fires was selected as a study case. The Modispinaster model was used as the basis of the DSS, to simulate growth scenarios and interventions along the optional rotation period. Two clear-cut ages were considered: 25 and 40 years. The results obtained were the input data for an integer linear programming (ILP) model to obtain the plan that maximizes the volume of timber harvested in the study area, during the planning horizon. The ILP model has constraints bounding the area of clearings, and sustainability, operational and forestry restrictions. The computational results are a powerful tool for guidance in the decision-making of scheduling and forecasting the execution of interventions determining the set of stands that are exploited according to the different scenarios and the period in which the clear-cut is made throughout the planning horizon. Considering all constraints, the solution allows a balanced extraction of a total of 685 m(3)center dot ha(-1), over the 50-year horizon, as well as the representation of all age classes at the end of the planning period.

Abstract
This article examines the impact of foreign and multinational ownership on firm exit using a sample of Portuguese firms for the period 2007-2016, with Kaplan-Meier survival functions and a Cox proportional hazard model. The results show that purely domestic firms endure worse survival prospects than multinationals, but this is more related to firm-level variables and not because of the effects of foreignness or multinational ownership. The disaggregated results at a sectoral level provide support for the contingent role of foreignness in very specific sectors of the Portuguese economy.