Abstract

Abstract
Burnout is increasingly present in organizations and in the most diverse professions, namely, in university students. Burnout can have negative repercussions on their well-being and can even lead them to abandon their studies. The objective of the study focuses on academic burnout and taking medication as a consequence of the requirements of the academic path of students at a Portuguese public university. To achieve this goal, a quantitative methodology was used, consisting of the distribution of a questionnaire to a sample of students from the analyzed university. The first study questionnaire obtained 207 responses, all valid. To perform the analysis of the quantitative data, the program IBM SPSS Statistics, version 25 was used. Inferential statistics were used, namely, Student t-test and one-way ANOVA (parametric tests), Spearman's correlation coefficient, and the Chi-square test, to test the previously defined research hypotheses. Among the variables for which statistically significant relationships with burnout were found, the following stand out: the arithmetic mean (course average); the professional situation; participation in extracurricular activities; the practice and frequency of physical exercise; the choice and expectations regarding the course; the uncertainty felt about the professional future; the evaluation of the relationship with colleagues.

Abstract
This paper addresses the development of a remote hyperspectral imaging system for detection and characterization of marine litter concentrations in an oceanic environment. The work performed in this paper is the following: (i) an in-situ characterization was conducted in an outdoor laboratory environment with the hyperspectral imaging system to obtain the spatial and spectral response of a batch of marine litter samples; (ii) a real dataset hyperspectral image acquisition was performed using manned and unmanned aerial platforms, of artificial targets composed of the material analyzed in the laboratory; (iii) comparison of the results (spatial and spectral response) obtained in laboratory conditions with the remote observation data acquired during the dataset flights; (iv) implementation of two different supervised machine learning methods, namely Random Forest (RF) and Support Vector Machines (SVM), for marine litter artificial target detection based on previous training. Obtained results show a marine litter automated detection capability with a 70-80% precision rate of detection in all three targets, compared to ground-truth pixels, as well as recall rates over 50%.

Abstract
Due to the pervasiveness of always connected devices, large amounts of heterogeneous data are continuously being collected. Beyond the benefits that accrue for the users, there are private and sensitive information that is exposed. Therefore, Privacy-Preserving Mechanisms (PPMs) are crucial to protect users' privacy. In this paper, we perform a thorough study of the state of the art on the following topics: heterogeneous data types, PPMs, and tools for privacy protection. Building from the achieved knowledge, we propose a privacy taxonomy that establishes a relation between different types of data and suitable PPMs for the characteristics of those data types. Moreover, we perform a systematic analysis of solutions for privacy protection, by presenting and comparing privacy tools. From the performed analysis, we identify open challenges and future directions, namely, in the development of novel PPMs. (C) 2021 The Authors. Published by Elsevier Inc.

Abstract
One of the main decisions that a car rental company has to make regards the definition of the fleet size and mix, i.e., the capacity to meet demand. This demand is highly unpredictable and price-sensitive; thus, the definition of the prices charged influences capacity decisions. Moreover, capacity decisions are also linked to other company strategies to meet demand, such as offering upgrades or transferring empty cars between stations. Typically, these problems are tackled focusing on the maximization of profits, disregarding the environmental impacts associated with these decisions. There is a growing need for models and analytical tools that can support decisions considering the trade-off between profit and environmental impact in mobility. Therefore, this work incorporates environmental concerns into the capacity-pricing problem for car rental, proposing a bi-objective model to tackle the trade-off between profit and environmental impact. The Life Cycle Assessment method is applied not only to vehicles but also to fuel to define environmental parameters accurately. Four types of vehicles are considered: internal combustion engine vehicles, hybrids, hybrids plug-in, and electric vehicles. Solving multi-objective models is a computationally challenging problem, which requires efficient and applicable methods. These methods can support policy and business decisions in a real-world context, running different scenarios and evaluating solutions under varying conditions. Due to its efficiency in solving bi-objective models, an Epsilon-constraint method is developed and applied in diverse situations to retrieve managerial insights. The results obtained enable quantifying the feasible trade-offs, overall showing that, on average, with a decrease of 14.44% in financial results, it is possible to obtain a decrease of 63.41% in environmental impact. Additional insights are also retrieved related to the fleet, fuel, prices and demand.

Abstract
Lung segmentation represents a fundamental step in the development of computer-aided decision systems for the investigation of interstitial lung diseases. In a holistic lung analysis, eliminating background areas from Computed Tomography (CT) images is essential to avoid the inclusion of noise information and spend unnecessary computational resources on non-relevant data. However, the major challenge in this segmentation task relies on the ability of the models to deal with imaging manifestations associated with severe disease. Based on U-net, a general biomedical image segmentation architecture, we proposed a light-weight and faster architecture. In this 2D approach, experiments were conducted with a combination of two publicly available databases to improve the heterogeneity of the training data. Results showed that, when compared to the original U-net, the proposed architecture maintained performance levels, achieving 0.894 +/- 0.060, 4.493 +/- 0.633 and 4.457 +/- 0.628 for DSC, HD and HD-95 metrics, respectively, when using all patients from the ILD database for testing only, while allowing a more efficient computational usage. Quantitative and qualitative evaluations on the ability to cope with high-density lung patterns associated with severe disease were conducted, supporting the idea that more representative and diverse data is necessary to build robust and reliable segmentation tools.