Abstract
Depression is a mental disease that affects over 264 million people worldwide and is responsible for causing great suffering, work dysfunction, faulty education, family relationships and can lead to suicide. Depression stigma prevents over half of the people who suffer from major depression from seeking professional help. Stigma mostly results from a deficient understanding of the mental disease. Research indicates that first-hand experiences of the perceptions of an individual diagnosed with a mental disorder in a simulated virtual reality environment can increase empathy and positive attitudes towards the individual. Interactive VR experiences have been described as a human-computer interface that enables users to immerse themselves in a computer generated, multi-dimensional environment. This project aims at examining the impact of a VR-assisted experience on reducing stigma and increase empathy towards individuals with depression. Following a methodology based on well-established results from psychology about common depression misunderstandings, we present a VR experience that simulates some of the most common difficulties encountered by people diagnosed with depression.

Abstract

Abstract
Driven by the high fidelity and low cost of the latest head-mounted devices reaching the consumer market, Virtual Reality (VR) is a technology upon which rests increased expectations for improving education and training outcomes. The unique capacity of VR to produce experiences with high levels of immersion, presence, and interactivity, opens a series of prospects to improve the learning of declarative, procedural, and practical knowledge through a new modality of educational content. This paper explores some of the most promising opportunities of VR through the development and evaluation of Sea of Cells, an immersive VR interactive experience to enhance the learning of the prokaryotic cell. Methodologies to introduce the VR experience, both inside and outside classes, were also explored by analysing assessments from several Portuguese biology teachers. A test pilot made through video demonstration, shows a promising future for VR in education. Despite the physical limitations of the pilot study, due to Covid, after presenting the project to 7 10th grade Biology teachers, it was concluded that VR might be a relevant and innovative tool for educational settings. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
There are Optimization Problems that are too complex to be solved efficiently by deterministic methods. For these problems, where deterministic methods have proven to be inefficient, if not completely unusable, it is common to use approximate methods, that is, optimization methods that solve the problems quickly, regardless of their size or complexity, even if they do not guarantee optimal solutions. In other words, methods that find acceptable solutions, efficiently. One particular type of approximate method, which is particularly effective in complex problems, are metaheuristics. Particle Swarm Optimization is a population-based metaheuristic, which has been particularly successful. In order to broaden the application and overcome the limitation of Particle Swarm Optimization, a discrete version of the metaheuristics is proposed. The Discrete Particle Swarm Optimization, DPSO, will change the PSO algorithm so it can be applied to discrete optimization problems. This alteration will focus on the velocity update equation. The DPSO was tested in an instance of the Traveling Salesman Problem, att48, 48 points problems proposed by Padberg and Rinaldi, which showed some promising results.

Abstract
There are different clean energy production technologies, including wind energy production. This type of energy, among renewable energies, is one of the least predictable due to the unpredictability of the wind. The wind prediction has been a deeply analysed field since has a considerable share on the green energy production, and the investments on this sector are growing. The efficiency and stability of power production can be increased with a better prediction of the main source of energy, in our case the wind. In this paper, some techniques inspired by Biological Inspired Optimization Techniques applied to wind forecast are compared. The wind forecast is very important to be able to estimate the electric energy production in the wind farms. As you know, the energy balance must be checked in the electrical system at every moment. In this study we are going to analyse different methodologies of wind and power prediction for wind farms to understand the method with best results.

Abstract
The need for sustainable power production has led to the development of more innovative approaches to production and storage. In light of this hydrogen production through wind power has emerged as sufficient in ensuring that the objectives of the Paris Agreement are made. This paper discusses the state-of-art models and controls used in ensuring that greater efficiency is achieved in the processes of energy to hydrogen transformation. The paper concludes with a comparison of the models and determination of one which suffices in ensuring that hydrogen/energy transformation is more efficient.