Abstract

Abstract

Abstract
Reducing office buildings’ energy consumption can contribute significantly towards carbon reduction commitments since it represents 10% of total energy consumption. Major components are lighting (40% of consumption), electrical equipment (35%), and heating and central cooling systems (25\%). Occupants’ behaviours impact these energy consumption components, with solid evidence on the role of individual behaviours. In this work, we propose a methodology that uses virtual choreographies to identify and prioritize behaviour-change interventions towards office users based on the potential impact on energy consumption. The data shows that some behaviours with significant consumption have little potential for behavioural change impact, while other behaviours hold substantial potential for lowering energy consumption via behavioural change.

Abstract

Abstract
In the areas of machine-learning/big data, feature selection is normally regarded as a very important problem to be solved, as it directly impacts both data analysis and model creation. The problem of optimizing the selected features of a given dataset is not always trivial, however, throughout the years various ways to counter this optimization problem have been presented. This work presents how feature-selection fits in the larger context of multi-objective problems as well as a review of how both multi-objective evolutionary algorithms and metaheuristics are being used in order to solve feature selection problems.

Abstract
The daily stress we, as population, are subjected has negative impacts on physical and mental health, making people age faster. With the increase in life expectancy, the population started to search for ways to ensure they reach the older ages with a healthier and better lifestyle, resulting in an increase of interest and demand for thermalism and wellness tourism. Although thermalism is recognized to have several benefits with regard to the combat and prevention of certain pathologies, this has not yet been fully proven. So, in this paper, we presented a sensing system, capable of not only recording thermalist's biometric data, but also getting the data ready and prepared to be processed by any analytic tool with the aim of showing the impact that this sector has on the health of its practitioners.