Abstract
The energy efficiency and the development of environmentally correct policies are current topics, especially when applied to the industrial sector with the objective of increasing the competitiveness of the enterprises. Portuguese textile dyeing sector, being a major consumer sector of primary energy, needs to adopt measures to improve its competitiveness. Biomass appears to be a viable and preferred alternative energy source for the sector, while simultaneously develops an entire forest industry devoted to the supply of forest solid fuels. This work carries out a comprehensive PEST (political, economic, social and technological) analysis, which analyses Political, Economic, Social and Technological aspects of the replacement of the fossil fuels traditionally used in this sector by biomass, providing a framework of environmental factors that influence the strategic management of the companies. The main advantages are the reduction of external dependence on imported fuel due to the use of an endogenous renewable resource, the creation and preservation of jobs, the increased competitiveness of the sector by reducing energy costs, the use of national technology and the reduction of greenhouse gases emissions.

Abstract
Human consciousness is a target of research in multiple fields of knowledge, that presents it as an important characteristic to better handle complex and diverse situations. Artificial consciousness models have arose, together with theories that attempt to model what we understand about consciousness, in a way that could be implemented an artificial conscious being. The main motivations to study artificial consciousness are related to the creation of agents more similar to human beings, in order to build more efficient machines. This paper presents an experiment using the Global Workspace Theory and the LIDA Model to build a conscious mobile robot in a virtual environment, using the LIDA framework as a implementation of the LIDA Model. The main objective is to evaluate if it is possible to use conscience as implemented by the LIDA framework to simplify decision making processes during navigation of a mobile robot subject to interaction with people, as part of a cicerone robot development.

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Abstract
We investigate learning of flexible Robot locomotion controller, i.e., the controllers should be applicable for multiple contexts, for example different walking speeds, various slopes of the terrain or other physical properties of the robot. In our experiments, contexts are desired walking linear speed and the direction of the gait. Current approaches for learning control parameters of biped locomotion controllers are typically only applicable for a single context. They can be used for a particular context, for example to learn a gait with highest speed, lowest energy consumption or a combination of both. The question of our research is, how can we obtain a flexible walking controller that controls the robot (near) optimally for many different contexts? We achieve the desired flexibility of the controller by applying the recently developed contextual relative entropy policy search(REPS) method. With such a contextual policy search algorithm, we can generalize the robot walking controller for different contexts, where a context is described by a real valued vector. In this paper we also extend the contextual REPS algorithm to learn a non-linear policy instead of a linear one over the contexts. In order to validate our method, we perform a simulation experiment using a simulated NAO humanoid robot. The robot now learns a policy to choose the controller parameters for a continuous set of walking speeds and directions.

Abstract
In recent years, periocular recognition has become a popular alternative to face and iris recognition in less ideal acquisition scenarios. An interesting example of such scenarios is the usage of mobile devices for recognition purposes. With the growing popularity and easy access to such devices, the development of robust biometric recognition algorithms to work under such conditions finds strong motivation. In the present work we assess the performance of extended versions of two state-of-the-art periocular recognition algorithms on the publicly available CSIP database, a recent dataset composed of images acquired under highly unconstrained and multi-sensor mobile scenarios. The achieved results show each algorithm is better fit to tackle different scenarios and applications of the biometric recognition problem.

Abstract
Objective: Physical exercise has been consistently documented as a complementary therapy in the treatment of depressive disorders. However, despite a higher prevalence among women compared to men, the trials developed in women are scarce. In addition, the optimal dosage of exercise capable of producing benefits that reduce depressive symptoms remains unclear. This clinical trial is designed to measure the effect of a structured physical exercise program as a complement to antidepressant medication in the treatment of women with depression. Methods: From July 2013 to May 2014, we implemented a randomized controlled trial (HAPPY BRAIN study). A total of 26 women (aged 50.16 +/- 12.08) diagnosed with clinical depression were randomized either to a supervised aerobic exercise group (45-50 min/week three times a week for four months) plus pharmacotherapy (intervention group), or only antidepressant medication (control group). Results: The exercise group presented a decrease in BDI-II and DASS-21 total score scales. Relatively to DASS-21, it showed a significant decrease in anxiety and stress. The exercise group when compared to a control group showed improvement in relation to physical functioning parameters between baseline and post-intervention. Moreover, anthropometric parameters presented only significant differences between groups in fat mass percentage. Nonetheless, no differences were found between groups in weight, body mass index, waist circumference, and self-esteem. Conclusion: Our results showed that supervised structured aerobic exercise training could be an effective adjuvant therapy for treating women with depression, reducing depressive symptomatology and improving physical fitness. A key factor of this improvement included strict control of exercise workload parameters and adjustment to each subject's capacity. In our study, due to the sample size there is an increase in the probability of type II errors.