Abstract
The objective of the project presented in this paper is to stimulate and evaluate the executive function in Parkinson's patients. This project is being developed in partnership with the Coimbra Hospital and Universitary Centre and the private social solidarity institution CASPAE. It aims to answer specific needs identified in the neurology service during the medical appointments. A common test to assess executive function is the Trail Making Test (TMT). This test is done on paper during the medical appointments for the diagnosis and follow-up of patients with the executive function diminished, such as Parkinson's disease patients. The way the TMT is done poses some problems that led to the development of an application for smartphones and tablets, with Android OS. This application has two operating modes: "Appointment", and "Train". The "Appointment Mode" makes the realization, reading, and the organization of the tests results easier. The "Train Mode" allows that patients improve their executive function performing tests that are randomly generated on your own smartphone.

Abstract
Over the years the energy needs have increased dramatically and we have become aware that our needs were and are having implications on the environment in which we live. Increasingly people are aware to saving electricity by turning off the equipment that is not been used, or connect electrical loads just outside the on-peak hours. However, these few efforts are not enough to reduce the global energy consumption, which is increasing. Regarding the field of optimization, researchers throughout the world have been making an effort in introducing better control schemes, both in industry and domestic sectors, for all types of loads from small lamps to large motors. Much of the reduction was due to mechanical improvements; however, with the advancing of the years' new types of control arise. All these factors provide a motive in this paper for introducing a new consumption reduction method in some residential loads via the implementation of Model Predictive Control ( MPC). A single cost function is required to set the reference output near the goal, and consequently through the variation of this cost function by changing the weights, thus specific control actions have priority over the remaining actions. Therefore, it is possible to have different goals during the day, determining the possible savings for each appliance that can be made during on-peak, mid-peak, off-peak and by providing simulations upon 24 hours in the household.

Abstract
This paper presents an overview and main features of Enki, a web-based learning environment for computer science languages. Enki was designed to be a sort of entry level IDE, aggregating tools for navigating and viewing course materials, for solving exercises and receiving automated feedback, as well as promoting the learning process. Enki uses services from several other systems, namely for content sequencing and recommendation, exercise assessment, and gamification.

Abstract
Interactions between internet users are mediated by their devices and the common support infrastructure in data centres. Keeping track of causality amongst actions that take place in this distributed system is key to provide a seamless interaction where effects follow causes. Tracking causality in large scale interactions is difficult due to the cost of keeping large quantities of metadata; even more challenging when dealing with resource-limited devices. In this paper, we focus on keeping partial knowledge on causality and address deduction from that knowledge. We provide the first proof-theoretic causality modelling for distributed partial knowledge. We prove computability and consistency results. We also prove that the partial knowledge gives rise to a weaker model than classical causality. We provide rules for offline deduction about causality and refute some related folklore. We define two notions of forward and backward bisimilarity between devices, using which we prove two important results. Namely, no matter the order of addition/ removal, two devices deduce similarly about causality so long as: (1) the same causal information is fed to both. (2) they start bisimilar and erase the same causal information. Thanks to our establishment of forward and backward bisimilarity, respectively, proofs of the latter two results work by simple induction on length.

Abstract
This paper presents the proposal of a Low cost Mobile Robot prototype with On Board Laser Scanner, prototyped to compete at the Robot (R) Factory Mobile Robot competition. The robot is equipped with a hacked Neato XV-11 Laser Scanner, being a very low cost, alternative, when compared with the current available laser scanners. It is presented the description of its sensors and actuators, providing valuable information that can be used to develop better designs of controllers and localization systems. The robot is equipped with the 37Dx52L, which is a low cost 12v motor equipped with encoders and a 29:1 reduction gearbox, being a very popular actuator in the mobile robotics domain. The robot is also equipped with an USB camera applied to acquire image, that will be processed, in order to provide information concerning the part material status.

Abstract
This paper studies the classification of high-dimensional Gaussian signals from low-dimensional noisy, linear measurements. In particular, it provides upper bounds (sufficient conditions) on the number of measurements required to drive the probability of misclassification to zero in the low-noise regime, both for random measurements and designed ones. Such bounds reveal two important operational regimes that are a function of the characteristics of the source: 1) when the number of classes is less than or equal to the dimension of the space spanned by signals in each class, reliable classification is possible in the low-noise regime by using a one-vs-all measurement design; 2) when the dimension of the spaces spanned by signals in each class is lower than the number of classes, reliable classification is guaranteed in the low-noise regime by using a simple random measurement design. Simulation results both with synthetic and real data show that our analysis is sharp, in the sense that it is able to gauge the number of measurements required to drive the misclassification probability to zero in the low-noise regime.