Abstract
We address optimal control problems for nonlinear systems with pathwise state-constraints. These are challenging nonlinear problems for which the number of discretization points is a major factor determining the computational time. Also, the location of these points has a major impact in the accuracy of the solutions. We propose an algorithm that iteratively finds an adequate time-grid to satisfy some predefined error estimate on the obtained trajectories, which is guided by information on the adjoint multipliers. The obtained results show a highly favorable comparison against the traditional equidistant spaced time grid methods, including the ones using discrete time models. This way, continuous time plant models can be directly used. The discretization procedure can be automated and there is no need to select a priori the adequate time step. Even if the optimization procedure is forced to stop in an early stage, as might be the case in real time problems, we can still obtain a meaningful solution, although it might be a less accurate one. The extension of the procedure to a Model Predictive Control (MPC) context is proposed here. By defining a time dependent accuracy threshold, we can generate solutions that are more accurate in the initial parts of the receding horizon, which are the most relevant for MPC.

Abstract
Seville, with a metropolitan population of about 1.5 million, is the capital and largest city of Andalusia (S Spain). It is the 30th most populous municipality in the European Union and contains three UNESCO World Heritage Sites. The Seville harbour, located about 80 km from the Atlantic Ocean, is the only river port in Spain. The city is located on the plain of the Guadalquivir River. Using Multi-Temporal InSAR with ERS-1/2 and Envisat data a subsidence behavior is detected in the period 1992-2010. The geometry of the subsiding areas suggests that it should be conditioned by the fluvial dynamics of the Guadalquivir River and its tributaries. Facies distribution along the fluvial system (paleochannels, flood plains.), with different grain size and matrix proportion, may explain the relative subsidence between the different sectors.

Abstract
We present a dynamic web tool that allows interactive search and visualization of large news archives using an entity-centric approach. Users are able to search entities using keyword phrases expressing news stories or events and the system retrieves the most relevant entities to the user query based on automatically extracted and indexed entity profiles. From the computational journalism perspective, TimeMachine allows users to explore media content through time using automatic identification of entity names, jobs, quotations and relations between entities from co-occurrences networks extracted from the news articles. TimeMachine demo is available at http://maquinadotempo.sapo.pt/.

Abstract
Sports and physical activities allow people with disabilities to have better quality of life. The proposed work aimed to develop a multimodal interaction platform of robotic devices in a simulated environment for users to train different interface options. The suggested scenarios allow a user to interact with an Intelligent Wheelchair (IW) and with an Intelligent Robotic Ramp (IRR) performing different tasks individually or with a multiplayer option. The main objective of this multimodal interaction platform is to allow users, with severe disabilities, to move around and inclusive to play the Boccia Game more independently and autonomously. A preliminary set of experiments with 27 volunteers tested the scenarios and the multimodal interface for driving the intelligent wheelchair and to maneuver the IRR. The results show excellent performance when users maneuver the IRR in which the success achieved 90%. All dimensions of CEGEQ questionnaire presented good results. Therefore the solution created is quite satisfactory for a user point of view.

Abstract
This paper proposes a real-time system to provide location based guidance and obstacle avoidance of blind persons in indoor environments. The system integrates navigation features based on visual recognition of markers and the detection and classification of possible obstacles in front of the blind person. The system uses the Microsoft Kinect sensor to acquire RGB-D images of the scene. The RGB camera provides input for a real-time tracking algorithm which identifies a trained set of wall-mounted visual markers. The user's pose is estimated combining marker information with GIS data. Depth information is used to classify nearby obstacles. The results of experimental tests with two blind subjects are presented and discussed.

Abstract
Background: Recurrence is an important cornerstone in breast cancer behavior, intrinsically related to mortality. In spite of its relevance, it is rarely recorded in the majority of breast cancer datasets, which makes research in its prediction more difficult. Objectives: To evaluate the performance of machine learning techniques applied to the prediction of breast cancer recurrence. Material and Methods: Revision of published works that used machine learning techniques in local and open source databases between 1997 and 2014. Results: The revision showed that it is difficult to obtain a representative dataset for breast cancer recurrence and there is no consensus on the best set of predictors for this disease. High accuracy results are often achieved, yet compromising sensitivity. The missing data and class imbalance problems are rarely addressed and most often the chosen performance metrics are inappropriate for the context. Discussion and Conclusions: Although different techniques have been used, prediction of breast cancer recurrence is still an open problem. The combination of different machine learning techniques, along with the definition of standard predictors for breast cancer recurrence seem to be the main future directions to obtain better results.