Abstract
Manufacturing environments require a real-time adaptation and optimization method to dynamically and intelligently maintain the current scheduling plan feasible. This way, the organization keeps clients satisfied and achieves its objectives (costs are minimized and profits maximized). This paper proposes an optimization approach - Selection Constructive based Hyper-heuristic for Dynamic Scheduling - to deal with these dynamic events, with the main goal of maintaining the current scheduling plan feasible and robust as possible. The development of this dynamic adaptation approach is inspired on evolutionary computation and hyper-heuristics. Our empirical results show that a selection constructive hyperheuristic could be advantageous on solving dynamic adaptation optimization problems.

Abstract
Deep Brain Stimulation (DBS) is the effective surgical treatment for drug-refractory movement disorders. In order to improve the therapeutic outcome precise anatomic location of electrodes must be achieved. Thus, neurologists can achieve better clinical decisions and take a more careful selection of the best stimulation parameters for DBS. In this paper, we present a system that accurately obtains the 3D positions of DBS electrodes relative to anatomical structures. The latter is based on the segmentation of deep brain structures and on a multimodal imaging approach. In this study, we examined 16 patients undergoing DBS (8 with Parkinson` s disease and 8 with dystonia). A "neuroscientist friendly" graphic user interface (GUI) was designed to support the processing pipeline to precisely detect the electrodes from the DBS lead. Using this system, we obtained the electrodes position and compared them with the ones manually calculated by an experienced physician. The differences observed were less than a voxel size for 89.9% of the cases and the automated procedure takes less 97.5% time than the manual procedure (1min vs 40min). The resulting masks were congruent in shape and position with the corresponding areas in the individuals' space. Using our automatic segmentation pipeline, clinicians save 77% of their time when compared with a manual segmentation (1.20min vs 5.26min). Both structures and electrodes masks were warped to the MNI space in order to provide a common reference space, for the clinical interpretations.

Abstract
The purpose of this paper is to propose new model for emotional interaction that uses learning styles and student emotional state to adapt the user interface and learning path. This aims to reduce the difficulty and emotional stain that students encounter while interacting with learning platforms. To this end will be used techniques of Affective Computer that can capture the student emotional state and base on that change the course parameters (flow, organization or difficulty) or even an emotional interaction in order to recapture the student attention.

Abstract
Software connectors encapsulate interaction patterns between services in complex, distributed service-oriented applications. Such patterns encode the interconnection between the architectural elements in a system, which is not necessarily fixed, but often evolves dynamically. This may happen in response to faults, degrading levels of QoS, new enforced requirements or the re-assessment of contextual conditions. To be able to characterise and reason about such changes became a major issue in the project of trustworthy software. This paper discusses what reconfiguration means within coordination-based models of software design. In these models computation and interaction are kept separate: components and services interact anonymously through specific connectors encoding the coordination protocols. In such a setting, of which Reo is a paradigmatic illustration, the paper introduces a model for connector reconfigurations, from both a structural and a behavioural perspective.

Abstract
The goal of this work is to develop a computational model of the human retina and simulate light scattering through its structure aiming to shed light on data obtained by optical coherence tomography in human retinas. Currently, light propagation in scattering media is often described by Mie's solution to Maxwell's equations, which only describes the scattering patterns for homogeneous spheres, thus limiting its application for scatterers of more complex shapes. In this work, we propose a discontinuous Galerkin method combined with a low-storage Runge-Kutta method as an accurate and efficient way to numerically solve the time-dependent Maxwell's equations. In this work, we report on the validation of the proposed methodology by comparison with Mie's solution, a mandatory step before further elaborating the numerical scheme towards the propagation of electromagnetic waves through the human retina.

Abstract
We describe the use of a phase-sensitive optical time domain reflectometer (phi OTDR) over an ultra-long Raman fiber laser cavity allowing fully distributed detection of vibrations over 125 km. Compared to a first-order Raman-assisted phi OTDR, this scheme shows an enhanced signal-to-noise ratio (SNR). This is due to the fact that the relative intensity noise introduced by the Raman amplification is mostly transferred to a lower frequency range, where the balanced detection implemented in the setup provides better suppression of the common-mode noise. The sensor was able to measure vibrations of up to 380 Hz (limit set by the time of flight of light pulses) in a distance of 125 km with a resolution of 10 m and an average SNR of 8 dB with no postprocessing. This implies a > 3 dB improvement in SNR over a first-order Raman-assisted setup with similar characteristics.