Abstract
This thesis aims at contributing with new research at the intersection of the research fields of information management and supply chain risk management. Information is a fundamental element for an effective risk management in supply networks. In spite of this, research on how to use internal and external information for risk management decisions in supply networks is scarce. Therefore, this thesis main goal is to characterize information models used in supply networks and the supply chain risk management process in order to propose an aggregated model of internal and external information for risk management decision support in supply networks. This work is based on the Design Science paradigm, which will be applied through several research methods: systematic literature review to analyse the current state of the art on information management in supply networks; field research to analyse the influence of different information organisation models in the process of supply chain risk management and to develop an information model based (artifact); and action research to implement the artifact for the definition of risk mitigation strategies in aeronautic supply networks.

Abstract
We present a methodology to assess cell level alterations on the human retina responsible for functional changes observable in the Optical Coherence Tomography data in healthy ageing and in disease conditions, in the absence of structural alterations. The methodology is based in a 3D multilayer Monte Carlo computational model of the human retina. The optical properties of each layer are obtained by solving the Maxwell's equations for 3D domains representative of small regions of those layers, using a Discontinuous Galerkin Finite Element Method (DG-FEM). Here we present the DG-FEM Maxwell 3D model and its validation against Mie's theory for spherical scatterers. We also present an application of our methodology to the assessment of cell level alterations responsible for the OCT data in Diabetic Macular Edema. It was possible to identify which alterations are responsible for the changes observed in the OCT scans of the diseased groups.

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.