Abstract
Carpooling is a car sharing practice first adopted in the United States of America during the fuel crisis in the 1970s. Since then, and after some ups and downs, this practice has been growing in recent years, being currently used throughout the world. With the evolution of mobile technologies, carpooling had the opportunity to expand, especially through mobile applications and web pages. With these technologies, it is possible for anyone in any part of the globe to search for others that wish to go to the same place and want to share their car. With this practice, people intend to save money, help preserve the environment, reduce congestions in cities, increase the number of places available to park and meet new people. This paper introduces MoCaS Mobile Carpooling System, a carpool service offered for registered users. In this system, each user can enter his travels and make appointments, assign ratings, register vehicles and add travel preferences. All this is possible via a web interface and also via a mobile application that together give greater support to those seeking such services. MoCaS distinguishes itself from other systems by offering innovative services, namely in the mobile component, that through location services allows for the booking of trips in real-time; in other words, not only trips that have not started, but trips that are already underway and that end up intersecting the user's position. Besides this novelty, this system provides a real-time map, where all trip stops are visible, as well as the location of carpoolers who are currently traveling. Both the web and the mobile applications were successfully developed, achieving good results in the performed tests, and are currently being prepared for deployment.

Abstract
Liver cancer is the sixth most frequently diagnosed cancer and, particularly, Hepatocellular Carcinoma (HCC) represents more than 90% of primary liver cancers. Clinicians assess each patient's treatment on the basis of evidence-based medicine, which may not always apply to a specific patient, given the biological variability among individuals. Over the years, and for the particular case of Hepatocellular Carcinoma, some research studies have been developing strategies for assisting clinicians in decision making, using computational methods (e.g. machine learning techniques) to extract knowledge from the clinical data. However, these studies have some limitations that have not yet been addressed: some do not focus entirely on Hepatocellular Carcinoma patients, others have strict application boundaries, and none considers the heterogeneity between patients nor the presence of missing data, a common drawback in healthcare contexts. In this work, a real complex Hepatocellular Carcinoma database composed of heterogeneous clinical features is studied. We propose a new cluster-based oversampling approach robust to small and imbalanced datasets, which accounts for the heterogeneity of patients with Hepatocellular Carcinoma. The preprocessing procedures of this work are based on data imputation considering appropriate distance metrics for both heterogeneous and missing data (HEOM) and clustering studies to assess the underlying patient groups in the studied dataset (K-means). The final approach is applied in order to diminish the impact of underlying patient profiles with reduced sizes on survival prediction. It is based on K-means clustering and the SMOTE algorithm to build a representative dataset and use it as training example for different machine learning procedures (logistic regression and neural networks). The results are evaluated in terms of survival prediction and compared across baseline approaches that do not consider clustering and/or oversampling using the Friedman rank test. Our proposed methodology coupled with neural networks outperformed all others, suggesting an improvement over the classical approaches currently used in Hepatocellular Carcinoma prediction models.

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
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
This article studies the economic crisis' impact on consumers' behavior, and aims to help defining green marketing strategies appropriate for these periods. We conducted a survey to 412 Portuguese individuals. The majority of the respondents shows a medium or high green consumer behavior, and demonstrates reduced consumption during crisis. The purchase of green products is more present in products whose use cost is lower than the use cost of the alternative products. The crisis may have a bipolar effect on green consumption, encouraging certain practices and reducing others.

Abstract