Corporate social responsibility and its effect on image and reputation: The case of L’Oréal and its acquisition of The Body Shop

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

A CSR communication strategy for Continente: Improving brand image and reinforcing local community bonds

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Image processing methodology for assessment of drilling induced damage in CFRP

Dissertação para obtenção do Grau de Mestre em Engenharia Mecânica

Time series morphological analysis applied to biomedical signals events detection

Dissertation submitted in the fufillment of the requirements for the Degree of Master in Biomedical Engineering

External brand extensions impact on Diesel's brand image

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

A comparison between digital breast tomosynthesis and mammography: dosimetric and image quality characterization of clinical system

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Equity in access to MRI equipment: the Portuguese case

Magnetic resonance imaging (MRI) is a method of image diagnose proven to be of undeniable importance when it comes to neuro and cardio related diseases. In fact, these diseases (such as: ischemic heart disease, stroke and acute myocardial infection) have high incidence in Portugal. For these reasons, the allocation of this medical technology should not be considered with light thoughts. In fact, making decision of resource allocation in health care can be a very complex and contested matter. The impacts of new technology allocation, such MRI, can be assessed in a variety of ways. However, a fundamental component should always be present: the use of evidence-based decision-making methods. One of these methods is Technology Assessment (TA). This paper aims to characterize the equity on access of the Portuguese population in general, to a specific medical device such as MRI, under the TA point of view. It is hoped to promote a bridge of scientific knowledge between the gap on research and policy-making through TA that can emerge as a tool to aid decision-makers in the organization of health systems. There are gaps in providing healthcare, due to geographical imbalances, with some areas unable to provide certain specialized services, as hospitals in the countryside do not provide all medical specialties. Portugal has also a large independent private sector that provides diagnostic and therapeutic services to NHS users under contracts called conventions. These medical contracts cover ambulatory health facilities for laboratory tests and examinations such as diagnostic tests and Radiology. However, there is no convention from the NHS when concerning the MRI exam. Therefore, this reality can be considered a limitation in the access of the general population to this kind of clinical exam. TA can play an useful and important role in helping the decision-makers to explore potential gains that might be achieved by introducing a more rational decision making into health care management, namely into the Radiology area, regarding the allocation of MRI equipment.

Structural time series models and the Kalman filter: A concise review

The continued increase in availability of economic data in recent years and, more importantly, the possibility to construct larger frequency time series, have fostered the use (and development) of statistical and econometric techniques to treat them more accurately. This paper presents an exposition of structural time series models by which a time series can be decomposed as the sum of a trend, seasonal and irregular components. In addition to a detailled analysis of univariate speci fications we also address the SUTSE multivariate case and the issue of cointegration. Finally, the recursive estimation and smoothing by means of the Kalman filter algorithm is described taking into account its different stages, from initialisation to parameter s estimation.

Portugal as a country exporting talent: Image of young high-skilled Portuguese people working outside Portugal

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

In-School CSR and its impact on brand image, brand identification and brand reputation: The moderator effect of brand familiarity, product involvement and cause-brand fit

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Microscopy image segmentation by active contour models

In this thesis a semi-automated cell analysis system is described through image processing. To achieve this, an image processing algorithm was studied in order to segment cells in a semi-automatic way. The main goal of this analysis is to increase the performance of cell image segmentation process, without affecting the results in a significant way. Even though, a totally manual system has the ability of producing the best results, it has the disadvantage of taking too long and being repetitive, when a large number of images need to be processed. An active contour algorithm was tested in a sequence of images taken by a microscope. This algorithm, more commonly known as snakes, allowed the user to define an initial region in which the cell was incorporated. Then, the algorithm would run several times, making the initial region contours to converge to the cell boundaries. With the final contour, it was possible to extract region properties and produce statistical data. This data allowed to say that this algorithm produces similar results to a purely manual system but at a faster rate. On the other hand, it is slower than a purely automatic way but it allows the user to adjust the contour, making it more versatile and tolerant to image variations.

Optimization of breast tomosynthesis image reconstruction using parallel computing

Breast cancer is the most common cancer among women, being a major public health problem. Worldwide, X-ray mammography is the current gold-standard for medical imaging of breast cancer. However, it has associated some well-known limitations. The false-negative rates, up to 66% in symptomatic women, and the false-positive rates, up to 60%, are a continued source of concern and debate. These drawbacks prompt the development of other imaging techniques for breast cancer detection, in which Digital Breast Tomosynthesis (DBT) is included. DBT is a 3D radiographic technique that reduces the obscuring effect of tissue overlap and appears to address both issues of false-negative and false-positive rates. The 3D images in DBT are only achieved through image reconstruction methods. These methods play an important role in a clinical setting since there is a need to implement a reconstruction process that is both accurate and fast. This dissertation deals with the optimization of iterative algorithms, with parallel computing through an implementation on Graphics Processing Units (GPUs) to make the 3D reconstruction faster using Compute Unified Device Architecture (CUDA). Iterative algorithms have shown to produce the highest quality DBT images, but since they are computationally intensive, their clinical use is currently rejected. These algorithms have the potential to reduce patient dose in DBT scans. A method of integrating CUDA in Interactive Data Language (IDL) is proposed in order to accelerate the DBT image reconstructions. This method has never been attempted before for DBT. In this work the system matrix calculation, the most computationally expensive part of iterative algorithms, is accelerated. A speedup of 1.6 is achieved proving the fact that GPUs can accelerate the IDL implementation.

State detection in a financial portfolio: a self-organizing maps approach for financial time series

This study analyses financial data using the result characterization of a self-organized neural network model. The goal was prototyping a tool that may help an economist or a market analyst to analyse stock market series. To reach this goal, the tool shows economic dependencies and statistics measures over stock market series. The neural network SOM (self-organizing maps) model was used to ex-tract behavioural patterns of the data analysed. Based on this model, it was de-veloped an application to analyse financial data. This application uses a portfo-lio of correlated markets or inverse-correlated markets as input. After the anal-ysis with SOM, the result is represented by micro clusters that are organized by its behaviour tendency. During the study appeared the need of a better analysis for SOM algo-rithm results. This problem was solved with a cluster solution technique, which groups the micro clusters from SOM U-Matrix analyses. The study showed that the correlation and inverse-correlation markets projects multiple clusters of data. These clusters represent multiple trend states that may be useful for technical professionals.

Image processing for displacement measurements

Since the invention of photography humans have been using images to capture, store and analyse the act that they are interested in. With the developments in this field, assisted by better computers, it is possible to use image processing technology as an accurate method of analysis and measurement. Image processing's principal qualities are flexibility, adaptability and the ability to easily and quickly process a large amount of information. Successful examples of applications can be seen in several areas of human life, such as biomedical, industry, surveillance, military and mapping. This is so true that there are several Nobel prizes related to imaging. The accurate measurement of deformations, displacements, strain fields and surface defects are challenging in many material tests in Civil Engineering because traditionally these measurements require complex and expensive equipment, plus time consuming calibration. Image processing can be an inexpensive and effective tool for load displacement measurements. Using an adequate image acquisition system and taking advantage of the computation power of modern computers it is possible to accurately measure very small displacements with high precision. On the market there are already several commercial software packages. However they are commercialized at high cost. In this work block-matching algorithms will be used in order to compare the results from image processing with the data obtained with physical transducers during laboratory load tests. In order to test the proposed solutions several load tests were carried out in partnership with researchers from the Civil Engineering Department at Universidade Nova de Lisboa (UNL).