Public Tertiary Education Expenditure in Portugal: a Non-Parametric Efficiency Analysis

We employ a non-parametric methodology, Data Envelopment Analysis, to estimate efficiency scores for Portuguese public universities for 2001. Due to the tertiary education organisational features we consider universities as well as faculties and institutes as decision entities. Using frontier analysis we are able to separate universities/faculties/institutes that might qualify, as “performing well” from those were some improvement might increase its efficiency. This is a first effort of checking efficiency and productivity in Portuguese public tertiary education using nonparametric analysis.expenditure in tertiary education; efficiency; production possibility frontier; DEA.

Development of a lesion localisation tool to improve outcome prediction in Traumatic Brain Injury patients

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2022Traumatic brain injury (TBI) is a highly heterogeneous pathology that poses severe health and socioeconomic problems on a global scale. Neuroimaging research and development has advanced its clinical care in numerous ways, as injured brains are being imaged and studied in greater detail. The size and location of TBI lesions are often necessary to accurately determine a prognosis, which is key in defining a patient-specific rehabilitation program. This dissertation aims to investigate the impact of lesion characteristics, such as volume and location, on outcome prediction in TBI patients. Lesion localisation was achieved by comparing annotated TBI lesions to a brain atlas. Furthermore, other lesion characteristics were examined across different Magnetic Resonance Imaging (MRI) sequences and scanners, with results suggesting that the use of different scanners or MRI contrasts introduced biases in said lesion characteristics. Patient outcome was predicted using four generalised linear models. Besides clinical variables, these models included lesion volume, group and location as predictors. Model comparison indicated that lesion volume could be beneficial for outcome prediction, but may be dependent on both lesion group and location. Overall, this methodology showed potential in uncovering the effect that certain lesion groups and/or locations have on patient outcome after TBI

Airframe assembly, systems integration and flight testing of a long endurance electric UAV

The need to adopt new techniques and practices in the Aerospace Industry branch is a consequence of technological development. The present work aims to study the use of solar power as a main energy source in the aviation, in this case for a flight of long endurance of an unmanned air vehicle. This master thesis follows on previous works of the LEEUAV, where it was done the design and construction of a long endurance unmanned aerial system. Thus, the main objective of this work is the systems integration, flight testing and concepts validation. LEEUAV, a prototype of 4.5 meters’ wingspan and ultralight structure partially covered by solar cells was designed to fulfil a continuous flight mission of at least 8 hours on the equinox. The 5.42Kg remotely piloted aircraft was successfully tested showing the agreement with theoretical calculations already made. The longest flight achieved lasted more than 8.5 hours’ resulting in a total distance travelled of more than 75 km. In order to validate the airworthiness concept of the LEEUAV several flight tests were performed and their respective data (static and total pressure, air temperature, ground speed and pitch angle) was collected for further analysis, using a flight controller with multiple sensors on board. The results obtained allowed to study the general performance of the aircraft, the main defects, agreement with the theoretical results as well as determine the real values of aerodynamic coefficients (????, ????), through a reading and processing flight data algorithm in Software MATLAB. Finally, some future expectations for upcoming work are suggested in order to make the LEEUAV an Unmanned Aerial Vehicle of reference.A necessidade de adoção de novas técnicas e práticas no ramo da Indústria Aeronáutica é uma consequência do desenvolvimento tecnológico. O presente trabalho aborda o uso de energia solar como principal fonte de energia na aviação, com enfoque num voo de grande autonomia de uma aeronave não tripulada. Esta tese de mestrado surge na sequência de trabalhos anteriores relativos ao LEEUAV, nos quais se efetuou o projeto e construção de uma aeronave não tripulada de grande autonomia. Assim, o principal objetivo deste trabalho é a integração de sistemas, testes de voo e validação de conceitos. O UAV Solar LEEUAV é um protótipo de 4.5 metros de envergadura e de estrutura ultraleve parcialmente coberto de células fotovoltaicas sendo projetado para cumprir uma missão de voo contínuo de pelo menos 8h no equinócio. O avião de 5.42kg foi testado com sucesso mostrando a concordância com os cálculos teóricos já elaborados. O voo mais longo conseguido foi de 3.13 horas correspondendo a uma distância total percorrida de 96.265 km. De modo a validar o conceito de aeronavegabilidade do LEEUAV foram efetuados vários voos de teste e recolhidos dados de voo (pressão estática e dinâmica, temperatura do ar, velocidade no solo e ângulo de arfagem) para posterior análise, utilizando um controlador de voo com múltiplos sensores a bordo. A análise dos resultados obtidos permitiu precisar o desempenho geral da aeronave, os principais defeitos, concordância com os resultados teóricos assim como determinar os valores reais dos coeficientes aerodinâmicos (???? , ????) através de um algoritmo de leitura e processamento de dados de voo, em Software MATLAB. Por fim, são referidas algumas sugestões para o desenvolvimento de novos trabalhos com o objetivo de tornar O LEEUAV num veículo aéreo não tripulado de referência

Students and Teachers: A DEA Approach to the Relative Efficiency of Portuguese Public Universities

We employ a non-parametric methodology, Data Envelopment Analysis, to estimate efficiency scores for Portuguese public universities, using data mainly for 2003. The input measures are constructed from the number of teachers and from universities' spending while the outputs measures are based on the undergraduate success rate and on the number of doctoral dissertations. Using frontier analysis we are able to separate universities that might qualify, as "performing well" from those were some improvement might be possible in terms of efficiency. This could imply a better allocation by the universities of the usually scarce public financial resources available to tertiary education

A DEA approach to the relative efficiency of portuguese public universities

We employ Data Envelopment Analysis, to estimate efficiency scores for Portuguese public universities. The input measures are constructed from the number of teachers and from universities' spending while the output measures are based on the undergraduate success rate and on the number of doctoral dissertations. Using frontier analysis we are able to separate universities that might qualify, as "performing well" from those where some improvement might be possible. This could imply a better allocation by the universities of the usually scarce public financial resources available to tertiary education by the universities

IFRS - a pathway to financial development: assessing the effects of international financial reporting standards adoption on financial development

IFRS has brought better quality and more transparent financial reports for companies, which can ultimately benefit investors and the financial system as a whole. The goal of this paper is therefore to assess the contribution of the adoption of IFRS in the financial development of a country. Moreover, an in-depth examination of the origin of such effect will be conducted. It was used panel data models with fixed effects based on 34 countries and 22 years. Overall, it was found that IFRS contributes positively to financial development, with the relation deriving from improvements in financial institutions

Predicting stock price direction using machine learning models

The present work aims to evaluate the efficiency of machine learning models in predicting next-day stock price direction, using technical indicators and candlestick patterns. This study considered 440 stocks from the Standard & Poor's 500 index and for each one of them, three different binary models were developed based on the following machine learning algorithms: Deep Neural Network, Support Vector Machine, and Random Forest. Additionally, a naive predictor was created to help compare the models results. The models were judged based on their accuracy and financial returns. The results showed that the machine learning models achieved similar results to the naive model and failed to accurately predict the next-day stock price direction using the selected features, indicating that there is no apparent relationship between them.O presente trabalho visa avaliar a eficiência dos modelos de aprendizagem automática na previsão da direção do preço de ações no dia seguinte, utilizando indicadores técnicos e padrões de vela. Este estudo considerou 440 ações do índice Standard & Poor's 500 e, para cada uma delas, foram desenvolvidos três modelos binários diferentes com base nos seguintes algoritmos de aprendizagem automática: Deep Neural Network, Support Vector Machine, e Random Forest. Além disso, foi criado um naive predictor para ajudar a comparar os resultados dos modelos. Os modelos foram julgados com base na sua precisão e retorno financeiro. Os resultados mostraram que os modelos de aprendizagem automática alcançaram resultados semelhantes aos do modelo naive e não conseguiram prever com precisão a direção do preço das ações no dia seguinte, utilizando as características selecionadas, indicando que não existe uma relação aparente entre os mesmos

Zap1 is required for Candida glabrata response to fluconazole

Invasive fungal infections are a major healthcare problem. In these diseases, the fungus enters the bloodstream and can affect any organ, causing serious hard-to-treat infections that are associated with high mortality rates. The first line of treatment for many invasive fungal infections is fluconazole, the most prescribed antifungal drug in the world. However, the fungistatic, rather than fungicidal, nature of this drug has fostered the emergence of resistant clinical isolates. To overcome this problem, great efforts have been made to fully understand the fungal response to fluconazole. In this work, we show that in Candida glabrata, one of the most prevalent fungal species causing nosocomial invasive infections, the transcription factor Zap1 is important for yeast growth under zinc-limited conditions. We also demonstrate that Zap1 down-regulates biofilm formation. Moreover, in line with our group’s previous work, showing that deletion of ZAP1 renders cells more sensitive to fluconazole, we found that the mutant CgΔzap1 accumulates higher levels of fluconazole, which correlates well with its lower levels of ergosterol. Surprisingly, we observed that Zap1 is a negative regulator of the drug efflux transporter gene CDR1 and of its regulator, PDR1. The apparent paradox of drug accumulation in cells where genes encoding transporters relevant for drug extrusion are being overexpressed, led us to test if their activity could be impaired. Our results indicate that Zap1-depleted cells present, in addition to decreased ergosterol levels, altered composition of membrane phospholipids, which together should impact membrane function and therefore prevent fluconazole detoxification. Overall, this study brings to light the multifaceted transcription factor Zap1 as an important hub in Candida glabrata response to zinc deficiency, biofilm formation and fluconazole detoxification.As infeções fúngicas invasivas são um problema de saúde preocupante, principalmente no meio hospitalar. Nestas doenças infeciosas, o fungo entra na corrente sanguínea e pode afetar qualquer órgão. Sendo infeções graves e de difícil tratamento que estão geralmente associadas a elevadas taxas de mortalidade. O tratamento de primeira linha para as infeções fúngicas invasivas é o fluconazol, um dos antifúngico mais prescrito mundialmente. No entanto, a sua natureza fungistática, em vez de fungicida, tem favorecido o aparecimento de isolados clínicos resistentes. De forma a superar este problema, têm sido envidados grandes esforços para compreender a resposta dos fungos ao fluconazol. Neste trabalho mostramos que, em Candida glabrata, uma das espécies fúngicas mais prevalentes em infeções invasivas nosocomiais, o fator de transcrição Zap1 é necessário para o seu crescimento em condições de deficiência de zinco. Demonstramos também que Zap1 é um regulador negativo da formação de biofilmes. Corroborando trabalhos anteriores do nosso grupo, onde se observou que a deleção de ZAP1 torna as células mais sensíveis ao fluconazol, verificámos ainda que o mutante CgΔzap1 acumula mais fluconazol e apresenta níveis menores de ergosterol. Surpreendentemente, observámos que Zap1 é um regulador negativo do transportador de efluxo de drogas CDR1 e do seu regulador, PDR1. O aparente paradoxo da acumulação da droga em células onde transportadores relevantes para a sua extrusão estão a ser sobrexpressos, levou-nos a considerar que a atividade destes poderia estar afectada. Corroborando esta hipótese, constatámos que células sem Zap1 apresentam, além da diminuição dos níveis de ergosterol, uma composição de fosfolipídios membranares alterada que, em conjunto, poderão afetar a função da membrana prejudicando a desintoxicação de fluconazol. Em suma, provamos que, em Candida glabrata, o fator de transcrição Zap1 é um regulador multifacetado, cuja função engloba a adaptação à deficiência de zinco, formação de biofilmes e desintoxicação de fluconazol

Modelling abstention rate using spatial regression

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementDuring the last few elections that were held in Portugal, there have been very low percentages of voter turnout. This will obviously impact the result of those elections and can maybe be related to the general disenchantment of the population regarding the country’s recent political environment. This study aims to contribute to a better understanding of the patterns in the abstention rate of the last elections in Portugal. Sociological and economic variables such as age, unemployment rate, education level and many others will be used in trying to find out if they influence the abstention rate. It is logical to assume that the abstention rate in a certain municipality will be related to the abstention in neighboring municipalities. Therefore, the study also investigates if there is spatial autocorrelation in the abstention rates. Modeling a phenomenon like this with a simple linear regression model, estimated by Ordinary Least Squares (OLS), will render less efficient and biased results because of the spatial correlation of the observations and possible spatial clustering of values. Spatial regression methods have been proposed to overcome these drawbacks, particularly the Geographically Weighted Regression (GWR). This method will take into account possible local influences, allowing the coefficients of the model to vary depending on the geographic location, possibly obtaining a more appropriate fit. Many different OLS and GWR models were investigated by considering different combinations of explanatory variables and diagnosing their results through statistical tests and goodness-of-fit measures. Results show that indeed the data exhibits a non-random spatial pattern, and that a GWR model is a better approach in modeling abstention rates, when compared to an OLS model. Hence, the percentage of voter turnout in a municipality is likely to be better modelled taking into account its geographic location