Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees : the PredictAL study

Background: Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. Methods: A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score >= 8 in men and >= 5 in women. Results: 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). Conclusions: The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

How to optimize Lipton’s business model in order to stimulate tea consumption in Portugal and increase Lipton Leaf tea market share?: The importance of distribution and sales channels management in a B2B2C market

Field Lab Entrepreneurial Innovative Ventures

Impacts of climate change scenarios on terrestrial productivity and biomass for energy in the Iberian Peninsula: assessment through the JSBACH model

Dissertação para obtenção do Grau de Mestre em Engenharia do Ambiente, Perfil de Gestão e Sistemas Ambientais

An RBC model with a rich fiscal sector

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

Predicting GDP growth in the Euro Area

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

Business model: What should be the strategy used by Smart Galp to implement and potentiate its value proposition in the Portuguese market?

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

Absorption in human capital and r&d effects in an endogenous growth model

Until now, in models of endogenous growth with physical capital, human capital and R&D such as in Arnold [Journal of Macroeconomics 20 (1998)] and followers, steady-state growth is independent of innovation activities. We introduce absorption in human capital accumulation and describe the steady-state and transition of the model. We show that this new feature provides an effect of R&D in growth, consumption and welfare. We compare the quantitative effects of R&D productivity with the quantitative effects of Human Capital productivity in wealth and welfare.

Drivers of participation in collaborative consumption ventures

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

Context-based identification of energy consumption in industrial plants

Nowadays, reducing energy consumption is one of the highest priorities and biggest challenges faced worldwide and in particular in the industrial sector. Given the increasing trend of consumption and the current economical crisis, identifying cost reductions on the most energy-intensive sectors has become one of the main concerns among companies and researchers. Particularly in industrial environments, energy consumption is affected by several factors, namely production factors(e.g. equipments), human (e.g. operators experience), environmental (e.g. temperature), among others, which influence the way of how energy is used across the plant. Therefore, several approaches for identifying consumption causes have been suggested and discussed. However, the existing methods only provide guidelines for energy consumption and have shown difficulties in explaining certain energy consumption patterns due to the lack of structure to incorporate context influence, hence are not able to track down the causes of consumption to a process level, where optimization measures can actually take place. This dissertation proposes a new approach to tackle this issue, by on-line estimation of context-based energy consumption models, which are able to map operating context to consumption patterns. Context identification is performed by regression tree algorithms. Energy consumption estimation is achieved by means of a multi-model architecture using multiple RLS algorithms, locally estimated for each operating context. Lastly, the proposed approach is applied to a real cement plant grinding circuit. Experimental results prove the viability of the overall system, regarding both automatic context identification and energy consumption estimation.

Predicting market direction with hidden Markov models

This paper develops the model of Bicego, Grosso, and Otranto (2008) and applies Hidden Markov Models to predict market direction. The paper draws an analogy between financial markets and speech recognition, seeking inspiration from the latter to solve common issues in quantitative investing. Whereas previous works focus mostly on very complex modifications of the original hidden markov model algorithm, the current paper provides an innovative methodology by drawing inspiration from thoroughly tested, yet simple, speech recognition methodologies. By grouping returns into sequences, Hidden Markov Models can then predict market direction the same way they are used to identify phonemes in speech recognition. The model proves highly successful in identifying market direction but fails to consistently identify whether a trend is in place. All in all, the current paper seeks to bridge the gap between speech recognition and quantitative finance and, even though the model is not fully successful, several refinements are suggested and the room for improvement is significant.

Proteomic analysis of a neurodegeneration cell model, treated with plant extracts with potential neuroprotective activity

Nowadays, a significant increase in chronic diseases is observed. Epidemiological studies showed a consistent relationship between the consumption of fruits and vegetables and a reduced risk of certain chronic diseases, namely neurodegenerative disorders. One factor common to these diseases is oxidative stress, which is highly related with proteins, lipids, carbohydrates and nucleic acids damage, leading to cellular dysfunction. Polyphenols, highly abundant in berries and associated products, were described as having antioxidant properties, with beneficial effect in these pathologies. The aims of this study were to evaluate by proteomic analyses the effect of oxidative insult in a neuroblastoma cell line (SK-N-MC) and understand the mechanisms involved in the neuroprotective effects of digested extracts from commercial and wild blackberry (R. vagabundus Samp.). The analysis of the total proteome by two-dimensional electrophoresis revealed that oxidative stress in SK-N-MC cells resulted in altered expression of 12 protein spots from a total of 318. Regarding some redox proteomics alterations, particularly proteins carbonylation and glutathionylation, protein carbonyl alterations during stress suggest that cells produce an early and late response; on the other hand, no glutathionylated polypeptides were detected. Relatively to the incubation of SK-N-MC cells with digested berry extracts, commercial blackberry promotes more changes in protein pattern of these cells than R. vagabundus. From 9 statistically different protein spots of cells incubated with commercial blackberry, only β-tubulin and GRP 78 were until now identified by mass spectrometry. Further studies involving the selection of sub proteomes will be necessary to have a better understanding of the mechanisms underlying the neuroprotective effects of berries.

Measuring the effects of price movements on private consumption

This paper studies how shocks in the prices of Food, Energy and Financial Assets affect private consumption using a VAR Model. Then, the total effects are broken into direct and indirect effects, using the coefficients taken from the previous model. We use quarterly data for the Portuguese economy from the last 20 years. We found that energy prices and financial assets have a strong connection with consumption, suggesting that the economy may be too exposed to shocks in these markets.

Predicting market movement with Artificial Neural Networks

This paper presents an application of an Artificial Neural Network (ANN) to the prediction of stock market direction in the US. Using a multilayer perceptron neural network and a backpropagation algorithm for the training process, the model aims at learning the hidden patterns in the daily movement of the S&P500 to correctly identify if the market will be in a Trend Following or Mean Reversion behavior. The ANN is able to produce a successful investment strategy which outperforms the buy and hold strategy, but presents instability in its overall results which compromises its practical application in real life investment decisions.