The combination of neural estimates in prediction and decision problems

In this dissertation, different ways of combining neural predictive models or neural-based forecasts are discussed. The proposed approaches consider mostly Gaussian radial basis function networks, which can be efficiently identified and estimated through recursive/adaptive methods. Two different ways of combining are explored to get a final estimate – model mixing and model synthesis –, with the aim of obtaining improvements both in terms of efficiency and effectiveness. In the context of model mixing, the usual framework for linearly combining estimates from different models is extended, to deal with the case where the forecast errors from those models are correlated. In the context of model synthesis, and to address the problems raised by heavily nonstationary time series, we propose hybrid dynamic models for more advanced time series forecasting, composed of a dynamic trend regressive model (or, even, a dynamic harmonic regressive model), and a Gaussian radial basis function network. Additionally, using the model mixing procedure, two approaches for decision-making from forecasting models are discussed and compared: either inferring decisions from combined predictive estimates, or combining prescriptive solutions derived from different forecasting models. Finally, the application of some of the models and methods proposed previously is illustrated with two case studies, based on time series from finance and from tourism.

In-depth search focused on furans, lactones, volatile phenols, and acetals as potential age markers of Madeira wines by comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry combined with solid phase microextraction

The establishment of potential age markers of Madeira wine is of paramount significance as it may contribute to detect frauds and to ensure the authenticity of wine. Considering the chemical groups of furans, lactones, volatile phenols, and acetals, 103 volatile compounds were tentatively identified; among these, 71 have been reported for the first time in Madeira wines. The chemical groups that could be used as potential age markers were predominantly acetals, namely, diethoxymethane, 1,1-diethoxyethane, 1,1-diethoxy-2-methyl-propane, 1-(1-ethoxyethoxy)-pentane, trans-dioxane and 2-propyl-1,3-dioxolane, and from the other chemical groups, 5-methylfurfural and cis-oak-lactone, independently of the variety and the type of wine. GC × GC-ToFMS system offers a more useful approach to identify these compounds compared to previous studies using GC−qMS, due to the orthogonal systems, that reduce coelution, increase peak capacity and mass selectivity, contributing to the establishment of new potential Madeira wine age markers. Remarkable results were also obtained in terms of compound identification based on the organized structure of the peaks of structurally related compounds in the GC × GC peak apex plots. This information represents a valuable approach for future studies, as the ordered-structure principle can considerably help the establishment of the composition of samples. This new approach provides data that can be extended to determine age markers of other types of wines.

Breadcrumbs: location and context aware mobile platform for story sharing

With the current proliferation of sensor equipped mobile devices such as smartphones and tablets, location aware services are expanding beyond the mere efficiency and work related needs of users, evolving in order to incorporate fun, culture and the social life of users. Today people on the move have more and more connectivity and are expected to be able to communicate with their usual and familiar social networks. That means communications not only with their peers and colleagues, friends and family but also with unknown people that might share their interests, curiosities or happen to use the same social network. Through social networks, location aware blogging, cultural mobile applications relevant information is now available at specific geographical locations and open to feedback and conversations among friends as well as strangers. In fact, nowadays smartphone technologies aloud users to post and retrieve content while on the move, often relating to specific physical landmarks or locations, engaging and being engaged in conversations with strangers as much as their own social network. The use of such technologies and applications while on the move can often lead people to serendipitous discoveries and interactions. Throughout our thesis we are engaging on a two folded investigation: how can we foster and support serendipitous discoveries and what are the best interfaces for it? In fact, to read and write content while on the move is a cognitively intensive task. While the map serves the function of orienting the user, it also absorbs most of the user’s concentration. In order to address this kind of cognitive overload issue with Breadcrumbs we propose a 360 degrees interface that enables the user to find content around them by means of scanning the surrounding space with the mobile device. By using a loose metaphor of a periscope, harnessing the power of the smartphone sensors we designed an interactive interface capable of detecting content around the users and display it in the form of 2 dimensional bubbles which diameter depends on their distance from the users. Users will navigate the space in relation to the content that they are curious about, rather than in relation to the traditional geographical map. Through this model we envisage alleviating a certain cognitive overload generated by having to continuously confront a two dimensional map with the real three dimensional space surrounding the user, but also use the content as a navigational filter. Furthermore this alternative mean of navigating space might bring serendipitous discovery about places that user where not aware of or intending to reach. We hence conclude our thesis with the evaluation of the Breadcrumbs application and the comparison of the 360 degrees interface with a traditional 2 dimensional map displayed on the devise screen. Results from the evaluation are compiled in findings and insights for future use in designing and developing context aware mobile applications.