Wow!: objects

Nowadays computers have advanced beyond the desktop into many parts of everyday life and objects. To achieve this we have to make the computer invisible, and making a computer invisible is not a matter of size of the hardware, it’s all about how the human perceives the computer. To make this possible, the interaction with the computer has to be done in an alternative way, such that the user doesn’t notice the usual computer interfaces (mouse and keyboard) when using it. Therefore this thesis focuses on physical objects that are interactive to achieve various purposes like persuasive objects for stress relief, persuasive objects to help the process of teaching, persuasive objects for fun, persuasive objects to display internet information and persuasive objects to make people feel more in community (exchange virtual emotions), persuasive objects are going to be created and evaluated to see if they have the power to simplify and turn our lives better. The persuasive objects developed employ technology like sensors, actuators, microcontrollers, and computer/web services’ communication. This Master thesis starts by presenting a comprehensive introduction of what are persuasive objects and some general information about several areas that are related to our persuasive objects like stress relief, work experience, multimedia education and other major aspects. It continues by describing related work done in this area. Then we have a detailed view of each persuasive object and finally this thesis finishes with a general conclusion and notion of future work.

Multivariate analysis for the classification and differentiation of Madeira wines according to main grape varieties

In order to differentiate and characterize Madeira wines according to main grape varieties, the volatile composition (higher alcohols, fatty acids, ethyl esters and carbonyl compounds) was determined for 36 monovarietal Madeira wine samples elaborated from Boal, Malvazia, Sercial and Verdelho white grape varieties. The study was carried out by headspace solid-phase microextraction technique (HS-SPME), in dynamic mode, coupled with gas chromatography–mass spectrometry (GC–MS). Corrected peak area data for 42 analytes from the above mentioned chemical groups was used for statistical purposes. Principal component analysis (PCA) was applied in order to determine the main sources of variability present in the data sets and to establish the relation between samples (objects) and volatile compounds (variables). The data obtained by GC–MS shows that the most important contributions to the differentiation of Boal wines are benzyl alcohol and (E)-hex-3-en-1-ol. Ethyl octadecanoate, (Z)-hex-3-en-1-ol and benzoic acid are the major contributions in Malvazia wines and 2-methylpropan-1-ol is associated to Sercial wines. Verdelho wines are most correlated with 5-(ethoxymethyl)-furfural, nonanone and cis-9-ethyldecenoate. A 96.4% of prediction ability was obtained by the application of stepwise linear discriminant analysis (SLDA) using the 19 variables that maximise the variance of the initial data set.