Isolation and encapsulation of a natural colourant using green technologies

The use of natural pigments instead of synthetic colourants is receiving growing interest in the food industry. In this field, cactus pears (Opuntia spp.) have been identified to be a promising betalainic crops covering a wide coloured spectrum. The aim of this work was to develop adequate clean and mild methodologies for the isolation and encapsulation of betacyanins, from cactus pear fruits (Opuntia spp.). Firstly, two different emerging technologies, namely PLE (Pressurized Liquid Extraction) and HPCDAE (High Pressure Carbon Dioxide-Assisted Extraction), were exploited to isolation of betacyanins form cactus pear fruits. Different process conditions were tested for the maximum recovery of betacyanins. Results showed that highest extraction yields were achieved for HPCDAE and mass ratio of pressurized carbon dioxide vs. acidified water was the parameter that most affected the betacyanins extraction. At optimum conditions of HPCDAE, Opuntia spp. extract presented a total betacyanin content of 211 ± 10 mg/100 g whereas extracts obtained using conventional extraction, PLE in static and in dynamic mode presented a total betacyanin content of 85 ± 3, 191 ± 2 and 153 ± 5 mg/100 g, respectively. HPCDAE has proven to be a successful technology to extract betacyanins from Opuntia spp. fruits. Afterward, Supercritical Fluid Technology was exploited to develop lipidic particles of betalain-rich extract. A betacyanin-rich conventional extract was encapsulated by PGSS® (Particles from Gas Saturated Solutions) technique. Different process conditions were tested in order to model the encapsulation of betacyanins. The pressure had a negative effect on betacyanin encapsulation. Lower pressures leads to an increase in the betacyanin encapsulation. This effect was more pronounced at higher temperatures and lower equilibrium time. At these conditions, Opuntia spp. particles presented 64.4 ± 4.5 mg/100 g and high antioxidant capacity. When compared with the Opuntia spp. dried extract, lipidic particles contributed to a better homogenization of the pink colour after incorporation in ice cream.

About using serious games to teach (Portuguese) sign language

Sign language is the form of communication used by Deaf people, which, in most cases have been learned since childhood. The problem arises when a non-Deaf tries to contact with a Deaf. For example, when non-Deaf parents try to communicate with their Deaf child. In most cases, this situation tends to happen when the parents did not have time to properly learn sign language. This dissertation proposes the teaching of sign language through the usage of serious games. Currently, similar solutions to this proposal do exist, however, those solutions are scarce and limited. For this reason, the proposed solution is composed of a natural user interface that is intended to create a new concept on this field. The validation of this work, consisted on the implementation of a serious game prototype, which can be used as a source for learning (Portuguese) sign language. On this validation, it was first implemented a module responsible for recognizing sign language. This first stage, allowed the increase of interaction and the construction of an algorithm capable of accurately recognizing sign language. On a second stage of the validation, the proposal was studied so that the pros and cons can be determined and considered on future works.

Social-media monitoring for cold-start recommendations

Generating personalized movie recommendations to users is a problem that most commonly relies on user-movie ratings. These ratings are generally used either to understand the user preferences or to recommend movies that users with similar rating patterns have rated highly. However, movie recommenders are often subject to the Cold-Start problem: new movies have not been rated by anyone, so, they will not be recommended to anyone; likewise, the preferences of new users who have not rated any movie cannot be learned. In parallel, Social-Media platforms, such as Twitter, collect great amounts of user feedback on movies, as these are very popular nowadays. This thesis proposes to explore feedback shared on Twitter to predict the popularity of new movies and show how it can be used to tackle the Cold-Start problem. It also proposes, at a finer grain, to explore the reputation of directors and actors on IMDb to tackle the Cold-Start problem. To assess these aspects, a Reputation-enhanced Recommendation Algorithm is implemented and evaluated on a crawled IMDb dataset with previous user ratings of old movies,together with Twitter data crawled from January 2014 to March 2014, to recommend 60 movies affected by the Cold-Start problem. Twitter revealed to be a strong reputation predictor, and the Reputation-enhanced Recommendation Algorithm improved over several baseline methods. Additionally, the algorithm also proved to be useful when recommending movies in an extreme Cold-Start scenario, where both new movies and users are affected by the Cold-Start problem.

EDP inovcity pilot study proposal: A set-top box interface

In this work I propose an additional test to be implemented in EDP’s residential electricity use feedback trials, under InovCity’s project scope. The proposed product to be tested consists of an interface between the smart meter and the television, through a set-top box. I provide a theoretical framework of the importance of feedback, an analysis of results from past studies involving smart metering, and a detailed description of my proposal. The results of a self-developed questionnaire related to the proposal and segmentation issues are also analyzed. Finally, general conclusions are drawn and potential future improvements and challenges are presented.