Characterization of processed materials by electrical currents: development of equipment and applications

Dissertação para obtenção do Grau de Mestre em Engenharia Mecânica

Synthesis, characterization and applications of new schiff base fluorescent chemosensors for metal and DNA interactions: conventional and "green" approaches

Dissertação apresentada para a obtenção do Grau de Doutor em Química Sustentável, especialidade de Química-Física Inorgânica, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Automatic extraction of concepts from texts and applications

The extraction of relevant terms from texts is an extensively researched task in Text- Mining. Relevant terms have been applied in areas such as Information Retrieval or document clustering and classification. However, relevance has a rather fuzzy nature since the classification of some terms as relevant or not relevant is not consensual. For instance, while words such as "president" and "republic" are generally considered relevant by human evaluators, and words like "the" and "or" are not, terms such as "read" and "finish" gather no consensus about their semantic and informativeness. Concepts, on the other hand, have a less fuzzy nature. Therefore, instead of deciding on the relevance of a term during the extraction phase, as most extractors do, I propose to first extract, from texts, what I have called generic concepts (all concepts) and postpone the decision about relevance for downstream applications, accordingly to their needs. For instance, a keyword extractor may assume that the most relevant keywords are the most frequent concepts on the documents. Moreover, most statistical extractors are incapable of extracting single-word and multi-word expressions using the same methodology. These factors led to the development of the ConceptExtractor, a statistical and language-independent methodology which is explained in Part I of this thesis. In Part II, I will show that the automatic extraction of concepts has great applicability. For instance, for the extraction of keywords from documents, using the Tf-Idf metric only on concepts yields better results than using Tf-Idf without concepts, specially for multi-words. In addition, since concepts can be semantically related to other concepts, this allows us to build implicit document descriptors. These applications led to published work. Finally, I will present some work that, although not published yet, is briefly discussed in this document.

Influence of the molecular weight in mechanical properties and degradation kinetics of chitosan inverted colloidal crystals

Tissue engineering arises from the need to regenerate organs and tissues, requiring the development of scaffolds, which can provide an optimum environment for tissue growth. In this work, chitosan with different molecular weights was used to develop biodegradable 3D inverted colloidal crystals (ICC) structures for bone regeneration, exhibiting uniform pore size and interconnected network. Moreover, in vitro tests were conducted by studying the influence of the molecular weight in the degradation kinetics and mechanical properties. The production of ICC included four major stages: fabrication of microspheres; assembly into a cohesive structure, polymeric solution infiltration and microsphere removal. Chitosan’s degree of deacetylation was determined by infrared spectroscopy and molecular weight was obtained via capillary viscometry. In order to understand the effect of the molecular weight in ICC structures, the mass loss and mechanical properties were analyzed after degradation with lysozyme. Structure morphology observation before and after degradation was performed by scanning electron microscopy. Cellular adhesion and proliferation tests were carried out to evaluate ICC in vitro response. Overall, medium molecular weight ICC revealed the best balance in terms of mechanical properties, degradation rate, morphology and biological behaviour.