New approaches to antimalarial chemotherapy: design and synthesis of bicyclic endoperoxides and of peptide and peptidomimetic carbonyl containing cysteine protease inhibitors

Tese dout., Química Orgânica, Universidade do Algarve, 2007

Effects of nutrient and light enrichment on phytoplankton growth

Alterations of freshwater flow regimes and increasing eutrophication lead to alterations in light availability and nutrient loading into adjacent estuaries and coastal areas. Phytoplankton community respond to these changes in many ways. Harmful phytoplankton blooms, for instance, may be a consequence of changes in nutrient supply, as well as the replacement of some phytoplankton species (like diatoms, that contribute for the development of large fish and shellfish populations) by ohers (like cyanobacteria, that may be toxic and represent an undesirable food source for higher trophic levels). Nutrient and light enrichment experiments allow us to understand and predict the effects of eutrophication on the growth of phytoplankton. This is a fundamental tool in water management issues, since it enables the prediction of changes in the phytoplankton community that may be harmful to the whole ecosystem, and the design of mitigation strategies (Zalewski 2000).

Design of neuro-fuzzy models by evolutionary and gradient-based algorithms

All systems found in nature exhibit, with different degrees, a nonlinear behavior. To emulate this behavior, classical systems identification techniques use, typically, linear models, for mathematical simplicity. Models inspired by biological principles (artificial neural networks) and linguistically motivated (fuzzy systems), due to their universal approximation property, are becoming alternatives to classical mathematical models. In systems identification, the design of this type of models is an iterative process, requiring, among other steps, the need to identify the model structure, as well as the estimation of the model parameters. This thesis addresses the applicability of gradient-basis algorithms for the parameter estimation phase, and the use of evolutionary algorithms for model structure selection, for the design of neuro-fuzzy systems, i.e., models that offer the transparency property found in fuzzy systems, but use, for their design, algorithms introduced in the context of neural networks. A new methodology, based on the minimization of the integral of the error, and exploiting the parameter separability property typically found in neuro-fuzzy systems, is proposed for parameter estimation. A recent evolutionary technique (bacterial algorithms), based on the natural phenomenon of microbial evolution, is combined with genetic programming, and the resulting algorithm, bacterial programming, advocated for structure determination. Different versions of this evolutionary technique are combined with gradient-based algorithms, solving problems found in fuzzy and neuro-fuzzy design, namely incorporation of a-priori knowledge, gradient algorithms initialization and model complexity reduction.