Combining non-dominance, objective-order and spread metric to extend firefly algorithm to multi-objective optimization

In this paper, we propose an extension of the firefly algorithm (FA) to multi-objective optimization. FA is a swarm intelligence optimization algorithm inspired by the flashing behavior of fireflies at night that is capable of computing global solutions to continuous optimization problems. Our proposal relies on a fitness assignment scheme that gives lower fitness values to the positions of fireflies that correspond to non-dominated points with smaller aggregation of objective function distances to the minimum values. Furthermore, FA randomness is based on the spread metric to reduce the gaps between consecutive non-dominated solutions. The obtained results from the preliminary computational experiments show that our proposal gives a dense and well distributed approximated Pareto front with a large number of points.

Cheese whey: a cost-effective alternative for hyaluronic acid production by Streptococcus zooepidemicus

This study focuses on the optimization of cheese whey formulated media for the production of hyaluronic acid HA by Streptococcus zooepidemicus. Culture media containing whey (W; 2.1 g/L) or whey hydrolysate (WH; 2.4 g/L) gave the highest HA productions. Both W and WH produced high yields on protein consumed, suggesting cheese whey is a good nitrogen source for S. zooepidemicus production of HA. Polysaccharide concentrations of 4.0 g/L and 3.2 g/L were produced in W and WH in a further scale-up to 5 L bioreactors, confirming the suitability of the low-cost nitrogen source. Cheese whey culture media provided high molecular weight (> 3000 kDa) HA products. This study revealed replacing the commercial peptone by the low-cost alternative could reduce HA production costs by up to a 70%compared to synthetic media.

Optimization of filling systems for low pressure by Flow 3D

Dissertação de mestrado integrado em Engenharia Mecânica

Optimization of magnetoelectric composites based on electroactive polymers for energy harvesting and sensor application

Tese de Doutoramento em Engenharia de Materiais.

Development of procedures for the design, optimization and manufacturing of customized orthopaedic and trauma implants: Geometrical/anatomical modelling from 3D medical imaging

Tese de Doutoramento (Programa Doutoral em Engenharia Biomédica)

Metabolic engineering with multi-objective optimization of kinetic models

Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

Development of an intelligent earthwork optimization system

Tese de Doutoramento em Engenharia Civil.

Optimization of peptide nucleic acid fluorescence in situ hybridization (PNA-FISH) for the detection of bacteria: the effect of pH, dextran sulfate and probe concentration

Fluorescence in situ hybridization (FISH) is a molecular technique widely used for the detection and characterization of microbial populations. FISH is affected by a wide variety of abiotic and biotic variables and the way they interact with each other. This is translated into a wide variability of FISH procedures found in the literature. The aim of this work is to systematically study the effects of pH, dextran sulfate and probe concentration in the FISH protocol, using a general peptide nucleic acid (PNA) probe for the Eubacteria domain. For this, response surface methodology was used to optimize these 3 PNA-FISH parameters for Gram-negative (Escherichia coli and Pseudomonas fluorescens) and Gram-positive species (Listeria innocua, Staphylococcus epidermidis and Bacillus cereus). The obtained results show that a probe concentration higher than 300 nM is favorable for both groups. Interestingly, a clear distinction between the two groups regarding the optimal pH and dextran sulfate concentration was found: a high pH (approx. 10), combined with lower dextran sulfate concentration (approx. 2% [w/v]) for Gram-negative species and near-neutral pH (approx. 8), together with higher dextran sulfate concentrations (approx. 10% [w/v]) for Gram-positive species. This behavior seems to result from an interplay between pH and dextran sulfate and their ability to influence probe concentration and diffusion towards the rRNA target. This study shows that, for an optimum hybridization protocol, dextran sulfate and pH should be adjusted according to the target bacteria.

Automated network resilience optimization using computational intelligence methods

This paper presents an automated optimization framework able to provide network administrators with resilient routing configurations for link-state protocols, such as OSPF or IS-IS. In order to deal with the formulated NP-hard optimization problems, the devised framework is underpinned by the use of computational intelligence optimization engines, such as Multi-objective Evolutionary Algorithms (MOEAs). With the objective of demonstrating the framework capabilities, two illustrative Traffic Engineering methods are described, allowing to attain routing configurations robust to changes in the traffic demands and maintaining the network stable even in the presence of link failure events. The presented illustrative results clearly corroborate the usefulness of the proposed automated framework along with the devised optimization methods.

By-products of beer fermentation

Among the most important factors influencing beer quality is the presence of well-adjusted amounts of higher alcohols and esters; as well as the successful reduction of undesirable by-products such as diacetyl. While higher alcohols and esters contribute rather positively to the beer aroma, diacetyl is mostly unwelcome for beer types with lighter taste. Thus, the complex metabolic pathways in yeast responsible for the synthesis of both pleasant and unpleasant by-products of fermentation were given special attention in this last chapter.

Microbial production of potent phenolic-antioxidants through solid state fermentation

The agroindustrial residues including plant tissues rich in polyphenols were explored for microbial production of potent phenolics under solid state fermentation processes. The fungal strains capable of hydrolyzing tannin-rich materials were isolated from Mexican semidesert zones. These microorganisms have been employed to release potent phenolic antioxidants during the solid state fermentation of different materials (pomegranate peels, pecan nut shells, creosote bush and tar bush). This chapter includes the critical parameters for antioxidants production from selective microbes. Technical aspects of the microbial fermentation of antioxidants have also been discussed.

Effects of fermentation residues on the melt processability and thermomechanical degradation of PHBV produced from cheese whey using mixed microbial cultures

"Available online 28 March 2016"

Olive pomace pretreatments to enhance its valorisation by solid-state fermentation

Dissertação de mestrado em Bioengenharia

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.

Production of biohydrogen from dark fermentation of organic wastes

Dissertação de mestrado em Bioengenharia