Interacting and making personalized recommendations of places of interest to tourists

Advances in Intelligent Systems and Computing, 353

Tunnel engineering – influence of the type and the quantity of measurements in the back analysis of geomechanical parameters

The monitoring data collected during tunnel excavation can be used in inverse analysis procedures in order to identify more realistic geomechanical parameters that can increase the knowledge about the interested formations. These more realistic parameters can be used in real time to adapt the project to the real structure in situ behaviour. However, monitoring plans are normally designed for safety assessment and not especially for the purpose of inverse analysis. In fact, there is a lack of knowledge about what types and quantity of measurements are needed to succeed in identifying the parameters of interest. Also, the optimisation algorithm chosen for the identification procedure may be important for this matter. In this work, this problem is addressed using a theoretical case with which a thorough parametric study was carried out using two optimisation algorithms based on different calculation paradigms, namely a conventional gradient-based algorithm and an evolution strategy algorithm. Calculations were carried for different sets of parameters to identify several combinations of types and amount of monitoring data. The results clearly show the high importance of the available monitoring data and the chosen algorithm for the success rate of the inverse analysis process.

Assessment of polymer-based nanocomposites biodegradability

The management of solid waste is a growing concern in many countries. Municipal solid waste is a major component of the total solid waste generated by society, and the composting of municipal solid waste has gained some attention even though a composting treatment for it is not yet widespread. It may not be realistic to replace large portions of these plastics with biodegradable materials, and it may be more important to separate plastics unsuitable for the composting process at the generating spots. However, for food packaging, there is still a great deal of interest in using biodegradable plastics that are difficult to sort at the generation spots. Under these circumstances, nanocomposites of biodegradable polymers as matrix and nanoparticles, that can be degraded along with organic wastes during composting could be a solution. Therefore, this chapter aims to give an overview on the biodegradability studies of bio-nanocomposites. It will focus on different polymers, nanocomposites containing different clay types and inorganic particles exposed under different environments.

Alcohol sponsored events: the impact on attitudes and intentions, and the mitigating impact of persuasion knowledge

Doctor of Philosophy in Marketing and Strategy

The effects of solvent composition on the affinity of a peptide towards hair keratin: experimental and molecular dynamics data

The study of the interaction between hair filaments and formulations or peptides is of utmost importance in fields like cosmetic research. Keratin intermediate filaments structure is not fully described, limiting the molecular dynamics (MD) studies in this field although its high potential to improve the area. We developed a computational model of a truncated protofibril, simulated its behavior in alcoholic based formulations and with one peptide. The simulations showed a strong interaction between the benzyl alcohol molecules of the formulations and the model, leading to the disorganization of the keratin chains, which regress with the removal of the alcohol molecules. This behavior can explain the increase of peptide uptake in hair shafts evidenced in fluorescence microscopy pictures. The model developed is valid to computationally reproduce the interaction between hair and alcoholic formulations and provide a robust base for new MD studies about hair properties. It is shown that the MD simulations can improve hair cosmetic research, improving the uptake of a compound of interest.

Nonparametric estimation of the survival function for ordered multivariate failure time data: a comparative study

In longitudinal studies of disease, patients may experience several events through a follow-up period. In these studies, the sequentially ordered events are often of interest and lead to problems that have received much attention recently. Issues of interest include the estimation of bivariate survival, marginal distributions and the conditional distribution of gap times. In this work we consider the estimation of the survival function conditional to a previous event. Different nonparametric approaches will be considered for estimating these quantities, all based on the Kaplan-Meier estimator of the survival function. We explore the finite sample behavior of the estimators through simulations. The different methods proposed in this article are applied to a data set from a German Breast Cancer Study. The methods are used to obtain predictors for the conditional survival probabilities as well as to study the influence of recurrence in overall survival.

RNA-seq analysis of the Quercus suber root response to drought

Dissertação de mestrado em Plant Molecular Biology, Biotechnology and Bioentrepreneurship

Cognitive functioning and efficacy of cognitive intervention in Multiple Sclerosis

Tese de Doutoramento em Psicologia Clínica / Psicologia

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.

Estimation of statistical cure from cancer using population-based data

Dissertação de mestrado em Estatística

Characterization of microbial populations in a wastewater treatment plant focusing on Staphylococcus aureus and Pseudomonas aeruginosa

Dissertação de mestrado em Bioengenharia