Microstructural evolution of Au/TiO2 nanocomposite films: The influence of Au concentration and thermal annealing

Nanocomposite thin films consisting of a dielectric matrix, such as titanium oxide (TiO2), with embedded gold (Au) nanoparticles were prepared and will be analysed and discussed in detail in the present work. The evolution of morphological and structural features was studied for a wide range of Au concentrations and for annealing treatments in air, for temperatures ranging from 200 to 800 °C. Major findings revealed that for low Au atomic concentrations (at.%), there are only traces of clustering, and just for relatively high annealing temperatures, T ≥ 500 °C. Furthermore, the number of Au nanoparticles is extremely low, even for the highest annealing temperature, T = 800 °C. It is noteworthy that the TiO2 matrix also crystallizes in the anatase phase for annealing temperatures above 300 °C. For intermediate Au contents (5 at.% ≤ CAu ≤ 15 at.%), the formation of gold nanoclusters was much more evident, beginning at lower annealing temperatures (T ≥ 200 °C) with sizes ranging from 2 to 25 nm as the temperature increased. A change in the matrix crystallization from anatase to rutile was also observed in this intermediate range of compositions. For the highest Au concentrations (> 20 at.%), the films tended to form relatively larger clusters, with sizes above 20 nm (for T ≥ 400 °C). It is demonstrated that the structural and morphological characteristics of the films are strongly affected by the annealing temperature, as well as by the particular amounts, size and distribution of the Au nanoparticles dispersed in the TiO2 matrix.

Transmission and progression to disease of Mycobacterium tuberculosis phylogenetic lineages in The Netherlands

The aim of this study was to determine if mycobacterial lineages affect infection risk, clustering, and disease progression among Mycobacterium tuberculosis cases in The Netherlands. Multivariate negative binomial regression models adjusted for patient-related factors and stratified by patient ethnicity were used to determine the association between phylogenetic lineages and infectivity (mean number of positive contacts around each patient) and clustering (as defined by number of secondary cases within 2 years after diagnosis of an index case sharing the same fingerprint) indices. An estimate of progression to disease by each risk factor was calculated as a bootstrapped risk ratio of the clustering index by the infectivity index. Compared to the Euro-American reference, Mycobacterium africanum showed significantly lower infectivity and clustering indices in the foreign-born population, while Mycobacterium bovis showed significantly lower infectivity and clustering indices in the native population. Significantly lower infectivity was also observed for the East African Indian lineage in the foreign-born population. Smear positivity was a significant risk factor for increased infectivity and increased clustering. Estimates of progression to disease were significantly associated with age, sputum-smear status, and behavioral risk factors, such as alcohol and intravenous drug abuse, but not with phylogenetic lineages. In conclusion, we found evidence of a bacteriological factor influencing indicators of a strain's transmissibility, namely, a decreased ability to infect and a lower clustering index in ancient phylogenetic lineages compared to their modern counterparts. Confirmation of these findings via follow-up studies using tuberculin skin test conversion data should have important implications on M. tuberculosis control efforts.

Sistema de apoio à decisão clínica: quality of life system

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Comparison of single and multi-objective evolutionary algorithms for robust link-state routing

Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.

Clustering-based approach for categorizing pregnant women in obstetrics and maternity care

When a pregnant woman is guided to a hospital for obstetrics purposes, many outcomes are possible, depending on her current conditions. An improved understanding of these conditions could provide a more direct medical approach by categorizing the different types of patients, enabling a faster response to risk situations, and therefore increasing the quality of services. In this case study, the characteristics of the patients admitted in the maternity care unit of Centro Hospitalar of Porto are acknowledged, allowing categorizing the patient women through clustering techniques. The main goal is to predict the patients’ route through the maternity care, adapting the services according to their conditions, providing the best clinical decisions and a cost-effective treatment to patients. The models developed presented very interesting results, being the best clustering evaluation index: 0.65. The evaluation of the clustering algorithms proved the viability of using clustering based data mining models to characterize pregnant patients, identifying which conditions can be used as an alert to prevent the occurrence of medical complications.