Ultrasonic sensor signals and optimum path forest classifier for the microstructural characterization of thermally-aged inconel 625 alloy

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Qualitative methodogy: revealing a new path for research in dentistry

Currently, it is easy to find health professionals who not only attach importance to qualitative methods, but also recognize their help to better understand their patients' lives. However, its use in dentistry is still incipient, either due to ignorance or because of technical / operational difficulties in identifying possibilities for their use in research. Thus, the purpose of this study was to review the literature on the characteristics and peculiarities of the qualitative methodology, demonstrating their techniques of collecting, recording and analyzing data. For this, we performed a descriptive literature, from a survey in the "LILACS", "BBO" and "PUBMED" databases, by keywords related to the theme, selecting only the papers that mentioned the "importance" of qualitative research, the "characteristics and fundamentals," and the "techniques of collecting, recording and data analysis" involving this methodology. It was found that all studies have highlighted the importance of qualitative research to the construction of new knowledge that cannot be achieved by quantitative data. We found many different techniques to gather, record and analyze qualitative data applied to the dentistry field. It was concluded that qualitative research represents a new path to be followed by dentistry, so that we are able to plan actions in ethical and humane public health dentistry, bringing better results to the population, because of the depth of knowledge that your date can.

Metamorphic evolution and P-T path of gneisses from Goiás Magmatic Arc in southwestern Goiás State

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A comparison about evolutionary algorithms for optimum-path forest clustering optimization

In this paper we deal with the problem of boosting the Optimum-Path Forest (OPF) clustering approach using evolutionary-based optimization techniques. As the OPF classifier performs an exhaustive search to find out the size of sample's neighborhood that allows it to reach the minimum graph cut as a quality measure, we compared several optimization techniques that can obtain close graph cut values to the ones obtained by brute force. Experiments in two public datasets in the context of unsupervised network intrusion detection have showed the evolutionary optimization techniques can find suitable values for the neighborhood faster than the exhaustive search. Additionally, we have showed that it is not necessary to employ many agents for such task, since the neighborhood size is defined by discrete values, with constrain the set of possible solution to a few ones.