3 resultados para r codes
em Universidade do Minho
Resumo:
According to the latest census (2011) covering the Portuguese housing stock, there is a considerable number of sub-occupied buildings and most of them were built before the first Portuguese Thermal Code (RCCTE). In view of the objectives of the European Union (20-20-20), it is necessary to contribute to the rehabilitation of existing buildings by promoting the densification of oversized houses, while complying with up-to-date comfort standards and codes. In this sense, this research is aimed to analyse this problem at the outskirts of Braga, by identifying the type-morphology of oversized buildings, assessing the degree of satisfaction and expectations of both owners and inhabitants and developing and testing a systematic design tool. Through the development of an intervention kit, wich systematizes and priorizes design strategies and solutions, the design team can have the necessary decision-making support tool to improve the spacial design of the building whilst considering energy efficency, sustainability and improvemente of interior environmental quality.
Resumo:
Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.
Resumo:
Doctoral thesis in Marketing and Strategy.