Quantifying, generating and mitigating radio interference in Low-Power Wireless Networks

Doctoral Programme in Telecommunication - MAP-tele

Listening to the public: enacting power : citizen access, Standing and influence in public participation discourses

Public participation in environmental governance is typically associated with citizen access to power despite many closures and limitations having been identified in participatory processes. This article proposes an analytical framework to analyse discursive practices involved in public consultation processes. Critical Discourse Analysis is used to examine and appraise citizens’ access, standing and influence. We apply that framework to a ‘notice and comment’ process on a hydroelectric power plan in Portugal and show that it was discursively managed to justify the decision of constructing 10 large dams and to reject critical or alternative views. Citizens’ access, standing and influence were constrained through diverse discursive practices which (re)produced very unequal power relationsbetween policy proponents and participating individuals. More generally, the article illustrates the potential of Critical Discourse Analysis to assess voice(s) in policy processes. Focusing on argumentative, interactional and rhetorical levels, and how they are interwoven in public consultation discourses, the proposed framework is conceivably applicable in other studies.

An R package for the integrated analysis of metabolomics and spectral data

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.