Social networks have provided the means for constant connectivity and fast information dissemination. In addition, real-time posting allows a new form of citizen journalism, where users can report events from a witness perspective. Therefore, information propagates through the network at a faster pace than traditional media reports it. However, relevant information is a small percentage of all the content shared. Our goal is to develop and evaluate models that can automatically detect journalistic relevance. To do it, we need solid and reliable ground truth data with a significantly large quantity of annotated posts, so that the models can learn to detect relevance over all the spectrum. In this article, we present and confront two different methodologies: an automatic and a human approach. Results on a test data set labelled by experts' show that the models trained with automatic methodology tend to perform better in contrast to the ones trained using human annotated data.

In recent years, the increase of average waiting times in waiting lists is an issue that has been felt in health institutions. Thus, the implementation of new administrative measures to improve the management of these organizations may be required. Hereupon, the aim of this present work is to support the decision-making process in appointments and surgeries waiting lists in a hospital located in the north of Portugal, through a pervasive Business Intelligence platform that can be accessed anywhere and anytime by any device connected within the hospital's private network. By representing information that facilitate the analysis of information and knowledge extraction, the Web tool allows the identification in real-time of average waiting times outside the outlined patterns. Thereby, the developed platform permits their identification, enabling their further understanding in order to take the necessary measures. Thus, the main purpose is to enable the reduction of average waiting times through the analysis of information in order to, subsequently, ensure the satisfaction of patients.

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This work describes the development of a capacitive-type sensor created from nanoporous anodic aluminium oxide (NP-AAO) prepared by the one-step anodization method conducted in potentiostatic mode and performed in a low-cost homemade system. A series of samples were prepared via an anodization campaign carried out on different acid electrolytes, in which the anodization parameters were adjusted to investigate the effect of pore size and porosity on the capacitive sensing performance. Two sensor test cases are investigated. The first case explores the use of highly uniform NP-AAO structures for humidity sensing applications while the second analyses the use of NP-AAO as a capacitive touch sensor for biological applications, namely, to detect the presence of small “objects” such as bacterial colonies of Escherichia Coli. A mathematical model based on equivalent electrical circuits was developed to evaluate the effect of humidity condensation (inside the pores) on the sensor capacitance and also to estimate the capacitance change of the sensor due to pore blocking by the presence of a certain number of bacterial microorganisms. Regarding the humidity sensing test cases, it was found that the sensitivity of the sensor fabricated in a phosphoric acid solution reaches up to 39 (pF/RH%), which is almost three times higher than the sensor fabricated in oxalic acid and about eight times higher than the sensor fabricated in sulfuric acid. Its improved sensitivity is explained in terms of the pore size effect on the mean free path and the loss of Brownian energy of the water vapour molecules. Concerning the touch sensing test case, it is demonstrated that the NP-AAO structures can be used as capacitive touch sensors because the magnitude of the capacitance change directly depends on the number of bacteria that cover the nanopores; the fraction of the electrode area activated by bacterial pore blocking is about 4.4% and 30.2% for B1 (E. Coli OD600nm = 0.1) and B2 (E. Coli OD600nm = 1) sensors, respectively.

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