Abstract
This paper presents a proposal for the use of the Hybrid Fuzzy Inference System algorithm (HyFIS) as solar intensity forecast mechanism. Fuzzy Inference Systems (FIS) are used to solve regression problems in various contexts. The HyFIS is a method based on FIS with the particular advantage of combining fuzzy concepts with Artificial Neural Networks (ANN), thus optimizing the learning process. This algorithm is part of several other FIS algorithms implemented in the Fuzzy Rule-Based Systems (FRBS) package of R. The ANN algorithms and Support Vector Machine (SVM), both widely used for solving regression problems, are also used in this study to allow the comparison of results. Results show that HyFIS presents higher performance when compared to the ANN and SVM, when applied to real data of Florianopolis, Brazil, which helps to reinforce the potential of this algorithm in solving the solar intensity forecasting problems. © 2016 IEEE.

Abstract
In this study an application to assist people with speech impairments in their speech therapy sessions is presented. AppVox simulates a vocalizer (audio stimulus feature) that can be used to train speech by repeating different words. In this paper, we aim at presenting the application as an assistive technology option and assess if this is a usable option for digital interaction for children with speech impairment. To assess the application we present a case study in which the participants were asked to perform tasks using the AppVox application. The results showed that this group of participants attained a good performance when interacting with the application.

Abstract
Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk1-4. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants1,5. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles1,6,7, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues8-11, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release12. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.

Abstract
Ethionamide (ETH) is an important second-line antituberculosis drug used for the treatment of patients infected with multidrug-resistant Mycobacterium tuberculosis. Recently, we reported that the loading of ETH into thermally carbonized-porous silicon (TCPSi) nanoparticles enhanced the solubility and permeability of ETH at different pH-values and also increased its metabolization process. Based on these results, we synthesized carboxylic acid functionalized thermally hydrocarbonized porous silicon nanoparticles (UnTHCPSi NPs) conjugated with ETH and its antimicrobial effect was evaluated against Mycobacterium tuberculosis strain H37Rv. The activity of the conjugate was increased when compared to free-ETH, which suggests that the nature of the synergy between the NPs and ETH is likely due to the weakening of the bacterial cell wall that improves conjugate-penetration. These ETH-conjugated NPs have great potential in reducing dosing frequency of ETH in the treatment of multidrug-resistant tuberculosis (MDR-TB).

Abstract
Ensemble approaches have revealed remarkable abilities to tackle different learning challenges, namely in dynamic scenarios with concept drift, e.g. in social networks, as Twitter. Several efforts have been engaged in defining strategies to combine the models that constitute an ensemble. In this work, we investigate the effect of using different metrics for combining ensembles' models, specifically performance-based metrics. We propose five performance combining metrics, having in mind that we may take advantage of diversity in classifiers, as their individual performance takes a leading role in defining their contribution to the ensemble. Experimental results on a Twitter dataset, artificially timestamped, suggest that using performance metrics to combine the models that constitute an ensemble can introduce relevant improvements in the overall ensemble performance.

Abstract
Traffic sampling is viewed as a prominent strategy contributing to lightweight and scalable network measurements. Although multiple sampling techniques have been proposed and used to assist network engineering tasks, these techniques tend to address a single measurement purpose, without detailing the network overhead and computational costs involved. The lack of a modular approach when defining the components of traffic sampling techniques also makes difficult their analysis. Providing a modular view of sampling techniques and classifying their characteristics is, therefore, an important step to enlarge the sampling scope, improve the efficiency of measurement systems, and sustain forthcoming research in the area. Thus, this paper defines a taxonomy of traffic sampling techniques resorting to a comprehensive analysis of the inner components of existing proposals. After identifying granularity, selection scheme, and selection trigger as the main components differentiating sampling proposals, the study goes deeper on characterizing these components, including insights into their computational weight. Following this taxonomy, a general-purpose architecture is established to sustain the development of flexible sampling-based measurement systems. Traveling inside packet sampling techniques, this paper contributes to a clearer positioning and comparison of existing proposals, providing a road map to assist further research and deployments in the area. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.