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.

Abstract
Nature and biologically inspired metaheuristics can be powerful tools to design PID controllers. The grey wolf optimization is one of these promising and interesting metaheuristics, recently introduced. In this study the grey wolf optimization algorithm is proposed to design PID controllers, and the results obtained compared with the ones obtained with gravitational search and particle swarm optimization algorithms. Simulation results obtained with these three bio-inspired metaheuristics applied to a set of benchmark linear plants are presented, considering the design objective of set-point tracking. The results are also compared with two non-iterative PID tuning techniques. © Springer International Publishing Switzerland 2017.

Abstract
Massive changes in electricity markets have occurred during the last years, as a consequence of the massive introduction of renewable energies. These changes have led to a restructuring process that had an impact throughout the electrical industry. The case of the electricity markets is a relevant example, where new forms of trading emerged and new market entities were created. With these changes, the complexity of electricity markets increased as well, which brought out the need from the involved players for adequate support to their decision making process. Artificial intelligence plays an important role in the development of these tools. Multi-agent systems, in particular, have been largely explored by stakeholders in the sector. Artificial intelligence also provides intelligent solutions for optimization, which enable troubleshooting in a short time and with very similar results to those achieved by deterministic techniques, which usually result from very high execution times. The work presented in this paper aims at solving a portfolio optimization problem for electricity markets participation, using an approach based on NPSO-LRS (New Particle Swarm Optimization with Local Random Search). The proposed method is used to assist decisions of electricity market players. © 2016 IEEE.

Abstract
A highly non-linear solid core photonic crystal fiber (SCPCF) is designed in the present work. Three hexagonal air hole rings in cladding region and four very small air holes are present in a symmetric manner in the core region. By using full vectorial finite element method (FVFEM) with the perfectly matched layer, we study numerically the effective area of modal pattern as well as the nonlinear coefficient of this proposed SCPCF. For this proposed fiber a small modal effective area 5.58 µm2 and a high nonlinear coefficient 21.38 W-1 km-1 are obtained at communication wavelength 1.55 µm for the small air holes in the core with diameter 0.15 µm. This type of SCPCF is useful for different nonlinear applications. © Springer Nature Singapore Pte Ltd. 2017.