Abstract
One of the major changes currently involving distribution network systems (DNSs) is the ever-increasing integration of renewable-based distributed generation (DG), wind and solar PV types in particular. This is dramatically influencing the planning and operation of distribution systems, in general. The traditional "fit-and-forget" approach is outdated. Current developments in the DNS would require new, efficient and robust planning and operation tools to support smooth integration of such DGs. The present work focuses on an optimal integration of renewable-based DGs with reactive power support capabilities. Accordingly, a stochastic mixed integer linear programming (S-MILP) model is developed that takes into account the optimal integration of RES-based DGs and reactive power sources. The developed model is tested using a standard IEEE distribution system. Test results show that integrating DGs with reactive power support capability significantly enhances voltage stability and improves the overall cost in the system. Simulation results show that setting the reactive power support capability of the RES-based DGs from 0.95 leading to 0.95 lagging leads to the maximum penetration level of wind and solar PV power in the system.

Abstract
Optical immersion clearing is a technique that has been widely studied for more than two decades and that is used to originate a temporary transparency effect in biological tissues. If applied in cooperation with clinical methods it provides optimization of diagnosis and treatment procedures. This technique turns biological tissues more transparent through two main mechanisms - tissue dehydration and refractive index (RI) matching between tissue components. Such matching is obtained by partial replacement of interstitial water by a biocompatible agent that presents higher RI and it can be completely reversible by natural rehydration in vivo or by assisted rehydration in ex vivo tissues. Experimental data to characterize and discriminate between the two mechanisms and to find new ones are necessary. Using a simple method, based on collimated transmittance and thickness measurements made from muscle samples under treatment, we have estimated the diffusion properties of glucose, ethylene glycol (EG) and water that were used to perform such characterization and discrimination. Comparing these properties with data from literature that characterize their diffusion in water we have observed that muscle cell membrane permeability limits agent and water diffusion in the muscle. The same experimental data has allowed to calculate the optical clearing (OC) efficiency and make an interpretation of the internal changes that occurred in muscle during the treatments. The same methodology can now be used to perform similar studies with other agents and in other tissues in order to solve engineering problems at design of inexpensive and robust technologies for a considerable improvement of optical tomographic techniques with better contrast and in-depth imaging.

Abstract
Nowadays, hydraulic sources are responsible for most of the Brazil's energy production. Hydroelectric power plants (HPP) operators in Brazil usually distribute equally the total power required among the generator units available in the plant. However, studies show that this configuration does not guarantee that each generator unit operate close to its optimal operation point. The energy dispatch optimization problem consists in determining which generation units need to be on or off and what is their respective power-set, so that both the overall HPP costs is minimized and the power required by the plant is met. This paper presents a GPU-based parallel implementation of NSGA-II, to solve the energy dispatch problem of a HPP complaying with the real time restrictions posed by the operation of a real HPP from the reception of the power demand to the energy dispatch. Our implementation obtains better solutions than the sequential implementation currently available.

Abstract
This paper focuses on dynamic performance control of modular multilevel converters (MMC) in high-voltage direct current (HVDC) transmission systems. To achieve this objective, a new mathematical model including six state variables of ac-currents and dc-link voltage of MMC, and circulating currents of converter arms are proposed for MMC in d-q reference frame. In addition, a robust control technique with three sub-control loops is designed to provide the stable operation of MMC. In the overall structure of the proposed controller, three outer, central and inner loops have the duties of 1) making the state variables error zero with changeable convergence rate, 2) adding robustness characteristic to the proposed controller, and 3) generating the appropriate reference values for MMC's currents, respectively. The effectiveness of the proposed control algorithm is investigated via MATLAB simulation. The simulation results highlight the capability of the proposed control algorithm in offering an accurate active and reactive power tracking through the control method of MMC, a stabilized dc-link voltage, capacitor voltage balancing of sub-modules, and minimization of circulating currents of converter arms during dynamic transitions and steady state operation.

Abstract
A traffic incident is defined by an event which provokes a disruption on the normal (free) flow condition of any highway. Such incidents must be caused by a recurrent excessive demand or, in alternative, by a series of possible stochastic occurrences which may suddenly reduce the road capacity (e.g. car accidents, extreme weather changes). This paper proposes a novel binary supervised learning method to classify congestion predictions regarding their causes - CJAMmer. It leverages on heterogeneous and ubiquitous data sources - such as weather, flow counts and traffic incident event logs -To generalize decision models able to understand the road congestion nature. CJAMmer settles on boosted decision trees using the well-known C4.5, as well as a straightforward feature generation process. A real world experiment was used to compare this method against other state-of-The-Art classifiers. The results uncovered the high potential impact of this methodology on industrial scale traffic control systems. © 2016 IEEE.

Abstract
Physical maps of a city or region are important pieces of geographical information for tourists and local citizens. Unfortunately the amount of information that can be presented on a piece of paper is limited. In order to extend the map information we propose an augmented reality (AR) system, ARTourMap, for additional information visualization and interaction. This system provides an abstraction layer to develop applications based on the concept of separated logic map tiles taking advantage of a multi-target system where several regions of the map trigger different superimposed graphics. This allows the map to be folded, to be partially occluded, and to have dematerialized information. To demonstrate the proposed system ARTourMap, three layers were developed: a location-based game with points of interest (POIs), a 3D building visualization and an historical map layer.