Abstract
In this paper a Distribution State Estimator (DSE) tool suitable for real-time monitoring in poorly characterized low voltage networks is presented. An Autoencoder (AE) properly trained with Extreme Learning Machine (ELM) technique is the "brain" of the DSE. The estimation of system state variables, i.e., voltage magnitudes and phase angles is performed with an Evolutionary Particle Swarm Optimization (EPSO) algorithm that makes use of the already trained AE. By taking advantage of historical data and a very limited number of quasi real-time measurements, the presented approach turns possible monitoring networks where information of topology and parameters is not available. Results show improvements in terms of estimation accuracy and time performance when compared to other similar DSE tools that make use of the traditional back-propagation based algorithms for training execution.

Abstract
We analyse a model of immune response by T cells (CD4), where regulatory T cells (Tregs) act by inhibiting IL-2 secretion. We introduced an asymmetry reflecting that the difference between the growth and death rates can be higher for the active T cells and the active Tregs than for the inactive T cells and inactive Tregs. This asymmetry mimics the presence of memory T cells. In this paper we start by analysing the model in the absence of Tregs. We obtain an explicit formula that gives approximately the antigenic stimulation of T cells from the concentration of Tregs. Afterwards, we present an explicit formula that describes approximately the balance between the concentration of T cells and the concentration of Tregs; and an explicit formula that relates approximately the antigenic stimulation of T cells, the concentration of T cells and the concentration of Tregs. For our parameter values, the relation between the antigenic stimulation of T cells and the concentration of T cells is an hysteresis that is unfold when some of the parameters are changed. We also consider a linear tuning between the antigenic stimulation of T cells and the antigenic stimulation of Tregs. Again, we have obtained an explicit formula relating approximately the antigenic stimulation of T cells, the concentration of T cells and the concentration of Tregs. With it, we can explain the appearance of an isola and a transcritical bifurcation. © 2015 NSP Natural Sciences Publishing Cor.

Abstract
The smart grid concept increases the observability and controllability of the distribution system, which creates conditions for bi-directional control of Distributed Energy Resources (DER). The high penetration of Renewable Energy Resources (RES) in the distribution grid may create technical problems (e.g., voltage problems, branch congestion) in both transmission and distribution systems. The flexibility from DER can be explored to minimize RES curtailment and increase its hosting capacity. This paper explores the use of the Monte Carlo Simulation to estimate the flexibility range of active and reactive power at the boundary nodes between transmission and distribution systems, considering the available flexibility at the distribution grid level (e.g., demand response, on-load tap changer transformers). The obtained results suggest the formulation of an optimization problem in order to overcome the limitations of the Monte Carlo Simulation, increasing the capability to find extreme points of the flexibility map and reducing the computational effort.

Abstract
This paper presents an enhanced simulation platform composed by the integration of two distinct multi-agent based simulators. The two simulators are: (i) the Multi-Agent Simulator of Competitive Electricity Markets (MASCEM), which provides a simulation platform for electricity markets participation, considering scenarios based on real data from several distinct market operators; and (ii) the Multi-Agent Smart Grid Platform (MASGriP), which facilitates the simulation of smart grids and microgrids, by modeling the power network at the distribution level, and representing the main entities that act in this scope. With the cooperation between the two simulation platforms, huge studying opportunities under different perspectives are provided, resulting in an important contribution in the fields of transactive energy, electricity markets, and smart grids. A case study is presented, showing the potentialities for interaction between players of the two ecosystems, namely by demonstrating a case in which a smart grid operator, which manages the internal resources of a smart grid, is able to participate in electricity market negotiations to sell the surplus of generation in some periods of the day, and buy the necessary power to satisfy the demand of smart grid consumers in other periods of the day. © 2015 IEEE.

Abstract
Fast volumetric cardiac imaging requires to reduce the number of transmit events within a single volume. One way of achieving this is by limiting the field-of-view (FOV) of the recording to the anatomically relevant domain only (e.g. the myocardium when investigating cardiac mechanics). Although fully automatic solutions towards myocardial segmentation exist, translating that information in a fast ultrasound scan sequence is not trivial. The aim of this study was therefore to develop a methodology to automatically define the FOV from a volumetric dataset in the context of anatomical scanning. Hereto, a method is proposed where the anatomical relevant space is automatically identified as follows. First, the left ventricular myocardium is localized in the volumetric ultrasound recording using a fully automatic real-time segmentation framework (i.e. BEAS). Then, the extracted meshes are employed to define a binary mask identifying myocardial voxels only. Later, using these binary images, the percentage of pixels along a given image line that belong to the myocardium is calculated. Finally, a spatially continuous FOV that covers 'T' percentage of the myocardium is found by means of a ring-shaped template matching, giving as a result the opening angle and 'thickness' for a conical scan. This approach was tested on 27 volumetric ultrasound datasets, a T = 85% was used. The mean initial opening angle for a conical scan was of 19.67±8.53° while the mean 'thickness' of the cone was 19.01±3.35°. Therefore, a reduction of 48.99% in the number of transmit events was achieved, resulting in a frame rate gain factor of 1.96. As a conclusion, anatomical scanning in combination with new scanning sequences techniques can increase frame rate significantly while keeping information of the relevant structures for functional imaging. © 2015 IEEE.

Abstract
In this paper, an indirect approach to the dual-mode adaptive robust controller (DMARC) is proposed, which combines the typical transient and robustness properties of variable structure systems with a smooth control signal in steady state, typical of conventional adaptive controllers, as model reference adaptive controller. The aim of this indirect version, here named indirect DMARC, is to provide a more intuitive controller design, based on physical plant parameters, as resistances, inertia moments, capacitances, and so on, maintaining DMARC properties. In this paper, a stability analysis for the proposed controller and simulations to an unstable second-order plant will be presented. Copyright (c) 2013 John Wiley & Sons, Ltd.