Abstract
This paper proposes the recently introduced Gravitational Search Algorithm (GSA) to tune a Sliding Mode Controller (SMC) applied on the temperature control of a grains drying system. The problem of maintaining the temperature precisely adjusted inside a silo is relevant to avoid thermal damage and spoilage losses, and thus guarantee the right conditions for storage. The objectives of setpoint tracking and disturbance rejection are incorporated into the minimization of the integral of the time-weighted absolute error. Simulation results are presented and compared with PID and with SMC tuned by Particle SwarmOptimization (PSO) and by earlier proposed tuning equations. © Springer International Publishing Switzerland 2017.

Abstract
This letter is an enhancement to our previous paper that quantifies additional reinforcement costs (ARCs) for low-voltage assets under moderate degree of three-phase imbalance. The original formulas cause an overestimation of the ARCs under severe imbalance. This letter first quantifies the threshold of the severe degree of imbalance (DIB), below which the original formulas are applicable. Then, the ARC formulas are extended to account for the whole range of DIB. Case studies demonstrate that when the asset loading level is below 33.3% (50%) for a feeder (a transformer), the DIB never exceeds the threshold and the original ARC formulas are applicable; otherwise, the DIB can exceed the threshold and the extended formulas yield correct ARCs.

Abstract
In this work we explore the dissimilarity between symmetric word pairs, by comparing the inter-word distance distribution of a word to that of its reversed complement. We propose a new measure of dissimilarity between such distributions. Since symmetric pairs with different patterns could point to evolutionary features, we search for the pairs with the most dissimilar behaviour. We focus our study on the complete human genome and its repeat-masked version. © Springer International Publishing AG 2017.

Abstract

Abstract
Using the finite element method (FEM), this paper presents a numerical investigation of the performance analysis of a D-type photonic crystal fiber (D-type PCF) for refractive index sensing, based on surface plasmon resonance (SPR) with a planar structure made out of a metamaterial. COMSOL Multiphysics was used to evaluate the design of the referred refractive index optical fiber sensor, with higher accuracy and considerable economy of time and resources. A study of different metamaterials concentrations conformed by aluminum oxide (Al2O3) and silver (Ag) is carried out. Another structural parameters, which influences the refractive index sensor performance, the thickness of the metamaterial, is also investigated. The results indicate that the use of metamaterials provides a way of improving the performance of SPR sensors on optical fibers and allows to tailor the working parameters of the sensor.

Abstract
Near-future electric distribution grids operation will have to rely on demand-side flexibility, both by implementation of demand response strategies and by taking advantage of the intelligent management of increasingly common small-scale energy storage. The Home energy management system (HEMS), installed at low voltage residential clients, will play a crucial role on the flexibility provision to both system operators and market players like aggregators. Modeling and forecasting multi-period flexibility from residential prosumers, such as battery storage and electric water heater, while complying with internal constraints (comfort levels, data privacy) and uncertainty is a complex task. This papers describes a computational method that is capable of efficiently learn and define the feasibility flexibility space from controllable resources connected to a HEMS. An Evolutionary Particle Swarm Optimization (EPSO) algorithm is adopted and reshaped to derive a set of feasible temporal trajectories for the residential net-load, considering storage, flexible appliances, and predefined costumer preferences, as well as load and photovoltaic (PV) forecast uncertainty. A support vector data description (SVDD) algorithm is used to build models capable of classifying feasible and non-feasible HEMS operating trajectories upon request from an optimization/control algorithm operated by a DSO or market player.