Abstract
This paper provides a new approach in decision making process for shunt capacitor placement in distribution networks. The main core of the evaluation process is a multi-objective framework to allocate the capacitor banks. The power loss and the total harmonic distortion (THD) are the objective functions of the system under study in a long-term planning horizon. In order to select the executive plan introduced by using a multi-objective model, transient switching overvoltages have been considered. As the size and location of shunt capacitors may result in unacceptable overvoltages, the proposed technical decision making framework can be applied to avoid corresponding damages. In this paper, an iterative conventional power flow technique is introduced. This technique can be applied to evaluate THD for distribution networks as well as other power flow based objectives, such as power losses calculation and voltage stability assessment. The presented framework is a two stage one where at the first stage, a non-dominated sorting genetic algorithm (NSGA-II) augmented with a local search technique is used in order to solve the addressed multi-objective optimization problem. Then, at the second stage, a decision making support technique is applied to determine the best solution from the obtained Pareto front. In order to evaluate the effectiveness of the proposed method, two benchmarks are addressed in this paper. The first test system is a 9-bus distribution network and the second one is an 85-bus large scale distribution network. The simulation results show that the presented method is satisfactory and consistent with the expectation.

Abstract

Abstract
Fusion splicing technique was explored for the fabrication of two sensing structures based on hollow microsphere Fabry-Perot cavity. The first sensor proposed was fabricated with a hollow microsphere tip, working as a probe sensor. This structure was studied for lateral load pressure, yielding a 1.56 +/- 0.01 nm/N sensitivity. The second sensing structure relied on an in-line hollow microsphere, which allowed the detection of lateral load, with a sensitivity of 2.62 +/- 0.02 nm/N. Furthermore, the proposed structure enabled strain sensing, with a sensitivity of 4.66 +/- 0.03 pm/mu epsilon. The two sensing structures were subjected to temperature, presenting low thermal cross-sensitivity.

Abstract
The prevalence of data science has grown exponentially in recent years. Increases in data exchange have created the need for standards and formats on handling data from different sources. Developing Metadata Application Profiles is an innovative reference source that discusses the latest trends and techniques for effectively managing and exchanging metadata. Including a range of perspectives on schemas and application profiles, such as interoperability, ontology-based design, and model-driven approaches, this book is ideally designed for researchers, academics, professionals, graduate students, and practitioners actively engaged in data science.

Abstract
This paper analyzes the impact on market prices of the policies that have been adopted in Brazil to foster electricity from renewable energy sources (RES-E), namely wind power. In recent years the Brazilian Government implemented a series of policies that enabled a strong growth of RES-E. Recently more than 14 GW of wind and solar power were contracted. However, as most of the assets are concentrated in specific regions, these policies will induce price differences among areas of the country. In this scope, this paper describes a System Dynamics based model of the Brazilian generation system to evaluate the impact on prices from the deployment of these new sources. The paper describes simulations using realistic data for the Brazilian power system and the results suggest that the difference of prices in the country tend to increase since the Northeast region of the country concentrates most of the wind parks.

Abstract
This paper proposes an ensemble method for time series forecasting tasks. Combining different forecasting models is a common approach to tackle these problems. State-of-the-art methods track the loss of the available models and adapt their weights accordingly. Metalearning strategies such as stacking are also used in these tasks. We propose a metalearning approach for adaptively combining forecasting models that specializes them across the time series. Our assumption is that different forecasting models have different areas of expertise and a varying relative performance. Moreover, many time series show recurring structures due to factors such as seasonality. Therefore, the ability of a method to deal with changes in relative performance of models as well as recurrent changes in the data distribution can be very useful in dynamic environments. Our approach is based on an ensemble of heterogeneous forecasters, arbitrated by a metalearning model. This strategy is designed to cope with the different dynamics of time series and quickly adapt the ensemble to regime changes. We validate our proposal using time series from several real world domains. Empirical results show the competitiveness of the method in comparison to state-of-the-art approaches for combining forecasters.