Abstract

Abstract
The main goal of the ANTAREX 1 project is to express by a Domain Specific Language (DSL) the application self-adaptivity and to runtime manage and autotune applications for green and heterogeneous High Performance Computing (HPC) systems up to the Exascale level. Key innovations of the project include the introduction of a separation of concerns between self-adaptivity strategies and application functionalities. The DSL approach will allow the definition of energy-efficiency, performance, and adaptivity strategies as well as their enforcement at runtime through application autotuning and resource and power management.

Abstract
Spreadsheets are nowadays used in a variety of contexts, including in in manipulatin large and complex data. This data is stored in a large unstructured matrix, which is hard to understand and to manipulate. Recent research has been done to manipulate and query such unstructured data, namely by proposing different query approaches to spreadsheets. In this paper we present an empirical study evaluating three recent query approaches to spreadsheets assessing their usage to query spreadsheets. The results of our study show that the end-users' productivity increases when using visual, model-driven queries are used. © 2016 ACM.

Abstract
The uncertainty and variability in electricity market price (EMP) signals and players' behavior, as well as in renewable power generation, especially wind power, pose considerable challenges. Hence, enhancement of forecasting approaches is required for all electricity market players to deal with the non-stationary and stochastic nature of such time series, making it possible to accurately support their decisions in a competitive environment with lower forecasting error and with an acceptable computational time. As previously published methodologies have shown, hybrid approaches are good candidates to overcome most of the previous concerns about time-series forecasting. In this sense, this paper proposes an enhanced hybrid approach composed of an innovative combination of wavelet transform (WT), differential evolutionary particle swarm optimization (DEEPSO), and an adaptive neuro-fuzzy inference system (ANFIS) to forecast EMP signals in different electricity markets and wind power in Portugal, in the short-term, considering only historical data. Test results are provided by comparing with other reported studies, demonstrating the proficiency of the proposed hybrid approach in a real environment.

Abstract
Generally, the more Renewable Energy Sources (RES) in generation mix the more complex is the problem of reliability assessment of generating systems, mainly because of the variability and uncertainty of generating capacity. These short-term concerns have been seen as a way of controlling the amount of spinning reserve, providing operators with information on operation system risks. For the medium and long-term assessment, such short-term concerns should be accounted for the system performance [1,2], assuring that investment options will result in more robust and flexible generating configurations that are consequently more secure. In order to deal with the spinning reserve needs, this work proposes the use of a risk based technique, Value-at-Risk and Conditional Value at-Risk, to assist the planners of the Electric Power Systems (EPS) as regards the design of the flexibility of generating systems. This methodology was applied in the IEEE-RTS-96 HW producing adequate results.

Abstract
This study aimed to assess the technical efficiency (TE) of individual companies and their respective sectors that are traded on the Portuguese stock market. We accomplished this by combining the internal input variables (e.g., market value and return') with exogenous variables (e.g., interest income', depreciation', cost of goods', employees' and net sales') into a Stochastic Frontier Analysis (SFA) model. The TE of the PSI-20 (Portuguese Stock Index) was estimated using factors that affect efficiency variability. The main advantage of using the SFA approach is its potential to discriminate between measurement error and systematic inefficiencies in the estimation process. The results demonstrated that TE is higher for enterprises in the industrial, construction and distribution sectors, whereas the commercial banking sector has the lowest TE scores. The employees' and depreciation' are the variables which most contribute to stock market inefficiency.