Abstract
Pressure fiber sensors play an important role in downhole high pressure measurements to withstand long term operation. The purpose of this paper is to present an application of hollow core photonic crystal fiber (HC-PCF) as a high pressure sensor head for downhole application based on dispersion variation. We used a high pressure stainless steel unit to exert pressure on the sensor. The experimental results show that different wavelengths based on sagnac loop interferometer have additive sensitivities from 5 x 10(-5) nm/psi at 1480 nm to 1.3 x 10(-3) nm/psi at 1680 nm. We developed a simulation to understand the reason for difference in sensitivity of wavelengths and also the relationship between deformation of HC-PCF and dispersion variation under pressure. For this purpose, by using the finite element method, we investigated the effect of structural variation of HC-PCF on spectral transformation of two linear polarizations under 1000 psi pressure. The simulation and experimental results show exponential decay behavior of dispersion variation from -3.4 x 10(-6) 1/psi to -1.3 x 10(- 6) 1/psi and from -5 x 10(-6) 1/psi to -1.8 x 10(-6) 1/psi, respectively, which were in a good accordance with each other. (C) 2014 AIP Publishing LLC.

Abstract
As a crucial factor in global energy consumption, environmental problems related to the greenhouse gas emissions and high oil prices have motivated the growth and incorporation of alternative sources of energy into power systems. However, one of the most important facets of such problems is their intermittent nature, which means that the operation and management of a power system is a difficult task. In this paper, a probabilistic approach to solving the economic dispatch (ED) problem under conditions of uncertainty is presented. The proposed methodology allows for obtaining the probability distribution function (PDF) of all generation units, the energy not supplied (ENS) and the total generation cost. A case study based on an insular power system is analyzed, with reference to the important relationship that exists between the PDF of net load demand and the PDF of ENS, power production and total generation cost.

Abstract
Recommender systems (RS) are increasingly adopted by e-business, social networks and many other user-centric websites. Based on the user's previous choices or interests, a RS suggests new items in which the user might be interested. With constant changes in user behavior, the quality of a RS may decrease over time. Therefore, we need to monitor the performance of the RS, giving timely information to management, who can than manage the RS to maximize results. Our work consists in creating a monitoring platform - based on Business Intelligence (BI) and On-line Analytical Processing (OLAP) tools - that provides information about the recommender system, in order to assess its quality, the impact it has on users and their adherence to the recommendations. We present a case study with Palco Principal(1), a social network for music.

Abstract
In this work we present a new low cost SPR (Surface Plasmon Resonance) sensor configuration based on efficient higher-order mode filtering in plastic multimode fibers, using a tapered POF (Plastic Optical Fiber) after the sensor system, without decreasing the sensitivity of the sensor. In particular, we present the experimental results obtained with this new configuration. The experimental results have shown as the tapered POF after the sensor system influences the performances in terms of refractive index range and Signal-to-Noise Ratio (SNR).

Abstract
We present here a report produced by a workshop on 'Addressing failures in exascale computing' held in Park City, Utah, 4-11 August 2012. The charter of this workshop was to establish a common taxonomy about resilience across all the levels in a computing system, discuss existing knowledge on resilience across the various hardware and software layers of an exascale system, and build on those results, examining potential solutions from both a hardware and software perspective and focusing on a combined approach.The workshop brought together participants with expertise in applications, system software, and hardware; they came from industry, government, and academia, and their interests ranged from theory to implementation. The combination allowed broad and comprehensive discussions and led to this document, which summarizes and builds on those discussions. © The Author(s) 2014.

Abstract
In real situations the evaluation of the global quality of either a product or a service depends on more than one quality characteristic. In order to monitor the variability of multivariate processes and identify the variables responsible for changes in the process, we will use the STATIS (Structuration des Tableaux A Trois Indices de la Statistique) methodology, a three-way data analysis method. For this purpose we consider a control chart based on a similarity measure between two positive semi-definite matrices, the RV coefficient, and we evaluate the performance of this control chart for monitoring multivariate normal data.