Abstract

Abstract
In this work a novel optical-fiber sensor for carbon dioxide measurement is presented. A polymeric sensitive layer based on the acid-base equilibrium of phenol and of its derivative 4-nitro-phenol is used for carbon dioxide determination. The sensitive material presents changes in color and in its refractive index. Colorimetric and refractometric measurements were performed. The results show the sensor is more sensitive for lower concentrations and a saturation effect occurs for higher levels. For the colorimetric response, a resolution of +/- 0.15% was estimated and a response time of 30s was measured. For the refractometric measurements, a resolution of +/- 0.50% could be estimated and a response time of 12s was measured. Reversibility and reproducibility were also demonstrated.

Abstract
An interferometric Fabry-Perot cavity based on hollow-core ring photonic crystal fiber (HCR-PCF) for pressure sensing is proposed. The sensing head is formed by splicing a small section of HCR-PCF to standard single mode fiber. The spectral response depends on the cavity length due to the geometry of the HCR-PCF. The sensing head is subjected to methane pressure variations, where it exhibits a sensitivity of 0.82 nm/MPa. Its response to nitrogen pressure variation is also studied. The sensing head's intrinsic sensitivity to the nitrogen refractive index variations inside the hollow-core is also estimated. Finally, temperature measurement is performed and a sensitivity of 3.77 pm/degrees C is obtained for temperatures below 200 degrees C.

Abstract
Time series motif discovery is the task of extracting previously unknown recurrent patterns from time series data. It is an important problem within applications that range from finance to health. Many algorithms have been proposed for the task of efficiently finding motifs. Surprisingly, most of these proposals do not focus on how to evaluate the discovered motifs. They are typically evaluated by human experts. This is unfeasible even for moderately sized datasets, since the number of discovered motifs tends to be prohibitively large. Statistical significance tests are widely used in the data mining communities to evaluate extracted patterns. In this work we present an approach to calculate time series motifs statistical significance. Our proposal leverages work from the bioinformatics community by using a symbolic definition of time series motifs to derive each motif's p-value. We estimate the expected frequency of a motif by using Markov Chain models. The p-value is then assessed by comparing the actual frequency to the estimated one using statistical hypothesis tests. Our contribution gives means to the application of a powerful technique-statistical tests-to a time series setting. This provides researchers and practitioners with an important tool to evaluate automatically the degree of relevance of each extracted motif. © 2012 Wiley Periodicals, Inc.

Abstract
In this paper we present a quality model for spreadsheets based on the ISO/IEC 9126 standard that defines a generic quality model for software. To each of the software characteristics defined in the ISO/IEC 9126, we associate an equivalent spreadsheet characteristic. Then, we propose a set of spreadsheet specific metrics to assess the quality of a spreadsheet in each of the defined characteristics. To obtain the normal distribution of expected values for a spreadsheet in each of the proposed metrics, we have executed them in the widely used EUSES spreadsheet corpus. Then, we quantify each characteristic of our quality model after computing the values of our metrics, and we define quality scores for the different ranges of values. Finally, to automate the quality assessment of a given spreadsheet, according to our quality model, we have integrated the computation of the metrics it includes in both a batch and a web-based tool.

Abstract
This paper presents an optimal load management strategy for residential consumers that utilizes the communication infrastructure of the future smart grid. The strategy considers predictions of electricity prices, energy demand, renewable power production, and power-purchase of energy of the consumer in determining the optimal relationship between hourly electricity prices and the use of different household appliances and electric vehicles in a typical smart house. The proposed strategy is illustrated using two study cases corresponding to a house located in Zaragoza (Spain) for a typical day in summer. Results show that the proposed model allows users to control their diary energy consumption and adapt their electricity bills to their actual economical situation.