Abstract
In this work, a Fabry-Perot optical fiber sensor for the measurement of strain at extreme temperatures is proposed. The cavity is formed by splicing a short section of a silica tube between two sections of single mode fiber. The tube, with a cladding similar to 14 mu m thick and a hollow core, presents four small rods, of similar to 20 mu m in diameter each, positioned in in diametrically opposite positions. This design ensures higher mechanical stability of the tube. Strain measurements are performed over a wide range of temperatures, until 900 degrees C. Some of the annealing effects are addressed in this study.

Abstract
At the present time there is a high prebure toward the improvement of all the production procebes. Those improvements can be sensed in several directions in particular those that involve energy efficiency. The definition of tight energy efficiency improvement policies is transversal to several operational areas ranging from industry to public services. As can be expected, agricultural procebes are not immune to this tendency. This statement takes more severe contours when dealing with indoor productions where it is required to artificially control the climate inside the building or a partial growing zone. Regarding the latter, this paper presents an innovative system that improves energy efficiency of a trees growing platform. This new system requires the control of both a water pump and a gas heating system based on information provided by an array of sensors. In order to do this, a multi-input, multi-output regulator was implemented by means of a Fuzzy logic control strategy. Presented results show that it is pobible to simultaneously keep track of the desired growing temperature set-point while maintaining actuators streb within an acceptable range.

Abstract
Wind energy production have been increasing in last years, with an annual growth of the installed capacity rate about 20%. It becomes important to develop optimization techniques to improve the effectiveness of the wind farms. One field in which this can be done is in the distribution network design that interconnects the turbines and the substation. This paper proposes two mathematical models to obtain the optimal electrical interconnection configuration of the wind farm turbines, considering technical constraints. One model minimizes the installation costs and the other one minimizes the installation costs and the energy losses costs registered during the wind farm lifetime. This problem corresponds to a capacitated minimum spanning tree with additional constraints. The proposed models were applied in two real wind farms. A sensitivity analysis is performed over two electrical parameters, the power factor and the load factor. The results show that the electrical losses of the wind farm must be taken into account in the optimization process.

Abstract

Abstract
In this work we present a simple and low cost setup that allows obtaining the light spectra and measuring the wavelength of its features. It is based on a cheap transmission diffraction grating, an ordinary digital camera and using Tracker software to increase measuring accuracy. This equipment can easily be found in most schools. The experimental setup is easy to implement (the typical setup for a pocket spectroscope) replacing the eye with the camera. The calibration is done using a light source with a well-known spectrum. The acquired images are analyzed with Tracker (freeware software frequently used for motion studies). With this system, we have analyzed several light sources. As an example, the analysis of the spectra obtained with compact fluorescent lamp allowed to recognize the spectrum of mercury in the lamp, as expected. This spectral analysis is therefore useful in schools, among other topics, to enable the recognition of chemical elements through spectroscopy, and to alert students to the different spectra of illuminating light sources used in houses and public places.

Abstract
Classic Collaborative Filtering (CF) algorithms rely on the assumption that data are static and we usually disregard the temporal effects in natural user-generated data. These temporal effects include user preference drifts and shifts, seasonal effects, inclusion of new users, and items entering the systemand old ones leavinguser and item activity rate fluctuations and other similar time-related phenomena. These phenomena continuously change the underlying relations between users and items that recommendation algorithms essentially try to capture. In the past few years, a new generation of CF algorithms has emerged, using the time dimension as a key factor to improve recommendation models. In this overview, we present a comprehensive analysis of these algorithms and identify important challenges to be faced in the near future.(C) 2015 John Wiley & Sons, Ltd.