Abstract
A curvature sensor based on a Fabry-Perot interferometer is proposed. A capillary tube of silica is fusion spliced between two single mode fibers, producing a Fabry-Perot cavity. The light propagates in air, when passing through the capillary tube. Two different cavities are subjected to curvature and temperature. The cavity with shorter length shows insensitivity to both measurands. The larger cavity shows two operating regions for curvature measurement, where a linear response is shown, with a maximum sensitivity of 18.77pm/m(-1) for the high curvature radius range. When subjected to temperature, the sensing head produces a similar response for different curvature radius, with a sensitivity of 0.87pm/degrees C.

Abstract
Temperature-independent strain and angle measurements are achieved resorting to a taper fabricated on a Bragg fiber using a CO2 laser. The characteristic bimodal interference of an untapered Bragg fiber is rendered multimode after taper fabrication and the resulting transmission spectra are analyzed as a function of strain, applied angle, and temperature variations. The intrinsic strain sensitivity exhibited by the Bragg fiber is increased 15 fold after tapering and reaches 22.68 pm/mu epsilon. The angle and temperature measurements are also performed with maximum sensitivities of 185.10 pm/deg and -12.20 pm/K, respectively. The difference in wavelength shift promoted by variations in strain, angle, and temperature for the two fringes studied is examined. Strain and angle sensing with little temperature sensitivity is achieved, presenting a response of 2.87 pm/mu epsilon and -57.31 pm/deg, respectively, for strain values up to 400 mu epsilon and angles up to 10 degrees. Simultaneous angle and strain measurements are demonstrated.

Abstract
Wireless Sensor Networks (WSNs) are susceptible to faults both in sensors and in communication. Information fusion techniques allow to extract precise information from a large amount of data. Detection, identification and treatment of outlier, in these techniques, is a key point. Outlier detection in WSNs is a challenge due to the low capacity of the nodes and low bandwidth of the network. This paper proposes a methodology that applies the clustering and lightweight statistics techniques for detection of outliers in WSNs. The assessment of the methodology involves a case study with temperature sensors in WSN nodes. The results show that this methodology is able to provide precise information, even in the presence of outliers.

Abstract
Scalability and topological stability are two of the most challenging issues in current wireless mesh networks (WMNs) deployments. In the literature, both the scalability and the topological stability of WMNs are described as likely to suffer from poor performance due to the ad hoc nature of the underlying IEEE 802.11 mechanisms. The main contribution of this article is a comprehensive review of the main topological stability and scalability-related issues in IEEE 802.11s-based networks. Moreover, the most relevant proposed solutions are surveyed, where both the drawbacks and the merits of each proposal are highlighted. At the end of the article, some open research challenges are presented and discussed. It is expected that this work may serve as motivation for more and deeper research on these issues to allow the design of future more stable and scalable IEEE 802.11s mesh networks deployments. Copyright (c) 2015 John Wiley & Sons, Ltd.

Abstract
There is growing recognition that decarbonisation of existing uses of electricity is only 'part of the story' and that closer attention needs to be given to demand for energy in heating or cooling and in transport, and to all the energy vectors and infrastructures that supply the end-use demand. In this respect, concepts such as 'multi-energy systems' (MES) have been put forward and are gaining increasing momentum, with the aim of identifying how multiple energy systems that have been traditionally operated, planned and regulated in independent silos can be integrated to improve their collective technical, economic, and environmental performance. This paper addresses the need for modelling of MES which is capable of assessing interactions between different sectors and the energy vectors they are concerned with, so as to bring out the benefits and potential unforeseen or undesired drawbacks arising from energy systems integration. Drivers for MES modelling and the needs of different users of models are discussed, along with some of the practicalities of such modelling, including the choices to be made in respect of spatial and temporal dimensions, what these models might be used to quantify, and how they may be framed mathematically. Examples of existing MES models and tools and their capabilities, as well as of studies in which such models have been used in the authors' own research, are provided to illustrate the general concepts discussed. Finally, challenges, opportunities and recommendations are summarised for the engagement of modellers in developing a new range of analytical capabilities that are needed to deal with the complexity of MES.

Abstract
Nearest neighbour collaborative filtering (NNCF) algorithms are commonly used in multimedia recommender systems to suggest media items based on the ratings of users with similar preferences. However, the prediction accuracy of NNCF algorithms is affected by the reduced number of items - the subset of items co-rated by both users typically used to determine the similarity between pairs of users. In this paper, we propose a different approach, which substantially enhances the accuracy of the neighbour selection process - a user-based CF (UbCF) with semantic neighbour discovery (SND). Our neighbour discovery methodology, which assesses pairs of users by taking into account all the items rated at least by one of the users instead of just the set of co-rated items, semantically enriches this enlarged set of items using linked data and, finally, applies the Collinearity and Proximity Similarity metric (CPS), which combines the cosine similarity with Chebyschev distance dissimilarity metric. We tested the proposed SND against the Pearson Correlation neighbour discovery algorithm off-line, using the HetRec data set, and the results show a clear improvement in terms of accuracy and execution time for the predicted recommendations.