Abstract
The design and modelling of a novel magnetic field sensor based on a long period fiber grating coated with a thin film of N doped ZnO is reported. The parameters of both, the grating and the thin film were carefully chosen to operate in the transition mode and near to the dispersion turning point. At this point, an LPFG shows its maximum sensitivity to external refractive index variations. The magnetic field induces variations in the coating refractive index, which changes the effective refractive index of the cladding mode and the consequent spectral response. In this work a sensitivity to the surrounding magnetic field of 2.9 nm/mT is reported with a maximum theoretical resolution of 2 mu T.

Abstract
In an increasingly aged society, with unmeasurable cultural richness kept by its elder elements for their vast life experience, it is urgent to preserve this cultural knowledge before it disappears. The capacity of the computational space to accommodate a virtualization of reality is evident, alongside with the possibility to preserve perspectives of reality with spontaneous and very easy to sort creations thanks to the web tools that now support content production by any common user. Despite all the relevant problematics brought by this context where the common user is simultaneously consumer and producer of information, the opportunities for the present and future of cultural identity maintenance are numerous. This paper approaches the idea of supporting the participatory maintenance of cultural identity through intergenerational storytelling and the dynamization of digital affinity spaces. Our contribution aims to grow the understanding of the role that interactive narratives can have in the real world and in the specific context of cultural identity maintenance, by developing new usage strategies to enhance cultural mediation with the tools we have available and with the help of social, ubiquitous and mobile storytelling strategies.

Abstract
The smart grid initiative, integrating advanced sensing technologies, intelligent control methods, and bi-directional communications into the contemporary electricity grid, offers excellent opportunities for energy efficiency improvements and better integration of distributed generation, coexisting with centralized generation units within an active network. A large share of the installed capacity for recent renewable energy sources already comprises insular electricity grids, since the latter are preferable due to their high potential for renewables. However, the increasing share of renewables in the power generation mix of insular power systems presents a significant challenge to efficient management of the insular distribution networks, mainly due to the variability and uncertainty of renewable generation. More than other electricity grids, insular electricity grids require the incorporation of sustainable resources and the maximization of the integration of local resources, as well as specific solutions to cope with the inherent characteristics of renewable generation. Insular power systems need a new generation of methodologies and tools to face the new paradigm of large-scale renewable integration. Smart and Sustainable Power Systems: Operations, Planning, and Economics of Insular Electricity Grids discusses the modeling, simulation, and optimization of insular power systems to address the effects of large-scale integration of renewables and demand-side management. This practical book: • Describes insular power systems, renewable energies, uncertainty, variability, reserves, and demand response • Examines state-of-the-art forecasting techniques, power flow calculations, and scheduling models • Covers probabilistic and stochastic approaches, scenario generation, and short-term operation • Includes comprehensive testing and validation of the mathematical models using real-world data • Explores electric price signals, competitive operation of distribution networks, and network expansion planning Smart and Sustainable Power Systems: Operations, Planning, and Economics of Insular Electricity Grids provides a valuable resource for the design of efficient methodologies, tools, and solutions for the development of a truly sustainable and smart grid. © 2015 by Taylor & Francis Group, LLC.

Abstract
A recent research trend is driven to increase the monitoring and control capabilities of low voltage networks. This paper describes a probabilistic forecasting methodology based on kernel density estimation and that makes use of distributed computing techniques to create a highly scalable forecasting system for LV networks. The results show that the proposed algorithm outperforms three benchmark models (one for point forecast and two for probabilistic forecasts) and demonstrate the applicability of the distributed in-memory computing solution for a practical operational scenario. The ultimate goal is to integrate information about net-load forecasts in power flow optimization frameworks for low voltage networks in order to solve technical constraints with the available home energy management system flexibility.

Abstract
HDR video on mobile devices is in its infancy and there are no solutions yet that can achieve full HDR video reproduction due to computational power limitations. In this paper we present a novel and versatile solution that allows the delivery of HDR video on mobile devices by taking into account contextual information and retro-compatibility for devices that do not have the computational power to decode HDR video. The proposed solution also enables the remote transmission of HDR video to mobile devices in real-time. This context-aware HDR video distribution solution for mobile devices is evaluated and discussed by considering the impact of HDR videos over conventional low dynamic range videos on mobile devices as well as the challenge of playing HDR videos directly locally or remotely.

Abstract
This paper presents a robust optimisation framework for long-term composite generation and transmission expansion planning problem which considers inherent uncertainties such as load growth, fuel cost and renewable energy output uncertainties. In this paper, a bi-level robust optimisation framework is proposed to accommodate wind output uncertainty in line with the uncertain demanded loads and uncertain fuel cost. The addressed optimisation problem is modelled as a mixed-integer optimisation framework with the objective of providing a robust expansion plan while maintaining the minimum cost expansion. In order to evaluate the robustness of each plan, an off-line Lattice Monte Carlo simulation technique is adopted in this study. The validity of the proposed method is examined on a simple six-bus and modified IEEE 118-bus test system as a large-scale case study. The simulation results show that the presented method is both satisfactory and consistent with expectation. © 2016 Informa UK Limited, trading as Taylor & Francis Group.