Abstract
A Mach-Zehnder sensor based on a large knot fiber resonator with a diameter of a few millimeters is designed using a single long taper. The long taper of some centimeters is fabricated with a CO2 laser technique. In air, light cannot couple between adjacent sections in the knot, and no signal is observed. However, in liquid, light is less confined and there is coupling between adjacent sections of the knot, resulting in a phase difference and consequent interference. The Mach-Zehnder is formed by the two contact points in the knot. The refractive index sensing of liquid compounds is achieved by monitoring the wavelength shift of the spectra. A sensitivity of 642 +/- 29 nm/refractive index unit (RIU) is achieved for refractive index sensing in the range of 1.3735-1.428 with a resolution of 0.009 RIU. For temperature sensing, a sensitivity of -42 +/- 9 pm/degrees C is observed. A low influence of temperature in the refractive index change is observed: 6.5 x 10(-5) RIU/degrees C.

Abstract
The secure integration of renewable generation into modern power systems requires an appropriate assessment of the security of the system in real-time. The uncertainty associated with renewable power makes it impossible to tackle this problem via a brute-force approach, i.e. it is not possible to run detailed online static or dynamic simulations for all possible security problems and realizations of load and renewable power. Intelligent approaches for online security assessment with forecast uncertainty modeling are being sought to better handle contingency events. This paper reports the platform developed within the iTesla project for online static and dynamic security assessment. This innovative and open-source computational platform is composed of several modules such as detailed static and dynamic simulation, machine learning, forecast uncertainty representation and optimization tools to not only filter contingencies but also to provide the best control actions to avoid possible unsecure situations. Based on High Performance Computing (IIPC), the iTesla platform was tested in the French network for a specific security problem: overload of transmission circuits. The results obtained show that forecast uncertainty representation is of the utmost importance, since from apparently secure forecast network states, it is possible to obtain unsecure situations that need to be tackled in advance by the system operator.

Abstract
This paper, which can be divided into two main interrelated studies, firstly describes the generic modelling of a sub-transmission network model to serve as a UK variant of the original IEEE 14-bus test system. The revised model, based on the actual/realistic power components found both in the UK and in European grids, provides an updated and complete technical description, ready for use in a variety of power system studies, in which the 14-bus test system is one of the most commonly used in the literature. Afterwards, this paper categorises the typical demand characteristics of the residential load sector in the UK, by providing a wider range of reference demand values and loading conditions for the planning and modelling studies of distribution networks, dividing them into four generic residential load subsectors. Different 'after diversity demand' values are therefore provided per residential load subsector, classes of customers and seasonal variations of annual power consumption.

Abstract
Similitude theory is a branch of engineering science concerned with establishing the necessary and sufficient conditions of similarity among phenomena, and has been applied to different fields such as structural engineering, vibration and impact problems. Testing of sub-scale models is still nowadays a valuable design tool, helping engineers to accurately predict the behavior of oversized prototypes through scaling laws applied to the obtained experimental results. In this manuscript it has been reviewed the developments in the methodologies used to create reduced scale models as a design tool, including those based in the use of: dimensional analysis, differential equations and energetic methods. Besides, given their importance, some major areas of research were reviewed apart: impacted structures, rapid prototyping of scale models and size effects. At last, some topics on which additional efforts can be undertaken are highlighted.

Abstract

Abstract
This paper presents a computational framework for the optimization of maintenance activities for infrastructure assets, with particular emphasis being placed on road network assets. This framework incorporates degradation and maintenance models for infrastructure assets along with multi-objective optimization for searching optimal maintenance schedules. Given a schedule of maintenance actions, the future performance is estimated by means of a Monte Carlo simulation that enables to account for inherent uncertainties. The design variables of optimization are the types of maintenance actions and their timing over the planning horizon. The objectives are to minimize both the asset degradation and maintenance cost. This includes satisfaction of constraints representing performance demands. The proposed framework is general and can be applied to different types of infrastructure assets. The numerical results, obtained for a road bridge managed by a highway operating agency, demonstrate the validity and usefulness of the proposed framework.