Abstract
Microfiber knot resonators find application in many different fields of action, of which an important one is the optical sensing. The large evanescent field of light can interact and sense the external medium, tuning the resonance conditions of the structure. The resonant property of microfiber knot resonators can also provide, in some cases, an enhancement in the sensing capability. Until nowadays, a wide variety of physical and chemical parameters have been possible to measure with this device. New developments and improvements are still being done in this field. A review on microfiber knot resonators as sensors is presented, with particular emphasis on their application as temperature and refractive index sensors. The properties of these structures are analyzed and different assembling configurations are presented. Important aspects in terms of the sensor stability are discussed, as well as alternatives to increase the sensor robustness. In terms of new advances, an overview on coated microfiber knot resonators is also presented. Finally, other microfiber knot configurations are explored and discussed.

Abstract
In this paper, the effect of renewable energy resources (RERs), demand response (DR) programs and electric vehicles (EVs) is evaluated on the optimal operation of a smart distribution company (SDISCO) in the form of a new bi-level model. According to the existence of private electric vehicle parking lots (PLs) in the network, the aim of both levels is to maximize the profits of SDISCO and the PL owners. Furthermore, due to the uncertainty of RERs and EVs, the conditional value-at-risk (CVaR) method is applied in order to limit the risk of expected profit. The model is transformed into a linear single-level model by the Karush-Kuhn-Tucker (KKT) conditions and tested on the IEEE 33-bus distribution system over a 24-h period. The results show that by using a proper charging/discharging schedule, as well as a time of use program, SDISCO gains more profit. Furthermore, by increasing the risk aversion parameter, this profit is reduced.

Abstract
Because of various developments in communications and technologies, each residential consumer has been enabled to contribute in Demand Response Programs (DRPs), manage its electrical usage and reduce its cost by using a Household Energy Management (HEM) system. An operational HEM model is investigated to find the minimum consumer's cost in every DRP and to guarantee the end-user's satisfaction, as well as to ensure the practical constraints of every battery and residential appliance. The numerical studies show that the presented method considerably affects the operational patterns of the HEM system in each DRP. According to the obtained results, by employing the presented method the consumer's cost is decreased up to 40%. © 2017 IEEE.

Abstract
The energy consumption of the current building stock represents about 40% of the total final energy consumption in Europe. New gamification techniques may play a significant role in helping users adopt new and more energy efficient behaviours. This paper presents the advances achieved within the context of the EU-funded project EnerGAware - Energy Game for Awareness of energy efficiency in social housing communities. The main objective of the project, funded by the European Union under the Horizon2020 programme, is to reduce the energy consumption and carbon emissions in a sample of European social housing by changing the energy efficiency behaviour of the social tenants through the implementation of a serious game linked to the real energy use of the participants' homes. © 2017 IEEE.

Abstract
While most physical systems or phenomena occur in continuous-time, identification methods based on discrete-time models are more widespread among practitioners and academic community, possibly due to the discrete-time nature of the data records. There has been a growing interest in estimating continuous-time (CT) models in the last decade. This work develops algorithms to estimate the parameters of multivariable state-space CT models from input-output samples using a method based on the recently developed MOLI-ZOFT approach. The performance of the algorithm is evaluated using real data from an industrial winding process.

Abstract