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
This paper presents REQCAP, an implementation of a new method that articulates hierarchical requirements modeling and test generation to assist in the process of capturing requirements for PLC-based control systems. REQCAP is based on a semi-formal graphical model that supports hierarchical modeling, thus enabling compositional specifications. The tool supports automated generation of test cases according to different coverage criteria. It can also import requirements directly from REQIF files and automatically generate Sequential Function Charts (SFCs). We use a real-world case study to show how REQCAP can be used to model realistic system requirements. We show how the automated generation of SFCs and test cases can support engineers (and clients) in visualizing and reviewing requirements. Moreover, all the tests listed in the original test document of the case study are also generated automatically by REQCAP, demonstrating that the tool can be used to effectively capture requirements and generate valid and useful test cases.

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
This paper presents a new proposal for sensor fusion in power system state estimation, analyzing the case of data sets composed of conventional measurements and phasor measurements from PMUs. The approach is based on multiple criteria decision-making concepts. The equivalence of an L-1 metric in the attribute space to the results from a Bar-Shalom-Campo fusion model is established. The paper shows that the new fusion proposal allows understanding the consequences of attributing different levels of confidence or trust to both systems. A case study provides insight into the new model.

Abstract
Distribution automation systems in terms of automatic and remote-controlled sectionalizing switches allows distribution utilities to implement flexible control of distribution networks, which is a successful strategy to enhance efficiency, reliability, and quality of service. The sectionalizing switches play a significant role in an automated distribution network, hence optimizing the allocation of switches can improve the quality of supply and reliability indices. This paper presents a mixed-integer nonlinear programming aiming to model the optimal placement of manual and automatic sectionalizing switches and protective devices in distribution networks. A value-based reliability optimization formulation is derived from the proposed model to take into consideration customer interruption cost and related costs of sectionalizing switches and protective devices. A probability distribution cost model is developed based on a cascade correlation neural network to have a more accurate reliability assessment. To ensure the effectiveness of the proposed formulation both technical and economic constraints are considered. Furthermore, introducing distributed generation into distribution networks is also considered subject to the island operation of DG units. The performance of the proposed approach is assessed and illustrated by studying on the bus 4 of the RBTS standard test system. The simulation results verify the capability and accuracy of the proposed approach.