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Abstract
Developments of the smart grid infrastructure can facilitate the upsurge of Demand Response (DR) share in power system resources. This paper models the effects of Demand Response Programs (DRPs) on the behavior of the electricity market in the Day-Ahead (DA) session. Decision makers look for the best DR tariff to employ it as a tool to obtain a flexible and sustainable energy market. Employing the most effective DRP is of crucial importance. An optimized DR model and the optimum rates for each DRP are found to meet the decision makers' requirements. Optimizing the nodal tariff and incentive values of different DRPs are proposed in the electricity market. In such environment, market interactions are considered by means of a security constrained unit commitment problem. Both types of Price-Based Demand Response (PBDR) and Incentive-Based Demand Response (IBDR) are modeled. The numerical results presented indicate the effectiveness of the proposed model.

Abstract
A remote sensor for radon continuous measurement using polymeric scintillation optical fibers was developed and evaluated. Successful preliminary results showed detection of natural occurring radon from a container with rocks rich in uranium oxides. © OSA 2018 © 2018 The Author(s)

Abstract
Creating formal models of interactive systems has wide reaching benefits, not only for verifying low-level correctness, but also as a tool for ensuring user interfaces behave logically and consistently. Despite this, tools for designing user experiences and tools for creating and working with formal models are typically distinctly separate systems. This work aims to bridge this divide by allowing the generation of state machine diagrams and formal models via a simple, interactive prototyping tool that mirrors the basic functionality of many modern digital prototyping applications.

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The gravitational search algorithm (GSA) is reviewed, by presenting a tutorial analysis of its key issues. As any other metaheuristic, GSA requires the selection of some heuristic parameters. One parameter which is crucial in regulating the exploratory capabilities of this algorithm is the gravitational constant. An analysis regarding this parameter selection is presented and a heuristic rule proposed for this purpose. The GSA performance is compared both with a hybridization with particle swarm optimization (PSO) and standard PSO. Preliminary simulation results are presented considering simple continuous functions optimization examples.

Abstract