Abstract
This article presents original probabilistic price forecasting meta-models (PPFMCP models), by aggregation of competitive predictors, for day-ahead hourly probabilistic price forecasting. The best twenty predictors of the EEM2016 EPF competition are used to create ensembles of hourly spot price forecasts. For each hour, the parameter values of the probability density function (PDF) of a Beta distribution for the output variable (hourly price) can be directly obtained from the expected and variance values associated to the ensemble for such hour, using three aggregation strategies of predictor forecasts corresponding to three PPFMCP models. A Reliability Indicator (RI) and a Loss function Indicator (LI) are also introduced to give a measure of uncertainty of probabilistic price forecasts. The three PPFMCP models were satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL). Results from PPFMCP models showed that PPFMCP model 2, which uses aggregation by weight values according to daily ranks of predictors, was the best probabilistic meta-model from a point of view of mean absolute errors, as well as of RI and LI. PPFMCP model 1, which uses the averaging of predictor forecasts, was the second best meta-model. PPFMCP models allow evaluations of risk decisions based on the price to be made.

Abstract
A reflective fiber optic sensor based on multimode interference for the measurement of glucose aqueous solutions is proposed. A maximum experimental resolution of 0.04 wt.% of glucose is achieved. © OSA 2018 © 2018 The Author(s)

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The use of the odometry and SLAM visual methods in autonomous vehicles has been growing. Optical sensors provide valuable information from the scenario that enhance the navigation of autonomous vehicles. Although several visual techniques are already available in the literature, their performance could be significantly affected by the scene captured by the optical sensor. In this context, this paper presents a comparative analysis of three monocular visual odometry methods and three stereo SLAM techniques. The advantages, particularities and performance of each technique are discussed, to provide information that is relevant for the development of new research and novel robotic applications. © Springer International Publishing AG 2018.

Abstract
This paper presents on-going work developing a formal framework for the model-based analysis of human-machine interaction in multiple critical systems. The framework builds on classical results from applied psychology on selective attention and working memory. The framework is intended for developers of interactive critical systems to identify plausible human multitasking strategies that are likely to be adopted by operators when using multiple interactive systems at the same time, and to estimate the memory load necessary to complete concurrent tasks. This type of analysis is especially useful at the early stages of system design, to better understand the effort necessary to operate the system when an implementation or a prototype of the system is unavailable. The analysis can also be used retrospectively, to analyse already implemented systems and complement results from user studies. An example based on infusion pumps, used in chemotherapy to infuse doses over a period, is employed to demonstrate the utility of the framework. The framework makes it possible to model the interactive tasks necessary to configure the pumps and start the infusion. The results of the analysis indicate situations where the operator is unable to carry out the task because of omission errors. These results are in line with experimental results reported in the literature, and may provide more detailed hypotheses that can be validated experimentally.