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
Industrial Symbiosis (IS) envisages a collaborative approach to resource efficiency, encouraging companies to recover, reprocess and reuse waste within the industrial network. Several challenges regarding the effective application of IS continue to limit a broader implementation of this area of Industrial Ecology. The MAESTRI project encompasses an Industrial Symbiosis approach within the scope of sustainable manufacturing for process industries that fosters the sharing of resources (energy, water, residues, etc.) between different processes of a single company or between multiple companies. The Industrial Symbiosis approach is integrated with Efficiency Framework in the so-called MAESTRI Total Efficiency Framework. Efficiency Framework is devoted to the combination of eco-efficiency (via ecoPROSYS) and the efficiency assessment (via MSM – Multi-Layer Stream Mapping). In this manuscript the benefit of the combination of the Efficiency Framework as an facilitator to a more effective application of Industrial Symbiosis, within or outside the company’s boundaries, is explored. © 2018 Taylor & Francis Group, London, UK.

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.

Abstract
Interest in autonomous vehicles has steadily increased in recent years. A number of tasks, like lane tracking, semaphore detection and decoding, are key features for a self-driving robot. This paper presents a path detection and tracking algorithm using the Inverse Perspective Mapping and Hough Transform methods compounded with real-time vision techniques and a semaphore recognition system based on color segmentation. An evaluation of the proposed algorithm is performed and a comparison between the results using real-time techniques is also presented. The suggested architecture has been put to test on autonomous driving robot who competed in the Portuguese autonomous vehicle competition called "Festival Nacional de Robotica". The overall process of the lane tracking algorithm, takes about 1.4 ms per image, almost 60 times faster than the first algorithm tested and a good accuracy, showing a translation error below 0.03m and a rotation error below 5 degrees. Regarding the real-time semaphore recognition, it takes about 0.35 ms to detect a semaphore and has achieved a perfect score in the laboratory tests performed.

Abstract
The arsenic content of dried baby shrimp (Acetes sp.) was investigated as part of an independent field study of human exposure to toxic metals/metalloids among the ethnic Chinese community located in Upstate New York. The dried baby shrimp were analyzed in a home environment using a portable X-Ray Fluorescence (XRF) instrument based on monochromatic excitation. Study participants had obtained their dried baby shrimp either from a local Chinese market or prepared them at home. The shrimp are typically between 10-20 mm in size and are consumed whole, without separating the tail from the head. Elevated levels of As were detected using portable XRF, ranging between 5-30 g g(-1). Shrimp samples were taken to the Cornell High Energy Synchrotron Source (CHESS) for Synchrotron Radiation XRF (SR-XRF) elemental mapping using a 384-pixel Maia detector system. The Maia detector provided high resolution trace element images for As, Ca, and Br, (among others) and showed localized accumulation of As within the shrimp's cephalothorax (head), and various abdominal segments. As quantification by SR-XRF was performed using a lobster hepatopancreas reference material pellet (NRC-CNRC TORT-2), with results in good agreement with both portable XRF and ICP-MS. Additional As characterization using X-ray Absorption Near Edge Spectroscopy (XANES) with the Maia XRF detector at CHESS identified arsenobetaine and/or arsenocholine as the possible As species present. Further arsenic speciation analysis by LC-ICP-MS/MS confirmed that the majority of As (>95%) is present as the largely non-toxic arsenobetaine species with trace amounts of arsenocholine, methylated As and inorganic As species detected.

Abstract
Within the smart grid (SG) paradigm, the microgrid (MG) concept has been pointed out as a pathway for the implementation of future smart distribution networks since it extends and decentralizes the distribution network monitoring and control capability and provides key self-healing capabilities to low voltage (LV) networks. The increased interest on the MG concept has led to several demonstration activities that have been exploited worldwide. Therefore, this chapter provides an overview regarding some of the laboratorial infrastructures and pilot sites dedicated to development of MG and SG concepts. Additionally, it is presented and discussed the development of a specific SG laboratorial infrastructure following the MG concept.