Abstract
Nowadays, high penetration of Distributed Generations (DG)s in power systems caused some protection issues. One of these issues is unintentional islanding. As regards IEEE 1547 standard, this situation must be recognized immediately, and DG must be separated from the load in less than 2 s. In this paper, to detection of islanding in an inverter-based distributed generation, a new hybrid method with high performance is proposed. In the proposed method, a primary detection of islanding is conducted by measuring the voltage harmonic distortion at the Point of Common Coupling (PCC), as well as comparing the variations to a specified threshold level. After this primary detection, a temporary reactive current signal is injected to the PCC by the inverter of DG, and its terminal voltage and frequency are measured. In the case of deviation of voltage or frequency from permissible range, definitive detection of islanding is determined. Simulation results indicate the efficiency and accuracy of the proposed detection method in different circumstances, especially for loads with the different quality factors.

Abstract
Acoustic parameters are very useful in voice screening, diagnosis and rehabilitation, and also in forensic voice comparison tasks. In this paper we present results for the acoustic analysis performed by two different voice analysis platforms and involving five sustained vowels uttered by 10 female speakers and 9 male speakers. We consider contemporaneous high-quality (HQ) and GSM voice recordings, as well as HQ and VOIP voice recordings. The analysis of the consistency of the acoustic analysis results by the two platforms provides useful insight regarding the intrinsic robustness of each acoustic parameter, the impact of the communication channel, and appropriate procedures seeking to extract full benefit of the available data in a forensic voice context. © 2017 IEEE.

Abstract
Supply chains are ubiquitous in any commercial delivery systems. The exchange of goods and services, from different supply points to distinct destinations scattered along a given geographical area, requires the management of stocks and vehicles fleets in order to minimize costs while maintaining good quality services. Even if the operating conditions remain constant over a given time horizon, managing a supply chain is a very complex task. Its complexity increases exponentially with both the number of network nodes and the dynamical operational changes. Moreover, the management system must be adaptive in order to easily cope with several disturbances such as machinery and vehicles breakdowns or changes in demand. This work proposes the use of a model predictive control paradigm in order to tackle the above referred issues. The obtained simulation results suggest that this strategy promotes an easy tasks rescheduling in case of disturbances or anticipated changes in operating conditions. © Springer International Publishing Switzerland 2017.

Abstract
In pharmacovigilance, reported cases are considered suspected adverse drug reactions (ADR). Health authorities have thus adopted structured causality assessment methods, allowing the evaluation of the likelihood that a medicine was the causal agent of an adverse reaction. The aim of this work was to develop and validate a new causality assessment support system used in a regional pharmacovigilance centre. A Bayesian network was developed, for which the structure was defined by an expert, aiming at implementing the current guidelines for causality assessment, while the parameters were learnt from 593 completely-filled ADR reports evaluated by the Portuguese Northern Pharmacovigilance Centre expert between 2000 and 2012. Precision, recall and time to causality assessment (TTA) was evaluated, according to the WHO causality assessment guidelines, in a retrospective cohort of 466 reports (April to September 2014) and a prospective cohort of 1041 reports (January to December 2015). Results show that the network was able to easily identify the higher levels of causality (recall above 80%), although strugling to assess reports with a lower level of causality. Nonetheless, the median (Q1:Q3) TTA was 4 (2:8) days using the network and 8 (5:14) days using global introspection, meaning the network allowed a faster time to assessment, which has a procedural deadline of 30 days, improving daily activities in the centre.

Abstract
This paper deals with integration of energy storage systems into electricity markets. We explain why the energy storage systems increase flexibility of both power systems and energy markets and why such flexibility is desirable, particularly when variable renewable energy sources are being used in existing power systems. As opposed to the existing literature, our model includes a dual technology energy storage system, acting in two different markets. We introduce a mathematical formulation for this model applied to two Dutch electricity markets. Adopting optimal control approach with the goal to maximize the yearly benefit, we show that the dual energy storage system can be profitable already when the same buying/selling strategies are adopted for the working days and weekends. We show that the profitability (slightly) increases with different buying/selling strategies for the weekdays and weekends. Finally, we demonstrate how the yearly benefit varies with size and efficiency of the devices chosen and market prices. (C) 2017 Published by Elsevier Ltd.

Abstract