Abstract
Power systems are showing a dynamic evolution in the last few years, caused in part by the adoption of smart grid technologies. The integration of new elements that represent a source of uncertainty, such as renewables generation, electric vehicles, variable loads and electricity markets, poses a higher degree of complexity causing that traditional mathematical formulations struggle in finding efficient solutions to problems in the smart grid context. In some situations, where traditional approaches fail, computational intelligence has demonstrated being a very powerful tool for solving optimization problems. In this paper, we analyze the application of Differential Evolution (DE) to address an energy resource management problem under uncertain environments. We perform a systematic parameter tuning to determine the best set of parameters of four state-of-the-art DE strategies. Having knowledge of the sensitivity of DE to the parameter selection, self-adaptive parameter control DE algorithms are also implemented, showing that competitive results can be achieved without the application of parameter tuning methodologies. Finally, a new hybrid-adaptive DE algorithm, HyDE, which uses a new 'DE/target - to - perturbed-best/1' strategy and an adaptive control parameter mechanism, is proposed to solve the problem. Results show that DE strategies with fixed parameters, despite very sensitive to the setting, can find better solutions than some adaptive DE versions. Overall, our HyDE algorithm excelled all the other tested algorithms, proving its effectiveness solving a smart grid application under uncertainty. © 2018 IEEE.

Abstract
The issue of environmental emissions has forced the power systems to use cleaner energy sources such as renewable and hydroelectric technologies. However, during recent decades due to the limitations on the available water in many regions, the optimal water reservoir usage has been highlighted. In this regard, this paper proposes a multi-objective model for short-term hydrothermal scheduling problem in the presence of the pumped-storage technology. It is noted that the framework well models the cascaded configuration of hydro reservoirs. Besides, in order to more accurately model the mentioned problem, a Mixed-Integer Non-Linear Programming (MINLP) optimization framework is presented. In this respect, the valve-loading effects occurred in thermal power generation technologies have been taken into account which turns the existing convex optimization problem into a non-convex one. In order to solve the mentioned problem, the Normal Boundary Intersection (NBI) method has been used while the VIKOR decision maker is employed to choose the most compromise solution amongst the Pareto optimal solutions obtained by NBI method. Finally, the efficiency of the proposed model has been verified through implementing four case studies and comparing the obtained results with those obtained by different methods. © 2017 Elsevier Ltd

Abstract
The emergence and widespread use of Information and Communication Technologies enabled government agencies to develop their capacity for citizens interaction, hence providing better public services. These electronic services, typically known as e-Government, have been undergoing significant development over the last few years. Information technologies have become one of the central elements in electronic governance today, and probably they will outstand in the future too. Despite the clear advantages inherent in e-Government services usage, the interaction rate between citizens and governments is still low and considered to be unsatisfactory. In order to understand which determinants may influence and trigger the adoption of e-Government services by civil society, a research methodology consisting of a theory-based qualitative validation, has been defined. This validation is performed by an Online Focus Group, where several experts were involved with the goal of reaching a set of remarkable considerations about potential determinants that can influence the use of e-Government services. © 2018, Springer International Publishing AG, part of Springer Nature.

Abstract
Monitoring of environmental radioactivity for the purpose of earthquake prediction requires the discrimination of anomalies of non-tectonic origin from seismically-induced anomalies. This is a challenging task as time series of environmental radioactivity display a complex temporal pattern reflecting a wide range of different physical processes, including meteorological and surface effects. The present study is based on the detailed time series of gamma radiation from the Eastern North Atlantic (ENA) site in the Azores, and on very high resolution precipitation intensity and soil moisture time series. The results show that an abrupt shift in the average level of the gamma radiation time series previously reported as a potential earthquake precursor can also be explained by a corresponding abrupt change in soil moisture. It was concluded that the reduction of false positive earthquake precursors requires the detailed assessment of both precipitation and soil moisture conditions at high temporal resolution.

Abstract
This paper presents EVA, a new concept for an hybrid ROV/AUV designed to support the underwater operation of an underwater mining machine, developed in the context of the European H2020 R&D VAMOS Project. This project is briefly presented, introducing the main components and concepts, providing the reader with clear picture of the operational scenario and allowing to understand better the functionality requirements of the support robotic vehicle developed. The design of EVA is detailed presented, addressing the mechanical design, hardware architecture, sensor system and navigation and control. The results of EVA both in water test tank, in the ! VAMOS! Field trials in Lee Moor, UK, and in an harbor scenario are presented and discussed

Abstract