Abstract
This paper presents a detailed study of the energy consumption of the different Java Collection Framework (JFC) implementations. For each method of an implementation in this framework, we present its energy consumption when handling different amounts of data. Knowing the greenest methods for each implementation, we present an energy optimization approach for Java programs: based on calls to JFC methods in the source code of a program, we select the greenest implementation. Finally, we present preliminary results of optimizing a set of Java programs where we obtained 6.2% energy savings. © 2016 ACM.

Abstract
Micro grid represents an emergent paradigm to address the challenges of recent smart electrical grid visions, where several small-scale and distributed electrical units cooperate to achieve higher levels of energy self-sustainability, by reducing the main grid dependence. Nevertheless, the realization of this paradigm requires advanced intelligent approaches that are able to effectively manage the micro grid infrastructure and its elements. Multi-agent systems provide a suitable framework to support the development of such systems, where autonomous agents endowed with predictive data analysis capabilities take advantage of the large amount of data produced to predict the renewable energy production and consumption. In this context, this paper presents a predictive data analysis driven multi-agent system for the management of micro grids renewable energy production. The proposed approach was applied to an experimental case study, considering different predictive algorithms and data sources for the short and midterm forecasting of the production of wind and photovoltaic energy-based units.

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This paper aims to make a comparison between the logistics costs of buying Wood Pellets (WP) and Torrefied Biomass Pellets (TBP) produced in Portugal and exported to the major consumer markets of Northern Europe. The starting point is to determine the value of a shipload of WP and TBP delivered to a North European port and loaded in Aveiro, the main Portuguese WP expeditor port. Torrefaction implies higher energy and bulk density pellets, which contributes to increase the logistics costs associated with them. The loss of mass is greater than the loss of energy. These changes in bulk and energy densities are an advantage in terms of logistics: more tonnes per unit of volume and more energy per tonne will decrease the transportation cost per energy unit. The analysis carried out in this paper determines the energy in gigajoules (GJ) per tonne and all the comparisons are based on the cost per energy unit. This analysis is supported by real data collected in the Argus Biomass Markets report.

Abstract
Fatigue, especially in its mental form, is one of the most worrying health problems nowadays. It affects not only health but also motivation, emotions and feelings and has an impact both at the individual and organizational level. Fatigue monitoring and management assumes thus, in this century, an increased importance, that should be promoted by private organizations and governments alike. While traditional approaches are mostly based on questionnaires, in this paper we present an alternative one that relies on the observation of the individual's interaction with the computer. We show that this interaction changes with the onset of fatigue and that these changes are significant enough to support the training of a neural network that can classify mental fatigue in real time. The main outcome of this work is the development of non-invasive systems for the continuous classification of mental fatigue that can support effective and efficient fatigue management initiatives, especially in the context of desk jobs.