Abstract
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.

Abstract

Abstract
Spatial Data Infrastructures (SDI) are repositories of large volumes of data, documented through standardized metadata. Data mining is one of the main techniques used to extract knowledge from large amounts of data, because of its versatility. The purpose of this article is to use clustering techniques and data mining to extract relationships and knowledge from metadata in SDI. For this reason, knowledge discovery techniques, clustering, text mining and data mining algorithms were used. In order to demonstrate the effectiveness of the proposed method, a case study was implemented to evaluate the performance of data mining techniques in this type of database. The results showed that the data mining process and clustering techniques guided to the classification proposed method for extracting relations and knowledge from a group of metadata extracted from within the database.

Abstract
The European Project Semester (EPS) is a one-semester capstone project/internship programme offered to engineering, product design and business undergraduates by 18 European engineering schools. EPS aims to prepare future engineers to think and act globally by adopting project-based learning and teamwork methodologies. The EPS@ISEP programme – the EPS programme provided by ISEP – the School of Engineering of the Polytechnic Institute of Porto – started in 2011 and has since welcomed 3rd and 4th year mobility students during the spring semester. In particular, sustainable development is a pervasive concern within EPS projects. It was in this context that, in 2012, a team of EPS@ISEP students decided to develop a water disinfection system. While the technical goal of the project was to design and develop a fluid disinfection system for removing bacteria, viruses and seaweeds, the overall objective was far more ambitious: to help students learn, develop and adopt sustainable practices for their future professional life. The system was intended to be a simple and effective solution for water treatment and recycling. At a larger scale, the project contributes to the preservation of the planet's fresh water resources and to the improvement of the population’s health by eliminating harmful microorganisms from the water. This challenge was, by itself, motivational and exposed the team to new learning experiences. The team found several approaches for water treatment and, after a detailed analysis, decided to adopt Ultraviolet (UV) irradiation for the removal of microorganisms. This multidisciplinary real world problem drove the team during the semester. The team surveyed and compared different methods for water cleansing and recycling, chose one approach and, then, designed, built and tested the prototype. In addition, the students also addressed marketing, sustainability as well as the ethic and deontological issues regarding the proposed solution while developing cross-cultural understanding, teamwork and communication skills. The project provided an excellent opportunity to foster the concept of sustainable development amongst students.

Abstract
Cognitive robots, able to adapt their actions based on sensory information and the management of uncertainty, have begun to find their way into manufacturing settings. However, the full potential of these robots has not been fully exploited, largely due to the lack of vertical integration with existing IT infrastructures, such as the manufacturing execution system (MES), as part of a large-scale cyber-physical entity. This paper reports on considerations and findings from the research project STAMINA that is developing such a cognitive cyber-physical system and applying it to a concrete and well-known use case from the automotive industry. Our approach allows manufacturing tasks to be performed without human intervention, even if the available description of the environment-the world model-suffers from large uncertainties. Thus, the robot becomes an integral part of the MES, resulting in a highly flexible overall system.