Abstract
This study aims to evaluate the implications of the use of maritime pine non-debarked wood chips as an alternative solid fuel in industrial boilers in Portugal, highlighting the energy properties and chemical composition of the ash produced. Several samples have been collected from different sources, on which an approximate analysis is carried out to determine the volatile matter content, fixed carbon content, ash content and higher heating value (HHV). The chemical composition of the ash samples is determined by the SEM - scanning electron microscope - method. Then, empirical indices are used to assess the behaviour of the ash and its tendency to create slagging and fouling deposits in industrial boilers during the combustion process. From the presented results it is clear that the use of maritime pine non-debarked wood chips can contribute significantly to the formation of slagging and fouling phenomena in industrial boilers. These phenomena will be responsible for increasing the number of technical stoppages of the equipment and for the increase in maintenance costs. © 2016 IEEE.

Abstract
Background: The way software for electronic health records and laboratory tests ordering systems are designed may influence physicians' prescription. A randomised controlled trial was performed to measure the impact of a diagnostic and laboratory tests ordering system software modification. Methods: Participants were family physicians working and prescribing diagnostic and laboratory tests. The intervention group had a modified software with a basic shortcut menu changes, where some tests were withdrawn or added, and with the implementation of an evidence-based decision support based on United States Preventive Services Task Force (USPSTF) recommendations. This intervention group was compared with usual software (control group). The outcomes were the number of tests prescribed from those: withdrawn from the basic menu; added to the basic menu; marked with green dots (USPSTF's grade A and B); and marked with red dots (USPSTF's grade D). Results: Comparing the monthly average number of tests prescribed before and after the software modification, from those tests that were withdrawn from the basic menu, the control group prescribed 33.8 tests per 100 consultations before and 30.8 after (p = 0075); the intervention group prescribed 31.3 before and 13.9 after (p < 0001). Comparing the tests prescribed between both groups during the intervention, from those tests that were withdrawn from the basic menu, the intervention group prescribed a monthly average of 14.0 vs. 29.3 tests per 100 consultations in the control group (p < 0.001). From those tests that are USPSTF's grade A and B, intervention group prescribed 66.8 vs. 74.1 tests per 100 consultations in the control group (p = 0.070). From those tests categorised as USPSTF grade D, the intervention group prescribed an average of 9.8 vs. 11.8 tests per 100 consultations in the control group (p = 0.003). Conclusions: Removing unnecessary tests from a quick shortcut menu of the diagnosis and laboratory tests ordering system had a significant impact and reduced unnecessary prescription of tests. The fact that it was not possible to perform the randomization at the family physicians' level, but only of the computer servers is a limitation of our study. Future research should assess the impact of different tests ordering systems during longer periods.

Abstract
Currently, it is possible to find various tools to deal with the unpredictability of electricity markets. However, they mainly focus on spot markets, disfavouring bilateral negotiations. A multi-agent decision support tool is proposed that addresses the identified gap, supporting players in the pre-negotiation and actual negotiation phases.

Abstract
The system operator is responsible for maintaining a constant balance between generation and load to keep frequency at the nominal value. This fundamental objective is achieved with upward (e.g., synchronized and nonsynchronized generation units) and downward (e.g., demand response, storage) reserve capacity. The system operator needs to define, in advance, the reserve capacity requirements that mitigate the risk of imbalances due to forecast errors and unplanned outages of generation units. The research trend is to apply probabilistic methodologies for setting the reserve requirements based on uncertainty forecasts for renewable generation and load, as well as a probabilistic modeling of units' outages. This chapter describes two probabilistic methods, which share a common modeling framework, for quantifying risk and reserve requirements in two types of electricity markets: (1) sequential markets with the reserves market after the energy market clearing and (2) cooptimization (or joint market clearing) of energy and reserves. Two case studies with real data are presented to illustrate the application of both methodologies.

Abstract
Research data are the cornerstone of science and their current fast rate of production is disquieting researchers. Adequate research data management strongly depends on accurate metadata records that capture the production context of the datasets, thus enabling data interpretation and reuse. This chapter reports on the authors' experience in the development of the metadata models, formalized as ontologies, for several research domains, involving members from small research teams in the overall process. This process is instantiated with four case studies: vehicle simulation; hydrogen production; biological oceanography and social sciences. The authors also present a data description workflow that includes a research data management platform, named Dendro, where researchers can prepare their datasets for further deposit in external data repositories. © 2017, IGI Global.

Abstract
The class imbalance problem is a key issue that has received much attention. This attention has been mostly focused on two-classes problems. Fewer solutions exist for the multi-classes imbalance problem. From an evaluation point of view, the class imbalance problem is challenging because a non-uniform importance is assigned to the classes. In this paper, we propose a relevance-based evaluation framework that incorporates user preferences by allowing the assignment of differentiated importance values to each class. The presented solution is able to overcome difficulties detected in existing measures and increases discrimination capability. The proposed framework requires the assignment of a relevance score to the problem classes. To deal with cases where the user is not able to specify each class relevance, we describe three mechanisms to incorporate the existing domain knowledge into the relevance framework. These mechanisms differ in the amount of information available and assumptions made regarding the domain. They also allow the use of our framework in common settings of multi-class imbalanced problems with different levels of information available. © 2017, Springer International Publishing AG.