Abstract
This work presents a risk analysis performed to stationary Li-ion batteries within the framework of the STABALID project. The risk analysis had as main objective analysing the variety of hazards and dangerous situations that might be experienced by the battery during its life cycle and providing useful information on how to prevent or manage those undesired events. The first task of the risk analysis was the identification of all the hazards (or risks) that may arise during the battery life cycle. Afterwards, the hazards identified were mapped in the different stages of the battery life cycle and two analyses were performed for each stage: an internal problem analysis and an external peril analysis. For both, the dangerous phenomena and the undesirable events resulting from each hazard was evaluated in terms of probability of occurrence and severity. Then, a risk assessment was carried out according to a predefined risk matrix and a preliminary set of risk mitigation measures were proposed to reduce their probability of occurrence and/or their severity level. The results obtained show that it is possible to reduce the probability of occurrence/severity of all the risks associated to the battery life cycle to acceptable or tolerable levels.

Abstract
In this chapter, we present clustering methods for symbolic data. We start by recalling that symbolic data is data presenting inherent variability, and the motivations for the introduction of this new paradigm.We then proceed by defining the different types of variables that allow for the representation of symbolic data, and recall some distance measures appropriate for the new data types. Then we present clustering methods for different types of symbolic data, both hierarchical and nonhierarchical. An application illustrates two well-known methods for clustering symbolic data. © 2016 by Taylor & Francis Group, LLC.

Abstract
This paper discusses the possibility of using adaptive signal processing techniques for maximum power point tracking controllers, in order to extract peak power from individual photovoltaic modules. A new technique grounded on unsupervised Hebbian learning theory (maximum eigenvector of the output power) is presented, which works on-online and is capable of operating without a desired response. Important modifications were made to the generic Hebbian adaptation to accommodate the intrinsic feedback loop between the controller and the plant. Analytic derivation of the new update rule is presented, as well as stability analysis by means of Lyapunov theory. Simulation results showing its effectiveness are presented, as well as experimental results.

Abstract
Scientific models are considered to be fundamental in scientific research and in science education. In true inquiry activities the focus is not to confirm ideas that have already been presented but to promote a serious inquiry action that involves student-centered exploration and problem solving. Models provide students with a broader framework to undertake inquiry activities. Modelling is a process that reflects the spirit of sciences by mirroring scientific entrepreneurship. One kind of models that can be used to develop inquiry in geoscience higher education is replicas of historical models used to collect evidences and to discover new knowledge. Replicas of historical models always help students to identify the problem, formulate questions, decide the factors involved in the questions, understand the investigation and record modelling activities notes. Within this framework, it was built a replica of a shaking table founded in a Collection of Glass Plate Negatives which belonged to Professor John Milne, now in Carisbrooke Castle Museum, Isle of Wight. A PowerPoint presentation and modelling activities with the shaking table were applied to a non-random selection of participants. The convenience sample was constituted by 31 students from a graduation in Geoscience: 19 (61,3%) females and 12 (38,7%) males with 21,9 age average. A pre-experimental study was undertaken, with the application of a validated questionnaire, before and after the two hours modelling workshop. During the workshop it was discussed particular model problems, resorting to a problem-based learning approach, having as the starting point a scenario referring to the research studies conducted by J. Milne in the XIX century. The pre-experimental study was used to do a follow up validation of the questionnaire and also to evaluate if students understood the role of models and of modelling activities in teaching and learning geosciences. After the workshop, the Wilcoxon test was used to verify whether the average difference of the pre and post questionnaire was statistically significant. The group showed an improvement of the average in the questionnaire, increasing from 13,7 to 14,9. The difference that was obtained was statistically significant (Wilcoxon Z = - 1.929; p = 0,03). Considering the main objective of this study, we consider that it was proved that modelling is significant in an inquiry based teaching.

Abstract
Performance appraisal increasingly assumes a more important role in any organizational environment. In the trucking industry, drivers are the company's image and for this reason it is important to develop and increase their performance and commitment to the company's goals. This paper aims to create a performance appraisal model for trucking drivers, based on a multi-criteria decision aid methodology. The PROMETHEE and MMASSI methodologies were adapted using the criteria used for performance appraisal by the trucking company studied. The appraisal involved all the truck drivers, their supervisors and the company's Managing Director. The final output is a ranking of the drivers, based on their performance, for each one of the scenarios used. The results are to be used as a decision-making tool to allocate drivers to the domestic haul service.

Abstract
Power transformers are important equipment of a substation from the generation, transmission, and distribution of electricity to end users. The costs associated with purchasing a power transformer in the voltage class of 500 kV (100 MVA) are a few million. The fines imposed by regulatory agencies are significant when there is unavailability of equipment for any defect or failure. Therefore, energy companies have been struggling in preventive and predictive maintenance in order to maintain this equipment in an effective maintenance program, avoiding the occurrence of failures. There are various techniques that are utilized for diagnosis and analysis of transformer failure, but little has been discussed about mechanisms that assist in decision making when it is necessary to overload the transformer, especially in emergency situations. In this paper, we present a framework that unifies the step of fault diagnosis of power transformers with the process of decision making, considering the current operating conditions as well as the life of the equipment. The assistance to the decision-making methodology is based on risk analysis with indicators derived from the failure rate and Arrhenius theory. The validation of the method is performed with a case study using data from a utility.