Abstract
The problem of learning label rankings is receiving increasing attention from several research communities. A number of common learning algorithms have been adapted for this task, including k-Nearest Neighbours (k-NN) and decision trees. Following this line, we propose an adaptation of the naive Bayes classification algorithm for the label ranking problem. Our main idea lies in the use of similarity between the rankings to replace the concept of probability. We empirically test the proposed method on some metalearning problems that consist of relating characteristics of learning problems to the relative performance of learning algorithms. Our method generally performs better than the baseline indicating that it is able to identify some of the underlying patterns in the data.

Abstract
Acoustic emission monitoring is often used in the diagnosis of electrical and mechanical incipient faults in high-voltage apparatus. Partial discharges are a source of failure in power transformers, and the differentiation from other sources of acoustic emissions is of the utmost importance. This paper reports the development of a new sensor concept-mandrel-based fiber-optic sensor-for the detection of incipient faults in oil-filled power transformers, taking direct measurements inside a transformer. These sensors can be placed in the inner surface of the transformer tank wall, not affecting the insulation integrity of the structure, and improving fault detection and location. The applicability of these acoustic sensors in air, water, and oil is investigated and the paper presents the promising results obtained, which will enable the industrial development of practical solutions.

Abstract
Purpose: The objective of this paper is to showthat grounded theory (GT), together with mixed methods, can be used to involve healthcare professionals in the design and enhancement of access control policies to Electronic Medical Record (EMR) systems. Methods: The mixed methods applied for this research included, in this sequence, focus groups (main qualitative method that used grounded theory for the data analysis) and structured questionnaires (secondary quantitative method). Results: Results showed that the presented methodology can be used to involve healthcare professionals in the definition of access control policies to EMR systems and explore these issues in a diversified and integrated way. The methodology allowed for the generation of great amounts of data in the beginning of the study and in a short time span. Results from the applied methodology revealed a first glimpse of the theories to be generated and integrated, with future research, into access control policies. Conclusions: The methodological research described in this paper is very rarely, if ever, applied in developing security tools such as access control. Nevertheless, it can be an effective way of involving healthcare professionals in the definition and enhancement of access control policies and in making information security more grounded into their workflows and daily practices.

Abstract
Target Controlled Infusion (TCI) systems are based in drug Pharmacokinetic (Pk) and Pharmacodynamic (Pd) models implemented in an algorithm to drive an infusion device. Several studies had compare manual titration of anesthesia and TCI system use; some studies evaluate the performance of the control algorithms for TCI systems, and a considerable number of studies assess the performance of Pk/ Pd models implemented into TCI systems. This study presents a set of tests to validate the performance of a TCI system as a computer- aided. The goal of the current study was to assess the performance of the TCI system, Anaesthesia Synchronization Software (ASYS), on clinical set up to evaluate communication consistence (computer - infusion device) and controller performance in real time. These measures provided quantitative and qualitative evidences of software robustness and accuracy to be used at clinical environment.

Abstract
Wind energy is the renewable energy source with a higher growing rate in the last decades and can be considered a hope in the future as a clean and sustainable energy. With the huge proliferation of wind farms, as an alternative to the traditional fossil power generation, the economic issues dictate the necessity of monitoring systems to optimize availability and profits. Tools to detect the onset of mechanical faults in wind turbines at a sufficiently early stage are very important for maintenance actions to be well planned, because these actions can reduce the outage time and can prevent bigger faults that may lead to machine stoppage.

Abstract
We propose a multiobjective local search metaheuristic for a mean-risk multistage capacity investment problem with irreversibility, lumpiness and economies of scale in capacity costs. Conditional value-at-risk is considered as a risk measure. Results of a computational study are presented and indicate that the approach is capable of producing high-quality approximations to the efficient sets with a modest computational effort. The best results are achieved with a new hybrid approach, combining Tabu Search and Variable Neighbourhood Search.