Abstract
The volume and velocity of data is increasing at astonishing rates. In order to extract knowledge from this huge amount of information there is a need for efficient on-line learning algorithms. Rule-based algorithms produce models that are easy to understand and can be used almost offhand. Ensemble methods combine several predicting models to improve the quality of prediction. In this paper, a new on-line ensemble method that combines a set of rule-based models is proposed to solve regression problems from data streams. Experimental results using synthetic and real time-evolving data streams show the proposed method significantly improves the performance of the single rule-based learner, and outperforms two state-of-the-art regression algorithms for data streams.

Abstract
Over the last few years, mechanical biological treatment systems for municipal solid waste have been introduced in many European countries. In most cases, this was driven by the European Union Landfill Directive, which requires the diversion of biodegradable municipal waste from landfill to alternative processes. Although this type of treatment allows energy recovery from municipal solid waste, the process of mechanical biological treatment appears to be an intensive energy consumer, due to high demand of electricity consumed by process equipment. This paper presents the main results of an energy audit performed to a Mechanical Biological Treatment facility in Portugal, which due to the amount of energy consumed must comply with the Portuguese Program called Intensive Energy Consumption Management System – SGCIE. The program was created in 2008 to promote energy efficiency and energy consumption monitoring in intensive energy facilities (energy consumption higher than 500 toe per year). Facilities operators are required to perform energy audits and take actions to draw up an action plan for energy efficiency, establishing targets for energy consumption reduction and greenhouse gases emissions indexes. To implement actions that improve energy efficiency, it is necessary for the facilities operation to be associated with an effective energy management methodology, as well as an efficient facilities management procedure. The implementation of any energy management system should start with an energy audit, which was carried out to identify potential energy conservation measures for improving energy efficiency, and also typical energy consumption patterns and sector/equipment load profiles. This tool gives managers the information to support decision making on improving energy performance and reducing greenhouse gas emissions. Results shown that there is a considerable potential for reducing energy consumption and greenhouse gases emissions on Mechanical Biological Treatment units. Here, as elsewhere in the industrial sector, energy efficiency can only be achieved through a continuous energy monitoring and management system.

Abstract
Penetration of distributed generation (DG) systems in conventional power systems leads to power quality (PQ) disturbances. This paper provides an improved PQ disturbances classification, which are associated with load changes and environmental factors. Various forms of PQ disturbances, including sag, swell, notch and harmonics, are taken into account. Several features are obtained through HS-transform, out of which optimal features are selected using a genetic algorithm (GA). These optimal features are used for PQ disturbances classification by employing support vector machines (SVM) and decision tree (DT) classifiers. The study is supported on three different case studies, considering experimental set-up prototypes for wind energy and photovoltaic (PV) systems, as well as the modified Nordic 32-bus test system. The robustness and precision of DT and SWM is performed with noise and harmonics in the disturbance signals, thus providing comprehensive results.

Abstract
In presented work, the tunable characteristics of magnetic fluid refractive index under the influence of magnetic field with single mode fiber (SMF) with tapered structure are investigated. The sensing fiber probe consists of special down taper joints formed by fusion splicer. Experimental result indicates that the proposed device has excellent magnetic fluid refractive index tunability. The sensitive performances of the sensors are controlled by designing the parameters of the tapered structure and the concentration of Magnetic fluid. Experimental result used to carry out to achieve the magnetic field (intensity and direction) dependent refractive index of the magnetic fluid. © OSA 2016.

Abstract

Abstract
The concern about injuries in sport are evident due to the implications it carries. To have the knowledge of the mechanisms of injuries is important either to prevent and recovery. This context generates the appropriate scenario to develop an electronic solution to measure dynamically the Center of Pressure (CoP) and surfboard's movement and support the understanding of the mechanisms responsible for the occurrence of injuries. Two matrices composed by 24 force sensors and Inertial Measurement Unit (IMU) controlled by ATEMEGA1280 microcontroller were developed. This system was tested using a dynamic protocol using one unstable structure at the bottom of the surfboard. The results have shown that the CoP displacement was largest during the tests that presented great rotation changes. Furthermore, the power oscillations were greater during transition moments. The proposed system is able to measure biomechanical variables dynamically, i.e., force, and surfboard's angle pitch and roll. This report reviews the surf injuries in literature and describes the electronic device used beyond to present the results of the tests performed.