Abstract
In this paper we fabricate a hetro core optical fiber sensor for concentration measurement. The obtained sensitivity for 10% to 45% is 0.24 dBm/% and for 45% to 100% It is 0.14 dBm/%. © OSA 2016.

Abstract
Over the last decade, impelled by the industry demand to achieve a technology capable of sending low amount of data payloads, but at the same time with a very low latency and ultra-low power consumption, several efforts in wireless network transmission standardization emerged, supporting new applications in health, sports and fitness, medical, sensor networking, and even the automotive industry field. Despite the competition from ANT+, ZigBee, Nike+, NFC and RF4CE, in 2010 the Bluetooth SIG (special interest groups) adopted a new wireless technology named Bluetooth Low Energy (BLE). BLE coexist with Bluetooth in the same chip (called dual mode) therefore assuring this technology a rapid growth among smartphones, iOS, tablets, laptops and PCs. In fact, Bluetooth SIG also announced that it shall be hard to find a smartphone or tablet-PC that does not integrate BLE in the near future. Despite this accelerated growth, BLE shares the same band with Wi-Fi and all other low power technologies, so in order to achieve QoS, a mandatory requirement in many systems, tests for interference and coexistence must be performed. This study analysis the impact of a BLE sensor network on a crowded 2.4GHz room, with multiple Wi-Fi routers, ZigBee sensors and Bluetooth technology. We also compare the results with the ones obtained inside an anechoic chamber on similar experiences.

Abstract
Model synchronization plays an important role in modeldriven software development. Bidirectional model transformation approaches provide techniques for developers to specify the bidirectional relationship between source and target models, while keeping related models synchronized for free. Since models of interest are usually not in a one-to-one correspondence, this synchronization process is inherently ambiguous. Nevertheless, existing bidirectional model transformation tools focus mainly on enforcing consistency and provide developers only limited control over how models are synchronized, solving the latent ambiguity via default strategies whose behavior is unclear to developers. In this paper, we propose a novel approach in which developers write update programs that succinctly describe how a target model can be used to update a source model, such that the bidirectional behavior is fully determined. The new approach mitigates the unpredictability of existing solutions, by enabling a finer and more transparent control of what a bidirectional transformation does, and suggests a research direction for building more robust bidirectional model transformation tools. Copyright © 2014 ACM.

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.