Benthic estuarine communities in Brazil: moving forward to long term studies to assess climate change impacts

Abstract Estuaries are unique coastal ecosystems that sustain and provide essential ecological services for mankind. Estuarine ecosystems include a variety of habitats with their own sediment-fauna dynamics, all of them globally undergoing alteration or threatened by human activities. Mangrove forests, saltmarshes, tidal flats and other confined estuarine systems are under increasing stress due to human activities leading to habitat and species loss. Combined changes in estuarine hydromorphology and in climate pose severe threats to estuarine ecosystems on a global scale. The ReBentos network is the first integrated attempt in Brazil to monitor estuarine changes in the long term to detect and assess the effects of global warming. This paper is an initial effort of ReBentos to review current knowledge on benthic estuarine ecology in Brazil. We herein present and synthesize all published work on Brazilian estuaries that has focused on the description of benthic communities and related ecological processes. We then use current data on Brazilian estuaries and present recommendations for future studies to address climate change effects, suggesting trends for possible future research and stressing the need for long-term datasets and international partnerships

Data-driven Detection and Identification of Undesirable Events in Subsea Oil Wells

Condition-Based Monitoring (CBM) systems have grown in popularity in recent years owing to innovations in areas, such as sensor-technology, communication systems, and computing. That has fostered the development of more efficient systems to monitor, analyze, and identify failures in industrial plants, production lines, and machinery. Gas and oil industries lose billions of dollars yearly related to abnormal events and systems failures. Thus, Abnormal Event Management (AEM), which aims at early detection and identification of these events, has become their number one priority so that preventive actions can be taken timely. This work addresses the issue of detection and classification of faults in offshore oil wells. The aim is to create a CBM system based on the random forest classifier to support decision-making. The events used in this work are part of the 3W database developed by Petrobras, Brazil, one of the world's largest oil producer. Seven events categorized as faulty events are considered, as well as several instances labeled as normal operation. We conducted two experiments related to two different classification scenarios. The proposed systems achieved an overall accuracy of 90\%, indicating that the system is not only able to detect faulty events but can also anticipate incoming failures successfully

High-Selectivity Filter Banks for Spectral Analysis of Music Signals

This paper approaches, under a unified framework, several algorithms for the spectral analysis of musical signals. Such algorithms include the fast Fourier transform (FFT), the fast filter bank (FFB), the constant- transform (C T), and the bounded- transform (B T), previously known from the associated literature. Two new methods are then introduced, namely, the constant- fast filter bank (C FFB) and the bounded- fast filter bank (B FFB), combining the positive characteristics of the previously mentioned algorithms. The provided analyses indicate that the proposed B FFB achieves an excellent compromise between the reduced computational effort of the FFT, the high selectivity of each output channel of the FFB, and the efficient distribution of frequency channels associated to the C T and B T methods. Examples are included to illustrate the performances of these methods in the spectral analysis of music signals.</p