Fisheries ecololgy and management of the Jaraqui (Semaprochilodus Taeniurus,S. Insignis) in central Amazonia

The decline of heavily exploited stocks of large, high quality, food fishes in central Amazonia has led to increasing fishing pressure on smaller taxa, especially the jaraqui (Semiprochilodus spp.). The Prochilodontids, now the most important species to the fisheries, are seined by commercial fishermen during three distinct peridos. At the beginning of the annual floods, schools of mature jaraquis in poor-water tributaries migrate downstream to spawn in the nutrient rich white-water rivers. During these rapid spawning runs, fishing effort is concentrated in the lowermost reaches of the tributaries. After spawning, they return in small groups to feed intensively in the flooded forest of the same tributaries from which they had migrated. This period, of approximately three months, may be considered as a natural closed season to the fisheries. The dispersal migration is most complex and requires distinct fishing strategies. In the middle of the floods large schools of fat jaraqui descend from the tributaries to the white-water rivers again. From there, they move upstream to different poor-water tributaries. As downstream movements are more diffcult to observe, fishermen remain at fixed fishing grounds. This contrasts with the dynamic strategies of the subsequent upstream fishing period which contributes 60 per cent of the annual catches. Fluctuations in catch are shown to reflect year to year variations in abundance, which are linked to the hydrological cycle. Considering that fishing over the stocks of jaraqui has already more than compensated for the deficit in catch of larger species, a combination of increased effort and environmental problems could lead, in a short period to a depletion of one of the most profitable fisheries of central Amazonia. Regulation of the fisheries could benefit from a more reasonable distribution of effort among other migratory illiophagous species which remain unexploited, if the goal of sustainable yield and conservation of these stocks is to be achieved. Reserved waters in large strategic units of at least 300 km along white-water rivers (which is equivalent to the maximum upstream displacement of jaraqui during their dispersal migrations) could also be useful to compensate for the loss of floodplain areas due to deforestaion, river regulation, use of pesticides, and mining. River impoundments in tributaries in central Amazonia may have little effect on jaraqui stocks, as spwning movements are unlikely to be directly interrupted by dams. However, alterations of the hydrological regime may also benefit from simulation of the flood cycle to mitigate potential negative impacts.     

Fish Quality Index Method: Principles,weaknesses, validation,and alternatives—A review

Fish is a high nutritional value matrix of which production and consumption have been increasing in the last years. Advancements in the efficient evaluation of freshness are essential to optimize the quality assessment, to improve consumer safety, and to reduce raw material losses. Therefore, it is necessary to use rapid, nondestructive, and objective methodologies to evaluate the quality of this matrix. Quality Index Method (QIM) is a tool applied to indicate fish freshness through a sensory evaluation performed by a group of assessors. However, the use of QIM as an official method for quality assessment is limited by the protocol, sampling size, specificities of the species, storage conditions, and assessor's experience, which make this method subjective. Also, QIM may present divergences regarding the development of microorganisms and chemical analysis. In this way, novel quality evaluation methods such as electronic noses, electronic tongues, machine vision system, and colorimetric sensors have been proposed, and novel technologies such as proteomics and mitochondrial analysis have been developed. In this review, the weaknesses of QIM were exposed, and novel methodologies for quality evaluation were presented. The consolidation of these novel methodologies and their use as methods of quality assessment are an alternative to sensory methods, and their understanding enables a more effective fish quality control.     

Forecasting of energy and diesel consumption and the cost of energy production in isolated electrical systems in the Amazon using a fuzzification process in time series models

Understanding the uncertainty inherent in the analysis of diesel fuel consumption and its impact on the generation of electricity is an important topic for planning the expansion of isolated thermoelectric systems in the state of Amazonas. In light of this, a decision support system has been developed to forecast the cost of electricity production using non-stationary data by integrating the methodology of time series models with fuzzy systems and optimization tools. The method presented herein combines the potential of the Autoregressive Integrated Moving Average (ARIMA) and the Seasonal ARIMA (SARIMA) models, such as the forecasting tool, with the advantages of fuzzy set theory to compensate for the uncertainties and errors encountered in the observed data, which would degrade the validity of forecasted values. The results show that incorporation of the α-cut concept facilitated the evaluation of risks while allowing simultaneous consideration of intervals for the unitary cost of energy production. This provides the analyst with the ability to make decisions using various predicted intervals with different membership values instead of the common practice of simply using the specific costs.     

Static Analysis of Buried Pipes Using Coupling between Layerwise Finite Element and Boundary Element Method

This work deals with the analysis of soil-structure interaction modeling of pipeline problems in static behavior using the coupling between FEM （finite element method） and BEM （boundary element method）. The representation of the pipe is made by MEF using one fmite element in the cylindrical panel formulated from the theory of equivalent discrete layers （Layerwise theory）, proposed by J. N. Reddy. The soil is represented by elastic continum infimite or semi-infinite and modeled using boundary elements with special curved surface, associated with cylindrical panel used to represent the soil-structure interaction within the soil, especially at the contact surface with the pipe.     

Green Cloud Meta-Scheduling

This article aims to evaluate the flexibility of GreenMACC (Metascheduling Green Architecture to Provide Quality of Service in Cloud Computing). The GreenMACC has a module called LRAM (Local Resource Allocation Manager) to automate the execution of all scheduling policies implemented in the architecture. This module enables the Meta-scheduler automatically adjust for each type of service requested by the user of a private cloud. Due to this function, can be ensure the most appropriate behavior to the principles of GreenIT while worrying about the quality of service. In this paper is shown the importance of the LRAM on GreenMACC. This article is also shown how to include a new policy in GreenMACC in a way that identifies the LRAM and automatically use. Through the performance evaluation of the new policy included it could be concluded that the GreenMACC is a flexible, reliable architecture and the LRAM module enables the automation of choosing the best scheduling mechanism in a private cloud.     

Development of Airfoils Based on Their Aerodynamic Characteristics Using Artificial Neural Networks

One of the main concerns in Engineering nowadays is the development of aircrafts of low consumption and high performance. For this purpose, airfoils are studied and designed to have an elevated lift coefficient and a low drag coefficient, thus generating a highly efficient airfoil. The higher the efficiency value is, the lower the aircraft fuel consumption will be; thus improving its performance. In this sense, this work aims to develop a tool for airfoil creation from some desired characteristics, such as the lift and drag coefficients and maximum efficiency rate, using an algorithm based on an ANN （artificial neural network）. In order to do so, a database of aerodynamic characteristics with a total of 300 airfoils was initially collected from the XFoil software. Then, through a routine implemented in the MATLAB software, network architectures of one, two, three and four modules were trained, using the back propagation algorithm and momentum. The cross-validation technique was applied to analyze the results, evaluating which network possesses the lowest value in RMS （root-mean-square） error. In this case, the best result obtained was from the two-module architecture with two hidden neuron layers. The airfoils developed by this network, in the regions with the lowest RMS, were compared to the same airfoils imported to XFoil. The presented work offers as a contribution, in relation to other works involving ANN applied to fluid mechanics, the development of airfoils from their aerodynamic characteristics.     

A middleware solution for integrating and exploring IoT and HPC capabilities

Even with the considerable advances in the development of middleware solutions, there is still a substantial gap in Internet of Things (IoT) and high-performance computing (HPC) integration. It is not possible to expose services such as processing, storage, sensing, security, context awareness, and actuating in a unified manner with the existing middleware solutions. The consequence is the utilization of several solutions with their particularities, thus requiring different skills. Besides that, the users have to solve the integration and all heterogeneity issues. To reduce the gap between IoT and HPC technologies, we present the JavaCá&Lá (JCL), a middleware used to help the implementation of distributed user-applications classified as IoT-HPC. This ubiquity is possible because JCL incorporates (1) a single application programming interface to program different device categories; (2) the support for different programming models; (3) the interoperability of sensing, processing, storage, and actuating services; (4) the integration with MQTT technology; and (5) security, context awareness, and actions services introduced through JCL application programming interface. Experimental evaluations demonstrated that JCL scales when doing the IoT-HPC services. Additionally, we identify that customized JCL deployments become an alternative when Java-Android and vice-versa code conversion is necessary. The MQTT brokers usually are faster than JCL HashMap sensing storage, but they do not perform distributed, so they cannot handle a huge amount of sensing data. Finally, a short example for monitoring moving objects exemplifies JCL facilities for IoT-HPC development.     

Enhanced LSCF oxygen deficiency through hydrothermal synthesis

High-performance perovskites are promising materials for diverse renewable energy technologies. Besides design characteristics, the devices performance depends on the material synthesis, since the processes occurring during synthesis may produce different structures and properties. In this work, the perovskite La_0.1Sr_0.9Co_0.9Fe_0.1O_3-δ (LSCF1991) was synthesized by different methods, and the phase composition and oxygen deficiency were assessed and discussed. We show that it is possible to increase oxygen deficiency by promoting oxygen release during crystallization within hydrothermal synthesis, producing a remarkable improvement of ?δ ~0.2 in comparison with the citrate method. The single phase LSCF1991 powder was characterized, shaped by different routes and sintered to demonstrate the stability and suitability to manufacture electrochemical devices.