Triplet entropy analysis of hemagglutinin and neuraminidase sequences measures influenza virus phylodynamics

The influenza virus has been a challenge to science due to its ability to withstand new environmental conditions. Taking into account the development of virus sequence databases, computational approaches can be helpful to understand virus behavior over time. Furthermore, they can suggest new directions to deal with influenza. This work presents triplet entropy analysis as a potential phylodynamic tool to quantify nucleotide organization of viral sequences. The application of this measure to segments of hemagglutinin (HA) and neuraminidase (NA) of H1N1 and H3N2 virus subtypes has shown some variability effects along timeline, inferring about virus evolution. Sequences were divided by year and compared for virus subtype (H1N1 and H3N2). The nonparametric Mann-Whitney test was used for comparison between groups. Results show that differentiation in entropy precedes differentiation in GC content for both groups. Considering the HA fragment, both triplet entropy as well as GC concentration show intersection in 2009, year of the recent pandemic. Some conclusions about possible flu evolutionary lines were drawn. © 2013 Elsevier B.V.

Neural network based on adaptive resonance theory with continuous training for multi-configuration transient stability analysis of electric power systems

This work presents a methodology to analyze electric power systems transient stability for first swing using a neural network based on adaptive resonance theory (ART) architecture, called Euclidean ARTMAP neural network. The ART architectures present plasticity and stability characteristics, which are very important for the training and to execute the analysis in a fast way. The Euclidean ARTMAP version provides more accurate and faster solutions, when compared to the fuzzy ARTMAP configuration. Three steps are necessary for the network working, training, analysis and continuous training. The training step requires much effort (processing) while the analysis is effectuated almost without computational effort. The proposed network allows approaching several topologies of the electric system at the same time; therefore it is an alternative for real time transient stability of electric power systems. To illustrate the proposed neural network an application is presented for a multi-machine electric power systems composed of 10 synchronous machines, 45 buses and 73 transmission lines. (C) 2010 Elsevier B.V. All rights reserved.

A comparative tunnelling network approach to assess interspecific competition effects in termites

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Neural network detection of grinding burn from acoustic emission

An artificial neural network (ANN) approach is proposed for the detection of workpiece `burn', the undesirable change in metallurgical properties of the material produced by overly aggressive or otherwise inappropriate grinding. The grinding acoustic emission (AE) signals for 52100 bearing steel were collected and digested to extract feature vectors that appear to be suitable for ANN processing. Two feature vectors are represented: one concerning band power, kurtosis and skew; and the other autoregressive (AR) coefficients. The result (burn or no-burn) of the signals was identified on the basis of hardness and profile tests after grinding. The trained neural network works remarkably well for burn detection. Other signal-processing approaches are also discussed, and among them the constant false-alarm rate (CFAR) power law and the mean-value deviance (MVD) prove useful.

A fast electric load forecasting using neural networks

The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, it is used Backpropagation algorithm with an adaptive process based on fuzzy logic. This methodology results in fast training, when compared to the conventional formulation of Backpropagation algorithm. Results are presented using data from a Brazilian Electric Company and the performance is very good for the proposal objective.

Transmission network expansion planning considering multiple generation scenarios

This paper presents a mathematical model and a methodology to solve a transmission network expansion planning problem considering open access. The methodology finds the optimal transmission network expansion plan that allows the power system to operate adequately in an environment with multiples generation scenarios. The model presented is solved using a specialized genetic algorithm. The methodology is tested in a system from the literature. ©2008 IEEE.

Market-driven security-constrained transmission network expansion planning

This paper presents a Bi-level Programming (BP) approach to solve the Transmission Network Expansion Planning (TNEP) problem. The proposed model is envisaged under a market environment and considers security constraints. The upper-level of the BP problem corresponds to the transmission planner which procures the minimization of the total investment and load shedding cost. This upper-level problem is constrained by a single lower-level optimization problem which models a market clearing mechanism that includes security constraints. Results on the Garver's 6-bus and IEEE 24-bus RTS test systems are presented and discussed. Finally, some conclusions are drawn. © 2011 IEEE.

Short-term multinodal load forecasting in distribution systems using general regression neural networks

Multinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, it is necessary a technique that is precise, trustable and has a short-time processing. This paper proposes two methodologies based on general regression neural networks for short-term multinodal load forecasting. The first individually forecast the local loads and the second forecast the global load and individually forecast the load participation factors to estimate the local loads. To design the forecasters it wasn't necessary the previous study of the local loads. Tests were made using a New Zealand distribution subsystem and the results obtained are compatible with the ones founded in the specialized literature. © 2011 IEEE.

Determinação de comunidades em redes biológicas integradas

To understand how biological phenomena emerge, the nonlinear interactions among the components envolved in these and the correspondent connected elements, like genes, proteins, etc., can be represented by a mathematical object called graph or network, where interacting elements are represented by edges connecting pairs of nodes. The analysis of various graph-related properties of biological networks has revealed many clues about biological processes. Among these properties, the community structure, i.e. groups of nodes densely connected among themselves, but sparsely connected to other groups, are important for identifying separable functional modules within biological systems for the comprehension of the high-level organization of the cell. Communities' detection can be performed by many algorithms, but most of them are based on the density of interactions among nodes of the same community. So far, the detection and analysis of network communities in biological networks have only been pursued for networks composed by one type of interaction (e.g. protein-protein interactions or metabolic interactions). Since a real biological network is simultaneously composed by protein-protein, metabolic and transcriptional regulatory interactions, it would be interesting to investigate how communities are organized in this type of network. For this purpose, we detected the communities in an integrated biological network of the Escherichia coli and Saccharomyces cerevisiae by using the Clique Percolation Method and we veri ed, by calculating the frequency of each type of interaction and its related entropy, if components of communities... (Complete abstract click electronic access below)

Integração de redes GNSS: uma proposta metodológica de densificação da rede SIRGAS na América do Sul

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Evaluating distributed generation impacts with a multiobjective index

Evaluating the technical impacts associated with connecting distributed generation to distribution networks is a complex activity requiring a wide range of network operational and security effects to be qualified and quantified. One means of dealing with such complexity is through the use of indices that indicate the benefit or otherwise of connections at a given location and which could be used to shape the nature of the contract between the utility and distributed generator. This paper presents a multiobjective performance index for distribution networks with distributed generation which considers a wide range of technical issues. Distributed generation is extensively located and sized within the IEEE-34 test feeder, wherein the multiobjective performance index is computed for each configuration. The results are presented and discussed.

Evaluating distributed time-varying generation through a multiobjective index

In the last decade, distributed generation, with its various technologies, has increased its presence in the energy mix presenting distribution networks with challenges in terms of evaluating the technical impacts that require a wide range of network operational effects to be qualified and quantified. The inherent time-varying behavior of demand and distributed generation (particularly when renewable sources are used), need to be taken into account since considering critical scenarios of loading and generation may mask the impacts. One means of dealing with such complexity is through the use of indices that indicate the benefit or otherwise of connections at a given location and for a given horizon. This paper presents a multiobjective performance index for distribution networks with time-varying distributed generation which consider a number of technical issues. The approach has been applied to a medium voltage distribution network considering hourly demand and wind speeds. Results show that this proposal has a better response to the natural behavior of loads and generation than solely considering a single operation scenario.

A novel single-phase hpf hybrid rectifier suitable for front-end trolleybus systems

This paper proposal presents the development and the experimental analysis of a new single-phase hybrid rectifier structure with high power factor (PF) and low harmonic distortion of current (THDI), suitable for application in traction systems of electrical vehicles pulled by electrical motors (trolleybus), which are powered by urban distribution network. This front-end rectifier structure is capable of providing significant improvements in trolleybuses systems and in the urban distribution network costs, and efficiency. The proposed structure is composed by an ordinary single-phase diode rectifier with parallel connection of a switched converter. It is outlined that the switched converter is capable of composing the input line current waveform assuring high power factor (HPF) and low THDI, as well as ordinary front-end converter. However, the power rating of the switched converter is about 34% of the total output power, assuring robustness and reliability. Therefore, the proposed structure was named single-phase HPF hybrid rectifier. A prototype rated at 15kW was developed and analyzed in laboratory. It was found that the input line current harmonic spectrum is in accordance with the harmonic limits imposed by IEC61000-3-4. The principle of operation, the mathematical analysis, the PWM control strategy, and experimental results of a 15kW prototype are also presented in this paper. © 2009 IEEE.

Traffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks

The prediction of the traffic behavior could help to make decision about the routing process, as well as enables gains on effectiveness and productivity on the physical distribution. This need motivated the search for technological improvements in the Routing performance in metropolitan areas. The purpose of this paper is to present computational evidences that Artificial Neural Network ANN could be use to predict the traffic behavior in a metropolitan area such So Paulo (around 16 million inhabitants). The proposed methodology involves the application of Rough-Fuzzy Sets to define inference morphology for insertion of the behavior of Dynamic Routing into a structured rule basis, without human expert aid. The dynamics of the traffic parameters are described through membership functions. Rough Sets Theory identifies the attributes that are important, and suggest Fuzzy relations to be inserted on a Rough Neuro Fuzzy Network (RNFN) type Multilayer Perceptron (MLP) and type Radial Basis Function (RBF), in order to get an optimal surface response. To measure the performance of the proposed RNFN, the responses of the unreduced rule basis are compared with the reduced rule one. The results show that by making use of the Feature Reduction through RNFN, it is possible to reduce the need for human expert in the construction of the Fuzzy inference mechanism in such flow process like traffic breakdown. © 2011 IEEE.

Metodologia para análise e interpretação de alarmes em tempo real de sistemas de distribuição de energia elétrica

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)