A novel neural model to electrical load forecasting in transformers

The paper describes a novel neural model to electrical load forecasting in transformers. The network acts as identifier of structural features to forecast process. So that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through load data extracted from a Brazilian Electric Utility taking into account time, current, tension, active power in the three phases of the system. The results obtained in the simulations show that the developed technique can be used as an alternative tool to become more appropriate for planning of electric power systems.

Influences of thermal and electric load fluctuations in the cogeneration attractiveness

The use of mean values of thermal and electric demand can be justifiable for synthesising the configuration and for estimating the economic results because it simplifies the analysis in a preliminary feasibility study of a cogeneration plant. For determining the cogeneration scheme that best fits the energetic needs of a process several cycles and combinations must be considered, and those technically feasible will be analysed according to economic models. Although interesting for a first approach, this procedure do not consider that the peaks and valleys present in the load patterns will impose additional constraints relatively to the equipment capacities. In this paper, the effects of thermal and electric load fluctuation to the cogeneration plant design were considered. An approach for modelling these load variability is proposed for comparing two competing thermal and electric parity competing schemes. A gas turbine associated to a heat recovery steam generator was then proposed and analysed for thermal- and electric-following operational strategies. Thermal-following option revealed to be more attractive for the technical and economic limits defined for this analysis. (c) 2006 Elsevier Ltd. All rights reserved.

Electrical load forecasting formulation by a fast neural network

The objective of this work is to develop a methodology for electric load forecasting based on a neural network. Here, backpropagation algorithm is used with an adaptive process that based on fuzzy logic and using a decaying exponential function to avoid instability in the convergence process. This methodology results in fast training, when compared to the conventional formulation of backpropagation algorithm. The results are presented using data from a Brazilian Electric Company, and shows a very good performance for the proposal objective.

The inhibitory effect of MK886 on the inflammatory process caused by Paracoccidioidomycosis brasiliensis infection in genetically selected mice

Leukotrienes are classic inflammatory response mediators considered chemotactic agents and microbicidal activity regulators in cells of the innate immune system, playing a protective role against different infectious agents. In this study, we investigated the involvement of leukotrienes in the course of murine paracoccidioidomycosis based on the following immunologic parameters: cell influx, mieloperoxydase activity, NO production, cytokine production, and fungal recovery in lungs of mice selected according to the intensity of their low (AIRmin) and high (AIRmax) acute inflammatory response. Infection by P. brasiliensis induced considerable production of IL-6, IL-10, IFN-gamma and TNF-alpha cytokines, and led to cell recruitment, as well as NO production in lungs at different study periods. In animals treated with MK886, a leukotriene biosynthesis inhibitor, IFN-gamma, IL-6 and TNF-alpha production was lower, while neutrophil influx and NO production decreased. These results may explain the higher fungal load in lungs of animals in which leukotriene synthesis was inhibited, suggesting that leukotrienes have a possible protective role in experimental paracoccidioidomycosis. AIRmax animals had lower fungal load in comparison with AIRmin ones, which can be related to the AIR phenotype regarding neutrophil migration, besides lower production of NO and pro-inflammatory cytokines. Thus, mice presenting AIRmax background are more resistant to infection by P. brasiliensis.

Generalised single-equation load flow method for unbalanced distribution systems

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

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.

A fast electric load forecasting using adaptive neural networks

This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.

Effect of diclofenac sodium on the healing process of end-to-end anastomosis in carotid arteries of rabbits. Histopathological and biomechanical studies

Aim. Diclofenac sodium is a non-steroidal anti-inflammatory drug commonly used to attenuate painful inflammatory reactions in surgery. However, it may delay healing in the skin and gastrointestinal tract. The aim of this study was to evaluate the influence of Diclofenac in vascular healing. Methods. Ninety rabbits had their carotid arteries sectioned and reconstructed by end-to-end anastomosis with interrupted sutures. The animals were randomly allocated into 3 groups of 30 each and treated by intramuscular route with saline (control), 5 mg/kg/day of diclofenac sodium (DS-5), and 10 mg/kg/day of diclofenac sodium (DS-10). Treatment began on the day of surgery and lasted 4 days. Angiography, biomechanical properties (failure load, failure elongation, yield point, yield point elongation, and stiffness were obtained from the load/elongation curve), macroscopic and histological examinations (hematoxylin-eosin, Masson, Calleja, Picrossirius-red), and scanning electron microscopy were studied in both arteries on the 3rd and 15th postoperative days. Results. No significant differences in biomechanical properties were observed either in the 3 groups or the experimental times. The carotid artery healing process was similar in the 3 groups. Conclusion. Diclofenac sodium did not cause alterations nor delayed carotid artery healing.

Analysis of efficiency and rationality in a sugar-alcohol mill using hyperboloid a-load and potency

Brazil is the world's largest producer of sugar cane and which in the state of São Paulo concentrate the greatest amount of sugar cane field of the country. The sugar-alcohol sector has the capacity to produce sufficient thermal and electrical energy to be used in their process of production and commercialize of surplus in electricity distribution network. Therefore it is necessary to evaluate the energy efficiency and rationality within the mill. Accordingly this research proposed analyze the sugar-alcohol mill's sectors globally and individually, located in the west center of the São Paulo state, using the valuation methodology employed by the Agência Nacional de Energia Elétrica (ANEEL) in the industries that do not have systems of cogeneration. In this analysis, the hyperboloids of load and potency were applied based on the indexes of potency factor and load factor that allow estimate the efficiency and rationality. © 2013 IEEE.

Influence of standard load micro and nanopatterned in surface roughness of bleached teeth and submitted to different surface treatments?

The aim of this study was evaluate the dental enamel after whitening treatment with Opalescence Boost PF® 38%, correlating the structural alterations in the surface of the enamel with its respective pH and verify if whitened teeth submitted to different finishing and polishing techniques show similar surface texture to healthy teeth (control group). Sixty premolars were divided in 6 groups (n = 10), which had been immersed in artificial saliva during all the experiment. Protocol whitening was performed according to the manufacturer recommendations, and then the specimens were submitted to different polishing technique with Sof-Lex Pop On® disks, Flex Diamond® felt disks using two different micrometric polishing pastes (Enamelize® and Diamond Polish®) and two nanometric polishing pastes (Lummina-E Diamond and Lummina-E Alumina), according to the groups. Representative specimens were analyzed in scanning electronic microscopy (SEM). Whitening gel used in this experiment had modified the morphologic aspect of the enamel surface. It was found that two nanometric polishing pastes (G5 and G6) promoted a less rough surface compared to control group even after the whitening process.