Partial characterization of the polluting load of swine wastewater treated with an integrated biodigestion system

The stabilization of swine wastewaters from swine confined housing by the combination of a upflow anaerobic sludge blanket (UASB) reactor and waste stabilization ponds is a viable alternative to minimize the environmental impact caused by inadequate disposal of swine wastewaters. In the present study, the polluting load of pre-decanted swine wastewater treated with a series of two 0.705 m(3) UASB reactors and then in parallel in aerated and non-aerated stabilization tanks was investigated from January to July, 2000. Physicochemical and microbiological analyses were made adopting standard methods (Standard Methods for Examination of Water and Wastewater, 19th ed., American Public Health Association, Washington, DC, 1995). COD values decreased as the wastewater ran through the integrated biodigestion system dropping from about 3492 +/- 511-4094 mg l(-1) +/- 481 to 124 +/- 52-490 mg l(-1) +/- 230, while nitrate and nitrite levels increased in stabilization tanks, ranging respectively from 4 +/- 0 to 20 mg l(-1) +/- 3 and 3 +/- 1 to 11 mg l(-1) +/- 24. Although the removal of Escherichia coli was more than 97% +/- 6, the effluents of the treatment system still contained unacceptable levels of E. coli (1.6 x 10(3)-1.2 x 10(6) 100 ml(-1)) according to WHO guidelines for use of wastewater in agriculture and aquaculture. These results indicate the necessity of changes on operational characteristics of the treatment system such as an increase of the hydraulic retention time in UASB reactors or in stabilization tanks. (C) 2003 Published by Elsevier Ltd.

Relative contributions of beta-cell function and tissue insulin sensitivity to fasting and postglucose-load glycemia

We performed hyperglycemic clamps in 283 nondiabetic Caucasians and, with multiple linear regression, determined the contribution of beta-cell function and tissue insulin sensitivity to variations in glycemia and insulinemia during oral glucose tolerance tests (OGTTs). Impaired glucose tolerance (IGT) subjects had reduced insulin sensitivity(P < .02) and beta-cell function (P < .0001). Normal glucose tolerance (NGT) subjects with first-degree type 2 diabetic relatives had reduced first and second phase insulin secretion (both, P < .05), but normal insulin sensitivity(P = .37). beta-Cell function and insulin sensitivity accounted for one fourth of the variability in glucose tolerance. Fasting plasma glucose in subjects with NGT (n = 185) was a function of both phases of insulin secretion and of insulin sensitivity tall, P < .05), whereas, in IGT subjects (n = 98), it was a function of first phase insulin secretion and insulin sensitivity(P < .01). Two-hour glycemia was a function of second phase secretion and insulin sensitivity (P < .01). Fasting and 2-hour plasma insulin levels were determined by insulin sensitivity land glycemia) in NGT subjects (P < .001), but by second phase secretion in IGT (P < .001). We conclude that beta-cell function is reduced in subjects with IGT; glycemia and insulinemia are not regulated by the same mechanisms in IGT and NGT; insulin sensitivity does not contribute to insulinemia in IGT; family history of diabetes influences beta-cell function, but not insulin sensitivity in Caucasians. Copyright (C) 2000 by W.B. Saunders Company.

Methodology for load compensation in four-wire electrical power systems based on instantaneous sampling

This work describes a methodology for power factor control and correction of the unbalanced currents in four-wire electric circuits. The methodology is based on the insertion of two compensation networks, one wye-grounded neutral and other in delta, in parallel to the load. The mathematical development has been proposed in previous work [3]. In this paper, however, the determination of the compensation susceptances is based on the instantaneous values of load currents. The results are obtained using the MatLab-Simulink enviroment

Load torque estimation in induction motors using artificial neural networks

The induction motors are largely used in several industry sectors. The dimensioning of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this paper is to use artificial neural networks as tool for dimensioning of induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Simulation results are also presented to validate the proposed approach.

Electromyographic study in Erector spinae, Rectus abdominis, Glutaeous maximus and Rectus femuris muscles, in standing and static posture with load on hands

Observe the loads associates application with in position of body, in the static or dynamic postures. Methods: the electromyographic study in erector spinae, rectus abdominis, glutaeous maximus and rectus femoris muscles was accomplished in female volunteers from 18 have 27 years old, previously selected. The muscles electric activities was gotten with surface electrodes, in standing and static posture, with the parallels and horizontal upper limbs with load on their hands. Conclusion: In this study it was clearly observed influence of the load and distance there is over studied musculature associated with standing erect posture.

Generalised single-equation load flow method for unbalanced distribution systems

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

Numeric evaluation of the stiffness of log-concrete composite beams submitted the cyclical load

In this paper was evaluated, using the software ANSYS, the stiffness (El) of the log-concrete composite beams, of section T, with connectors formed by bonded-in steel rods, type CA-50, disposed in X, with application of cyclical load. The stiffness of the system was evaluated through the simulation of bending tests, considered 1/2 beam, with cyclical shipment varying among 40 % and 5 % of the strength of the connection with the load relationship R=0,125, for a total of 10 load cycles applied. The numeric results show a good agreement with experimental tests.

Multi-Agent Framework for Spatial Load Forecasting

A multi-agent framework for spatial electric load forecasting, especially suited to simulate the different dynamics involved on distribution systems, is presented. The service zone is divided into several sub-zones, each subzone is considered as an independent agent identified with a corresponding load level, and their relationships with the neighbor zones are represented as development probabilities. With this setting, different kind of agents can be developed to simulate the growth pattern of the loads in distribution systems. This paper presents two different kinds of agents to simulate different situations, presenting some promissory results.

Synchronous machines parameters identification using load rejection test data

This work shows a computational methodology for the determination of synchronous machines parameters using load rejection test data. The quadrature axis parameters are obtained with a rejection under an arbitrary reference, reducing the present difficulties.

Staphylococcal carriage and antibodies to toxic shock syndrome toxin 1 in Brazilian women

A study of 215 women from different socioeconomic backgrounds in Botucatu, Brazil, was conducted to reveal possible clues why toxic shock syndrome (TSS) is seldom diagnosed in Brazil. Of the 215 women, 79 were colonized with Staphylococcus aureus either in the nasal passages and/or in the vaginal area, which is comparable to the colonization of individuals in the developed countries Thirteen of the women were colonized with S. aureus that produced toxic shock syndrome toxin-1 (TSST-1), the toxin responsible for the majority of cases of TSS. Eleven strains produced enterotoxin B, the only enterotoxin implicated in TSS, primarily in non-menstrual TSS. Enterotoxin A was produced by 15 strains and is commonly associated with the production of TSST-1, but has not been implicated in TSS. Seven strains produced enterotoxin D and one strain produced enterotoxin C, but these have not been implicated in TSS. Only 9 women used tampons which may be a major reason for the lack of menstrual TSS in Brazil, Only two of the 49 women whose sera were examined for the presence of antibodies to TSST-1 had no or very low antibody titers, the major protection against the development of TSS, both menstrual and non-menstrual TSS. This is a lower percentage than has been observed in the developed countries. Although another possibility for the lack of TSS in Brazil is the failure to recognize the disease, however, the results of this limited study indicate the importance of low usage of tampons and the high percentage of individuals with antibodies to TSST-1. The socioeconomic backgrounds of the participants were of little significance.

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.

Identification of synchronous machine parameters using load rejection test data

This work shows a computational methodology for the determination of synchronous machines parameters using load rejection test data. By machine modeling one can obtain the quadrature parameters through a load rejection under an arbitrary reference, reducing the present difficulties. The proposed method is applied to a real machine.

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.