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.

Cultivation of Rubrivivax gelatinosus in fish industry effluent for depollution and biomass production

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

Tratamento de esgoto e produção de água de reúso com o emprego de filtros de areia

In this research it was studied a system composed by the anaerobic filter combined with a sand filter for the wastewater treatment. For this, three anaerobic filters were operated with hydraulic detention time of nine hours which had the effluent disposed over four sand filters in different frequencies of application. on the first sand filter, 50 L.m(-2) were applied once a day. on the second, the third and the fourth filters, the same load was disposed in twice, three and four times a day, distributed between 9 a. m. and 4 p. m. The system presented a final effluent suitable for the COD and BOD legislation maximum limit to be discharged into water body (Decreto Paulista no 8,468/1976), showing the viability of dispose a higher quantity of effluent then the one suggested by NBR 13969/1997, which guides the constructive aspects for this kind of treatment.

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.

Uso de macroalgas para avaliação da Poluição orgânica no Rio Preto, noroeste do estado de São Paulo.

The Preto River, located in the northwest of São Paulo State, receives a total wastewater load of 15.150 kg DBO day-1, from which 13.685 kg DBO day-1 (90.5%) corresponds to domestic sewage, and the city of São José do Rio Preto contributes with 12.400 kg DBO day-1 (90% of domestic sewage). During the period from August 1990 through January 1991, monthly sampling was carried out to evaluate the use of macroalgae as bioindicator of organic pollution. Five sampling sites were established along the main river and the following variables were analised: temperature, conductance, turbidity, dissolved oxygen, BOD, COD, total and fecal coliforms, and composition and abundance of macroalgal communities. Data were submitted to analysis of variance, correlation coefficient, cluster analysis (four different approaches) and converted to biological indices (species deficit, relative pollution, saprobity, diversity and uniformity indices). A wide range in water quality was found (particularly for conductance, oxygen, BOD and COD) among the sampling sites, which were classified into three groups (polluted, moderately polluted and unpolluted/weakly polluted). As regards the occurrence and abundance of macroalgae the Rhodophyta were found only in unpolluted or weakly polluted sites, whereas Cyanophyta occurred mostly under high pollution load; the Chlorophyta species were observed under a wide range of conditions. Among the biological indices, saprobity was the most sensitive and correlated to all water variables and the other indices. Cluster analyses showed that the composition of macroalgal communities was consistent with the levels of organic pollution in the Preto River.

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.

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.

A load cell for grain-propelled ballistic rocket thrust measurement

A 1000-kgf resistive strain-gauge load cell has been developed for quality testing of rocket propellant grain. A 7075-T6 aluminum alloy has been used for the elastic column, in which 8 uniaxial, 120-Ω strain gauges have been bonded and connected to form a full Wheatstone bridge to detect the strain. The chosen geometry makes the transducer insensitive to moments and, also, to the temperature. Experimental tests using a universal testing machine to imposed compression force to the load cell have demonstrated that its behavior is linear, with sensitivity of 2.90 μV/kgf ± 0.34%, and negligible hysteresis. The designed force transducer response to a dynamic test has been comparable to that of a commercial load cell. © 2005 IEEE.