The adaptive compensations in endocrine pancreas from glucocorticoid-treated rats are reversible after the interruption of treatment

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

Effect of haematocrit on pump and kidney dysfunction after myocardial revascularization

Objective - Kidney dysfunction is a common complication after cardiac surgery. It occurs in 7 to 31% of the patients. The lowest haematocrit after cardiopulmonary bypass surgery (LHCT) has been identified as a risk factor for kidney dysfunction after cardiac surgery. The aim of this study is to determine whether different levels of haematocrit during cardiopulmonary bypass surgery are related to kidney dysfunction.Methods and results-A prospective study was conducted on consecutive adult patients undergoing myocardial revascularization. Preoperative renal function was assessed by baseline serum creatinine level (CrPre). Peak postoperative creatinine (CrPost) was defined as the highest daily in-hospital postoperative value. Peak fractional change in creatinine (% Delta Cr) was defined as the difference between the CrPre and CrePost represented as a percentage of the preoperative value. The LHTC was defined as the lowest recorded haematocrit prior to weaning from the initial pump run. A category variable was created for haematocrit based on the distribution of values. The category variable had the following cut-off points: less than 23%, 23.1 to 28% and greater than 28.1 %. Lowest haematocrit (26.62 +/- 4.15%), CPB (74.71 +/- 24.90 min), CrPre (1.23 +/- 0.37 mg/dl) and highest CrPost (1.52 +/- 0.47 mg/dl) data varied in near-normal fashion. Statistical significance has been observed in the < 23% lowest haematocrit group (CrIPOD and Cr5POD; P = 0.006) and the 23.1 28% lowest haematocrit level group (CrPre and Cr2POD; P = 0.047). CrPre and Cr5POD did not differ between groups (P > 0.05). The multiple linear regression model confirmed that the determinants for higher %Delta Cr were age, body surface area and preoperative serum creatinine level.Conclusion - The LHTC was not identified as a risk factor for kidney dysfunction after myocardial revascularization.

An adaptive view of caste differentiation in the neotropical wasp Polybia (Trichothorax) sericea Olivier (Hymenoptera: Vespidae)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

An adaptive system based on artificial neural networks for NPK fluid fertilizer application

The application process of fluid fertilizers through variable rates implemented by classical techniques with feedback and conventional equipments can be inefficient or unstable. This paper proposes an open-loop control system based on artificial neural network of the type multilayer perceptron for the identification and control of the fertilizer flow rate. The network training is made by the algorithm of Levenberg-Marquardt with training data obtained from measurements. Preliminary results indicate a fast, stable and low cost control system for precision fanning. Copyright (C) 2000 IFAC.

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 genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.