Movement of Heterorhabditis amazonensis and Steinernema arenarium in search of corn fall armyworm larvae in artificial conditions

Spodoptera frugiperda (Smith, 1797) (Lepidoptera: Noctuidae) is considered to be the main pest of maize crops in Brazil. Entomopathogenic nematodes (EPN) may be used to control this pest and exhibit different, unique abilities to search for their hosts. The movement of EPN in relation to S. frugiperda was evaluated. To test for horizontal movement, a styrofoam enclosure filled with sand was divided into segments, nematodes were placed at the entrance to the enclosure and a larva was placed at the end of each division. The same approach was used to evaluate vertical movement; however, PVC pipes were used in this case. In general, the mortality was inversely proportional to the initial distance between host and nematodes. In the vertical displacement test, both nematodes were able to kill the larvae up to a distance of 25 cm. Therefore, the infective juveniles of H. amazonensis and S. arenarium can search out, infect and kill larvae of S. frugiperda at distances of up to 60 cm and 25 cm of horizontal and vertical displacement, respectively.

MORPHOLOGY AND WEIGHT OF AFRICANIZED QUEEN BEES PRODUCED IN DIFFERENT DIAMETERS OF ARTIFICIAL, CUPS

The present work showed that the newly emerged queen weight, the number of ovarioles. and the spermatheca mass are not influenced by the sizes of the wax cups. The first two characters suffered time transference influence, and the best acceptances were obtained mainly, in wax cups with diameters of 7, 8, and 9 min.

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.

Prediction of occurrences of diverse chemical classes in the Asteraceae through artificial neural networks

The training and the application of a neural network system for the prediction of occurrences of secondary metabolites belonging to diverse chemical classes in the Asteraceae is described. From a database containing about 604 genera and 28,000 occurrences of secondary metabolites in the plant family, information was collected encompassing nine chemical classes and their respective occurrences for training of a multi-layer net using the back-propagation algorithm. The net supplied as output the presence or absence of the chemical classes as well as the number of compounds isolated from each taxon. The results provided by the net from the presence or absence of a chemical class showed a 89% hit rate; by excluding triterpenes from the analysis, only 5% of the genera studied exhibited errors greater than 10%. Copyright (C) 2004 John Wiley Sons, Ltd.

Using artificial neural networks for identification of electrical losses in transformers during the manufacturing phase

The paper describes a novel neural model to estimate electrical losses in transformer during the manufacturing phase. The network acts as an identifier of structural features on electrical loss process, so that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through experimental data taking into account core losses, copper losses, resistance, current and temperature. The results obtained in the simulations have shown that the developed technique can be used as an alternative tool to make the analysis of electrical losses on distribution transformer more appropriate regarding to manufacturing process. Thus, this research has led to an improvement on the rational use of energy.

Potentiometric determination of saccharin in commercial artificial sweeteners using a silver electrode

A simple, precise, rapid and low-cost potentiometric method for saccharin determination in commercial artificial sweeteners is proposed. Saccharin present in several samples of artificial sweeteners is potentiometrically titrated with silver nitrate solution using a silver wire as the indicator electrode, coupled to a titroprocessor. The best pH range was from 3.0 to 3.5 and the detection limit of sodium saccharin was 2.5 mg/ml. Substances normally found along with saccharin in several commercial artificial sweeteners such as maltodextrin, glucose, sucrose, fructose, aspartame, cyclamate, caffeine, sorbitol, lactose, nitrate, methyl- and n-propyl-p-hydroxybenzoate, benzoic, citric and ascorbic acids do not interfere even in significant amounts (e.g. 20 excess relative to saccharin). Chloride ion interferes when present in concentrations larger than 10 mg l(-1); this interference is eliminated with previous extraction of the sweetener from the aqueous medium with ethyl acetate. The results obtained by applying the proposed method compared very favorably with those given by the HPLC method recommended by the FDA. (C) 2003 Elsevier Ltd. All rights reserved.

LH and FSH concentration and follicular development in Nellore heifers submitted to fixed-time artificial insemination protocols with different progesterone concentrations

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