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.

Response of Corriedale and Crioula Lanada sheep to artificial primary infection with Haemonchus contortus

Clinical, parasitological and biochemical parameters were evaluated in Corriedale and Crioula Lanada sheep after a single experimental infection with Haemonchus contortus. Ten 4-month-old worm-free lambs, of each breed, were infected with 200 L-3 H. contortus per kg live weight,and four uninfected animals of each breed were used as controls. Every week, the animals were weighed and blood and faecal samples were collected for measurement of packed cell volume (PCV), total serum protein (TSP) and albumin (ALB), and the number of eggs per gram of faeces (EPG), respectively. Twelve weeks after infection, the animals were slaughtered. The worm burden was determined and samples of the abomasal mucosa were processed for determination of the number of eosinophils, mast cells and globule leukocytes. No significant differences in PCV, TSP, ALB, parasite burden or the cell populations of the abomasal mucosa were observed between breeds, but Crioula lambs had a lower EPG count. The comparison of the infected groups with their respective controls revealed significant alterations in PCV, TSP and ALB in the Corriedale lambs and in PCV, TSP, ALB and the density of eosinophils and mast cells in the Crioula lambs.

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.

APPLICATIONS OF ARTIFICIAL-INTELLIGENCE IN ORGANIC-CHEMISTRY .17. NEW PROGRAMS IN THE SISTEMAT PROJECT

This work describes the new improvements of the SISTEMAT project, one system for structural elucidation mainly in the field of Natural Products Chemistry. Some examples of the resolution of problems using C-13 Nuclear Magnetic Resonance and Mass Spectroscopy are given. Programs to discover new heuristic rules for structure generation are discussed. The data base contains about 10000 C-13 NMR spectra.

Weather-based prediction of anthracnose severity using artificial neural network models

Data were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides. Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21.9% for the Australian and 22.1% for the South American model. of the six cross-continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development.

Homogeneous photodegradation of CI Reactive Blue 4 using a photo-Fenton process under artificial and solar irradiation

The oxidation of C.I. Reactive Blue 4 (RB4) by photo-Fenton process mediated by lerrioxalate was investigated under artificial and solar irradiation. The RB4 degradation in acidic medium (pH 2.5) was evaluated by the decrease in Total Organic Carbon (TOC) content and color, measured by the decrease in chromophore absorption band (600 nm). The influence of ferrioxalate and H2O2 concentrations on the dye degradation was studied and best results were obtained using 1.0 mM ferrioxalate and 10 nM of hydrogen peroxide. Under these experimental conditions, 80% of TOC and 100% of color removal were obtained for a 0.1 mM RB4 dye in 35 min of solar irradiation. (c) 2006 Elsevier Ltd. All rights reserved.

Efeito da lavagem artificial na eficiência de acaricidas-ovicidas no controle de Brevipalpus phoenicis (Geijskes, 1939) (Acari: Tenuipalpidae) em citros

The objective of this research was to test the control of Brevipalpus phoenicis (Geijskes, 1939) by the hexythiazox and quinometionato products in citrus crop with and without adhesive spread and when submitted to artificial rain. The plants were sprayed at 2, 4, 8, 24 and 48 hours. When dried, the fruits were collected (eight/plants) and half of them were washed in laboratory with artificial rainfall on the basis of 30 mm/h during 15 minutes establishing the following treatments: T1 - hexythiazox + washing; T2 - hexythiazox + agral + washing; T3 - quinometionato + washing; T4 - quinometionato + agral + washing; T5 - control + agral + washing; T6 - hexythiazox; T7 - hexythiazox + agral; T8 - quinometionato; T9 - quinometionato + agral; T10 - control + agral. Thus, all the fruits were inoculated with ten females of B. phoenicis and five days later the mites alive were counted and eliminated. Approximately 15 days later the number of larvae alive were also counted. The results obtained allowed the following conclusions: a) the acaricides were efficient to control B. phoenicis; b) the rainfall (washing) did not alter the efficiency; c) the agral did not change the results.

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.

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.