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.

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.

Smart structures health monitoring using artificial neural network

This paper presents a non-model based technique to detect and locate structural damage with the use of artificial neural networks. This method utilizes high frequency structural excitation (typically greater than 30 kHz) through a surface-bonded piezoelectric sensor/actuator to detect changes in structural point impedance due to the presence of damage. Two sets of artificial neural networks were developed in order to detect, locate and characterize structural damage by examining changes in the measured impedance curves. A simulation beam model was developed to verify the proposed method. An experiment was successfully performed in detecting damage on a 4-bay structure with bolted-joints, where the bolts were progressively released.

Influência do intervalo entre a inseminação artificial com sêmen diluído resfriado e transportado e a ovulação sobre a fertilidade de éguas

One hundred forty seven cycles of mares were allocated in a completely randomized experiment, with different number of replications and divided in four treatments (T1 = 24 hours preovulation; T2 = 48 hours preovulation; T3 = 48 hours preovulation and in tbe same day of ovulation; T4 = 72 hours preovulation and in the same day of ovulation), in order to study the effect of AI/ovulation interval on mare fertility. The mares were inseminated three times for wek (monday, wednesday and friday), with semen of only one stallion diluted in extender skim milk-glucose, using a volume of 15ml, with 400 x 10(6) sptz viable, cooled at 14 degrees C/3.6 hours, and transported in modified container Celle. Conception rates were not different according to the treatments. So, observed spermatic survival for 60 hours showed the practicability of the inseminations on monday, wednesday and friday.

Uterine leucocyte infiltration after artificial insemination in bitches

In the present study, polymorphonuclear neutrophils (PMN) were enumerated to evaluate acute uterine inflammation after artificial insemination in the bitch. It was concluded that the canine seminal plasma possessed an immunomodulating action. However, the most commonly used extender for freezing canine semen (Tris glucose with egg yolk and glycerol) was a potential inducer of uterine inflammation. (c) 2006 Published by Elsevier B.V.

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.

Structural health evaluation by optimization techinique and artificial neural network

This paper presents two different approaches to detect, locate, and characterize structural damage. Both techniques utilize electrical impedance in a first stage to locate the damaged area. In the second stage, to quantify the damage severity, one can use neural network, or optimization technique. The electrical impedance-based, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations, this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors, and therefore, it is able to detect the damage in its early stage. Optimization approaches must be used for the case where a good condensed model is known, while neural network can be also used to estimate the nature of damage without prior knowledge of the model of the structure. The paper concludes with an experimental example in a welded cubic aluminum structure, in order to verify the performance of these two proposed methodologies.