Evaluation and identification of lightning models by artificial neural networks

This paper describes a novel approach for mapping lightning models using artificial neural networks. The networks acts as identifier of structural features of the lightning models so that output parameters can be estimated and generalized from an input parameter set. Simulation examples are presented to validate the proposed approach. More specifically, the neural networks are used to compute electrical field intensity and critical disruptive voltage taking into account several atmospheric and structural factors, such as pressure, temperature, humidity, distance between phases, height of bus bars, and wave forms. A comparative analysis with other approaches is also provided to illustrate this new methodology.

Identification of Mechanical Systems through Kautz Filter

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

A fuzzy inference system for identification of crossflow filtration processes

The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behavior. In this paper, a system based on fuzzy logic systems is developed to overcome the problems usually found in the conventional mathematical models. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the fuzzy approach. Simulation results are presented to justify the validity of the proposed approach.

A SequenceSpace analysis of Lys49 phospholipases A2: Clues towards identification of residues involved in a novel mechanism of membrane damage and in myotoxicity

'SequenceSpace' analysis is a novel approach which has been used to identify unique amino acids within a subfamily of phospholipases A2 (PLA2) in which the highly conserved active site residue Asp49 is substituted by Lys (Lys49-PLA2s). Although Lys49-PLA2s do not bind the catalytic co-factor Ca2+ and possess extremely low catalytic activity, they demonstrate a Ca2+-independent membrane damaging activity through a poorly understood mechanism, which does not involve lipid hydrolysis. Additionally, Lys49-PLA2s possess combined myotoxic, oedema forming and cardiotoxic pharmacological activities, however the structural basis of these varied functions is largely unknown. Using the 'SequenceSpace' analysis we have identified nine residues highly unique to the Lys49-PLA2 sub-family, which are grouped in three amino acid clusters in the active site, hydrophobic substrate binding channel and homodimer interface regions. These three highly specific residue clusters may have relevance for the Ca2+-independent membrane damaging activity. Of a further 15 less stringently conserved residues, nine are located in two additional clusters which are well isolated from the active site region. The less strictly conserved clusters have been used in predictive sequence searches to correlate amino acid patterns in other venom PLA2s with their pharmacological activities, and motifs for presynaptic and combined toxicities are proposed.

Modeling and identification of fertility maps using artificial neural networks

The application of agricultural fertilizers using variable rates along the field can be made through fertility maps previously elaborated or through real-time sensors. In most of the cases applies maps previously elaborated. These maps are identified from analyzes done in soil samples collected regularly (a sample for each field cell) or irregularly along the field. At the moment, mathematical interpolation methods such as nearest neighbor, local average, weighted inverse distance, contouring and kriging are used for predicting the variables involved with elaboration of fertility maps. However, some of these methods present deficiencies that can generate different fertility maps for a same data set. Moreover, such methods can generate inprecise maps to be used in precision farming. In this paper, artificial neural networks have been applied for elaboration and identification of precise fertility maps which can reduce the production costs and environmental impacts.

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.

Identification of structural damage in flexible structures using system norm and neural networks

Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.

Identification of structural parameters for active vibration control

The study of algorithms for active vibration control in flexible structures became an area of enormous interest for some researchers due to the innumerable requirements for better performance in mechanical systems, as for instance, aircrafts and aerospace structures. Intelligent systems, constituted for a base structure with sensors and actuators connected, are capable to guarantee the demanded conditions, through the application of diverse types of controllers. For the project of active controllers it is necessary, in general, to know a mathematical model that enable the representation in the space of states, preferential in modal coordinates to permit the truncation of the system and reduction in the order of the controllers. For practical applications of engineering, some mathematical models based in discrete-time systems cannot represent the physical problem, therefore, techniques of identification of system parameters must be used. The techniques of identification of parameters determine the unknown values through the manipulation of the input (disturbance) and output (response) signals of the system. Recently, some methods have been proposed to solve identification problems although, none of them can be considered as being universally appropriate to all the situations. This paper is addressed to an application of linear quadratic regulator controller in a structure where the damping, stiffness and mass matrices were identified through Chebyshev's polynomial functions.

Collective screening tools for early identification of dyslexia

Current response to intervention models (RTIs) favor a three-tier system. In general, Tier 1 consists of evidence-based, effective reading instruction in the classroom and universal screening of all students at the beginning of the grade level to identify children for early intervention. Non-responders to Tier 1 receive small-group tutoring in Tier 2. Nonresponders to Tier 2 are given still more intensive, individual intervention in Tier 3. Limited time, personnel and financial resources derail RTI's implementation in Brazilian schools because this approach involves procedures that require extra time and extra personnel in all three tiers, including screening tools which normally consist of tasks administered individually. We explored the accuracy of collectively and easily administered screening tools for the early identification of second graders at risk for dyslexia in a two-stage screening model. A first-stage universal screening based on collectively administered curriculum-based measurements was used in 45 7 years old early Portuguese readers from 4 second-grade classrooms at the beginning of the school year and identified an at-risk group of 13 academic low-achievers. Collectively administered tasks based on phonological judgments by matching figures and figures to spoken words [alternative tools for educators (ATE)] and a comprehensive cognitive-linguistic battery of collective and individual assessments were both administered to all children and constituted the second-stage screening. Low-achievement on ATE tasks and on collectively administered writing tasks (scores at the 25th percentile) showed good sensitivity (true positives) and specificity (true negatives) to poor literacy status defined as scores <= 1 SD below the mean on literacy abilities at the end of fifth grade. These results provide implications for the use of a collectively administered screening tool for the early identification of children at risk for dyslexia in a classroom setting.

COMPOSITION OF FRESHLY HARVESTED BRAZILIAN ROYAL JELLY - IDENTIFICATION OF CARBOHYDRATES FROM THE SUGAR FRACTION

Samples of Brazilian royal jelly from Africanized Apis mellifera were analysed in order to determine the gross composition: crude moisture ranged from 67.80% to 69.40%, crude protein from 15.80% to 16.70%, crude lipid from 2.90% to 3.98% and-total sugars from 11.40% to 11.50%. The sugar fraction was investigated and revealed the presence of the following compounds identified by their retention time during HPLC analysis: ribose, fructose, glucose, sucrose, mannose, trehalose, erythritol, adonitol and mannitol.

Identification of a novel melittin isoform from Africanized Apis mellifera venom

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

Identification of new isolates of Bacillus thuringiensis using rep-PCR products and delta-endotoxin electron microscopy

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Identification of Salmonella spp. isolates from chicken abattoirs by multiplex-PCR

The present study was carried out to report the occurrence Salmonella spp., Salmonella Enteritidis, and Salmonella Typhimurium in chicken abattoirs. Samples of feces; feathers; scald, evisceration, and chiller water; and rinse water of non-eviscerated, eviscerated, and chilled carcass were collected from six chicken abattoirs. Salmonella isolates were identified by a multiplex-PCR using three sets of primers targeting the inuA, pefA, and sefA gene sequences from Salmonella spp., S. Typhimurium and S. Enteritidis, respectively. Salmonella spp. was detected in 10% (29/288) of the samples, whereas serovars Enteritidis and Typhimurium were identified in 62% (7/288), respectively. The results indicate the need to improve hygiene and sanitary standards in poultry slaughter lines, besides the education of food handlers and information to consumers. (c) 2006 Elsevier Ltd. All rights reserved.

Identification of a bisegmented double-stranded RNA virus (picobirnavirus) in calf faeces

To determine the incidence of rotavirus infection among dairy herds in the State of Sdo Paulo, Brazil, 576 faecal samples obtained from calves aged 1-45 days with and without diarrhoea, reared on 63 dairy cattle farms, were analyzed. Polyacrylamide gel electrophoresis (PAGE) identified 28 samples positive for group A rotavirus, while four samples, two diarrhoeic and two non-diarrhoeic, showed a bisegmented genome with a typical picobirnavirus pattern. Electron microscopy revealed spherical virus particles with a diameter of 37 nm and without a defined surface structure. The present study is the first report of a bisegmented virus identified in cattle in Brazil. (C) 2003 Elsevier B.V. Ltd. All rights reserved.