Desfolha artificial simulando danos de pragas na cultura do girassol (Helianthus annuus L., Asteraceae)

O girassol está sujeito às perdas de área foliar por diferentes fatores, dentre eles os insetos desfolhadores, contra aos quais geralmente são dirigidas aplicações de inseticidas na cultura. A desfolha artificial em plantas de importância econômica é uma metodologia útil na simulação de ataques dessas pragas em lavouras na determinação dos níveis de dano econômico. O objetivo deste estudo foi avaliar componentes de produção das plantas de girassol submetidas a níveis crescentes de desfolha de 0, 10, 25, 50, 75 e 100%, realizada em três distintos estádios fenológicos da cultura, a saber: V6 (seis folhas com no mínimo 4,0 cm de comprimento), R1 (quando a inflorescência circundada pela bráctea imatura torna-se visível) e R5.5 (50% das flores do disco estão fertilizadas ou em antese), perfazendo um total de 18 tratamentos, os quais foram dispostos em blocos ao acaso, com quatro repetições. Para todos os componentes de produção avaliados (diâmetro do capítulo, biomassa total de sementes da planta e biomassa de 100 aquênios) houve efeito significativo da interação dos tratamentos, evidenciando que o efeito da desfolha será dependente do estágio fenológico da planta. O estádio R5.5 foi mais sensível à desfolha, ocasionando perdas em todos os componentes de produção avaliados.

Testes para verificar a igualdade de parâmetros e a identidade de modelos de regressão não-linear em dados de experimento com delineamento em blocos casualizados

Considerou-se o ajustamento de equações de regressão não-linear e o teste da razão de verossimilhança, com aproximações pelas estatísticas qui-quadrado e F, para testar as hipóteses de igualdade de qualquer subconjunto de parâmetros e de identidade dos modelos para dados com repetições provenientes de experimento com delineamento em blocos completos casualizados. Concluiu-se que as duas aproximações podem ser utilizadas, mas a aproximação pela estatística F deve ser preferida, principalmente para pequenas amostras.

Dose inseminante utilizada na fertilização artificial de ovócito de piracanjuba (Brycon orbignyanus)

A piracanjuba (Brycon orbignyanus Valenciennes, 1849) é uma espécie de peixe migratória, ameaçada de extinção. O objetivo do presente estudo foi determinar a dose inseminante na fertilização artificial de ovócitos de piracanjuba. Para isso, utilizou-se delineamento em blocos casualizados, com quatro tratamentos e três repetições. Três casais de piracanjuba, selecionados dos tanques de reprodutores da Estação Ambiental de Itutinga (EAI - CEMIG), no período de piracema 2006/2007, receberam aplicação de hormônio extrato bruto de hipófise de carpa (EBHC) para obtenção dos gametas. Adotaram-se quatro tratamentos diferentes para a fertilização de 0,1 grama de ovócitos: 10µL, 20µL, 30µL e 40µL de sêmen. As amostras foram ativadas com 5 mL de água do próprio tanque e, em seguida, levadas para incubadoras, dotadas de renovação constante de água, à temperatura de 28ºC. Após 8 e 16 horas, analisaram-se as taxas de fertilização (ovos viáveis) e de eclosão dos ovos, respectivamente. Os resultados obtidos foram comparados pelo teste de Tukey a 5%. As relações sêmen-ovócitos testadas não alteraram as taxas de fertilização e eclosão (P>0,05). O número de espermatozoides-ovócitos, variando de 10,4 x10(5) a 41,6 x10(5), foi eficiente para obtenção de boas taxas de fertilidade.

Mechanical, electrical and electro-mechanical properties of thermoplastic elastomer styrene–butadiene–styrene/multiwall carbon nanotubes composites

Composites of styrene–butadiene–styrene (SBS) block copolymer with multiwall carbon nanotubes were processed by solution casting to investigate the influence of filler content, the different ratios of styrene/butadiene in the copolymer and the architecture of the SBS matrix on the electrical, mechanical and electro-mechanical properties of the composites. It was found that filler content and elastomer matrix architecture influence the percolation threshold and consequently the overall composite electrical conductivity. The mechanical properties are mainly affected by the styrene and filler content. Hopping between nearest fillers is proposed as the main mechanism for the composite conduction. The variation of the electrical resistivity is linear with the deformation. This fact, together with the gauge factor values in the range of 2–18, results in appropriate composites to be used as (large) deformation sensors.

Atributos físicos de um Latossolo Vermelho correlacionados linear e espacialmente com a consorciação de guandu com milheto

O sistema cultivo mínimo, por possibilitar pouca movimentação de solo, menor número de operações agrícolas sem incorporação dos resíduos vegetais, apresenta vantagens em razão do menor custo de preparo e da redução das perdas de solo e água. No ano agrícola de 2006/2007, na Fazenda de Ensino e Pesquisa da Faculdade de Engenharia de Ilha Solteira, SP, Brasil - FEIS/UNESP, situada nas condições do Cerrado Brasileiro, objetivou-se analisar a produtividade de massa de matéria seca da consorciação de forragem (guandu+milheto) (MSF), em função de atributos físicos do solo, tais como resistência à penetração (RP), umidade gravimétrica (UG), umidade volumétrica (UV) e densidade do solo (DS) nas profundidades de 0,0-0,10 m; 0,10-0,20 m e 0,20-0,30 m. Para tanto, foi instalado um ensaio, contendo 117 pontos amostrais, em um Latossolo Vermelho distroférrico, sob pivô central, numa área experimental de 1600 m² sob cultivo mínimo. A análise estatística constou de análise descritiva inicial dos atributos e análise das correlações lineares simples entre eles, e, finalmente, de análise geoestatística. Do ponto de vista da correlação espacial, o atributo que mais bem explica a produtividade de massa de matéria seca da consorciação é a densidade do solo na camada de 0,20-0,30 m, com uma correlação inversa, indicando que as espécies se desenvolvem bem em solos adensados.

Chronic stress disrupts neural coherence between cortico-limbic structures

Chronic stress impairs cognitive function, namely on tasks that rely on the integrity of cortico-limbic networks. To unravel the functional impact of progressive stress in cortico-limbic networks we measured neural activity and spectral coherences between the ventral hippocampus (vHIP) and the medial prefrontal cortex (mPFC) in rats subjected to short term stress (STS) and chronic unpredictable stress (CUS). CUS exposure consistently disrupted the spectral coherence between both areas for a wide range of frequencies, whereas STS exposure failed to trigger such effect. The chronic stress-induced coherence decrease correlated inversely with the vHIP power spectrum, but not with the mPFC power spectrum, which supports the view that hippocampal dysfunction is the primary event after stress exposure. Importantly, we additionally show that the variations in vHIP-to-mPFC coherence and power spectrum in the vHIP correlated with stress-induced behavioral deficits in a spatial reference memory task. Altogether, these findings result in an innovative readout to measure, and follow, the functional events that underlie the stress-induced reference memory impairments.

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Artificial neural networks for automatic modelling of the pectus excavatum corrective prosthesis

Pectus excavatum is the most common deformity of the thorax and usually comprises Computed Tomography (CT) examination for pre-operative diagnosis. Aiming at the elimination of the high amounts of CT radiation exposure, this work presents a new methodology for the replacement of CT by a laser scanner (radiation-free) in the treatment of pectus excavatum using personally modeled prosthesis. The complete elimination of CT involves the determination of ribs external outline, at the maximum sternum depression point for prosthesis placement, based on chest wall skin surface information, acquired by a laser scanner. The developed solution resorts to artificial neural networks trained with data vectors from 165 patients. Scaled Conjugate Gradient, Levenberg-Marquardt, Resilient Back propagation and One Step Secant gradient learning algorithms were used. The training procedure was performed using the soft tissue thicknesses, determined using image processing techniques that automatically segment the skin and rib cage. The developed solution was then used to determine the ribs outline in data from 20 patient scanners. Tests revealed that ribs position can be estimated with an average error of about 6.82±5.7 mm for the left and right side of the patient. Such an error range is well below current prosthesis manual modeling (11.7±4.01 mm) even without CT imagiology, indicating a considerable step forward towards CT replacement by a 3D scanner for prosthesis personalization.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Validation of percutaneous puncture trajectory during renal access using 4D ultrasound reconstruction

Background: An accurate percutaneous puncture is essential for disintegration and removal of renal stones. Although this procedure has proven to be safe, some organs surrounding the renal target might be accidentally perforated. This work describes a new intraoperative framework where tracked surgical tools are superimposed within 4D ultrasound imaging for security assessment of the percutaneous puncture trajectory (PPT). Methods: A PPT is first generated from the skin puncture site towards an anatomical target, using the information retrieved by electromagnetic motion tracking sensors coupled to surgical tools. Then, 2D ultrasound images acquired with a tracked probe are used to reconstruct a 4D ultrasound around the PPT under GPU processing. Volume hole-filling was performed in different processing time intervals by a tri-linear interpolation method. At spaced time intervals, the volume of the anatomical structures was segmented to ascertain if any vital structure is in between PPT and might compromise the surgical success. To enhance the volume visualization of the reconstructed structures, different render transfer functions were used. Results: Real-time US volume reconstruction and rendering with more than 25 frames/s was only possible when rendering only three orthogonal slice views. When using the whole reconstructed volume one achieved 8-15 frames/s. 3 frames/s were reached when one introduce the segmentation and detection if some structure intersected the PPT. Conclusions: The proposed framework creates a virtual and intuitive platform that can be used to identify and validate a PPT to safely and accurately perform the puncture in percutaneous nephrolithotomy.