Indução de embriões somáticos a partir de embriões zigóticos de Heliconia bihai (L.) L. cv. Lobster Claw Two

As flores tropicais têm contribuído para o aumento do mercado florístico no Brasil; dentre elas destacam-se as do gênero Heliconia. A propagação de helicônias que atinge maior número de mudas ocorre por via vegetativa, por meio da divisão de rizomas, aumentando a probabilidade de disseminação de doenças, podendo, num futuro próximo, comprometer a produção de flores. O cultivo in vitro surge como importante alternativa para proporcionar a produção de mudas de helicônia com elevado padrão de qualidade. Entretanto, a micropropagação de helicônias via ápices caulinares depara-se com problemas de contaminação endofítica, dificultando a multiplicação do explante. Diante disso, fez-se necessário avaliar alternativas que promovam a produção de mudas in vitro, por meio da utilização de outros explantes. Para tanto, realizou-se um experimento, utilizando embriões zigóticos, provenientes de frutos imaturos e maduros de H. bihai (L.) L. cv. Lobster Claw Two, em combinações de AIA (0; 5,70 e 11,41 µm L-1) e 2,4-D (0; 22,62; 45,24; 67,86 e 90,48 µm L-1), para induzir a formação de embriões somáticos durante 90 dias. Por meio de análise histológica, observou-se a formação de embriões somáticos apenas nos embriões zigóticos, provenientes de frutos maduros, cultivados na ausência de reguladores de crescimento.

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.

Indução, análises morfológicas e ultraestruturais de calos de maracujazeiro nativo

A análise de calos que apresentem características embriogênicas é importante para posterior regeneração, in vitro, de espécies com características agronômicas desejáveis, como o maracujazeiro nativo Passiflora gibertii. Diante do exposto, objetivou-se, com este trabalho, analisar a indução de calos oriundos de explantes foliares de Passiflora gibertiiN. E. Brown, bem como caracterizá-los, morfológica e ultraestruturalmente. Para obtenção de calos, folhas cotiledonares foram inoculadas, em meio de cultura, suplementado com picloram e 2,4-D, combinados com cinetina. Após 30 dias em meio de cultura, no escuro, os calos obtidos foram preparados para a visualização em microscopia eletrônica (transmissão e varredura) e microscopia de luz. Os resultados permitem afirmar que a adição de picloram e cinetina ao meio de cultura promove maior formação de calos em explantes foliares de P. gibertii que 2,4-D e cinetina. O regulador 2,4-D proporciona a obtenção de calos com células de formato isodiamétrico, pequenas e com pequeno espaço intercelular, sistema celular organizado e predominância de mitocôndrias de formato arredondado. Já com a utilização do regulador de crescimento picloram, observa-se a predominância de células grandes e de formato alongado, de espaços intercelulares, de sistema celular desorganizado e de mitocôndrias de formato alongado.

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.

Applications of artificial intelligence to condition-based maintenance

In this paper we discuss interesting developments of expert systems for machine diagnosis and condition-based maintenance. We review some elements of condition-based maintenance and its applications, expert systems for machine diagnosis, and an example of machine diagnosis. In the last section we note some problems to be resolved so that expert systems for machine diagnosis may gain wider acceptance in the future.

Aspectos ecológicos da leishmaniose tegumentar americana: 2. ecótopo artificial como abrigo de Psychodopygus intermedius e observações sobre alimentação e reprodução sob influência de fatores físicos naturais

As observações levadas a cabo em um galinheiro experimental mostraram que Psychodopygus intermedius tem a capacidade de nele abrigar-se. Para se chegar a este resultado, a metodologia utilizada consistiu na coleta total diurna e exame do estado de alimentação e digestão sangüínea dos Ps. intermedius como parâmetro da sua maior ou menor permanência no ecótopo estudado. Além disso, observou-se paralelamente, os tempos para o repasto sangüíneo, digestão completa, oviposição, sobrevivência e cópula sob a influência direta dos fatores físicos naturais. A importância epidemiológica dos resultados reside em novas elucidações experimentais sobre a viabilidade da transmissão da leshmaniose tegumentar ocorrer em ambiente domiciliar.

Controlo de velocidade e monitorização de um motor de indução trifásico suportados em microcontrolador

O bom desempenho dos motores de indução trifásicos, ao nível do binário, em velocidades de funcionamento abaixo da velocidade nominal, faz deles uma boa opção para realizar o controlo de velocidade nesta gama de velocidades. Actualmente, com o rápido avanço da electrónica de potência é mais acessível a implementação de dispositivos que permitam variar a velocidade dos motores de indução trifásicos, contribuindo para que estas máquinas sejam cada vez mais utilizadas em accionamentos de velocidade variável. Este trabalho tem como objectivo o estudo prático da utilização da técnica de controlo escalar por variação simultânea da tensão e frequência (V/f) no accionamento do motor de indução trifásico. Para o efeito, foi implementado um conversor de potência compacto do tipo ondulador de tensão trifásico. Os sinais de comando para o conversor, que utilizam a modulação por largura de impulso, são gerados por um microcontrolador, que para além das capacidades normais de um dispositivo desse tipo, pemite ainda o processamento digital de sinal. O microcontrolador permite ainda a monitorização da velocidade de rotação do motor e da corrente no motor. A análise do desempenho do sistema incide essencialmente sobre o controlo da velocidade de rotação do motor, tendo sido criadas várias condições de funcionamento, com diferentes inclinações das rampas de aceleração e desaceleração.

Opinião de médicos brasileiros sobre fecundação artificial do ponto de vista ético

Foi realizado levantamento da opinião dos médicos que compareceram ao "XIV Congresso Brasileiro de Reprodução Humana", sob o ponto de vista ético, a respeito da fecundação artificial. Foram analisados os resultados, chegando-se a selecionar alguns itens comportamentais como preliminarmente aceitos. Propõe-se a realização de estudos mais profundos que objetivam abordar os vários aspectos aceitos ou não pela sociedade.

Duration of larval and pupal development stages of Aedes albopictus in natural and artificial containers

Aedes albopictus were reared in different containers: a tree hole, a bamboo stump and an auto tire. The total times from egg hatching to adult emergence were of 19.6,27.3 and 37.5 days, respectively, according to the container. The first, second and third-instar larvae presented growth periods with highly similar durations. The fourth-instar larvae was longer than the others stages. The pupation time was longer than the fourth-instar larvae growth period. The temperature of the breeding sites studied, which was of 18° C to 22° C on average, was also taken into consideration. The mortality of the immature stages was analysed and compared as between the experimental groups; it was lower in the natural containers than in the discarded tire. The average wing length of adult females emerging from tree hole was significantly larger (p < 0.05) than that of those emerging from the tire.

Encontro de Aedes scapularis (Diptera: Culicidae) em criadouro artificial em localidade da região Sul do Brasil

Duas larvas de Aedes scapularis foram encontradas em um criadouro artificial, no Município de Sertaneja, Norte do Estado do Paraná, Brasil, durante atividade de rotina para o controle de vetores da dengue.

An artificial neural network approach for short-term wind power forecasting in Portugal

This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.