Effect of cattle manure on sunflower production and water use in two types of soil

The aim of the present study was to evaluate water consumption, use efficiency and yield components of sunflower variety Embrapa 122 V/2000 cultivated in two types of soil (Fluvissol and Haplic Luvisol) subjected to increasing doses of cattle manure. The experiment was carried out in a greenhouse at Universidade Estadual da Paraíba. The experimental design was completely randomized in a factorial scheme. The irrigation was performed every other day, replacing the water absorbed by the plants. The water consumption and the use efficiency were evaluated, being the use efficiency determined by the ratio of the total dry mass of sunflower and the amount of water used to produce it in each treatment. Plants were harvested at 95 days after sowing when the following parameters were evaluated: number of seeds per plant, weight of seeds per plant, weight of 1000 seeds and the outer diameter of the capitulum (head). The results showed that the sunflower was positively affected by cattle manure application, increasing the production components and the water use efficiency, regardless of the type of soil. Excepting for the 1000 seeds weight and the water use efficiency, the type of soil affected significantly the water use, the number and weight of seeds per plant. The plants cultivated in Haplic Luvisol had a better performance.

Sugarcane productivity correlated with physical-chemical attributes to create soil management zone

The socioeconomic importance of sugar cane in Brazil is unquestionable because it is the raw material for the production of ethanol and sugar. The accurate spatial intervention in the management of the crop, resulting zones of soil management, increases productivity as well as its agricultural yields. The spatial and Person's correlations between sugarcane attributes and physico-chemical attributes of a Typic Tropustalf were studied in the growing season of 2009, in Suzanápolis, State of São Paulo, Brazil (20°28'10'' S lat.; 50°49'20'' W long.), in order to obtain the one that best correlates with agricultural productivity. Thus, the geostatistical grid with 120 sampling points was installed to soil and data collection in a plot of 14.6 ha with second crop sugarcane. Due to their substantial and excellent linear and spatial correlations with the productivity of the sugarcane, the population of plants and the organic matter content of the soil, by evidencing substantial correlations, linear and spatial, with the productivity of sugarcane, were indicators of management zones strongly attached to such productivity.

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.

Heavy metal toxicity in rice and soybean plants cultivated in contaminated soil

Heavy metals can accumulate in soil and cause phytotoxicity in plants with some specific symptoms. The present study evaluated the specific symptoms on rice and soybeans plants caused by excess of heavy metals in soil. Rice and soybean were grown in pots containing soil with different levels of heavy metals. A completely randomized design was used, with four replications, using two crop species and seven sample soils with different contamination levels. Rice and soybean exhibited different responses to the high concentrations of heavy metals in the soil. Rice plants accumulated higher Cu, Mn, Pb and Zn concentrations and were more sensitive to high concentrations of these elements in the soil, absorbing them more easily compared to the soybean plants. However, high available Zn concentrations in the soil caused phytotoxicity symptoms in rice and soybean, mainly chlorosis and inhibited plant growth. Further, high Zn concentrations in the soil reduced the Fe concentration in the shoots of soybean and rice plants to levels considered deficient.

Nutrient accumulation and biomass production of alfafa after soil amendment with silicates

Studies on the use of silicate correctives in agriculture show that they have great potential to improve soil chemical characteristics, however, little information is available on the reactivity rates of their particle-size fractions. This study investigated whether the reactivity rates obtained experimentally could be considered in the calculation of ECC (effective calcium carbonate) for soil liming, promoting adequate development of alfalfa plants. Six treatments were evaluated in the experiment, consisting of two slag types applied in two rates. The experimental ECC was used to calculate one of the rates and the ECC determined in the laboratory was used to calculate the other. Rates of limestone and wollastonite were based on the ECC determined in laboratory. The rates of each soil acidity corretive were calculated to increase the base saturation to 80%. The treatments were applied to a Rhodic Hapludox and an Alfisol Ferrudalfs. The methods for ECC determination established for lime can be applied to steel slag. The application of slag corrected soil acidity with consequent accumulation of Ca, P, and Si in alfalfa, favoring DM production.

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.

Soil chemical properties affected by cover crops under no-tillage system

The use of cover crops in no-tillage systems (NTS) can significantly improve the soil's fertility. Thus, a study was performed to evaluate changes in chemical properties of soil caused by cover crops in a no-tillage system. The field experiment consisted of the following crop rotation: cover crops/rice/cover crops/rice. The experimental design was in randomized blocks with three replications. Treatments consisted of four cover crops (Brachiaria brizantha(Hochst. ex A. Rich.) Stapf. cv. Marandu, Brachiaria ruziziensis R. Germ. and C.M. Evrard, Panicum maximum Jacq. cv. Colonião, and Pennisetum glaucum(L.) R. Br. cv. BN-2) and fallow (control treatment). Soil samples were collected at the beginning of the summer crop in Oct 2007, Oct 2008 and Oct 2009 at 0-5 cm soil depth. The use of cover crops provided for a significant increase in the level of nutrients, soil organic matter, cation exchange capacity, and base saturation in the soil. Soil fertility improved from the first to second year with the growing of cover crops. The soil under cover crops P. glaucum, B. ruziziensis, and B. brizantha showed higher fertility than the area under fallow.

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.

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.