Nymph developmental time and survivorship, adult longevity, reproduction and body weight of Dichelops melacanthus (Dallas) feeding on natural and artificial diets

The biology of nymphs and adults of the neotropical pentatomid, Dichelops melacanthus (Dallas), feeding on the natural foods, soybean, Glycine max (L.) Merrill immature pods, and corn, Zea mays L. immature seeds, and on an artificial dry diet, was studied in the laboratory. Nymph developmental time was shorter on the natural foods (ca. 21-22 days) than on the artificial diet (28 days), and most nymphs reached adulthood on the food plants (55% on soybean and 73% on corn) than on the artificial diet (40%). Fresh body weight at adult emergence was similar and higher for females raised as nymphs on the natural foods, compared to females from nymphs raised on the artificial diet; for males, weights were similar on all foods. Mean (female and male) survivorship up to day 20, decreased from 55% on soybean to 40% on corn, down to 0% on the artificial diet. Total longevity for females was higher on soybean, while for males was similar on all foods. About three times more females oviposited on soybean than on corn, but fecundity/female was similar on both foods. On the artificial diet, only one out of 30 females oviposited. Fresh body weight of adults increased significantly during the first week of adult life, and at the end of the 3rd week, weight gain was similar on all foods.

The adequacy of artificial oviposition substrates for laboratory rearing of Piezodorus guildinii (Westwood) (Heteroptera, Pentatomidae)

Studies in the laboratory tested the suitability of synthetic wool string, cotton string, cheesecloth, and commercial cotton ball as artificial oviposition substrates for the small green stink bug, Piezodorus guildinii (Westwood) (Heteroptera, Pentatomidae). In confined cages, 54% of total egg masses was laid on synthetic wool string, 31% on cotton string, and only 15% on cheesecloth. In an additional test, the best substrate selected, synthetic wool string, received 92% of egg masses compared to 8% on the commonly used substrate, cotton ball. Synthetic wool string received the most egg masses of any size, in particular those in the range 11-20 eggs/mass. Because the eggs of P. guildinii are laid in two parallel double rows, the egg masses fit the wool string perfectly.

Selection of artificial hosts for oviposition by wild Anastrepha obliqua (Macquart) (Diptera, Tephritidae): influence of adult food and effect of experience

Several factors influence the selection of oviposition substrates by insects. The aim of the present work was to find answers to the following questions related to the oviposition behavior of Anastrepha obliqua. Can carbohydrates (glucose or sucrose) present in the adult diet have influence on the female preference for an oviposition substrate with similar composition? Can the previous experience with a host containing one of mentioned carbohydrates interfere in further selection of oviposition substrates? The results showed that the kind of carbohydrate present in the adult diet did not affect the female preference for an artificial oviposition substrate, neither when it was presented by itself nor in combination with brewer's yeast. The effect of experience in the oviposition behavior was observed when there was a previous contact with artificial oviposition substrates containing yeast and sucrose. The data are discussed in terms of the behavioral plasticity presented by this species in relation to feeding and oviposition behaviors.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Biology and morphometrics of the immature stages of Epinotia aporema on artificial diet

The objective of this work was to establish a life table for the immature stages of Epinotia aporema, as part of a wider investigation on its biological control. Insects were reared on an artificial diet at 25±1ºC and a 16:8 (light:dark) hour photoperiod. For the identification of larval instars for the study of pathogen-insect interactions under laboratory conditions, head capsule widths (HCWs) were also determined. The egg incubation period was 4.13±0.30 days, larval stage took 11.64±0.49 days, and the development time of the pupal phase was sex-dependent with 8.51±0.69 days for the females and 9.41±0.65 days for the males. Five larval instars were identified.

Comparative demography of the exotic Harmonia axyridis with other aphidophagous coccinellids reared on artificial diet

The objective of this work was to compare biological aspects and life table parameters of the coccinellids Harmonia axyridis, Cycloneda sanguineaand Hippodamia convergens. Insects were fed eggs of Anagasta kuehniella, and reared at 24.5±1ºC, 70±10% relative humidity, with a 12 hour photophase. Hippodamia convergenstook about 1.6 day to complete development, longer than H. axyridis, and 2.4 day longer than C. sanguinea.At immature stages, H. axyridisexhibited the highest survival percentage (49.2%), in comparison to the other coccinellids. For mean adult longevity, H. convergenswas deficient, in comparison with the other species. Mean period of pre oviposition was the longest in C. sanguinea; the longest oviposition time occurred for H. axyridis; and the post oviposition period was similar between the coccinellids. Considering the reproductive parameters, H. axyridisshowed the best performance in all aspects. For life table, the values of H. convergenswere higher than, although close, to those of H. axyridis. Nevertheless, the high net reproductive rate of H. axyridis showed this species potential to increase population size. The biological characteristics of the exotic H. axyridis favors its invasion and establishment in Brazil, corroborating results noticed in other countries.

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

Simple method for mass production of polypyrrole/carbon nanotubes hybrid artificial muscle

Large scale preparation of hybrid electrical actuators represents an important step for the production of low cost devices. Interfacial polymerization of polypyrrole in the presence of multi-walled carbon nanotubes represents a simple technique in which strong interaction between components is established, providing composite materials with potential applications as actuators due to the synergistic interaction between the individual components, i.e., fast response of carbon nanotubes, high strain of polypyrrole, and diversity in the available geometry of resulting samples.

Rainfall erosivity for the State of Rio de Janeiro estimated by artificial neural network

The Artificial Neural Networks (ANNs) are mathematical models method capable of estimating non-linear response plans. The advantage of these models is to present different responses of the statistical models. Thus, the objective of this study was to develop and to test ANNs for estimating rainfall erosivity index (EI30) as a function of the geographical location for the state of Rio de Janeiro, Brazil and generating a thematic visualization map. The characteristics of latitude, longitude e altitude using ANNs were acceptable to estimating EI30 and allowing visualization of the space variability of EI30. Thus, ANN is a potential option for the estimate of climatic variables in substitution to the traditional methods of interpolation.

Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network

The present study aimed at evaluating the use of Artificial Neural Network to correlate the values resulting from chemical analyses of samples of coffee with the values of their sensory analyses. The coffee samples used were from the Coffea arabica L., cultivars Acaiá do Cerrado, Topázio, Acaiá 474-19 and Bourbon, collected in the southern region of the state of Minas Gerais. The chemical analyses were carried out for reducing and non-reducing sugars. The quality of the beverage was evaluated by sensory analysis. The Artificial Neural Network method used values from chemical analyses as input variables and values from sensory analysis as output values. The multiple linear regression of sensory analysis values, according to the values from chemical analyses, presented a determination coefficient of 0.3106, while the Artificial Neural Network achieved a level of 80.00% of success in the classification of values from the sensory analysis.

Hydrographic and hydrogeological basin of Entre-Ribeiros: probable recharge zone delimitation and environmental impact assessment

The probable recharge zone delimitation of Entre-Ribeiros Basin (Northwest of the state of Minas Gerais / Brazil) is proposed in this study. The delimitation is based upon stratigraphy, geomorphology, geo-environmental domains and hydrogeology studies. Combining the recharge zone map with the land use variation between 1975 and 2008, the occupation trends of possible recharge zones are identified. Concluding, the environmental impacts for this basin are discussed.

Technical and economic analysis of replacing artificial lighting system to induction of photoperiod effect in begonia's seedlings in greenhouse

The type of artificial light used for inducing photoperiod effect in begonia's seedlings at greenhouse has fundamental importance in the growth and development of these plants and directly reflects in the electrical energy consumption used in this production process. The objective of this research was to analyze the technical and economic feasibility of replacing the current technology of artificial lighting used by the producers (incandescent lamps), by the technology of discharge lamps with the purpose of inducing photoperiod in a greenhouse. The analysis results indicate that the discharge lamp of 32 W Tubular Fluorescent discharge lamp was the one that presented the lower peak demand and lower average energy consumption of 85.01% compared to incandescent filament lamp of 100 W that is the technology of bigger consumption and currently used by the producer.

Technical evaluation of artificial ligthing systems for broiler houses

The spatial distribution of illuminance and the electric consumption of artificial lighting system is one of the main problems related to broiler production. Therefore, the aim of this study was to evaluate the spatial distribution of luminance level and energy efficiency of different lighting systems for broiler houses. Six types of lamps were tested in two different configurations to find the minimum illuminance of 20 and 5 lux. The tested lamps were incandescent (IL) 100 W, compact fluorescent (CFL) 34 W, mixed (ML) 160 W, sodium vapor (SVL) 70 W, T8 fluorescent tube (T8 FTL) 40 W and T5 fluorescent tube (T5 FTL) 28 W. The first four were evaluated with and without reflective light fixture and the latter two without light fixture. It was observed that the tested system with light fixtures negatively affected the spatial distribution of illuminance inside the house. The systems composed by IL and ML without light fixture led to better results in meeting the minimum illuminance of 20 lux and 5 lux, respectively. T5 FTL presented the lowest energy demand.

Application of artificial neural networks as an alternative to volumetric water balance in drip irrigation management in watermelon crop

Precision irrigation seeks to establish strategies which achieve an efficient ratio between the volume of water used (reduction in input) and the productivity obtained (increase in production). There are several studies in the literature on strategies for achieving this efficiency, such as those dealing with the method of volumetric water balance (VWB). However, it is also of great practical and economic interest to set up versatile implementations of irrigation strategies that: (i) maintain the performance obtained with other implementations, (ii) rely on few computational resources, (iii) adapt well to field conditions, and (iv) allow easy modification of the irrigation strategy. In this study, such characteristics are achieved when using an Artificial Neural Network (ANN) to determine the period of irrigation for a watermelon crop in the Irrigation Perimeter of the Lower Acaraú, in the state of Ceará, Brazil. The Volumetric Water Balance was taken as the standard for comparing the management carried out with the proposed implementation of ANN. The statistical analysis demonstrates the effectiveness of the proposed management, which is able to replace VWB as a strategy in automation.