A municipal level approach to the management of schistosomiasis control in Peri-Peri, MG, Brazil

A schistosomiasis control program was implemented between 1974/87 in Peri-Peri,. MG (622 inhabitants). Molluscicide (niclosamide) was applied at three monthly intervals in water sources with Biomphalaria glabrata, and individuals eliminating Schistosoma mansoni eggs in the feces were treated annually with oxamniquine. From 1974 to 1983 the control measures were undertaken by staff of the "René Rachou" Research Center FIOCRUZ (CPqRR), and from 1984 to 1987 these measures were included in the Capim Branco basic health network activities. During both periods, the prevalence, incidence, intensity of infection and hepatosplenic form as well as the number of infected snails decreased significantly. The prevalence decreased from 43.5 to 4.4%, the incidence from 19.0 to 2.9%, the overall intensity of S. mansoni from 281 to 87 and of the hepatosplenic form from 5.9 to 0.0%. The results obtained suggest that the municipal management of control measures was as effective as the vertical program conducted by CPqRR staff.

Soil infiltration based on bp neural network and grey relational analysis

Soil infiltration is a key link of the natural water cycle process. Studies on soil permeability are conducive for water resources assessment and estimation, runoff regulation and management, soil erosion modeling, nonpoint and point source pollution of farmland, among other aspects. The unequal influence of rainfall duration, rainfall intensity, antecedent soil moisture, vegetation cover, vegetation type, and slope gradient on soil cumulative infiltration was studied under simulated rainfall and different underlying surfaces. We established a six factor-model of soil cumulative infiltration by the improved back propagation (BP)-based artificial neural network algorithm with a momentum term and self-adjusting learning rate. Compared to the multiple nonlinear regression method, the stability and accuracy of the improved BP algorithm was better. Based on the improved BP model, the sensitive index of these six factors on soil cumulative infiltration was investigated. Secondly, the grey relational analysis method was used to individually study grey correlations among these six factors and soil cumulative infiltration. The results of the two methods were very similar. Rainfall duration was the most influential factor, followed by vegetation cover, vegetation type, rainfall intensity and antecedent soil moisture. The effect of slope gradient on soil cumulative infiltration was not significant.

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.