RESUMEN
Background and Objectives: rheological characteristics of dough are important for achieving useful information about raw-material quality, dough behavior during mechanical handling, and textural characteristics of products. Our purpose in the present research is to apply soft computation tools for predicting the rheological properties of dough out of simple measurable factors
Materials and Methods: one hundred samples of white flour were collected from different provinces of Iran. Seven physicochemical properties of flour and Farinogram parameters of dough were selected as neural network's inputs and outputs, respectively. Trial-and-error and genetic algorithm [GA] were applied for developing an artificial neural network [ANN] with an optimized structure. Feed-forward neural networks with a back-propagation learning algorithm were employed. Sensitivity analyses were conducted to explore the ability of inputs in changing the Farinograph properties of dough
Results: the optimal neural network is an ANN-GA that evolves a four-layer network with eight nodes in the first hidden layer and seven neurons in the second hidden layer. The average of normalized mean square error, mean absolute error and correlation coefficient in estimating the test data set was 0.222, 0.124 and 0.953, respectively. According to the results of sensitivity analysis, gluten index was selected as the most important physicochemical parameter of flour in characterization of dough's Farinograph properties
Conclusions: an ANN is a powerful method for predicting the Farinograph properties of dough. Taking advantages of performance criteria proved that the GA is more powerful than trial-and-error in determining the critical parameters of ANN's structure, and improving its performance
RESUMEN
Short birth intervals have been associated with adverse health outcomes, including infant, child and maternal mortality. We aimed to investigate the duration and determinants of inter birth intervals among women of reproductive age in Yazd, Iran. A cluster sampling technique was used to select 400 ever-married women aged 15-49 years in Yazd, Islamic Republic of Iran. Data were obtained by interview questionnaire and analyzed with life table, Kaplan-Meier survival and Cox regression analyses. The mean duration of inter birth interval was 49.76 [standard error 1.82] months [95% CI: 46.19-53.34 months] with a median of 39. In 28.5% of women the birth interval was <2 years, in 28% it was 3-5 years and in 25.5% it was >/= 6 years. Among explanatory variables of interest, age of marriage, and woman's education were significant predicators of the birth interval. Women who stated an ideal preference of two children constituted 59.9% of the sample and 16% had 3 children as well as 10.7% had an ideal preference for 4 or 5. The study recommended an educational program to have optimal birth intervals and ideal number of children per family for the prevention of adverse prenatal outcomes.