A temperature predicting model for manufacturing processes requiring coiling

A model for predicting temperature evolution for automatic controling systems in manufacturing processes requiring the coiling of bars in the transfer table is presented. Although the method is of a general nature, the presentation in this work refers to the manufacturing of steel plates in hot rolling mills. The predicting strategy is based on a mathematical model of the evolution of temperature in a coiling and uncoiling bar and is presented in the form of a parabolic partial differential equation for a shape changing domain. The mathematical model is solved numerically by a space discretization via geometrically adaptive finite elements which accomodate the change in shape of the domain, using a computationally novel treatment of the resulting thermal contact problem due to coiling. Time is discretized according to a Crank-Nicolson scheme. Since the actual physical process takes less time than the time required by the process controlling computer to solve the full mathematical model, a special predictive device was developed, in the form of a set of least squares polynomials, based on the off-line numerical solution of the mathematical model.

APPRAISAL OF THE SNAP MODEL FOR PREDICTING NITROGEN MINERALIZATION IN TROPICAL SOILS UNDER EUCALYPTUS

The Soil Nitrogen Availability Predictor (SNAP) model predicts daily and annual rates of net N mineralization (NNM) based on daily weather measurements, daily predictions of soil water and soil temperature, and on temperature and moisture modifiers obtained during aerobic incubation (basal rate). The model was based on in situ measurements of NNM in Australian soils under temperate climate. The purpose of this study was to assess this model for use in tropical soils under eucalyptus plantations in São Paulo State, Brazil. Based on field incubations for one month in three, NNM rates were measured at 11 sites (0-20 cm layer) for 21 months. The basal rate was determined in in situ incubations during moist and warm periods (January to March). Annual rates of 150-350 kg ha-1 yr-1 NNM predicted by the SNAP model were reasonably accurate (R2 = 0.84). In other periods, at lower moisture and temperature, NNM rates were overestimated. Therefore, if used carefully, the model can provide adequate predictions of annual NNM and may be useful in practical applications. For NNM predictions for shorter periods than a year or under suboptimal incubation conditions, the temperature and moisture modifiers need to be recalibrated for tropical conditions.

Predicting the occurrence of alate aphids in Brassicaceae

The objective of this work was to predict the occurrence of alates of Brevicoryne brassicae, Lipaphis erysimi, and Myzus persicae (Hemiptera, Aphididae) in Brassicaceae. The alate aphids were collected in yellow water traps from July 1997 to August 2005. Aphid population peaks were predicted using a degree‑day model. The meteorological factors, temperature, air relative humidity, rainfall, and sunshine hours, were used to provide precision indexes to evaluate the best predictor for the date of the first capture of alate aphids by the traps. The degree‑day model indicated that the peak population of the evaluated aphid species can be predicted using one of the following biofix dates: January 1st, June 1st, and the date of the first capture of the alate aphid species by the yellow water traps. The best predictor of B. brassicae occurrence is the number of days with minimum temperature >15°C, and of L. erysimi and M. persicae, the number of days with rainfall occurrence.

PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX

The quantitative structure property relationship (QSPR) for the boiling point (Tb) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was investigated. The molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and Tb was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN), respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the models developed. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation was 1.77 and 1.23, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation was 1.65 and 1.16, respectively. A quantitative relationship between the MDEV index and Tb of PCDD/Fs was demonstrated. Both MLR and ANN are practicable for modeling this relationship. The MLR model and ANN model developed can be used to predict the Tb of PCDD/Fs. Thus, the Tb of each PCDD/F was predicted by the developed models.

Predicting variety composite means without diallel crossing

Prediction of variety composite means was shown to be feasible without diallel crossing the parental varieties. Thus, the predicted mean for a quantitative trait of a composite is given by: Yk = a1 sigmaVj + a2sigmaTj + a3 - a4, with coefficients a1 = (n - 2k)/k²(n - 2); a2 = 2n(k - 1)/k²(n - 2); a3 = n(k - 1)/k(n - 1)(n - 2); and a4 = n²(k - 1)/k(n - 1)(n - 2); summation is for j = 1 to k, where k is the size of the composite (number of parental varieties of a particular composite) and n is the total number of parent varieties. Vj is the mean of varieties and Tj is the mean of topcrosses (pool of varieties as tester), and and are the respective average values in the whole set. Yield data from a 7 x 7 variety diallel cross were used for the variety means and for the "simulated" topcross means to illustrate the proposed procedure. The proposed prediction procedure was as effective as the prediction based on Yk = - ( -)/k, where and refer to the mean of hybrids (F1) and parental varieties, respectively, in a variety diallel cross. It was also shown in the analysis of variance that the total sum of squares due to treatments (varieties and topcrosses) can be orthogonally partitioned following the reduced model Yjj’ = mu + ½(v j + v j’) + + h j+ h j’, thus making possible an F test for varieties, average heterosis and variety heterosis. Least square estimates of these effects are also given

Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.