The aim of the present study was to investigate interactions between feed form and dietary inclusion of probiotics (live organisms) and enzymes on growth performance, cellular and humoral immunity, and relative weights of lymphoid organs of broiler chicks fed a triticale-based diet. A total of 640 broiler chicks were allocated to eight treatments when they were one day old, with four replicates (n = 20 chicks in each). The dietary treatments consisted of feed form (pellets or mash) and dietary supplementation with or without a Bacillus-based probiotic (0.03% diet) and enzyme (carbohydrase, 0.05% diet) in a 2 × 2 × 2 factorial arrangement. Feed conversion ratio (FCR) was calculated based on feed intake (FI) and weight gain (WG). The WG of the broiler chicks fed the pelleted diets containing the enzyme and probiotic (alone or in combination) was greater than that of the birds fed the mash diets without these additives. Significant interaction effects of the enzyme and probiotic on FCR and FI were observed in the starter period. The heterophil : lymphocyte (H : L) ratio of the chicks fed the pelleted diet containing the enzyme and probiotic was lower than that of the birds fed the other diets. The cellular immunity of the birds fed this diet increased after 24 hours, as shown by the results of a challenge experiment with dinitrochlorobenzene (DNCB) or an injection of phytohemagglutinin (PHA). In conclusion, feed form and supplementation of a triticale-based diet with feed enzymes and probiotics can greatly improve the growth performance and immunity of broiler chicks.

In this paper, a control algorithm for improving vehicle handling has been proposed by applying correction angle to the steered wheel, based on the optimal and adaptive theory using Simulink MATLAB software. A 4DOF model with nonlinear tire and SBW subsystem is presented using hardware in the loop method. Since some space variables cannot be measured, an estimator is used to extract the measurable variables from the simulated model and convert them to the required variables of the controller. These variables are transmitted to the controller and then it adopts itself with new conditions and applies the best modification on the steering. The results reveal that the proposed controller can significantly improve vehicle handling during severe maneuvers.

Equations of state are useful for description of fluid properties such as pressure-volume-temperature (PVT). However, the success estimation of such correlations depends mainly on the range of data which have originated. Therefore new models are highly required. In this work a new method is proposed based on Artificial Neural Network (ANN) for estimation of PVT properties of compounds. The data sets were collected from Perry's Chemical Engineers' Handbook. Different training schemes for the back-propagation learning algorithm, such as; Scaled Conjugate Gradient (SCG), Levenberg-Marquardt (LM) and Resilient back Propagation (RP) methods were used. The accuracy and trend stability of the trained networks were tested against unseen data. The LM algorithm with sixty neurons in the hidden layer has proved to be the best suitable algorithm with the minimum Mean Square Error (MSE) of 0.000606. The ANN's capability to estimate the PVT properties is one of the best estimating method with high performance.