Cumin seed meal with enzyme and polyethylene glycol as an alternative to wheat bran in broiler diets

Iran produces about 7000 metric tons of cumin seed meal (CSM) as a by‐product of cumin oil extraction factories, annually. To evaluate the nutritional significance of cumin seed meal as a broiler feedstuff, an experiment was conducted using 288 male broiler chicks (14 days old) receiving diets containing 0, 25 and 50 g kg⁻¹ of CSM with and without polyethylene glycol (PEG) and enzyme (Grindazyme^™ GP 15000) for 28 days. Total body weight (BW), body weight gain (WG), feed intake (FI), feed conversion ratio (FCR), weight of carcass and percentage of legs, breast and edible parts of carcass were measured. The results showed that, inclusion of CSM in broiler diets had no negative influence on parameters evaluated compared to the control diet containing wheat bran (P > 0.05). PEG and enzyme had no influence on the bird performance (P > 0.05). There was an increase in relative weight of gizzard when the amount of CSM in the diet was increased (P < 0.01). An increase in relative weight of gizzard in birds that received the CSM diet was likely to be due to the increase in fibre content of CSM diets. In respect of the low price of CSM, it could be concluded that inclusion of CSM at levels used in this experiment has no negative effect on broiler performance and reduces the overall cost of broiler production. Copyright © 2006 Society of Chemical Industry     

The VIM theory of molecular thermodynamics: Part I: Analytic equation of state of nonpolar fluids

Variational inequality minimization (VIM) theory of statistical mechanics is used to derive analytic equations of state for nonpolar fluids. Pair- and tripletintermolecular potential energy functions are taken into account in the formulation of this equation of state. The VIM theory has made it possible to derive rather simple expressions for the thermodynamic properties of nonpolar fluids. Predictions of the thermodynamic properties of argon and methane in the liquid phase, in the vapor phase, and in the critical region by the VIM equation of state are shown to be in excellent agreement with the experimental data.     

Subcritical and Supercritical Water Radial Distribution Function

A theoretical and analytic expression for the first shell, and an analytic empirical expression for the whole radial distribution function (RDF) of water are introduced. All the asymptotic limits and functionalities of the RDF with temperature and density are incorporated in these expressions. An effective Kihara pair potential function is presented for water intermolecular interactions which incorporates the hydrogen bonding by using the chain association theory. The intermolecular pair potential parameters are adjusted to the experimental x-ray diffraction data of water RDF at various temperatures. The predicted first-shell results for water near critical and in supercritical conditions compare satisfactorily with the available neutron diffraction RDF data, with the simulation RDF results, and with the empirical RDF curves. The empirical expression initially proposed for the RDF of the Lennard–Jones fluid is extended to predict the RDF and the isothermal compressibility of water to conditions where experimental or simulated data are not available. Comparison with the Lennard–Jones fluid shows that the height of the first peak of water RDF changes much less at subcritical and supercritical conditions compared to that of the Lennard–Jones fluid which decreases appreciably going from subcritical to supercritical conditions.     

Equilibrium in multiphase polydisperse fuids

Based on the principle of Gibbs phase equilibrium, three different algorithms are developed for phase equilibrium calculations of polydisperse mixtures. These algorithms are based on (i) the equality of the chemical potentials of components in each phase, (ii) the minimization of the total Gibbs free energy of the system with respect to all the system variables, and (iii) the equilibrium ratios constraint between the phases. All three algorithms demonstrate applicability to mixtures with different mole fraction distribution functions and different equations of state. The results of calculations using the three phase algorithms are compared with the simulated multicomponent hydrocarbon mixture data, and the results are in good agreement.Nomenclature a, b Interaction parameter in the equation of state - CP Critical point - F(I) Density distribution function - f Fugacity - G Gibbs free energy - I Distributed variable - I ₀ Initial value of density distribution function - K(I) Equilibrium ratio of component I - P Pressure (atm) - R Gas constant - T Temperature (K) - v Molar volume - Z Compressibility factor - h Variance of density distribution function - Chemical potential - F _v Moles vapor per moles in system - Y Fugacity coefficient - i, j Component identifiers - f Feed stream - L Liquid phase - V Vapor phase     

An Artificial Neural Network Model to Solve the Fuzzy Shortest Path Problem

Neural Processing Letters - In the present research, we are going to obtain the solution of the fuzzy shortest path (FSP) problem. According to our search in the scientific reported papers, this is...     

Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease

Coronary artery disease (CAD) is one of the major causes of mortality worldwide. Knowledge about risk factors that increase the probability of developing CAD can help to understand the disease better and assist in its treatment. Recently, modern computer‐aided approaches have been used for the prediction and diagnosis of diseases. Swarm intelligence algorithms like particle swarm optimization (PSO) have demonstrated great performance in solving different optimization problems. As rule discovery can be modelled as an optimization problem, it can be mapped to an optimization problem and solved by means of an evolutionary algorithm like PSO. An approach for discovering classification rules of CAD is proposed. The work is based on the real‐world CAD data set and aims at the detection of this disease by producing the accurate and effective rules. The proposed algorithm is a hybrid binary‐real PSO, which includes the combination of categorical and numerical encoding of a particle and a different approach for calculating the velocity of particles. The rules were developed from randomly generated particles, which take random values in the range of each attribute in the rule. Two different feature selection methods based on multi‐objective evolutionary search and PSO were applied on the data set, and the most relevant features were selected by the algorithms. The accuracy of two different rule sets were evaluated. The rule set with 11 features obtained more accurate results than the rule set with 13 features. Our results show that the proposed approach has the ability to produce effective rules with highest accuracy for the detection of CAD.     

Simultaneous Learning of Nonlinear Manifolds Based on the Bottleneck Neural Network

Manifold learning methods are important techniques for nonlinear extraction of high-dimensional data structures. These methods usually extract a global manifold for data. However, in many real-world problems, there is not only one global manifold, but also additional information about the objects is shared by a large number of manifolds. These manifolds can share information for data reconstruction. To simultaneously extract these data manifolds, this paper proposes a nonlinear method based on the deep neural network (NN) named nonlinear manifold separator NN (NMSNN). Unlike unsupervised learning of bottleneck NN, data labels were used for simultaneous manifold learning. This paper makes use of NMSNN for extracting both expression and identity manifolds for facial images of the CK+ database. These manifolds have been evaluated by different metrics. The identity manifold is used for changing image identity. The result of identity recognition by K-nearest neighbor classifier shows that virtual identities are exactly sanitized. The virtual images for different expressions of test subjects are generated by expression manifold. The facial expression recognition rate of 92.86 % is achieved for virtual expressions of test persons. It is shown that NMSNN can be used to enrich datasets by sanitizing virtual images. As a result, 8 and 19 % improvements are gained in the face recognition task by a single image of each person on CK+ and Bosphorus databases, respectively.     

An integrated model based on statistical process control and maintenance

The close relationship between quality and maintenance of manufacturing systems has contributed to the development of integrated models, which use the concept of statistical process control (SPC) and maintenance. Such models not only help to improve quality of products but also lead to lower maintenance cost. In this paper, an integrated model is presented which considers complete failure and planned maintenance simultaneously. This model leads to six different scenarios. A new procedure for calculating average cost per time unit is also presented. Finally, a numerical example is used to evaluate sensitivity of the model parameters and compare performance of the developed model to a planned maintenance model. Results indicate satisfactory performance for the developed model.     

Efficient numerical algorithms for the solution of “good” Boussinesq equation in water wave propagation

This paper proposes three fast and high accuracy numerical methods for solving a nonlinear partial differential equation (PDE) describing water waves and called the Boussinesq (Bq) equation. We numerically solve the Bq equation with fourth-order time-stepping schemes in combination with discrete Fourier transform. We discretize the original PDE with discrete Fourier transform in space and obtain a system of ordinary differential equations (ODEs) which will be solved with fourth-order time-stepping methods. After transforming the equation to a system of ODEs, the linear operator is not diagonal, but we can implement the methods such as diagonal case which reduces the CPU time. Comparing numerical solutions with analytical solutions demonstrates that those methods are accurate and readily implemented. Also we investigate the conservation of mass for Bq equation.