Development of healthy extruded maize snacks; Effects of soybean flour and feed moisture content

The main aim of this research was to introduce a green and clean method of increasing the nutritional quality while preserving the quality of extruded maize snacks. This was achieved by addition of soybean flour (5%, 10%, 15% and 20%, w/w) as a source of plant protein, fibre and bioactive compounds and changing the feed moisture content. Extruded samples were produced using a single-screw extruder. Increasing the soybean flour and feed moisture content resulted in reduced specific mechanical energy of the extruder and consequently several changes in physicochemical properties of the snacks including higher moisture content, lower expansion and volume, reduced crispiness, reduction in L-and a-values but an increase in b-value and formation of more wrinkly and thicker air bubbles cell walls as observed under scanning electron microscope. The addition of <20% soybean flour and feed moisture content resulted in snacks with improved nutritional value and physical properties.     

Characterization of Erbium,Indium, and Erbium–Indium co-doped 8%mol Yttria-stabilized Zirconia

Journal of Materials Science: Materials in Electronics - In this work, the cubic structure of Yttria-stabilized Zirconia (8YSZ) and Indium and Erbium co-doped 8YSZ nanopowders have been prepared by...     

Online measurement of particle size distribution during crystallization using ultrasonic spectroscopy

Particle growth during crystallization of paracetamol was monitored on-line using a new modified method based on ultrasonic spectroscopy. The model was earlier developed and tested with inert particles systems and accounts for low angle scattering and probe geometry. A graphic user interface was developed and integrated with the model based deconvolution algorithm for real-time data acquisition, analysis and display of results. The crystal growth and changes in size distribution were measured during the crystallization of paracetamol from paracetamol–isopropanol–water solution. A simplified pre-calibration procedure using acoustic velocity to estimate solution properties was established. The model based particle size distribution showed good agreement with offline measurements using laser diffraction analysis of a sample retrieved from the crystallizer.     

Using Coal Fly Ash and Wastewater for Microwave Synthesis of LTA Zeolite

The reuse of industrial wastes from a coal‐fired power plant and a plasma electrolytic oxidation process was attempted to realize a zero discharge. The batch composition was adjusted by adding sodium hydroxide and sodium aluminate. A single‐mode microwave oven equipped with reflux condenser was used for crystallization under atmospheric pressure. The synthesized samples were characterized by X‐ray diffraction, scanning electron microscopy, BET, thermogravimetric analysis, and cation‐exchange capacity (CEC) measurement. Analytical results indicated that Na‐A zeolite with a defined maximum crystallinity could be successfully synthesized by hydrothermal treatment of fly ash with wastewater. Due to the high CEC, the product can be applied for gas purification and soil remediation processes.     

Prediction of Air Specific Heat Ratios at Elevated Pressures Using a Novel Modeling Approach

An accurate and efficient model based on least‐squares support vector machines (LSSVM) is developed for the determination of air specific heat ratios at elevated pressures. Additionally, the coupled simulated annealing optimization strategy is used to calculate the optimal values of the LSSVM parameters. A large dataset of air specific heat ratios as a function of temperature and pressure for about 170 samples is used to develop and validate the model. The leverage approach (Williams plot) is used to determine the applicability domain of the model and to detect probably erroneous data points. Comparison of the obtained results with a previously published correlation as well as an intelligent method demonstrates that the performance of the presented model is more satisfactory than that of other methods.     

Properties enhancement using oil palm shell nanoparticles of fibers reinforced polyester hybrid composites

Oil palm shell (OPS) nanoparticles were utilized as filler in fibers reinforced polyester hybrid composites. The OPS nanoparticles were successfully produced from the raw OPS using high-energy ball milling process. Fundamental properties including morphology, crystalline size, and particle size of the OPS nanoparticles were determined. Tri-layer natural fiber reinforcement (kenaf–coconut–kenaf fiber mat) polyester hybrid composites were prepared by hand lay-up techniques. The influences of the OPS nanoparticles loading in the natural fibers reinforced polyester hybrid composites were determined by analyzing physical, mechanical, morphological, and thermal properties of the composites. Results showed that the incorporation of the OPS nanoparticles into the hybrid composites enhanced the composite properties. Further, the natural fibers reinforced polyester hybrid composite had the highest physical, mechanical, morphological, and thermal characteristics at 3 wt.% OPS nanoparticles loading.     

Directionally sensitive weighted adaptive multivariate CUSUM mean charts

In many service and manufacturing industries, process monitoring involves multivariate data, instead of univariate data. In these situations, multivariate charts are employed for process monitoring. Very often when the mean vector shifts to an out-of-control situation, the exact shift size is unknown; hence, multivariate charts for monitoring a range of the mean shift sizes in the mean vector are adopted. In this paper, directionally sensitive weighted adaptive multivariate CUSUM charts are developed for monitoring a range of the mean shift sizes. Directionally sensitive charts are useful in situations where the aim lies in monitoring either an increasing or a decreasing shift in the mean vector of the quality characteristics of interest. The Monte Carlo simulation is used to compute the run length characteristics in comparing the sensitivities of the proposed and existing multivariate CUSUM charts. In general, the directionally sensitive and weighted adaptive features enhance the sensitivities of the proposed multivariate CUSUM charts in comparison with the existing multivariate CUSUM charts without the adaptive feature or those that are directionally invariant. It is also found that the variable sampling interval feature enhances the sensitivities of the proposed and existing charts as compared to their fixed sampling interval counterparts. The implementation of the proposed charts in detecting upward and downward shifts in the in-control process mean vector is demonstrated using two different datasets.     

Artificial neural network approach for solving fuzzy differential equations

The current research attempts to offer a novel method for solving fuzzy differential equations with initial conditions based on the use of feed-forward neural networks. First, the fuzzy differential equation is replaced by a system of ordinary differential equations. A trial solution of this system is written as a sum of two parts. The first part satisfies the initial condition and contains no adjustable parameters. The second part involves a feed-forward neural network containing adjustable parameters (the weights). Hence by construction, the initial condition is satisfied and the network is trained to satisfy the differential equations. This method, in comparison with existing numerical methods, shows that the use of neural networks provides solutions with good generalization and high accuracy. The proposed method is illustrated by several examples.     

An application of a merit function for solving convex programming problems

This paper presents a gradient neural network model for solving convex nonlinear programming (CNP) problems. The main idea is to convert the CNP problem into an equivalent unconstrained minimization problem with objective energy function. A gradient model is then defined directly using the derivatives of the energy function. It is also shown that the proposed neural network is stable in the sense of Lyapunov and can converge to an exact optimal solution of the original problem. It is also found that a larger scaling factor leads to a better convergence rate of the trajectory. The validity and transient behavior of the neural network are demonstrated by using various examples.