On the stabilization mechanism of Doogh (Iranian yoghurt drink) by gum tragacanth

Serum separation in Doogh, a favorite acidic dairy drink in the Middle East, is a major problem which happens due to low pH (≤4.00) and aggregation of caseins. Therefore, in the present study the influence of gum tragacanth (GT) and its soluble and insoluble fractions on prevention of serum separation as well as stabilization mechanisms were investigated using rheological, zeta potential and microscopic measurements. According to our findings, the soluble tragacanthin (T) and GT prevented serum separation at concentrations of 0.100 and 0.200%, respectively. Furthermore, the proper rheological model for control was Newtonian whereas for the others it was Power law. Based on viscoelastic experiments, Dooghs containing T and GT exhibited dominant viscose and elastic behaviors, respectively. In addition, the presence of T and GT changed the zeta potential values from positive to negative. It was elucidated that T adsorbs onto casein and induces stabilization via electrostatic and steric repulsions. Moreover, the insoluble bassorin (B) may assist stabilization by increasing the viscosity.     

Optimum design of structures by an improved genetic algorithm using neural networks

Optimum design of large-scale structures by standard genetic algorithm (GA) makes the computational burden of the process very high. To reduce the computational cost of standard GA, two different strategies are used. The first strategy is by modifying the standard GA, called virtual sub-population method (VSP). The second strategy is by using artificial neural networks for approximating the structural analysis. In this study, radial basis function (RBF), counter propagation (CP) and generalized regression (GR) neural networks are used. Using neural networks within the framework of VSP creates a robust tool for optimum design of structures.     

An efficient parallel algorithm for the longest path problem in meshes

The longest path problem is the problem of finding a simple path with the maximum number of vertices in a given graph, and so far it has been solved polynomially only for a few classes of graphs. This problem generalizes the well-known Hamiltonian path problem, hence it is NP-hard in general graphs. In this paper, first we give a sequential linear-time algorithm for the longest path problem in meshes. Then based on this algorithm, we present a constant-time parallel algorithm for the problem, which can be run on every parallel machine.     

Comparing the performance of multispectral vegetation indices and machine-learning algorithms for remote estimation of chlorophyll content: a case study in the Sundarbans mangrove forest

Optical vegetation indices (VIs) have been used to retrieve and assess biophysical variables from satellite reflectance data. These indices, however, also are sensitive to a number of confounding factors, such as canopy geometry, soil optical properties, and solar position. This suggests that VIs should be used cautiously for biophysical parameter estimation. Among biophysical variables, chlorophyll content is of particular importance as an indicator of photosynthetic activity. The goal of this study is to investigate the performance of multispectral optical VIs for chlorophyll content estimation in the world’s largest mangrove forest, the Sundarbans, and to compare these with machine-learning algorithms (MLAs). To this end, we have investigated the performance of 15 multispectral VIs and six state-of-the-art MLAs that are widely used for adaptive data fitting. The MLAs are Artificial Neural Networks (ANNs), Genetic Algorithm (GA), Gaussian Processes for Machine Learning (GPML), Kernel Ridge Regression (KRR), Locally Weighted Polynomials (LWP), and Multivariate Adaptive Regression Splines (MARS). We use an in situ data set of reflectance and chlorophyll measurements to develop and validate our models. Each MLA was evaluated 500 times with random partitions of training and validation data. Results showed that the weight optimization and term selection used within GA produce the most reliable chlorophyll content estimation. However, green normalized difference VI (GNDVI) is a simple and computationally efficient VI that produces results that are nearly as accurate as GA in terms of model fit and performance. Results also show that all methods except ANNs and MARS produce a quasi-linear relationship between spectral reflectance and chlorophyll content. Statistical transformations of GNDVI and chlorophyll content have the capability of further reducing model error.     

Surface Patches from Unorganized Space Curves

Recent 3D sketch tools produce networks of three‐space curves that suggest the contours of shapes. The shapes may be non‐manifold, closed three‐dimensional, open two‐dimensional, or mixed. We describe a system that automatically generates intuitively appealing piecewise‐smooth surfaces from such a curve network, and an intelligent user interface for modifying the automatically chosen surface patches. Both the automatic and the semi‐automatic parts of the system use a linear algebra representation of the set of surface patches to track the topology. On complicated inputs from ILoveSketch [ [BBS08] ], our system allows the user to build the desired surface with just a few mouse‐clicks.     

Estimating the rate constant in second-order kinetics using hard-soft-net analyte signal (HS-NAS) method

Rank deficiency is the major problem associated with the chemometrics modeling of the second-order chemical reactions of the form of A + B → C. In this article, the application of the hard-soft-net analyte signal (HS-NAS), as a newly proposed method in our research group, is described for modeling of second-order reactions. This combined hard-soft method is based on the net analyte signal (NAS) concept, which is defined as a part of total signal that is directly related to the concentration of the component of interest. Therefore, concentration changes versus time can be obtained by calculating NAS for any chemical species involved in the reaction without requiring any pure component spectra or extra runs. The power of the method was verified by applying it to the resolution of simulated data sets containing noises added at different levels. The resolution of the second-order reaction between amoxicillin and 1, 2-naphthoqoinone was also tested as a real chemical system.     

Equilibrium,kinetic and thermodynamic study of removal of reactive orange 12 on platinum nanoparticle loaded on activated carbon as novel adsorbent

The proposed research describes the synthesis and characterization of platinum nanoparticles loaded on activated carbon (Pt-NP-AC) and its efficient application as novel adsorbent for efficient removal of reactive orange 12 (RO-12). The influences of effective parameters following the optimization of variables on removal percentages, their value was set as 0.015 g Pt-NP-AC, pH 1, contact time of 13 min. At optimum values of all variables at 25 and 50 mgL⁻¹ of RO-12 enthalpy (ΔH⁰) and entropy (ΔS⁰) changes was found to be 59.89 and 225.076, respectively, which negative value of ΔG⁰ shows a spontaneous nature, and the positive values of ΔH⁰ and ΔS⁰ indicate the endothermic nature and adsorption organized of dye molecule on the adsorbent surface. Experimental data was fitted to different kinetic models including first-order, pseudo-second-order, Elovich and intra-particle diffusion models, and it was seen that the adsorption process follows pseudo-second-order model in consideration to intra-particle diffusion mechanism. At optimum values of all variables, the adsorption process follows the second-order kinetic and Langmuir isotherm model with adsorption capacity 285.143 mg g⁻¹ at room temperature.