Estimation of time-to-flashover characteristics of contaminatedelectrolytic surfaces using a neural network

A major field of neural networks (NN) application is function estimation, because the useful properties of NN such as adaptivity and nonlinearity are well suited to function estimation tasks where the equation describing the function is unknown. In this paper the prerequisite training data are obtained from experimental studies performed on a flat plate model for a polluted insulator under power frequency voltage. Detailed studies have been carried to determine the NN parameters which give the best results. Studies have also been carried out to assess the effect of the presence of inadequate data in the training set on modeling accuracy. It is found that, when training is completed, NN is capable of estimating the function t=f(V,L,R_p ) very efficiently and effectively even when the inadequate data are incorporated in the training set     

An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization

Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this paper, we propose a new mutation strategy, a fitness-induced parent selection scheme for the binomial crossover of DE, and a simple but effective scheme of adapting two of its most important control parameters with an objective of achieving improved performance. The new mutation operator, which we call DE/current-to-gr_best/1, is a variant of the classical DE/current-to-best/1 scheme. It uses the best of a group (whose size is q% of the population size) of randomly selected solutions from current generation to perturb the parent (target) vector, unlike DE/current-to-best/1 that always picks the best vector of the entire population to perturb the target vector. In our modified framework of recombination, a biased parent selection scheme has been incorporated by letting each mutant undergo the usual binomial crossover with one of the p top-ranked individuals from the current population and not with the target vector with the same index as used in all variants of DE. A DE variant obtained by integrating the proposed mutation, crossover, and parameter adaptation strategies with the classical DE framework (developed in 1995) is compared with two classical and four state-of-the-art adaptive DE variants over 25 standard numerical benchmarks taken from the IEEE Congress on Evolutionary Computation 2005 competition and special session on real parameter optimization. Our comparative study indicates that the proposed schemes improve the performance of DE by a large magnitude such that it becomes capable of enjoying statistical superiority over the state-of-the-art DE variants for a wide variety of test problems. Finally, we experimentally demonstrate that, if one or more of our proposed strategies are integrated with existing powerful DE variants such as jDE and JADE, their performances can also be enhanced.     

Low temperature synthesis of bismuth ferrite nanoparticles by a ferrioxalate precursor method

The synthesis of bismuth ferrite by solid-state reaction of Bi₂O₃ and Fe₂O₃ results in the formation of multiphase products. Even coprecipitation followed by calcination leads to the formation of impurity phases. Here, we report the synthesis of magnetoelectric bismuth ferrite by a ferrioxalate precursor method. In this process, bismuth ferrite, synthesized through solutions of some specific salts led to the formation of phase pure (perovskite) nanocrystalline powder (11–22 nm as evident from X-ray diffraction analysis) at a temperature of 600 °C. The synthesized powders were characterized by X-ray diffractometry, thermogravimetry and differential thermal analysis, Fourier transformation infrared spectroscopy and scanning electron microscopy. The synthesis route is simple, energy saving and cost-effective. Such nanosized bismuth ferrite powder may have a potential application in making lead free piezoelectric materials for actuators as well as magnetoelectric sensors.     

A UML-based pattern specification technique

Informally described design patterns are useful for communicating proven solutions for recurring design problems to developers, but they cannot be used as compliance points against which solutions that claim to conform to the patterns are checked. Pattern specification languages that utilize mathematical notation provide the needed formality, but often at the expense of usability. We present a rigorous and practical technique for specifying pattern solutions expressed in the unified modeling language (UML). The specification technique paves the way for the development of tools that support rigorous application of design patterns to UML design models. The technique has been used to create specifications of solutions for several popular design patterns. We illustrate the use of the technique by specifying observer and visitor pattern solutions.     

Thermodynamic and kinetic modeling of the Cr-Ti-V system

A synergistic approach of thermodynamic and kinetic modeling is applied to the Cr-Ti-V system. To assist the design of (α+β) and β titanium alloys for structural applications and vanadium alloys for fusion reactor applications, a set of self-consistent and optimized thermodynamic model parameters is presented to describe the phase equilibria of the Cr-Ti, Cr-V, Ti-V, and Cr-Ti-V systems. The Laves phases, α-Cr₂Ti, β-Cr₂Ti, and γ-Cr₂Ti, are described by a two-sublattice model assuming antistructure atoms on both sublattices. The calculated thermodynamic quantities and phase diagrams are in good accord with the corresponding experimental data. To assist the simulation of the kinetics of diffusional transformations in bodycentered cubic (bcc) alloys, the atomic mobilities of Cr, Ti, and V are modeled. A set of optimized mobility parameters is given. Very good agreement between the calculated and experimental diffusivities was found.     

Molecular Modeling Studies of N-phenylpyrimidine-4-amine Derivatives for Inhibiting FMS-like Tyrosine Kinase-3

Overexpression and frequent mutations in FMS-like tyrosine kinase-3 (FLT3) are considered risk factors for severe acute myeloid leukemia (AML). Hyperactive FLT3 induces premature activation of multiple intracellular signaling pathways, resulting in cell proliferation and anti-apoptosis. We conducted the computational modeling studies of 40 pyrimidine-4,6-diamine-based compounds by integrating docking, molecular dynamics, and three-dimensional structure–activity relationship (3D-QSAR). Molecular docking showed that K644, C694, F691, E692, N701, D829, and F830 are critical residues for the binding of ligands at the hydrophobic active site. Molecular dynamics (MD), together with Molecular Mechanics Poison–Boltzmann/Generalized Born Surface Area, i.e., MM-PB(GB)SA, and linear interaction energy (LIE) estimation, provided critical information on the stability and binding affinity of the selected docked compounds. The MD study suggested that the mutation in the gatekeeper residue F691 exhibited a lower binding affinity to the ligand. Although, the mutation in D835 in the activation loop did not exhibit any significant change in the binding energy to the most active compound. We developed the ligand-based comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models. CoMFA (q² = 0.802, r² = 0.983, and QF32 = 0.698) and CoMSIA (q² = 0.725, r² = 0.965 and QF32 = 0.668) established the structure–activity relationship (SAR) and showed a reasonable external predictive power. The contour maps from the CoMFA and CoMSIA models could explain valuable information about the favorable and unfavorable positions for chemical group substitution, which can increase or decrease the inhibitory activity of the compounds. In addition, we designed 30 novel compounds, and their predicted pIC₅₀ values were assessed with the CoMSIA model, followed by the assessment of their physicochemical properties, bioavailability, and free energy calculation. The overall outcome could provide valuable information for designing and synthesizing more potent FLT3 inhibitors.     

Rheology of short fiber filled thermoplastics

We examine the applicability of the conformation tensor to describe the fiber orientation and rheology of moderately concentrated fiber-filled thermoplastics subjected to large deformation flow. To retain computational simplicity, we assume a Newtonian matrix. We present a model that can account for orientation effects, Brownian motion, semiflexibility, and interactions through excluded volume effect, of the fibers. The model predicts a wide variety of rheological effects. We present predictions of steady shear viscosity, primary normal stress and the creep functions, as well as uniaxial elongational viscosity, due to the fibers. We have compared rheological data for 9.54 wt% carbon fibers in polyethylene and 30 wt% glass fibers in polypropylene, with the model predictions. By defining an “effective fiber concentration,” we have been able to correlate the model well with data. With fitting parameters from the steady state viscosity vs. shear rate data, we have been able to predict the steady state primary stress coefficient data as well as the creep data.