Prediction of reaeration rates in square, stirred tanks

Experiments were conducted on the oxygen transfer coefficient, k(L)a(20), through surface aeration in geometrically similar square tanks, with a rotor of diameter D fitted with six flat blades. An optimal geometric similarity of various linear dimensions, which produced maximum k(L)a(20) for any rotational speed of rotor N by an earlier study, was maintained. A simulation equation uniquely correlating k = k(L)a(20)(nu/g(2))(1/3) (nu and g are kinematic viscosity of water and gravitational constant, respectively), and a parameter governing the theoretical power per unit volume, X = (ND2)-D-3/(g(4/3)nu(1/3)), is developed. Such a simulation equation can be used to predict maximum k for any N in any size of such geometrically similar square tanks. An example illustrating the application of results is presented. Also, it has been established that neither the Reynolds criterion nor the Froude criterion is singularly valid to simulate either k or K = k(L)a(20)/N, simultaneously in all the sizes of tanks, even through they are geometrically similar. Occurrence of "scale effects" due to the Reynolds and the Froude laws of similitude on both k and K are also evaluated.

Multivariate nonlinear ensemble prediction of daily chaotic rainfall with climate inputs

The basic characteristic of a chaotic system is its sensitivity to the infinitesimal changes in its initial conditions. A limit to predictability in chaotic system arises mainly due to this sensitivity and also due to the ineffectiveness of the model to reveal the underlying dynamics of the system. In the present study, an attempt is made to quantify these uncertainties involved and thereby improve the predictability by adopting a multivariate nonlinear ensemble prediction. Daily rainfall data of Malaprabha basin, India for the period 1955-2000 is used for the study. It is found to exhibit a low dimensional chaotic nature with the dimension varying from 5 to 7. A multivariate phase space is generated, considering a climate data set of 16 variables. The chaotic nature of each of these variables is confirmed using false nearest neighbor method. The redundancy, if any, of this atmospheric data set is further removed by employing principal component analysis (PCA) method and thereby reducing it to eight principal components (PCs). This multivariate series (rainfall along with eight PCs) is found to exhibit a low dimensional chaotic nature with dimension 10. Nonlinear prediction employing local approximation method is done using univariate series (rainfall alone) and multivariate series for different combinations of embedding dimensions and delay times. The uncertainty in initial conditions is thus addressed by reconstructing the phase space using different combinations of parameters. The ensembles generated from multivariate predictions are found to be better than those from univariate predictions. The uncertainty in predictions is decreased or in other words predictability is increased by adopting multivariate nonlinear ensemble prediction. The restriction on predictability of a chaotic series can thus be altered by quantifying the uncertainty in the initial conditions and also by including other possible variables, which may influence the system. (C) 2011 Elsevier B.V. All rights reserved.

DNA Free Energy-Based Promoter Prediction and Comparative Analysis of Arabidopsis and Rice Genomes

The cis-regulatory regions on DNA serve as binding sites for proteins such as transcription factors and RNA polymerase. The combinatorial interaction of these proteins plays a crucial role in transcription initiation, which is an important point of control in the regulation of gene expression. We present here an analysis of the performance of an in silico method for predicting cis-regulatory regions in the plant genomes of Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa) on the basis of free energy of DNA melting. For protein-coding genes, we achieve recall and precision of 96% and 42% for Arabidopsis and 97% and 31% for rice, respectively. For noncoding RNA genes, the program gives recall and precision of 94% and 75% for Arabidopsis and 95% and 90% for rice, respectively. Moreover, 96% of the false-positive predictions were located in noncoding regions of primary transcripts, out of which 20% were found in the first intron alone, indicating possible regulatory roles. The predictions for orthologous genes from the two genomes showed a good correlation with respect to prediction scores and promoter organization. Comparison of our results with an existing program for promoter prediction in plant genomes indicates that our method shows improved prediction capability.

Generalized model for infrared perception from an engine exhaust

A comprehensive scheme has been developed for the prediction of radiation from engine exhaust and its incidence on an arbitrarily located sensor. Existing codes have been modified for the simulation of flows inside nozzles and jets. A novel view factor computation scheme has been applied for the determination of the radiosities of the discrete panels of a diffuse and gray nozzle surface. The narrowband model has been used to model the radiation from the gas inside the nozzle and the nonhomogeneous jet. The gas radiation from the nozzle inclusive of nozzle surface radiosities have been used as boundary conditions on the jet radiation. Geometric modeling techniques have been developed to identify and isolate nozzle surface panels and gas columns of the nozzle and jet to determine the radiation signals incident on the sensor. The scheme has been validated for intensity and heat flux predictions, and some useful results of practical importance have been generated to establish its viability for infrared signature analysis of jets.

Structural investigation of combustion synthesized Cu/CeO2 catalysts by EXAFS and other physical techniques: Formation of a Ce1-xCuxO2-delta solid solution

The structure and chemical environment of Cu in Cu/CeO2 catalysts synthesized by the solution combustion method have been investigated by X-ray diffraction (XRD), transmission electron microscopy (TEM), electron paramagnetic resonance (EPR) spectroscopy, X-ray photoelectron spectroscopy (XPS), cyclic voltammetry (CV), and extended X-ray fine structure (EXAFS) spectroscopy. High-resolution XRD studies of 3 and 5 atom % Cu/CeO2 do not show CuO lines in their respective patterns. The structure could be refined for the composition Ce1-xCuxO2-delta (x = 0.03 and 0.05; delta similar to 0.13 and 0.16) in the fluorite structure with 5-8% oxide ion vacancy. High-resolution TEM did not show CuO particles in 5 atom % Cu/CeO2. EPR as well as XPS studies confirm the presence of Cu2+ species in the CeO2 matrix. Redox potentials of Cu species in the CeO2 matrix are lower than those in CuO. EXAFS investigations of these catalysts show an average coordination number of 3 around the Cu2+ ion in the first shell at a distance of 1.96 Angstrom, indicating the O2- ion vacancy around the Cu2+ ion. The Cu-O bond length also decreases compared to that in CuO. The second and third shell around the Cu2+ ion in the catalysts are attributed to -Cu2+-O2--Cu2+ - at 2.92 Angstrom and -Cu2+-O2--Ce4+- at the distance of 3.15 Angstrom, respectively. The present results provide direct evidence for the formation of a Ce1-xCuxO2-delta type of solid solution phase having -square-Cu2+-O-Ce4+- kind of linkages.

Techniques for realistic visualization of fluids: A survey

Visualization of fluids has wide applications in science, engineering and entertainment. Various methodologies Of visualizing fluids have evolved which emphasize on capturing different aspects of the fluids accurately. In this survey the existing methods for realistic visualization of fluids are reviewed. The approaches are classified based on the key concept they rely on for fluid modeling. This classification allows for easy selection of the method to be adopted for visualization given an application. It also enables identification of alternative techniques for fluid modeling.

An investigation into the feasibility of fetal lung maturity prediction using statistical textural features

Fetal lung and liver tissues were examined by ultrasound in 240 subjects during 24 to 38 weeks of gestational age in order to investigate the feasibility of predicting the maturity of the lung from the textural features of sonograms. A region of interest of 64 X 64 pixels is used for extracting textural features. Since the histological properties of the liver are claimed to remain constant with respect to gestational age, features obtained from the lung region are compared with those from liver. Though the mean values of some of the features show a specific trend with respect to gestation age, the variance is too high to guarantee definite prediction of the gestational age. Thus, we restricted our purview to an investigation into the feasibility of fetal lung maturity prediction using statistical textural features. Out of 64 features extracted, those features that are correlated with gestation age and less computationally intensive are selected. The results of our study show that the sonographic features hold some promise in determining whether the fetal lung is mature or immature.

Online character recognition using regression techniques

This paper introduces a scheme for classification of online handwritten characters based on polynomial regression of the sampled points of the sub-strokes in a character. The segmentation is done based on the velocity profile of the written character and this requires a smoothening of the velocity profile. We propose a novel scheme for smoothening the velocity profile curve and identification of the critical points to segment the character. We also porpose another method for segmentation based on the human eye perception. We then extract two sets of features for recognition of handwritten characters. Each sub-stroke is a simple curve, a part of the character, and is represented by the distance measure of each point from the first point. This forms the first set of feature vector for each character. The second feature vector are the coeficients obtained from the B-splines fitted to the control knots obtained from the segmentation algorithm. The feature vector is fed to the SVM classifier and it indicates an efficiency of 68% using the polynomial regression technique and 74% using the spline fitting method.