An application of component analysis to the study of South Indian sculptures

Conclusion The primary contribution of this study is advancing the use of computers to an additional field of art historical research. The study deals with an important body of material of the Pallava period from a temple which is one of the earliest structural temples of the Madras region in South India. There are no other comparable materials of this period. Further work on other important material of a later period is in progress. In addition to proportions, the distinctive costumes and jewellery (Lockwood, Siromoney and Dayanandan, 1974) depicted on the different categories will also be of value in restoration work.     

Phrase-based correction model for improving handwriting recognition accuracies

We propose a method for increasing word recognition accuracies by correcting the output of a handwriting recognition system. We treat the handwriting recognizer as a black box, such that there is no access to its internals. This enables us to keep our algorithm general and independent of any particular system. We use a novel method for correcting the output based on a “phrase-based” system in contrast to traditional source-channel models. We report the accuracies of two in-house handwritten word recognizers before and after the correction. We achieve highly encouraging results for a large synthetically generated dataset. We also report results for a commercially available OCR on real data.     

Potential improvement of classifier accuracy by using fuzzymeasures

Typical digit recognizers classify an unknown digit pattern by computing its distance from the cluster centers in a feature space. In this paper, we propose a methodology that has many salient aspects. First, the classification rule is dependent on the “difficulty” of the unknown sample. Samples “far” from the center, which tend to fall on the boundaries of classes are error prone and, hence, “difficult”. An “overlapping zone” is defined in the feature space to identify such difficult samples. A table is precomputed to facilitate an efficient lookup of the class corresponding to all the points in the overlapping zone. The lookup function itself is defined by a modification of the KNN rule. A characteristic function defining the new boundaries is computed using the topology of the set of samples in the overlapping zones. Our two-pronged approach uses different classification schemes with the “difficult” and “easy” samples. The method described has improved the performance of the gradient structural concavity digit recognizer described by Favata et al. (1996)     

Cyclic Oxidation Behavior and Microstructure of Nanocrystalline Ni–20Cr–4Al Coating

The cyclic oxidation behavior and microstructure of a nanocrystalline Ni–20Cr–4Al coating have been investigated. Cyclic oxidation tests were conducted on uncoated and coated samples at peak temperatures of 750 and 1010 °C for up to 2070 thermal cycles between the peak and room temperatures. The results showed that a dense Al₂O₃ scale formed on the coated samples during thermal cycling to both peak temperatures. Evidence of internal oxidation of the coating was observed only on the samples exposed to 1472 one-hour thermal cycles at the peak temperature of 1010 °C. The external scale exhibited good spallation resistance during cyclic oxidation testing at both temperatures. Thermal exposure led to depletion of Al from the coating and grain coarsening within the coating. The improvement in oxide scale spallation resistance and accelerated depletion of aluminum are believed to be related to the fine-grained structure of the coating.     

In vitro biological evaluation of anti‐diabetic activity of organic–inorganic hybrid gold nanoparticles

Diabetes mellitus has been considered as a heterogeneous metabolic disorder characterised by complete or relative impairment in the production of insulin by pancreatic β‐cells or insulin resistance. In the present study, propanoic acid, an active biocomponent isolated from Cassia auriculata is employed for the synthesis of propanoic acid functionalised gold nanoparticles (Pa@AuNPs) and its anti‐diabetic activity has been demonstrated in vitro. In vitro cytotoxicity of synthesised Pa@AuNPs was performed in L6 myotubes. The mode of action of Pa@AuNPs exhibiting anti‐diabetic potential was validated by glucose uptake assay in the presence of Genistein (insulin receptor tyrosine kinase inhibitor) and Wortmannin (Phosphatidyl inositide kinase inhibitor). Pa@AuNPs exhibited significant glucose uptake in L6 myotubes with maximum uptake at 50 ng/ml. Assays were performed to study the potential of Pa@AuNPs in the inhibition of protein‐tyrosine phosphatase 1B, α‐glucosidases, and α‐amylase activity.Inspec keywords: molecular biophysics, biomedical materials, sugar, enzymes, nanofabrication, gold, patient treatment, organic‐inorganic hybrid materials, biochemistry, diseases, cellular biophysics, nanoparticles, toxicology, nanomedicineOther keywords: glucose uptake assay, α‐amylase activity, organic–inorganic hybrid gold nanoparticles, diabetes mellitus, heterogeneous metabolic disorder, pancreatic β‐cells, insulin resistance, propanoic acid, antidiabetic potential, antidiabetic activity, in vitro cytotoxicity, L6 myotubes, Genistein, IRTK inhibitor, Wortmannin, P13K inhibitor, protein‐tyrosine phosphatase 1B, α‐glucosidases, Cassia auriculata, Au     

Silver, gold and bimetallic nanoparticles production using single-cell protein (Spirulina platensis) Geitler

Interaction of single-cell protein of Spirulina platensis with aqueous AgNO₃ and HAuCl₄ was investigated for the synthesis of Ag, Au and Au core—Ag shell nanoparticles. Biological reduction and extracellular synthesis of nanoparticles were achieved in 120 h at 37 °C at pH 5.6. The nanometallic dispersions were characterized by surface plasmon absorbance measuring at 424 and 530 nm for Ag and Au nanoparticles, respectively. For bimetallic nanoparticles, absorption peak was observed at 509, 486 and 464 nm at 75:25, 50:50 and 25:75 (Au:Ag) mol concentrations, respectively. High-resolution transmission electron microscopy showed formation of nanoparticles in the range of 7–16 (silver), 6–10 (gold) and 17–25 nm (bimetallic 50:50 ratio). XRD analysis of the silver and gold nanoparticles confirmed the formation of metallic silver and gold. Fourier transform infrared spectroscopic measurements revealed the fact that the protein is the possible biomolecule responsible for the reduction and capping of the biosynthesized nanoparticles.     

Temporal Moment Analysis for Volatile Organic Compounds in Dual-Porosity Porous Media: Loss Fractions and Effective Parameters

Using transform techniques, analytical expressions for potential losses by volatilization and degradation are developed for several organic compounds in dual porosity porous media. A sensitivity analysis is conducted to study the importance of different physical/chemical processes on volatilization, degradation, and leaching losses. To obtain estimates for overall solute behavior, expressions for effective Péclet numbers and degradation rates for organic contaminants are presented using method of moments. Results indicate that large fractions of many organic compounds are likely to volatilize into the atmosphere for sandy and clayey soils under typical flow conditions. It is found that nondimensional degradation influences both advective and dispersive effects. Thus, the Péclet number from effective-parameter equation tends to be enhanced when the nondimensional degradation is rather high. The simple expressions for moments and effective parameters can be used as screening tools to assess the behavior of volatile compounds in vadoze zone of soils.     

Generalized regression model for sequence matching and clustering

Linear relation has been found to be valuable in rule discovery of stocks, such as if stock X goes up a, stock Y will go down b. The traditional linear regression models the linear relation of two sequences faithfully. However, if a user requires clustering of stocks into groups where sequences have high linearity or similarity with each other, it is prohibitively expensive to compare sequences one by one. In this paper, we present generalized regression model (GRM) to match the linearity of multiple sequences at a time. GRM also gives strong heuristic support for graceful and efficient clustering. The experiments on the stocks in the NASDAQ market mined interesting clusters of stock trends efficiently. Hansheng Lei received his BE from Ocean University of China in 1998, MS from the University of Science and Technology of China in 2001, and Ph.D. from the University at Buffalo, the State University of New York in February 2006, all in computer science. He is currently an assistant professor in CS/CIS Department, University of Texas at Brownsville. His research interests include biometrics, pattern recognition, machine learning, and data mining. Venu Govindaraju is a professor of Computer Science and Engineering at the University at Buffalo (UB), State University of New York. He received his B.-Tech. (Honors) from the Indian Institute of Technology (IIT), Kharagpur, India in 1986, and his Ph.D. degree in Computer Science from UB in 1992. His research is focused on pattern recognition applications in the areas of biometrics and digital libraries.     

Equivalence of different methods for slant and skew corrections inword recognition applications

Normalization of slant and skew is often used in processing a word image before recognition. In this paper, we prove the theoretical equivalence of different methods for slant and skew corrections. In particular, we show that correcting first for skew by rotation and then for slant by a shear transformation in the horizontal direction is equivalent to first correcting for slant by a shear transformation in the horizontal direction and then for skew by a shear transformation in the vertical direction. Our proof can be easily modified to prove equivalence of other methods for correcting the slant and skew