<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

Center for Informatics

Xylanases are an industrially important class of hydrolytic enzymes that degrade xylans. Production of xylanase from a fungal culture by submerged fermentation and optimization of the operating conditions for maximum activity are the two aims of the present study. Penicillium janthinellum NCIM 1169 with Mandels-Weber medium, sugarcane bagassse (40#) as a carbon source and beef extract as a nitrogen source were used in the experiments. We did 41 experiments to see the effect of variations in carbon, nitrogen source, pH, and inoculum on xylanase activity. This data was then used to build an input/output model using multiple linear regression, back propagation neural network and lazy learning algorithm. It was found that lazy learning model correlated well in mapping input/output data. This model was then utilized as an objective function in genetic algorithm to find the optimal combination of the operating conditions to get the maximum xylanase activity. It was observed that with carbon source, 1.63%, nitrogen source, 0.16%, pH, 4.1, and inoculum, 5.5%, maximum xylanase activity of 28.98 ± 1.73 U/ml was achieved. © 2008 Elsevier B.V. All rights reserved.

World University root node

Biochemical Engineering Journal

Optimal xylanase production using Penicilium janthinellum NCIM 1169: A model based approach

Nanotubular and nanowire structures of silicon are currently of great interest for miniature devices. Recently, using cluster assembly approach, nanotubular forms of silicon have been shown to be stabilized by encapsulation of metal atoms. This chapter reviews these developments and discusses the stability of such nanostructures and their electronic properties including metallic, semiconducting and magnetic behaviors. Hydrogenated and oxygenated structures of silicon can also be made in tubular forms. These could be among the thinnest semiconducting nanostructures of silicon. Thicker quasi-one-dimensional structures of silicon have been grown in the form of nanowires which could be metallic or semiconducting. This chapter discusses the surface reconstruction in such nanowires and their electronic properties. Further effects of p- or n-type doping as well as hydrogen defects on the atomic and electronic structures of hydrogenated Si nanowires are presented. The metallic, semiconducting and optical properties of silicon in such nanostructures could make it possible to develop novel silicon-based nanodevices. {\textcopyright} 2008 Elsevier Ltd All rights reserved.

Nanosilicon

Theoretical Advances in the Electronic and Atomic Structures of Silicon Nanotubes and Nanowires

Calculations on encapsulation of a metal atom in silicon clusters have led to the predictions of novel forms of nanosilicon and the discovery of silicon fullerenes. There is renewed interest in the study of silicon nanoclusters and the understanding of the effects of doping with metal atoms. Experiments on doped clusters of silicon as well as other group 14 elements have led to the realization of some of the predictions. Encapsulation of a metal atom enhances the stability of nanoclusters and often leads to a striking preference for a specific size. This can facilitate the design and production of nanoclusters of silicon with specific properties in high abundance and the development of assemblies of such species. This chapter discusses the electronic origin of the stability of such species as well as their magnetic, vibrational and optical properties. Hydrogen termination has been used to predict empty cage silicon fullerenes as well as their endohedral and exohedral forms that could have potential for the development of many derivatives and functional materials similar to carbon fullerenes as well as their interesting optical and magnetic properties for nanoscale applications. {\textcopyright} 2008 Elsevier Ltd All rights reserved.

Metal Encapsulated Clusters of Silicon. Silicon Fullerenes and Other Polyhedral Forms

Ayurveda is one of the most comprehensive healing systems in the world and has classified the body system according to the theory of Tridosha to overcome ailments. Diagnosis similar to the traditional pulse-based method requires a system of clean input signals, and extensive experiments for obtaining classification features. In this paper we briefly describe our system of generating pulse waveforms and use various feature detecting methods to show that an arterial pulse contains typical physiological properties. The beat-to-beat variability is captured using a complex B-spline mother wavelet based peak detection algorithm. We also capture - to our knowledge for the first time - the self-similarity in the physiological signal, and quantifiable chaotic behavior using recurrence plot structures. © 2007 IEEE.

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

Arterial pulse system: Modem methods for traditional indian medicine

In this work, we integrate a non-linear signal analysis method, recurrence quantification analysis (RQA), with the well-known machine-learning algorithm, support vector machines for the binary classification of protein sequences. Two different classification problems were selected, discriminating between aggregating and non-aggregating proteins and mostly disordered and completely ordered proteins, respectively. It has also been shown that classification performance of SVM models improve on selection of the most informative RQA descriptors as SVM input features. © 2007 Taylor & Francis Group, LLC.

Journal of Biomolecular Structure and Dynamics

Using recurrence quantification analysis descriptors for protein sequence classification with support vector machines

Physical Review-Section B-Condensed Matter

Structure, structural phase transitions, mechanical properties, defects-First-principles study of the structural, electronic and elastic properties of RRh3BxC1-x (R= Sc and Y)

Using ab initio calculations, we find high stability of octahedral Mo 3nS3n+2 clusters, which can further be condensed to form Mo3nS3n+2 (n, an integer) nanowires. These linear structures are energetically more favorable compared with other closed-packed polyhedral isomers of Mo-S clusters. The octahedral units in nanowires are stabilized by strong Mo-Mo interactions and p-d hybridization between Mo 4d and S 2p orbitals. There is a free electron-like band that crosses the Fermi energy in infinite nanowires and leads to their metallic character. Iodine doping acts as electron donor and can be used to tailor the electronic conductivity. For Mo12S 8I4 nanowires, both electrons and holes are found to contribute to conduction. These nanowires are energetically more favorable than the experimentally obtained Mo12S6I12 nanowires. {\textcopyright} 2007 American Chemical Society.

Nano Letters

Assembling nanowires from Mo-S clusters and effects of iodine doping on electronic structure

This paper proposes PSACO (particle swarm ant colony optimization) algorithm for highly non-convex optimization problems. Both particle swarm optimization (PSO) and ant colony optimization (ACO) are co-operative, population-based global search swarm intelligence metaheuristics. PSO is inspired by social behavior of bird flocking or fish schooling, while ACO imitates foraging behavior of real life ants. In this study, we explore a simple pheromone-guided mechanism to improve the performance of PSO method for optimization of multimodal continuous functions. The proposed PSACO algorithm is tested on several benchmark functions from the usual literature. Numerical results comparisons with different metaheuristics demonstrate the effectiveness and efficiency of the proposed PSACO method. © 2006 Elsevier Inc. All rights reserved.

Applied Mathematics and Computation

Particle swarm and ant colony algorithms hybridized for improved continuous optimization

Materials Transactions

Special issue on ACCMS working group meeting on clusters and nanomaterials

In this study we have developed a support vector regression (SVR) based data-driven model for predicting two important design parameters of surface aerators, namely, the volumetric mass transfer coefficient (kLa) and fractional gas hold-up (∈G)- The dynamical state of the surface aerator system was captured by acquiring pressure fluctuation signals (PFSs) at various design and operating conditions. The most informative features from PFS were extracted using the chaos analysis technique, which includes estimation of Lyapunov exponent, correlation dimensions, and Kolmogorov entropy. At similar conditions the values of kLa and ∈G were also measured. Two different SVR models for predicting the volumetric mass transfer coefficient (kLa) and overall gas hold-up (∈G) as a function of chaotic invariants, design parameters, and operating parameters were developed showing test accuracies of 98.8% and 97.1%, respectively. Such SVM based models for the surface aerator can be potentially useful on a commercial scale for online monitoring and control of desired process output variables. © 2007 American Chemical Society.