<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>

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APS March Meeting Abstracts 2014, M31.

Atomic structures of magic ZnSe clusters from first principles calculation

Bulletin of the American Physical Society

\\textit{Ab initio} investigation of the electronic properties of HgTe clusters

Large scale fed-batch operations are industrially important in agricultural and pharmaceutical sectors. In fed-batch operations, we need to optimize the flow rates of rate limiting substrates to maximize the required performance index. Dynamic optimization of the feed rate profiles renders the problem quite complex and requires intelligent techniques. In this context, evolutionary algorithms have proven to be very useful. We introduce the application of a fairly recent nature-inspired evolutionary optimization technique-biogeography-based optimization (BBO), not so widely known as other established evolutionary optimization techniques-for the fed-batch bioreactor-based dynamic optimization problems. We demonstrate with the help of two case studies that BBO can achieve performance indices in close agreement or better than the results in the literature for the considered problems. © 2014 Springer International Publishing Switzerland. All rights reserved.

Applications of Metaheuristics in Process Engineering

Biogeography-based optimization for dynamic optimization of chemical reactors

Microarray gene expression datasets comprise of a large number of genes in contrast to a small number of samples, thus having a high dimension of variables. Analysis of microarray data can lead us to many useful conclusions. In many microarray data analyses, selecting a small subset of genes which are of significance for a particular type of disease is an important issue but selection of such genes become difficult due to many irrelevant genes and noisy genes. The process of gene selection helps to extract the most informative genes, which consequently aid to build a robust prediction model using those genes. In this study, we employ a hybrid Chemical Reaction Optimization (CRO) based filter-wrapper methodology, which uses an information gain gene ranking heuristic to simultaneously extract informative gene subsets and build robust cancer classification models. The performance of the proposed method was tested on three benchmark gene expression datasets obtained from the Kent Ridge Biomedical datasets collection and the LIBSVM data repository. CRO results demonstrate its capability to select relevant genes with high confidence in comparison to the results reported earlier.

Lecture Notes in Engineering and Computer Science

Simultaneous gene selection and cancer classification using chemical reaction optimization

Process fault detection concerns itself with monitoring process variables and identifying when a fault has occurred in the process workflow. Sophisticated learning algorithms may be used to select the relevant process state variables out of a massive search space and can be used to build more efficient and robust fault detection models. In this study, we present a recently proposed swarm intelligencebased hybrid intelligent water drop (IWD) optimization algorithm in combination with support vector machines and an information gain heuristic for selecting a subset of relevant fault indicators. In the process, we demonstrate the successful application and effectiveness of this swarm intelligence-based method to variable selection and fault identification. Moreover, performance testing on standard machine learning benchmark datasets also indicates its viability as a strong candidate for complex classification and prediction tasks. © Springer India 2014.

Advances in Intelligent Systems and Computing

Variable selection and fault detection using a hybrid intelligent water drop algorithm

An atomistic model for the effect of carbon (C) doping on the structural reorganization of boron suboxide (B6O) was developed from first principles density functional theory. The results indicate that it is energetically preferable for a single C atom to substitute into an oxygen (O) site rather than a B site. The lattice parameters and cell volume increase to relieve the residual stress created by the C substitution. Interstitial substitutions are not favorable for single atom substitutions. However, when 2 C atoms replace 2 neighboring O atoms within the B6O unit cell, it becomes energetically favorable to dope an interstitial O, B, or C atom along the C-C chain. If the interstitial dopant is either B or C, a local boron carbide (B4C)-like structure with either a C-B-C or C-C-C chain is created within the B6O unit cell, which could serve as an intermediate phase in the transformation of B6O into B4C. By promoting the formation of a local B4C-like structure, it may be possible to improve the mechanical stability and elastic properties of B6O.

ARMY RESEARCH LAB ABERDEEN PROVING GROUND MD WEAPONS AND MATERIALS RESEARCH {\ldots}

First Principles Atomistic Model for Carbon-Doped Boron Suboxide

APS March Meeting Abstracts 2013, C23.

Non-stoichiometric Mn doped ZnO clusters: First principles calculations

We report results of ab initio calculations on yttrium oxide clusters using a plane wave pseudopotential method within density functional theory. (Y 2O3)n clusters in the size range n = 1-10 prefer compact and symmetric globular configurations where preference for an octahedron unit of Y6O8 is seen. The evolution of the atomic structures shows similarity with that of the local structure in the bulk cubic (C-Y2O3) phase. The maximum coordinations of Y and O atoms are 6 and 4, respectively. The addition (removal) of an electron to (from) the lowest energy configurations of the neutral clusters induces significant changes for some of the cluster sizes. Sequential addition of a Y2O3 unit to the (Y2O3)n cluster leads to an increase in the binding energy. However, the HOMO-LUMO gap, ionization potential, and electron affinity do not show any systematic variation in these clusters with increasing size. The bonding characteristics have been studied using charge density and Bader charge analysis. The charge transfer from Y atoms to oxygens increases with the increase in the cluster size and approaches the value in bulk. The stability of the clusters is dominated by ionic Y-O interactions. However, a small degree of covalency is also seen in Y-O bonding. All the lowest energy configurations of neutral clusters prefer the lowest spin state and the ionic clusters prefer a doublet state. © 2013 American Chemical Society.

Journal of Physical Chemistry A

Density functional calculations of the structural and electronic properties of (Y2O3)n0,±1 clusters with n = 1-10

Ab initio calculations on interaction of a C atom on small Ptn clusters (n = 1-10) show strong carbidic bonding and a decrease in the magnetic moments of Pt clusters. The adsorption energy decreases in an oscillatory way with increasing size but suggests no graphitic phase on small Pt clusters. Ptn clusters (n &lt; 7) remain nearly planar after C adsorption but larger clusters have 3D structure. Further calculations of infrared and Raman spectra are reported. Interaction of H on pure and PtnC clusters shows stronger binding of H on PtnC clusters suggesting their better reactivity. © 2013 Elsevier B.V. All rights reserved.