<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

There is a need for developing accurate learning algorithms for analyzing large-scale medical diagnostic, prognostic, and treatment datasets. Success of classifiers like support vector machines lies in employment of best informative features out of a huge noisy feature space. In this work, we employ a hybrid filter– wrapper approach to build high-performance classification models. We tested our algorithms using popular datasets containing clinic-bio-pathological parameters of leukemia, hepatitis, breast cancer, and colon cancer taken from publically available datasets. Our results indicate that the hybrid algorithm can discover informative subsets possessing very high classification accuracy. © Springer India 2015.

Life Sciences

(SoNS) School of Natural Sciences

Advances in Intelligent Systems and Computing

Hybrid ACO chaos-assisted support vector machines for classification of medical datasets

World University root node

Fulltext

Frontiers in Genetics

Editorial: Annotation and curation of uncharacterized proteins: Systems biology approaches

The structural, electronic, and elastic properties of pristine and carbon-doped boron suboxide (B6O) are calculated using density functional theory. The results indicate that it is energetically preferable for a single carbon atom to substitute into an oxygen site rather than a boron site. The lattice parameters and cell volume increase to relieve the residual stress created by the carbon substitution. The interstitial position is not favorable for a single atom substitution. However, if two carbon atoms substitute for two neighboring oxygen atoms, then it becomes energetically favorable to dope an interstitial oxygen, boron, or carbon atom along the C-C chain. If the interstitial dopant is either boron or carbon, a local B4C-like structure with either a C-B-C or C-C-C chain is created within the boron suboxide unit cell. The resulting structure shows improvements in the bulk modulus at the expense of the shear and Young's moduli. The moduli further improve if an additional carbon is substituted within a polar or equatorial site of the neighboring B12 icosahedron. Based on these calculations, we conclude that carbon doping can either harden or soften B6O depending on the manner in which the substitutions are populated. Furthermore, as B6O samples are often oxygen deficient, C doping can occupy such sites and improve the elastic properties. © 2015 The American Ceramic Society.

Journal of the American Ceramic Society

Carbon Doping in Boron Suboxide: Structure, Energetics, and Elastic Properties

The low-energy structures of PtnSnn (n = 1–10) and Pt3mSnm (m = 1–5) clusters have been determined using genetic algorithm incorporated with density functional theory. Platinum and tin atoms tend to mix with each other due to the energetically favorable Pt–Sn bonds. However, due to the larger atomic radius of Sn atoms, we find segregation of Sn atoms on the surface of PtnSnn clusters. This leaves one or two Pt atoms available for reaction and for larger clusters segregation of Sn could block the Pt sites. For Pt3mSnm clusters, Sn atoms are well separated in the cluster structures and prefer to form sharp vertices leaving triangular faces of three Pt atoms available for reactivity. The electronic properties such as highest occupied molecular orbital–lowest unoccupied molecular orbital gap, distribution of frontier orbitals, Mayer bond order, Mülliken atomic charge, and the density of states are discussed. Significant hybridization between the d orbitals of Pt and the p orbitals of Sn is revealed. These theoretical results provide the general trends for the structural and bonding characteristics of the Pt–Sn alloy clusters and help understand their catalytic behavior. © 2014, Springer Science+Business Media New York.

Journal of Cluster Science

Low-Energy Structures of Binary Pt–Sn Clusters from Global Search Using Genetic Algorithm and Density Functional Theory

Ab initio calculations on Eu doped (GaN)n (n-=-12, 13, and 32) nanoparticles show that Eu doping in nanoparticles is favorable compared with bulk GaN as a large fraction of atoms lie on the surface where strain can be released compared with bulk where often Eu doping is associated with a N vacancy. Co-doping of Si further facilitates Eu doping as strain from an oversized Eu atom and an undersized Si atom is compensated. These results along with low symmetry sites in nanoparticles make them attractive for developing strongly luminescent nanomaterials. The atomic and electronic structures are discussed using generalized gradient approximation (GGA) for the exchange-correlation energy as well as GGA + U formalism. In all cases of Eu (Eu-+-Si) doping, the magnetic moments are localized on the Eu site with a large value of 6μB (7μB). Our results suggest that co-doping can be a very useful way to achieve rare-earth doping in different hosts for optoelectronic materials. © 2015 AIP Publishing LLC.

Journal of Applied Physics

Enhanced stability of Eu in GaN nanoparticles: Effects of Si co-doping

Ab initio calculations on CsSnX3 perovskites and mixed halides CsSn(XxY 1-x)3, X and Y = I, Cl, and Br, show that all of them have a direct band gap of ∼1 eV which can be tuned by varying the compositions of X and Y. The optimized supercells are tetragonal, orthorhombic or monoclinic. The top of the valence band arises from hybridization of Sn 4s and halogen p valence orbitals while the bottom of the conduction band has predominantly Sn p character. Similar to organo-metallic lead halides this is expected to facilitate p-p optical transitions that are highly favourable for photoabsorption. Our results suggest that these inorganic perovskites have the desired features to achieve high efficiency of photo-response with appropriate combination of halogens. © 2015 Author(s).

AIP Advances

Ab initio design of CsSn(XxY 1-x)3 (X and y = Cl, Br, and I) perovskites for photovoltaics

Silicene, a graphene analogue of silicon, has been generating immense interest due to its potential for applications in miniaturized devices. Unlike planar graphene, silicene prefers a buckled structure. Here we explore the possibility of stabilizing the planar form of silicene by Ni doping using first principles density functional theory based calculations. It is found that planar as well as buckled structure is stable for Ni-doped silicene, but the buckled sheet has slightly lower total energy. The planar silicene sheet has unstable phonon modes. A comparative study of the mechanical properties reveals that the in-plane stiffness of both the pristine and the doped planar silicene is higher compared to that of the buckled silicene. This suggests that planar silicene is mechanically more robust. Electronic structure calculations of the planar and buckled Ni-doped silicene show that the energy bands at the Dirac point transform from linear behavior to parabolic dispersion. Furthermore, we extend our study to Ge and Sn sheets that are also stable and the trends of comparable mechanical stability of the planar and buckled phases remain the same.

Physical Chemistry Chemical Physics

Mechanical and electronic properties of pristine and Ni-doped Si, Ge, and Sn sheets

Metaheuristics exhibit desirable properties like simplicity, easy parallelizability and ready applicability to different types of optimization problems such as real parameter optimization, combinatorial optimization and mixed integer optimization. They are thus beginning to play a key role in different industrially important process engineering applications, among them the synthesis of heat and mass exchange equipment, synthesis of distillation columns and static and dynamic optimization of chemical and bioreactors. This book explains cutting-edge research techniques in related computational intelligence domains and their applications in real-world process engineering. It will be of interest to industrial practitioners and research academics. © 2014 Springer International Publishing Switzerland. All rights reserved.

Applications of Metaheuristics in Process Engineering

Applications of metaheuristics in process engineering

This chapter presents an overview of applications of metaheuristics to solve different real-world chemical process engineering problems over the last 30 years. The first part of this chapter describes some fundamental characteristics of metaheuristics, a class of global stochastic methods and also provides the standard description of some of the most widely used metaheuristics such as simulated annealing, tabu search, genetic algorithms, and ant colony optimization (ACO). In the second part, different practical applications of these metaheuristics related to chemical process industry are covered such as heat exchanger networks (HENs), short-term scheduling of batch processes, dynamic optimization of chemical and biochemical processes, parameter estimation, and multiobjective optimization with extensive list of references. © 2014 Springer International Publishing Switzerland. All rights reserved.

Metaheuristics in process engineering: A historical perspective

Nuclear receptors constitute a super family of protein hormones that serve as transcription factors. They typically reside in the cytosol and, after ligand binding, migrate to the nucleus to exert their biological action.Ligands are lipophilic, small molecules including retinoids, steroids, thyroxine, and vitamin D. Nuclear receptors being important regulators of gene expression, constitute 13\% of proteins targeted by various drugs. Thus it becomes important to identify the ligand binding pockets on these proteins. Support Vector Machine (SVM) classifier was built to identify nuclear receptor ligand binding pockets. Positive dataset consisted of the ligand binding pockets of known nuclear receptor-Ligand complex structures. Negative dataset consisted of ligand binding pockets of proteins other than nuclear receptors and nonligand binding pockets of nuclear receptors. SVM model yielded a 10 fold cross-validation accuracy of 96\% using linear kernel. Also, it is helpful to find out the class of nuclear receptor in order to design a "class-specific" drug. In case of the multiclass nuclear receptor dataset comprising of nuclear receptors belonging to three different classes, SVM model for classification yielded an average 10-Fold cross validation accuracy of 92 \% for this dataset. SVM algorithm identifies and classifies nuclear receptor binding pockets with excellent accuracy. Top ranked features indicate the hydrophobic nature of ligand binding pocket of nuclear receptors. Conserved Leucine and phenylalanine residues form a distinguishing feature of these binding pockets. Along-with identification of NR binding pockets, important top ranked features are listed which would be useful in screening of possible drug molecules with NRs as molecular targets. ©2014 Bentham Science Publishers.