Smoothed functional and Quasi-Newton algorithms for routing in multi-stage queueing network with constraints

We consider the problem of optimal routing in a multi-stage network of queues with constraints on queue lengths. We develop three algorithms for probabilistic routing for this problem using only the total end-to-end delays. These algorithms use the smoothed functional (SF) approach to optimize the routing probabilities. In our model all the queues are assumed to have constraints on the average queue length. We also propose a novel quasi-Newton based SF algorithm. Policies like Join Shortest Queue or Least Work Left work only for unconstrained routing. Besides assuming knowledge of the queue length at all the queues. If the only information available is the expected end-to-end delay as with our case such policies cannot be used. We also give simulation results showing the performance of the SF algorithms for this problem.

Context aware M-commerce services: C-IOB model approach

Context-aware computing is useful in providing individualized services focusing mainly on acquiring surrounding context of user. By comparison, only very little research has been completed in integrating context from different environments, despite of its usefulness in diverse applications such as healthcare, M-commerce and tourist guide applications. In particular, one of the most important criteria in providing personalized service in a highly dynamic environment and constantly changing user environment, is to develop a context model which aggregates context from different domains to infer context of an entity at the more abstract level. Hence, the purpose of this paper is to propose a context model based on cognitive aspects to relate contextual information that better captures the observation of certain worlds of interest for a more sophisticated context-aware service. We developed a C-IOB (Context-Information, Observation, Belief) conceptual model to analyze the context data from physical, system, application, and social domains to infer context at the more abstract level. The beliefs developed about an entity (person, place, things) are primitive in most theories of decision making so that applications can use these beliefs in addition to history of transaction for providing intelligent service. We enhance our proposed context model by further classifying context information into three categories: a well-defined, a qualitative and credible context information to make the system more realistic towards real world implementation. The proposed model is deployed to assist a M-commerce application. The simulation results show that the service selection and service delivery of the system are high compared to traditional system.

A Bottom-Up Approach toward Fabrication of Ultrathin PbS Sheets

Two-dimensional (2D) sheets are currently in the spotlight of nanotechnology owing to high-performance device fabrication possibilities. Building a free-standing quantum sheet with controlled morphology is challenging when large planar geometry and ultranarrow thickness are simultaneously concerned. Coalescence of nanowires into large single-crystalline sheet is a promising approach leading to large, molecularly thick 2D sheets with controlled planar morphology. Here we report on a bottom-up approach to fabricate high-quality ultrathin 2D single crystalline sheets with well-defined rectangular morphology via collective coalescence of PbS nanowires. The ultrathin sheets are strictly rectangular with 1.8 nm thickness, 200-250 nm width, and 3-20 mu m length. The sheets show high electrical conductivity at room and cryogenic temperatures upon device fabrication. Density functional theory (DFT) calculations reveal that a single row of delocalized orbitals of a nanowire is gradually converted into several parallel conduction channels upon sheet formation, which enable superior in-plane carrier conduction.

Exploration of deleterious single nucleotide polymorphisms in the components of human P bodies: An in silico approach

P bodies are 100-300 nm sized organelles involved in mRNA silencing and degradation. A total of 60 human proteins have been reported to localize to P bodies. Several human SNPs contribute to complex diseases by altering the structure and function of the proteins. Also, SNPs alter various transcription factors binding, splicing and miRNA regulatory sites. Owing to the essential functions of P bodies in mRNA regulation, we explored computationally the functional significance of SNPs in 7 P body components such as XRN1, DCP2, EDC3, CPEB1, GEMIN5, STAU1 and TRIM71. Computational analyses of non-synonymous SNPs of these components was initiated using well utilized publicly available software programs such as the SIFT, followed by PolyPhen, PANTHER, MutPred, I-Mutant-2.0 and PhosSNP 1.0. Functional significance of noncoding SNPs in the regulatory regions were analysed using FastSNP. Utilizing miRSNP database, we explored the role of SNPs in the context that alters the miRNA binding sites in the above mentioned genes. Our in silico studies have identified various deleterious SNPs and this cataloguing is essential and gives first hand information for further analysis by in vitro and in vivo methods for a better understanding of maintenance, assembly and functional aspects of P bodies in both health and disease. (C) 2013 Elsevier B.V. All rights reserved.

Network, nodes and nexus: systems approach to multitarget therapeutics

Systems biology is revealing multiple layers of regulatory networks that manifest spatiotemporal variations. Since genes and environment also influence the emergent property of a cell, the biological output requires dynamic understanding of various molecular circuitries. The metabolic networks continually adapt and evolve to cope with the changing milieu of the system, which could also include infection by another organism. Such perturbations of the functional networks can result in disease phenotypes, for instance tuberculosis and cancer. In order to develop effective therapeutics, it is important to determine the disease progression profiles of complex disorders that can reveal dynamic aspects and to develop mutitarget systemic therapies that can help overcome pathway adaptations and redundancy.

A multi-level multi-scale approach to study essential genes in Mycobacterium tuberculosis

Background: The set of indispensable genes that are required by an organism to grow and sustain life are termed as essential genes. There is a strong interest in identification of the set of essential genes, particularly in pathogens, not only for a better understanding of the pathogen biology, but also for identifying drug targets and the minimal gene set for the organism. Essentiality is inherently a systems property and requires consideration of the system as a whole for their identification. The available experimental approaches capture some aspects but each method comes with its own limitations. Moreover, they do not explain the basis for essentiality in most cases. A powerful prediction method to recognize this gene pool including rationalization of the known essential genes in a given organism would be very useful. Here we describe a multi-level multi-scale approach to identify the essential gene pool in a deadly pathogen, Mycobacterium tuberculosis. Results: The multi-level workflow analyses the bacterial cell by studying (a) genome-wide gene expression profiles to identify the set of genes which show consistent and significant levels of expression in multiple samples of the same condition, (b) indispensability for growth by using gene expression integrated flux balance analysis of a genome-scale metabolic model, (c) importance for maintaining the integrity and flow in a protein-protein interaction network and (d) evolutionary conservation in a set of genomes of the same ecological niche. In the gene pool identified, the functional basis for essentiality has been addressed by studying residue level conservation and the sub-structure at the ligand binding pockets, from which essential amino acid residues in that pocket have also been identified. 283 genes were identified as essential genes with high-confidence. An agreement of about 73.5% is observed with that obtained from the experimental transposon mutagenesis technique. A large proportion of the identified genes belong to the class of intermediary metabolism and respiration. Conclusions: The multi-scale, multi-level approach described can be generally applied to other pathogens as well. The essential gene pool identified form a basis for designing experiments to probe their finer functional roles and also serve as a ready shortlist for identifying drug targets.

The relationship between classification of multi-domain proteins using an alignment-free approach and their functions: a case study with immunoglobulins

Establishing functional relationships between multi-domain protein sequences is a non-trivial task. Traditionally, delineating functional assignment and relationships of proteins requires domain assignments as a prerequisite. This process is sensitive to alignment quality and domain definitions. In multi-domain proteins due to multiple reasons, the quality of alignments is poor. We report the correspondence between the classification of proteins represented as full-length gene products and their functions. Our approach differs fundamentally from traditional methods in not performing the classification at the level of domains. Our method is based on an alignment free local matching scores (LMS) computation at the amino-acid sequence level followed by hierarchical clustering. As there are no gold standards for full-length protein sequence classification, we resorted to Gene Ontology and domain-architecture based similarity measures to assess our classification. The final clusters obtained using LMS show high functional and domain architectural similarities. Comparison of the current method with alignment based approaches at both domain and full-length protein showed superiority of the LMS scores. Using this method we have recreated objective relationships among different protein kinase sub-families and also classified immunoglobulin containing proteins where sub-family definitions do not exist currently. This method can be applied to any set of protein sequences and hence will be instrumental in analysis of large numbers of full-length protein sequences.

Biogenic nanosilver incorporated reverse osmosis membrane for antibacterial and antifungal activities against selected pathogenic strains: An enhanced eco-friendly water disinfection approach

Reverse osmosis (RO) membranes have been used extensively in water desalination plants, waste water treatment in industries, agricultural farms and drinking water production applications. The objective of this work is to impart antibacterial and antifungal activities to commercially available RO membrane used in water purification systems by incorporating biogenic silver nanoparticles (AgNPs) synthesized using Rosa indica wichuriana hybrid leaf extract. The morphology and surface topography of uncoated and AgNPs-coated RO membrane were studied using Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM). Elemental composition of the AgNPs-coated RO membrane was analyzed by energy-dispersive X-ray spectroscopy (EDAX). The functional groups were identified by Fourier Transform Infrared spectroscopy (FT-IR). Hydrophilicity of the uncoated and AgNPs-coated RO membrane was analyzed using water contact angle measurements. The thermal properties were studied by thermogravimetric analysis (TGA). The AgNPs incorporated RO membrane exhibited good antibacterial and antifungal activities against pathogenic bacterial strains such as E. coli, S. aureus, M. luteus, K. pneumoniae, and P. aeruginosa and fungal strains such as Candida tropicalis, C. krusei, C. glabrata, and C. albicans.

Cu-II-Azide Polynuclear Complexes of Three Different Building Clusters with the Same Schiff-Base Ligand: Synthesis, Structures, Magnetic Behavior, and Density Functional Theory Studies

Three copper-azido complexes Cu-4(N-3)(8)(L-1)(2)(MeOH)(2)](n) (1), Cu-4(N-3)(8)(L-1)(2)] (2), and Cu-5(N-3)(10)(L-1)(2)](n) (3) L-1 is the imine resulting from the condensation of pyridine-2-carboxaldehyde with 2-(2-pyridyl)ethylamine] have been synthesized using lower molar equivalents of the Schiff base ligand with Cu(NO3)(2)center dot 3H(2)O and an excess of NaN3. Single crystal X-ray structures show that the basic unit of the complexes 1 and 2 contains Cu-4(II) building blocks; however, they have distinct basic and overall structures due to a small change in the bridging mode of the peripheral pair of copper atoms in the linear tetranudear structures. Interestingly, these changes are the result of changing the solvent system (MeOH/H2O to EtOH/H2O) used for the synthesis, without changing the proportions of the components (metal to ligand ratio 2:1). Using even lower proportions of the ligand, another unique complex was isolated with Cu-5(II) building units, forming a two-dimensional complex (3). Magnetic susceptibility measurements over a wide range of temperature exhibit the presence of both antiferromagnetic (very weak) and ferromagnetic exchanges within the tetranuclear unit structures. Density functional theory calculations (using B3LYP functional, and two different basis sets) have been performed on the complexes 1 and 2 to provide a qualitative theoretical interpretation of their overall magnetic behavior.

Smoothed Functional Algorithms for Stochastic Optimization Using q-Gaussian Distributions

Smoothed functional (SF) schemes for gradient estimation are known to be efficient in stochastic optimization algorithms, especially when the objective is to improve the performance of a stochastic system However, the performance of these methods depends on several parameters, such as the choice of a suitable smoothing kernel. Different kernels have been studied in the literature, which include Gaussian, Cauchy, and uniform distributions, among others. This article studies a new class of kernels based on the q-Gaussian distribution, which has gained popularity in statistical physics over the last decade. Though the importance of this family of distributions is attributed to its ability to generalize the Gaussian distribution, we observe that this class encompasses almost all existing smoothing kernels. This motivates us to study SF schemes for gradient estimation using the q-Gaussian distribution. Using the derived gradient estimates, we propose two-timescale algorithms for optimization of a stochastic objective function in a constrained setting with a projected gradient search approach. We prove the convergence of our algorithms to the set of stationary points of an associated ODE. We also demonstrate their performance numerically through simulations on a queuing model.

Polymorphic phase transition among the titania crystal structures using a solution-based approach: from precursor chemistry to nucleation process

Nanocrystalline titania are a robust candidate for various functional applications owing to its non-toxicity, cheap availability, ease of preparation and exceptional photochemical as well as thermal stability. The uniqueness in each lattice structure of titania leads to multifaceted physico-chemical and opto-electronic properties, which yield different functionalities and thus influence their performances in various green energy applications. The high temperature treatment for crystallizing titania triggers inevitable particle growth and the destruction of delicate nanostructural features. Thus, the preparation of crystalline titania with tunable phase/particle size/morphology at low to moderate temperatures using a solution-based approach has paved the way for further exciting areas of research. In this focused review, titania synthesis from hydrothermal/solvothermal method, conventional sol-gel method and sol-gel-assisted method via ultrasonication, photoillumination and ILs, thermolysis and microemulsion routes are discussed. These wet chemical methods have broader visibility, since multiple reaction parameters, such as precursor chemistry, surfactants, chelating agents, solvents, mineralizer, pH of the solution, aging time, reaction temperature/time, inorganic electrolytes, can be easily manipulated to tune the final physical structure. This review sheds light on the stabilization/phase transformation pathways of titania polymorphs like anatase, rutile, brookite and TiO2(B) under a variety of reaction conditions. The driving force for crystallization arising from complex species in solution coupled with pH of the solution and ion species facilitating the orientation of octahedral resulting in a crystalline phase are reviewed in detail. In addition to titanium halide/alkoxide, the nucleation of titania from other precursors like peroxo and layered titanates are also discussed. The nonaqueous route and ball milling-induced titania transformation is briefly outlined; moreover, the lacunae in understanding the concepts and future prospects in this exciting field are suggested.

Application of Qualitative Imaging Methods to Electrical Performance-Aware Package Board Design

Package-board co-design plays a crucial role in determining the performance of high-speed systems. Although there exist several commercial solutions for electromagnetic analysis and verification, lack of Computer Aided Design (CAD) tools for SI aware design and synthesis lead to longer design cycles and non-optimal package-board interconnect geometries. In this work, the functional similarities between package-board design and radio-frequency (RF) imaging are explored. Consequently, qualitative methods common to the imaging community, like Tikhonov Regularization (TR) and Landweber method are applied to solve multi-objective, multi-variable package design problems. In addition, a new hierarchical iterative piecewise linear algorithm is developed as a wrapper over LBP for an efficient solution in the design space.

Human Action Recognition in Compressed Domain using PBL-McRBFN Approach

Large variations in human actions lead to major challenges in computer vision research. Several algorithms are designed to solve the challenges. Algorithms that stand apart, help in solving the challenge in addition to performing faster and efficient manner. In this paper, we propose a human cognition inspired projection based learning for person-independent human action recognition in the H.264/AVC compressed domain and demonstrate a PBL-McRBEN based approach to help take the machine learning algorithms to the next level. Here, we use gradient image based feature extraction process where the motion vectors and quantization parameters are extracted and these are studied temporally to form several Group of Pictures (GoP). The GoP is then considered individually for two different bench mark data sets and the results are classified using person independent human action recognition. The functional relationship is studied using Projection Based Learning algorithm of the Meta-cognitive Radial Basis Function Network (PBL-McRBFN) which has a cognitive and meta-cognitive component. The cognitive component is a radial basis function network while the Meta-Cognitive Component(MCC) employs self regulation. The McC emulates human cognition like learning to achieve better performance. Performance of the proposed approach can handle sparse information in compressed video domain and provides more accuracy than other pixel domain counterparts. Performance of the feature extraction process achieved more than 90% accuracy using the PTIL-McRBFN which catalyzes the speed of the proposed high speed action recognition algorithm. We have conducted twenty random trials to find the performance in GoP. The results are also compared with other well known classifiers in machine learning literature.

Insights into the Functional Roles of N-Terminal and C-Terminal Domains of Helicobacter pylori DprA

DNA processing protein A (DprA) plays a crucial role in the process of natural transformation. This is accomplished through binding and subsequent protection of incoming foreign DNA during the process of internalization. DprA along with Single stranded DNA binding protein A (SsbA) acts as an accessory factor for RecA mediated DNA strand exchange. H. pylori DprA (HpDprA) is divided into an N-terminal domain and a C-terminal domain. In the present study, individual domains of HpDprA have been characterized for their ability to bind single stranded (ssDNA) and double stranded DNA (dsDNA). Oligomeric studies revealed that HpDprA possesses two sites for dimerization which enables HpDprA to form large and tightly packed complexes with ss and dsDNA. While the N-terminal domain was found to be sufficient for binding with ss or ds DNA, C-terminal domain has an important role in the assembly of poly-nucleoprotein complex. Using site directed mutagenesis approach, we show that a pocket comprising positively charged amino acids in the N-terminal domain has an important role in the binding of ss and dsDNA. Together, a functional cross talk between the two domains of HpDprA facilitating the binding and formation of higher order complex with DNA is discussed.

Solid-state optical absorption from optimally tuned time-dependent range-separated hybrid density functional theory

We present a framework for obtaining reliable solid-state charge and optical excitations and spectra from optimally tuned range-separated hybrid density functional theory. The approach, which is fully couched within the formal framework of generalized Kohn-Sham theory, allows for the accurate prediction of exciton binding energies. We demonstrate our approach through first principles calculations of one- and two-particle excitations in pentacene, a molecular semiconducting crystal, where our work is in excellent agreement with experiments and prior computations. We further show that with one adjustable parameter, set to produce the known band gap, this method accurately predicts band structures and optical spectra of silicon and lithium fluoride, prototypical covalent and ionic solids. Our findings indicate that for a broad range of extended bulk systems, this method may provide a computationally inexpensive alternative to many-body perturbation theory, opening the door to studies of materials of increasing size and complexity.