Convergence of Power Control in a Random Channel Environment

In this paper, we study the Foschini Miljanic algorithm, which was originally proposed in a static channel environment. We investigate the algorithm in a random channel environment, study its convergence properties and apply the Gerschgorin theorem to derive sufficient conditions for the convergence of the algorithm. We apply the Foschini and Miljanic algorithm to cellular networks and derive sufficient conditions for the convergence of the algorithm in distribution and validate the results with simulations. In cellular networks, the conditions which ensure convergence in distribution can be easily verified.

Immune responses to hemagglutinin-neuraminidase protein of peste des petits ruminants virus expressed in transgenic peanut plants in sheep

Peste des petits ruminants (PPR) is an acute, highly contagious disease of small ruminants caused by a morbillivirus, Peste des petits ruminants virus (PPRV). The disease is prevalent in equatorial Africa, the Middle East, and the Indian subcontinent. A live attenuated vaccine is in use in some of the countries and has been shown to provide protection for at least three years against PPR. However, the live attenuated vaccine is not robust in terms of thermotolerance. As a step towards development of a heat stable subunit vaccine, we have expressed a hemagglutinin-neuraminidase (HN) protein of PPRV in peanut plants (Arachis hypogea) in a biologically active form, possessing neuraminidase activity. Importantly. HN protein expressed in peanut plants retained its immunodominant epitopes in their natural conformation. The immunogenicity of the plant derived HN protein was analyzed in sheep upon oral immunization. Virus neutralizing antibody responses were elicited upon oral immunization of sheep in the absence of any mucosal adjuvant. In addition, anti-PPRV-HN specific cell-mediated immune responses were also detected in mucosally immunized sheep. (C) 2010 Elsevier B.V. All rights reserved.

Improving the convergence rate of the inverse iteration method

Solution of generalized eigenproblem, K phi = lambda M phi, by the classical inverse iteration method exhibits slow convergence for some eigenproblems. In this paper, a modified inverse iteration algorithm is presented for improving the convergence rate. At every iteration, an optimal linear combination of the latest and the preceding iteration vectors is used as the input vector for the next iteration. The effectiveness of the proposed algorithm is demonstrated for three typical eigenproblems, i.e. eigenproblems with distinct, close and repeated eigenvalues. The algorithm yields 29, 96 and 23% savings in computational time, respectively, for these problems. The algorithm is simple and easy to implement, and this renders the algorithm even more attractive.

From polarization to convergence: need to mend the broken patent system

For more than two hundred years, the world has discussed the issue of whether to continue the process of patenting or whether to do away with it. Developed countries remain polarized for various reasons but nevertheless the pro patent regime continued. The result was a huge volume of patents. The present article explains the implications of excessive volume of patents and conditions under which prior art search fails. This article highlights the importance and necessity of standardization efforts so as to bring about convergence of views on patenting.

On Convergence of Differential Evolution Over a Class of Continuous Functions With Unique Global Optimum

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms of current interest. Since its inception in the mid 1990s, DE has been finding many successful applications in real-world optimization problems from diverse domains of science and engineering. This paper takes a first significant step toward the convergence analysis of a canonical DE (DE/rand/1/bin) algorithm. It first deduces a time-recursive relationship for the probability density function (PDF) of the trial solutions, taking into consideration the DE-type mutation, crossover, and selection mechanisms. Then, by applying the concepts of Lyapunov stability theorems, it shows that as time approaches infinity, the PDF of the trial solutions concentrates narrowly around the global optimum of the objective function, assuming the shape of a Dirac delta distribution. Asymptotic convergence behavior of the population PDF is established by constructing a Lyapunov functional based on the PDF and showing that it monotonically decreases with time. The analysis is applicable to a class of continuous and real-valued objective functions that possesses a unique global optimum (but may have multiple local optima). Theoretical results have been substantiated with relevant computer simulations.

Convergence problems in a class of model reference adaptive control systems

A class of model reference adaptive control system which make use of an augmented error signal has been introduced by Monopoli. Convergence problems in this attractive class of systems have been investigated in this paper using concepts from hyperstability theory. It is shown that the condition on the linear part of the system has to be stronger than the one given earlier. A boundedness condition on the input to the linear part of the system has been taken into account in the analysis - this condition appears to have been missed in the previous applications of hyperstability theory. Sufficient conditions for the convergence of the adaptive gain to the desired value are also given.

Reducing convergence times of self-propelled swarms via modified nearest neighbor rules

Vicsek et al. proposed a biologically inspired model of self-propelled particles, which is now commonly referred to as the Vicsek model. Recently, attention has been directed at modifying the Vicsek model so as to improve convergence properties. In this paper, we propose two modification of the Vicsek model which leads to significant improvements in convergence times. The modifications involve an additional term in the heading update rule which depends only on the current or the past states of the particle's neighbors. The variation in convergence properties as the parameters of these modified versions are changed are closely investigated. It is found that in both cases, there exists an optimal value of the parameter which reduces convergence times significantly and the system undergoes a phase transition as the value of the parameter is increased beyond this optimal value. (C) 2012 Elsevier B.V. All rights reserved.

An operator-splitting Galerkin/SUPG finite element method for population balance equations : stability and convergence

We present a heterogeneous finite element method for the solution of a high-dimensional population balance equation, which depends both the physical and the internal property coordinates. The proposed scheme tackles the two main difficulties in the finite element solution of population balance equation: (i) spatial discretization with the standard finite elements, when the dimension of the equation is more than three, (ii) spurious oscillations in the solution induced by standard Galerkin approximation due to pure advection in the internal property coordinates. The key idea is to split the high-dimensional population balance equation into two low-dimensional equations, and discretize the low-dimensional equations separately. In the proposed splitting scheme, the shape of the physical domain can be arbitrary, and different discretizations can be applied to the low-dimensional equations. In particular, we discretize the physical and internal spaces with the standard Galerkin and Streamline Upwind Petrov Galerkin (SUPG) finite elements, respectively. The stability and error estimates of the Galerkin/SUPG finite element discretization of the population balance equation are derived. It is shown that a slightly more regularity, i.e. the mixed partial derivatives of the solution has to be bounded, is necessary for the optimal order of convergence. Numerical results are presented to support the analysis.

Establishment of an in vitro transcription system for Peste des petits ruminant virus

Background: Peste-des-petits ruminants virus (PPRV) is a non segmented negative strand RNA virus of the genus Morbillivirus within Paramyxoviridae family. Negative strand RNA viruses are known to carry nucleocapsid (N) protein, phospho (P) protein and RNA polymerase (L protein) packaged within the virion which possess all activities required for transcription, post-transcriptional modification of mRNA and replication. In order to understand the mechanism of transcription and replication of the virus, an in vitro transcription reconstitution system is required. In the present work, an in vitro transcription system has been developed with ribonucleoprotein (RNP) complex purified from virus infected cells as well as partially purified recombinant polymerase (L-P) complex from insect cells along with N-RNA (genomic RNA encapsidated by N protein) template isolated from virus infected cells. Results: RNP complex isolated from virus infected cells and recombinant L-P complex purified from insect cells was used to reconstitute transcription on N-RNA template. The requirement for this transcription reconstitution has been defined. Transcription of viral genes in the in vitro system was confirmed by PCR amplification of cDNAs corresponding to individual transcripts using gene specific primers. In order to measure the relative expression level of viral transcripts, real time PCR analysis was carried out. qPCR analysis of the transcription products made in vitro showed a gradient of polarity of transcription from 3' end to 5' end of the genome similar to that exhibited by the virus in infected cells. Conclusion: This report describes for the first time, the development of an in vitro transcription reconstitution system for PPRV with RNP complex purified from infected cells and recombinant L-P complex expressed in insect cells. Both the complexes were able to synthesize all the mRNA species in vitro, exhibiting a gradient of polarity in transcription.