Robust filtering for stochastic genetic regulatory networks with time-varying delay

This is the post print version of the article. The official published version can be obtained from the link - Copyright 2009 Elsevier LtdThis paper addresses the robust filtering problem for a class of linear genetic regulatory networks (GRNs) with stochastic disturbances, parameter uncertainties and time delays. The parameter uncertainties are assumed to reside in a polytopic region, the stochastic disturbance is state-dependent described by a scalar Brownian motion, and the time-varying delays enter into both the translation process and the feedback regulation process. We aim to estimate the true concentrations of mRNA and protein by designing a linear filter such that, for all admissible time delays, stochastic disturbances as well as polytopic uncertainties, the augmented state estimation dynamics is exponentially mean square stable with an expected decay rate. A delay-dependent linear matrix inequality (LMI) approach is first developed to derive sufficient conditions that guarantee the exponential stability of the augmented dynamics, and then the filter gains are parameterized in terms of the solution to a set of LMIs. Note that LMIs can be easily solved by using standard software packages. A simulation example is exploited in order to illustrate the effectiveness of the proposed design procedures.This work was supported in part by the Biotechnology and Biological Sciences Research Council (BBSRC) of the U.K. under Grants BB/C506264/1 and 100/EGM17735, an International Joint Project sponsored by the Royal Society of the U.K., the Research Grants Council of Hong Kong under Grant HKU 7031/06P, the National Natural Science Foundation of China under Grant 60804028, and the Alexander von Humboldt Foundation of Germany

Low energy nuclear scattering and sub-threshold spectra from a multi-channel algebraic scattering theory

A multi-channel algebraic scattering theory, to find solutions of coupled-channel scattering problems with interactions determined by collective models, has been structured to ensure that the Pauli principle is not violated. Positive (scattering) and negative (sub-threshold) solutions can be found to predict both the compound nucleus sub-threshold spectrum and all resonances due to coupled channel effects that occur on a smooth energy varying background.Comment: 5 pages, 4 figures, FINUSTAR conference, Kos, Greece, Sept. 200

A-STAR: The All-Sky Transient Astrophysics Reporter

The small mission A-STAR (All-Sky Transient Astrophysics Reporter) aims to locate the X-ray counterparts to ALIGO and other gravitational wave detector sources, to study the poorly-understood low luminosity gamma-ray bursts, and to find a wide variety of transient high-energy source types, A-STAR will survey the entire available sky twice per 24 hours. The payload consists of a coded mask instrument, Owl, operating in the novel low energy band 4-150 keV, and a sensitive wide-field focussing soft X-ray instrument, Lobster, working over 0.15-5 keV. A-STAR will trigger on ~100 GRBs/yr, rapidly distributing their locations.Comment: Accepted for the European Astronomical Society Publications Series: Proceedings of the Fall 2012 Gamma-Ray Burst Symposium held in Marbella, Spain, 8-12 Oct 201

A Memetic Algorithm for whole test suite generation

The generation of unit-level test cases for structural code coverage is a task well-suited to Genetic Algorithms. Method call sequences must be created that construct objects, put them into the right state and then execute uncovered code. However, the generation of primitive values, such as integers and doubles, characters that appear in strings, and arrays of primitive values, are not so straightforward. Often, small local changes are required to drive the value toward the one needed to execute some target structure. However, global searches like Genetic Algorithms tend to make larger changes that are not concentrated on any particular aspect of a test case. In this paper, we extend the Genetic Algorithm behind the EvoSuiTE test generation tool into a Memetic Algorithm, by equipping it with several local search operators. These operators are designed to efficiently optimize primitive values and other aspects of a test suite that allow the search for test cases to function more effectively. We evaluate our operators using a rigorous experimental methodology on over 12,000 Java classes, comprising open source classes of various different kinds, including numerical applications and text processors. Our study shows that increases in branch coverage of up to 53% are possible for an individual class in practice