Regularization of Descriptor Systems by Derivative and Proportional State Feedback

For linear multivariable time-invariant continuous or discrete-time singular systems it is customary to use a proportional feedback control in order to achieve a desired closed loop behaviour. Derivative feedback is rarely considered. This paper examines how derivative feedback in descriptor systems can be used to alter the structure of the system pencil under various controllability conditions. It is shown that derivative and proportional feedback controls can be constructed such that the closed loop system has a given form and is also regular and has index at most 1. This property ensures the solvability of the resulting system of dynamic-algebraic equations. The construction procedures used to establish the theory are based only on orthogonal matrix decompositions and can therefore be implemented in a numerically stable way. The problem of pole placement with derivative feedback alone and in combination with proportional state feedback is also investigated. A computational algorithm for improving the “conditioning” of the regularized closed loop system is derived.

Minimum norm regularization of descriptor systems by mixed output feedback

We study the regularization problem for linear, constant coefficient descriptor systems Ex' = Ax+Bu, y1 = Cx, y2 = Γx' by proportional and derivative mixed output feedback. Necessary and sufficient conditions are given, which guarantee that there exist output feedbacks such that the closed-loop system is regular, has index at most one and E+BGΓ has a desired rank, i.e., there is a desired number of differential and algebraic equations. To resolve the freedom in the choice of the feedback matrices we then discuss how to obtain the desired regularizing feedback of minimum norm and show that this approach leads to useful results in the sense of robustness only if the rank of E is decreased. Numerical procedures are derived to construct the desired feedback gains. These numerical procedures are based on orthogonal matrix transformations which can be implemented in a numerically stable way.

System identification of Wiener systems with B-spline functions using De Boor recursion

In this article a simple and effective algorithm is introduced for the system identification of the Wiener system using observational input/output data. The nonlinear static function in the Wiener system is modelled using a B-spline neural network. The Gauss–Newton algorithm is combined with De Boor algorithm (both curve and the first order derivatives) for the parameter estimation of the Wiener model, together with the use of a parameter initialisation scheme. Numerical examples are utilised to demonstrate the efficacy of the proposed approach.

Bibliographic analysis of persuasive systems: techniques, methods and domains of application

This paper presents findings of our study on peer-reviewed papers published in the International Conference on Persuasive Technology from 2006 to 2010. The study indicated that out of 44 systems reviewed, 23 were reported to be successful, 2 to be unsuccessful and 19 did not specify whether or not it was successful. 56 different techniques were mentioned and it was observed that most designers use ad hoc definitions for techniques or methods used in design. Hence we propose the need for research to establish unambiguous definitions of techniques and methods in the field.

Codon and mRNA sequence optimization of microdystrophin transgenes improves expression and physiological outcome in dystrophic mdx mice following AAV2/8 gene transfer

Duchenne muscular dystrophy is a fatal muscle-wasting disorder. Lack of dystrophin compromises the integrity of the sarcolemma and results in myofibers that are highly prone to contraction-induced injury. Recombinant adenoassociated virus (rAAV)-mediated dystrophin gene transfer strategies to muscle for the treatment of Duchenne muscular dystrophy (DMD) have been limited by the small cloning capacity of rAAV vectors and high titers necessary to achieve efficient systemic gene transfer. In this study, we assess the impact of codon optimization on microdystrophin (ΔAB/R3-R18/ΔCT) expression and function in the mdx mouse and compare the function of two different configurations of codon-optimized microdystrophin genes (ΔAB/R3-R18/ΔCT and ΔR4-R23/ΔCT) under the control of a muscle-restrictive promoter (Spc5-12). Codon optimization of microdystrophin significantly increases levels of microdystrophin mRNA and protein after intramuscular and systemic administration of plasmid DNA or rAAV2/8. Physiological assessment demonstrates that codon optimization of ΔAB/R3-R18/ΔCT results in significant improvement in specific force, but does not improve resistance to eccentric contractions compared with noncodon-optimized ΔAB/ R3-R18/ΔCT. However, codon-optimized microdystrophin ΔR4-R23/ΔCT completely restored specific force generation and provided substantial protection from contraction-induced injury. These results demonstrate that codon optimization of microdystrophin under the control of a muscle-specific promoter can significantly improve expression levels such that reduced titers of rAAV vectors will be required for efficient systemic administration.

Evolution of immune systems: specificity and autoreactivity

Multicellularity evolved well before 600 million years ago, and all multicellular animals have evolved since then with the need to protect against pathogens. There is no reason to expect their immune systems to be any less sophisticated than ours. The vertebrate system, based on rearranging immunoglobulin-superfamily domains, appears to have evolved partly as a result of chance insertion of RAG genes by horizontal transfer. Remarkably sophisticated systems for expansion of immunological repertoire have evolved in parallel in many groups of organisms. Vaccination of invertebrates against commercially important pathogens has been empirically successful, and suggests that the definition of an adaptive and innate immune system should no longer depend on the presence of memory and specificity, since these terms are hard to define in themselves. The evolution of randomly-created immunological repertoire also carries with it the potential for generating autoreactive specificities and consequent autoimmune damage.While invertebrates may use systems analogous to ours to control autoreactive specificities, they may have evolved alternative mechanisms which operate either at the level of individuals-within-populations rather than cells-within-individuals, by linking self-reactive specificities to regulatory pathways and non-self-reactive to effector pathways.

Complex-valued b-spline neural networks for modelling and inverse of wiener systems

Communication signal processing applications often involve complex-valued (CV) functional representations for signals and systems. CV artificial neural networks have been studied theoretically and applied widely in nonlinear signal and data processing [1–11]. Note that most artificial neural networks cannot be automatically extended from the real-valued (RV) domain to the CV domain because the resulting model would in general violate Cauchy-Riemann conditions, and this means that the training algorithms become unusable. A number of analytic functions were introduced for the fully CV multilayer perceptrons (MLP) [4]. A fully CV radial basis function (RBF) nework was introduced in [8] for regression and classification applications. Alternatively, the problem can be avoided by using two RV artificial neural networks, one processing the real part and the other processing the imaginary part of the CV signal/system. A even more challenging problem is the inverse of a CV

Optimization of a sea ice model using basinwide observations of Arctic sea ice thickness, extent, and velocity

A stand-alone sea ice model is tuned and validated using satellite-derived, basinwide observations of sea ice thickness, extent, and velocity from the years 1993 to 2001. This is the first time that basin-scale measurements of sea ice thickness have been used for this purpose. The model is based on the CICE sea ice model code developed at the Los Alamos National Laboratory, with some minor modifications, and forcing consists of 40-yr ECMWF Re-Analysis (ERA-40) and Polar Exchange at the Sea Surface (POLES) data. Three parameters are varied in the tuning process: Ca, the air–ice drag coefficient; P*, the ice strength parameter; and α, the broadband albedo of cold bare ice, with the aim being to determine the subset of this three-dimensional parameter space that gives the best simultaneous agreement with observations with this forcing set. It is found that observations of sea ice extent and velocity alone are not sufficient to unambiguously tune the model, and that sea ice thickness measurements are necessary to locate a unique subset of parameter space in which simultaneous agreement is achieved with all three observational datasets.

Continuous-flow processing of gaseous ammonia using a Teflon AF-2400 tube-in-tube reactor: synthesis of thioureas and in-line titrations

A simple tube-in-tube reactor based on the gas-permeable membrane Teflon AF-2400 was used in the continuous flow reaction of gaseous ammonia with isothiocyanates and one isocyanate. A colourimetric in-line titration technique is also reported as a simple method to quantify the amount of ammonia taken up by the solvent in the system.