Tuning of intramolecular electron transfer between Ru(II) and the disulfide bond

Reaction of 2,2'-dithiodipyridine (DTDP) with cis-Ru(bpy)(2)Cl-2 (bpy = 2,2'-bipyridine) and cis-Ru(phen)(2)Cl-2 (phen = 1,10-phenanthroline) respectively yields the dicationic species [Ru(bpy) (2)(DTDP)](2+) and [Ru(phen)(2) (DTDP)](2+) in which the S-S bond of DTDP remains intact. The S-S bond undergoes a reductive cleavage when DTDP is reacted with cis-Ru(bisox)(2)Cl-2 (bisox = 4,4,4',4'-tetramethyl-2,2'-bisoxazoline) under identical conditions to generate the monocationic species [Ru(bisox)(2)(2-thiolatopyridine)]. The intramolecular electron transfer between the metal and the S-S bond is found to be subtly controlled by the crystal field strength of the ancillary bidentate N-donor ligands.

Mapping fibre orientation in complex-shaped biological systems with micrometre resolution by scanning X-ray microdiffraction

A fully automated procedure to extract and to image local fibre orientation in biological tissues from scanning X-ray diffraction is presented. The preferred chitin fibre orientation in the flow sensing system of crickets is determined with high spatial resolution by applying synchrotron radiation based X-ray microbeam diffraction in conjunction with advanced sample sectioning using a UV micro-laser. The data analysis is based on an automated detection of azimuthal diffraction maxima after 2D convolution filtering (smoothing) of the 2D diffraction patterns. Under the assumption of crystallographic fibre symmetry around the morphological fibre axis, the evaluation method allows mapping the three-dimensional orientation of the fibre axes in space. The resulting two-dimensional maps of the local fibre orientations - together with the complex shape of the flow sensing system - may be useful for a better understanding of the mechanical optimization of such tissues.

Fastidiosipila sanguinis gen. nov., sp nov., a new Gram-positive, coccus-shaped organism from human blood

Phenotypic and phylogenetic studies were performed on two strains of an unidentified Gram-positive, fastidious, non-spore-forming, coccus-shaped bacterium recovered from human blood. The organism was catalase-negative and grew under strictly anaerobic conditions and in the presence of 2 and 6% O-2. Comparative 16S rRNA gene sequencing demonstrated that the unidentified bacterium was, phylogenetically, far removed from peptostreptococci and related Gram-positive coccus-shaped organisms, but exhibited a phylogenetic association with Clostridium rRNA cluster III [as defined by Collins et al, Int J Syst Bacteriol 44 (1994), 812-826]. Sequence divergence values of 15% or more were observed between the unidentified bacterium and all other recognized species within this and related rRINIA clostridial clusters. Treeing analysis showed that the unknown bacterium formed a deep line branching at the periphery of rRNA cluster III and represents a hitherto unknown genus within this supra-generic grouping. On the basis of both phylogenetic and phenotypic evidence, it is proposed that the unknown bacterium from blood be classified in a new genus, Fastidiosipila gen. nov., as Fastidiosipila sanguinis sp, nov. The type strain of Fastidiosipila sanguinis is CCUG 47711(T) (= CIP 108292(T)).

Atopococcus tabaci gen. nov., sp nov., a novel Gram-positive, catalase-negative, coccus-shaped bacterium isolated from tobacco

A novel Gram-positive, aerobic, catalase-negative, coccus-shaped organism originating from tobacco was characterized using phenotypic and molecular taxonomic methods. The organism contained a cell wall murein based on L-lysine (variation A4 alpha, type L-lysine-L-glutamic acid), synthesized long-chain cellular fatty acids of the straight-chain saturated and monounsaturated types (with C(16:1)omega 9, C-16:0 and C(18:1)omega 9 predominating) and possessed a DNA G+C content of 46 mol%. Based on morphological, biochemical and chemical characteristics, the coccus-shaped organism did not conform to any presently recognized taxon. Comparative 16S rRNA gene sequencing studies confirmed the distinctiveness of the unknown coccus, with the bacterium displaying sequence divergence values of greater than 7% with other recognized Gram-positive taxa. Treeing analysis reinforced its distinctiveness, with the unidentified organism forming a relatively long subline branching at the periphery of an rRNA gene sequence cluster which encompasses the genera Alloiococcus, Allolustis, Alkalibacterium, Atopostipes, Dolosigranulum and Marinilactibacillus. Based on phenotypic and molecular phylogenetic evidence, it is proposed that the unknown organism from tobacco be classified as a new genus and species, Atopococcus tabaci gen. nov., sp. nov. The type strain of Atopococcus tabaci is CCUG 48253(T) (= CIP 108502(T)).

Allofustis seminis gen. nov., sp. nov., a novel Gram-positive, catalase-negative, rod-shaped bacterium from pig semen

An unknown Gram-positive, catalase-negative, facultatively anaerobic, non-spore-forming, rod-shaped bacterium originating from semen of a pig was characterized using phenotypic, molecular chemical and molecular phylogenetic methods. Chemical studies revealed the presence of a directly cross-linked cell wall murein based on L-lysine and a DNA G + C content of 39 mol%. Comparative 16S rRNA gene sequencing showed that the unidentified rod-shaped organism formed a hitherto unknown subline related, albeit loosely, to Alkalibacterium olivapovliticus, Alloiococcus otitis, Dolosigranulum pigrum and related organisms, in the low-G + C-content Gram-positive bacteria. However, sequence divergence values of > 11 % from these recognized taxa. clearly indicated that the novel bacterium represents a separate genus. Based on phenotypic and phylogenetic considerations, it is proposed that the unknown bacterium from pig semen be classified as a new genus and species, Allofustis seminis gen. nov., sp. nov. The type strain is strain 01-570-1(T) (=CCUG 45438(T)=CIP 107425(T)).

Autonomous intelligent cruise control using a novel multiple-controller framework incorporating fuzzy-logic-based switching and tuning

This paper presents a novel intelligent multiple-controller framework incorporating a fuzzy-logic-based switching and tuning supervisor along with a generalised learning model (GLM) for an autonomous cruise control application. The proposed methodology combines the benefits of a conventional proportional-integral-derivative (PID) controller, and a PID structure-based (simultaneous) zero and pole placement controller. The switching decision between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using a fuzzy-logic-based supervisor, operating at the highest level of the system. The supervisor is also employed to adaptively tune the parameters of the multiple controllers in order to achieve the desired closed-loop system performance. The intelligent multiple-controller framework is applied to the autonomous cruise control problem in order to maintain a desired vehicle speed by controlling the throttle plate angle in an electronic throttle control (ETC) system. Sample simulation results using a validated nonlinear vehicle model are used to demonstrate the effectiveness of the multiple-controller with respect to adaptively tracking the desired vehicle speed changes and achieving the desired speed of response, whilst penalising excessive control action. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.

Design of a minimum variance multiple input-multiple output neuro self-tuning proportional-integral-derivative controller for non-linear dynamic systems

In this study a minimum variance neuro self-tuning proportional-integral-derivative (PID) controller is designed for complex multiple input-multiple output (MIMO) dynamic systems. An approximation model is constructed, which consists of two functional blocks. The first block uses a linear submodel to approximate dominant system dynamics around a selected number of operating points. The second block is used as an error agent, implemented by a neural network, to accommodate the inaccuracy possibly introduced by the linear submodel approximation, various complexities/uncertainties, and complicated coupling effects frequently exhibited in non-linear MIMO dynamic systems. With the proposed model structure, controller design of an MIMO plant with n inputs and n outputs could be, for example, decomposed into n independent single input-single output (SISO) subsystem designs. The effectiveness of the controller design procedure is initially verified through simulations of industrial examples.

Investigation of liquid MALDI and optimization for instrument tuning and quantitative measurements

The success of Matrix-assisted laser desorption / ionisation (MALDI) in fields such as proteomics has partially but not exclusively been due to the development of improved data acquisition and sample preparation techniques. This has been required to overcome some of the short comings of the commonly used solid-state MALDI matrices such as - cyano-4-hydroxycinnamic acid (CHCA) and 2,5-dihydroxybenzoic acid (DHB). Solid state matrices form crystalline samples with highly inhomogeneous topography and morphology which results in large fluctuations in analyte signal intensity from spot to spot and positions within the spot. This means that efficient tuning of the mass spectrometer can be impeded and the use of MALDI MS for quantitative measurements is severely impeded. Recently new MALDI liquid matrices have been introduced which promise to be an effective alternative to crystalline matrices. Generally the liquid matrices comprise either ionic liquid matrices (ILMs) or a usually viscous liquid matrix which is doped with a UV lightabsorbing chromophore [1-3]. The advantages are that the droplet surface is smooth and relatively uniform with the analyte homogeneously distributed within. They have the ability to replenish a sampling position between shots negating the need to search for sample hot-spots. Also the liquid nature of the matrix allows for the use of additional additives to change the environment to which the analyte is added.

Neural network enhanced generalised minimum variance self-tuning controller for nonlinear discrete-time systems

A neural network enhanced self-tuning controller is presented, which combines the attributes of neural network mapping with a generalised minimum variance self-tuning control (STC) strategy. In this way the controller can deal with nonlinear plants, which exhibit features such as uncertainties, nonminimum phase behaviour, coupling effects and may have unmodelled dynamics, and whose nonlinearities are assumed to be globally bounded. The unknown nonlinear plants to be controlled are approximated by an equivalent model composed of a simple linear submodel plus a nonlinear submodel. A generalised recursive least squares algorithm is used to identify the linear submodel and a layered neural network is used to detect the unknown nonlinear submodel in which the weights are updated based on the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model therefore the nonlinear submodel is naturally accommodated within the control law. Two simulation studies are provided to demonstrate the effectiveness of the control algorithm.

Filtering techniques in self-tuning

In the last few years a state-space formulation has been introduced into self-tuning control. This has not only allowed for a wider choice of possible control actions, but has also provided an insight into the theory underlying—and hidden by—that used in the polynomial description. This paper considers many of the self-tuning algorithms, both state-space and polynomial, presently in use, and by starting from first principles develops the observers which are, effectively, used in each case. At any specific time instant the state estimator can be regarded as taking one of two forms. In the first case the most recently available output measurement is excluded, and here an optimal and conditionally stable observer is obtained. In the second case the present output signal is included, and here it is shown that although the observer is once again conditionally stable, it is no longer optimal. This result is of significance, as many of the popular self-tuning controllers lie in the second, rather than first, category.

Self-tuning regulators—a state space approach

This paper employs a state space system description to provide a pole placement scheme via state feedback. It is shown that when a recursive least squares estimation scheme is used, the feedback employed can be expressed simply in terms of the estimated system parameters. To complement the state feedback approach, a method employing both state feedback and linear output feedback is discussed. Both methods arc then compared with the previous output polynomial type feedback schemes.

An implicit pole-assignment self-tuning regulator

A new self-tuning implicit pole-assignment algorithm is presented which, through the use of a pole compression factor and different RLS model and control structures, overcomes stability and convergence problems encountered in previously available algorithms. Computational requirements of the technique are much reduced when compared to explicit pole-assignment schemes, whereas the inherent robustness of the strategy is retained.

Auto-weighted minimum-variance self-tuning controller

A self-tuning controller which automatically assigns weightings to control and set-point following is introduced. This discrete-time single-input single-output controller is based on a generalized minimum-variance control strategy. The automatic on-line selection of weightings is very convenient, especially when the system parameters are unknown or slowly varying with respect to time, which is generally considered to be the type of systems for which self-tuning control is useful. This feature also enables the controller to overcome difficulties with non-minimum phase systems.

A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares

A self-tuning proportional, integral and derivative control scheme based on genetic algorithms (GAs) is proposed and applied to the control of a real industrial plant. This paper explores the improvement in the parameter estimator, which is an essential part of an adaptive controller, through the hybridization of recursive least-squares algorithms by making use of GAs and the possibility of the application of GAs to the control of industrial processes. Both the simulation results and the experiments on a real plant show that the proposed scheme can be applied effectively.