Statistical Monitoring of Dynamic Multivariate Processes: Part I: Modeling Auto- and Cross-Correlation

The work in this paper is of particular significance since it considers the problem of modelling cross- and auto-correlation in statistical process monitoring. The presence of both types of correlation can lead to fault insensitivity or false alarms, although in published literature to date, only autocorrelation has been broadly considered. The proposed method, which uses a Kalman innovation model, effectively removes both correlations. The paper (and Part 2 [2]) has emerged from work supported by EPSRC grant GR/S84354/01 and is of direct relevance to problems in several application areas including chemical, electrical, and mechanical process monitoring.

Prescription of Epinephrine Auto-injectors - Can we reach a consensus?

A consensus view of Irish Clinical Immunologists as to when auto injection syringes of adrenaline should be prescribed

Application of auto-associative neural networks to transient fault detection in an IC engine

The tailpipe emissions from automotive engines have been subject to steadily reducing legislative limits. This reduction has been achieved through the addition of sub-systems to the basic four-stroke engine which thereby increases its complexity. To ensure the entire system functions correctly, each system and / or sub-systems needs to be continuously monitored for the presence of any faults or malfunctions. This is a requirement detailed within the On-Board Diagnostic (OBD) legislation. To date, a physical model approach has been adopted by me automotive industry for the monitoring requirement of OBD legislation. However, this approach has restrictions from the available knowledge base and computational load required. A neural network technique incorporating Multivariant Statistical Process Control (MSPC) has been proposed as an alternative method of building interrelationships between the measured variables and monitoring the correct operation of the engine. Building upon earlier work for steady state fault detection, this paper details the use of non-linear models based on an Auto-associate Neural Network (ANN) for fault detection under transient engine operation. The theory and use of the technique is shown in this paper with the application to the detection of air leaks within the inlet manifold system of a modern gasoline engine whilst operated on a pseudo-drive cycle. Copyright © 2007 by ASME.

Integração de evidências em redes credais e a regra de Jeffrey

Credal networks provide a scheme for dealing with imprecise probabilistic models. The inference algorithms often used in credal networks compute the interval of the posterior probability of an event of interest given evidence of the specific kind -- evidence that describe the current state of a set of variables. These algorithms do not perform evidential reasoning in case of the evidence must be processed according to the conditioning rule proposed by RC Jeffrey. This paper describes a procedure to integrate evidence with Jeffrey's rule when performing inferences with credal nets.

In Helicobacter pylori auto-inducer-2, but not LuxS/MccAB catalysed reverse transsulphuration, regulates motility through modulation of flagellar gene transcription

BACKGROUND: LuxS may function as a metabolic enzyme or as the synthase of a quorum sensing signalling molecule, auto-inducer-2 (AI-2); hence, the mechanism underlying phenotypic changes upon luxS inactivation is not always clear. In Helicobacter pylori, we have recently shown that, rather than functioning in recycling methionine as in most bacteria, LuxS (along with newly-characterised MccA and MccB), synthesises cysteine via reverse transsulphuration. In this study, we investigated whether and how LuxS controls motility of H. pylori, specifically if it has its effects via luxS-required cysteine metabolism or via AI-2 synthesis only.

RESULTS: We report that disruption of luxS renders H. pylori non-motile in soft agar and by microscopy, whereas disruption of mccAHp or mccBHp (other genes in the cysteine provision pathway) does not, implying that the lost phenotype is not due to disrupted cysteine provision. The motility defect of the DeltaluxSHp mutant was complemented genetically by luxSHp and also by addition of in vitro synthesised AI-2 or 4, 5-dihydroxy-2, 3-pentanedione (DPD, the precursor of AI-2). In contrast, exogenously added cysteine could not restore motility to the DeltaluxSHp mutant, confirming that AI-2 synthesis, but not the metabolic effect of LuxS was important. Microscopy showed reduced number and length of flagella in the DeltaluxSHp mutant. Immunoblotting identified decreased levels of FlaA and FlgE but not FlaB in the DeltaluxSHp mutant, and RT-PCR showed that the expression of flaA, flgE, motA, motB, flhA and fliI but not flaB was reduced. Addition of DPD but not cysteine to the DeltaluxSHp mutant restored flagellar gene transcription, and the number and length of flagella.

CONCLUSIONS: Our data show that as well as being a metabolic enzyme, H. pylori LuxS has an alternative role in regulation of motility by modulating flagellar transcripts and flagellar biosynthesis through production of the signalling molecule AI-2.

Laser driven MeV proton beam focussing by auto-charged electrostatic lens configuration

Significant reduction of inherent large divergence of the laser driven MeV proton beams is achieved by strong (of the order of 10^9 V/m ) electrostatic focussing field generated in the confined region of a suitably shaped structure attached to the proton generating foil. The scheme exploits the positively charging of the target following an intense laser interaction. Reduction in the proton beam divergence, and commensurate increase in proton flux is observed while preserving the beam laminarity. The underlying mechanism has been established by the help of particle tracing simulations. Dynamic focussing power of the lens, mainly due to the target discharging, can also be exploited in order to bring up the desired chromaticity of the lens for the proton beams of broad energy range.