The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances. ing a Decision Support System for the Histopathalogical Diagnosis of Chronic Idiopathic Inflammatory Bowel Disease- Comparison of Radial Basis Function Neural Networks and Logistic Regression

The medical problem domain that is investigated i this study is the histopathological diagnosis of chronic idiopathic inflammatory bowel disease (CIBD). CIBD is a generic category that describes diseases of the bowel which are characterised by acute and chronic inflammation and which have no identified aetiological agent (such as an infective agent) The two major diseases within this category are ulcerative colitis (UC) and Crohn's disease. Both diseases are chronic conditions characterised by periods of relapse and remission and may produce life threatening complications such as intestinal perforation, sepsis and carcinoma. Many conditions mimic the clinical symptoms of CIBID (Farmer, 1990: Hamilton 1987: Moxon et al. 1994 Shepherd 1991: Shivananda et al. 1991: Suarwicz et al. 1994) and thus histopathalogical examination of colorectal biopsies is important, both in confirming the diagnosis and in excluding other conditions such as infective colitis..........

A New Non-Linear Design Method For Active Vehicle Suspension Systems.

A novel non-linear design method based on linear quadratic optimal control theory is presented that applies both to linear and (a wide class of) non-linear systems. The method is easy to apply and results in a globally stabilising, near optimal solution, that can be implemented in real time. The key feature of the design method is the introduction of state-dependence in the weight matrices of the usual linear quadratic cost function, leading to a non-linear design method, even for linear dyanmics. To demonstrate the method, a simple linear suspension model is used, in conjunction with a non-linear state penalty, which better reflects the engineering objectives of active vehicle vibration suppression. Non-linear dynamics can equally well be accommodated. A number of simulations is conducted and compared, favourably with a passively mounted vehicle. These preliminary results indicate the potential of the method

An Automated Pattern Recognition System for the Quantification of Inflammatory Cells in Hepatitis C Infected Liver Biopsies.

Hepatitis C is a common viral infection of the liver. The degree of inflammation associated with the infection is normally estimated manually from a liver biopsy, by considering the quantity and nature of inflammatory cells. This paper presents an automated pattern recognition system for the quantification of inflammatory cells in liver biopsies. Initially, images are corrected for colour variation. Features are then extracted for from colour biopsy images at positions of interest identified by adaptive thresholding and clump decomposition. A sequential floating search method and principal component analysis are used to reduce the dimensionality of the feature vector. Manually annotated training images allow supervised training by providing the class membership for each position of interest. Gaussian parametric and Gaussian mixture model density estimation methods are compared and are used to classify cells as either inflammatory or healthy via Bayes' theorem. The system is optimised using a response surface method, where the response or system performance is derived from the area under the receiver operating characteristic curve. The optimised system is then tested on test images previously ranked by a number of observers with varing levels of pathology experience. The observers results are compared to the automated system using Spearman rank correlation. Results show that this system can rank 15 test images, with varying degrees of inflammation, in strong agreement with five expert pathologists

Adaptive Resonance Theory: A Foundation for "Apprentice" Systems in Clinical Decision Support?

The idea of an "apprentice" system in contrast to an expert system, is introduced, as one which continues, perpetually, to refine its knowledge-base. Neural networks appear to offer the necessary learning ability for this task, and the Adaptive Resonance Theory family is particularly suited to on-line (casual) learning. The ability of these networks accurately to represent decision problems and to disclose their acquired knowledge is discussed, and their practical application is assessed. Two problems of medical decision making are considered using the approach. The first is the early diagnosis of myocardial infarction from clinical and electrocardiographic data gathered at presentation. The second is the cytopathological diagnosis of breast lesions from fine needle aspirate samples. In both cases good performance is obtained along with sets of "if-then" rules which are in accordance with medical opinion. In the first case, examples of on-line learning are given and the system is seen to be behaving as expected, with performance improving with increasing sample size

Characteristics of PM2.5 Episodes Revealed by Semi-Continuous Measurements at the Baltimore Supersite at Ponca St

Highly time-resolved measurements of PM2.5, its major constituents, particle size distributions (9 nm to 20 μ m), CO, NO/NO2, and O3, and meteorological parameters were made from February through November 2002, at the Baltimore Supersite at Ponca St. using commercial and prototype semi-continuous instruments. The average PM2.5 mass concentration during the study period was 16.9 μ g/m3 and a total of 29 PM2.5 pollution episodes, each in which 24-h averaged PM2.5 mass concentrations exceeded 30.0 μ g/m3 for one or more days, were observed. Herein, 6 of the worst episodes are discussed. During these events, PM2.5 excursions were often largely due to elevations in the concentration of one or two of the major species. In addition, numerous short-term excursions were observed and were generally attributable to local sources. Those in OC, EC, nitrate, CO, and NOx levels were often observed in the morning traffic hours, particularly before breakdown of nocturnal inversions. Moreover, fresh accumulation aerosols from local stationary combustion sources were observed on several occasions, as evidenced by elevations in elemental markers when winds were aligned with sources resulting in PM2.5 increments of ∼ 17 μ g/m3. Overall, the results described herein show that concentrations of PM2.5 and its major constituents vary enormously on time scales ranging from < 1 hr to several days, thus imposing a more highly complex pattern of pollutant exposure than can be captured by 24-hr integrated methods, alone. The data suggest that control of a limited number of local sources might achieve compliance with daily and annual PM2.5 standards

Quiescence near the X-point of MAST measured by high speed visible imaging

Using high speed imaging of the divertor volume, the region close to the X-point in MAST is shown to be quiescent. This is confirmed by three different analysis techniques and the quiescent X-point region (QXR) spans from the separatrix to the ψ N = 1 . 02 flux surface. Local reductions to the atomic density and effects associated with the camera viewing geometry are ruled out as causes of the QXR, leaving quiescence in the local plasma conditions as being the most likely cause. The QXR is found to be ubiquitous across a significant operational space in MAST including L-mode and H-mode discharges across maximal ranges of 9 . 8 × 10 19 m − 2 in line integrated density, 0 . 36MA in plasma current, 0 . 11T in toroidal magnetic field and 3 . 2MW in NBI power. When mapped to the divertor target the QXR occupies approximately an e-folding length of the heat-flux profile, containing ∼ 60% of the total heat flux to the target, and also shows a tendency towards higher frequency shorter lived fluctuations in the ion-saturation current. This is consistent with short- lived divertor localised filamentary structures observed further down the outer divertor leg in the camera images, and suggests a complex multi-region picture of filamentary transport in the divertor

Implementing Reliability: The Interaction of Requirements, Tactics and Architecture Patterns

An important way that the reliability of a software system is enhanced is through the implementation of specific run-time measures called runtime tactics. Because reliability is a system-wide property, tactic implementations affect the software structure and behavior at the system, or architectural level. For a given architecture, different tactics may be a better or worse fit for the architecture, depending on the requirements and how the architecture patterns used must change to accommodate the tactic: different tactics may be a better or worse fit for the architecture. We found three important factors that influence the implementation of reliability tactics. One is the nature of the tactic, which indicates whether the tactic influences all components of the architecture or just a subset of them. The second is the interaction between architecture patterns and tactics: specific tactics and patterns are inherently compatible or incompatible. The third is the reliability requirements which influence which tactics to use and where they should be implemented. Together, these factors affect how, where, and the difficulty of implementing reliability tactics. This information can be used by architects and developers to help make decisions about which patterns and tactics to use, and can also assist these users in learning what modifications and additions to the patterns are needed.</p

Optimising cardiometabolic risk factors in pregnancy: a review of risk prediction models targeting gestational diabetes and hypertensive disorders

Cardiovascular disease, especially coronary heart disease and cerebrovascular disease, is a leading cause of mortality and morbidity in women globally. The development of cardiometabolic conditions in pregnancy, such as gestational diabetes mellitus and hypertensive disorders of pregnancy, portend an increased risk of future cardiovascular disease in women. Pregnancy therefore represents a unique opportunity to detect and manage risk factors, prior to the development of cardiovascular sequelae. Risk prediction models for gestational diabetes mellitus and hypertensive disorders of pregnancy can help identify at-risk women in early pregnancy, allowing timely intervention to mitigate both short- and long-term adverse outcomes. In this narrative review, we outline the shared pathophysiological pathways for gestational diabetes mellitus and hypertensive disorders of pregnancy, summarise contemporary risk prediction models and candidate predictors for these conditions, and discuss the utility of these models in clinical application.Eleanor P. Thong, Drishti P. Ghelani, Pamada Manoleehakul, Anika Yesmin, Kaylee Slater, Rachael Taylor, Clare Collins, Melinda Hutchesson, Siew S. Lim, Helena J. Teede, Cheryce L. Harrison, Lisa Moran, and Joanne Enticot