Asthma, current detection methods and metabolites proposed as asthma markers are described. The limitation of the disease diagnosis is outlined and metabolomics is introduced as the approach carried out within this research with the potential to measure the group metabolites that characterise the metabolic responses of a biological system to a specific disease. Chemistry underlying breathing, current breath collection and analytical techniques are described as well as detection and data processing technology associated within our research. A work-flow for the collection, analysis and processing of exhaled breath samples in respiratory diseases is described. The non-invasive sampling method allows collection of exhaled breath samples on children and adults without experiencing any discomfort. The analysis of exhaled breath samples using thermal desorption gas chromatography mass spectrometry outlines the use of retention index for the alignment of VOCs retention time shifting over time. This methodology enables the creation of a breath matrix for multivariate analysis data processing where each VOC is defined by retention index and most intense fragments of the mass spectrum. This methodology is tested in two cohorts of participants: paediatric asthma and severe asthmatic participants whose breath profiles are compared against healthy controls and within the two asthmatic phenotypes to prospect the markers that differentiate between the different groups. Eight candidate markers are identified to discriminate between asthmatic children and healthy children and seven markers between asthmatics undergoing therapy and healthy controls. The database from severe and paediatric asthma is compared, establishing seven non-age related markers between the two groups. A new interface is developed for the faster analysis of exhaled breath samples using thermal desorption ion mobility mass spectrometry. The interface front end has been modified and optimised to achieve the best sensitivity and resolution of VOCs in exhaled breath. A preliminary study carried out in a small cohort of volunteers shows the feasibility of the technique for the differentiation of asthmatic and healthy adults.

published_or_final_version / Applied Statistics / Master / Master of Social Sciences

published_or_final_version / Applied Statistics / Master / Master of Social Sciences

published_or_final_version / Applied Statistics / Master / Master of Social Sciences

Reliability issues are always salient as behavioral researchers observe human behavior and classify individuals from criterion-referenced test scores. This has created a need for studies to assess agreement between observers, recording the occurrance of various behaviors, to establish the reliability of their classifications. In addition, there is a need for measuring the consistency of dichotomous and polytomous classifications established from criterion-referenced test scores. The development of several log linear univariate models for measuring agreement has partially met the demand for a probability-based measure of agreement with a directly interpretable meaning. However, multi-variate repeated measures agreement produres are necessary because of the development of complex intrasubject and intersubject research designs. The present investigation developed applications of the log linear, latent class, and weighted least squares procedures for the analysis of multivariate repeated measures designs. These computations tested the model-data fit and calculated the multivariate measure of the magnitude of agreement under the quasi-equiprobability and quasi-independence models. Applications of these computations were illustrated with real and hypothetical observational data. It was demonstrated that employing log linear, latent class, and weighted least squares computations resulted in identical multi-variate model-data fits with equivalent chi-square values. Moreover, the application of these three methodologies also produced identical measures of the degree of agreement at each point in time and for the multivariate average. The multivariate methods that were developed also included procedures for measuring the probability of agreement for a single response classification or subset of classifications from a larger set. In addition, procedures were developed to analyze occurrences of systematic observed disagreement within the multivariate tables. The consistency of dichotomous and polytomous classifications over repeated assessments of the identical examinees was also suggested as a means of conceptualizing criterion-referenced reliability. By applying the univariate and multivariate models described, the reliability of these classifications across repeated testings could be calculated. The procedures utilizing the log linear, latent structure, and weighted least squares concepts for the purpose of measuring agreement have the advantages of (1)yielding a coefficient of agreement that varies between zero and one and measures agreement in terms of the probability that the observers' judgements will agree, as estimated under a quasi-equiprobability or quasi-independence model, (2)correcting for the proportion of "chance" agreement, and (3) providing a directly interpretable coefficient of "no agreement." Thus, these multivariate procedures may be regarded as a more refined psychometric technology for measuring inter-observer agreement and criterion-referenced test reliability.

No description available.

No description available.

Process control and monitoring is a common problem in high value added chemical manufacturing industries where batch processes are used to produce wide range of products on the same piece of equipment. This results in frequent adjustments on control and monitoring schemes. A chemical toner manufacturing process is representative of an industrial case which is used in this thesis. Process control and monitoring problem of batch processes have been researched, mostly through the simulation, and published in the past . However, the concept of applying the subject to chemical toner manufacturing process or to use a single indicator for multiple pieces of equipment have never been visited previously. In the case study of this research, there are many different factors that may affect the final quality of the products including reactor batch temperature, jacket temperature, impeller speed, rate of the addition of material to the reactor, or process variable associated with the pre-weight tank. One of the challenging tasks for engineers is monitoring of these process variables and to make necessary adjustments during the progression of a batch and change controls strategy of future batches upon completion of an existing batch. Another objective of the proposed research is the establishment of the operational boundaries to monitor the process through the usage of process trajectories of the history of the past successful batches. In this research, process measurements and product quality values of the past successful batches were collected and projected into matrix of data; and preprocessed through time alignment, centering, and scaling. Then the preprocessed data was projected into lower dimensions (latent variables) to produce latent variables and their trajectories during successful batches. Following the identification of latent variables, an empirical model was built through a 4-fold cross validation that can represent the operation of a successful batch. The behavior of two abnormal batches, batch 517 and 629, is then compared to the model by testing its statistical properties. Once the abnormal batches were flagged, their data set were folded back to original dimension to form a localization path for the time of abnormality and process variables that contributed to the abnormality. In each case the process measurement were used to establish operational boundaries on the latent variable space.

No description available.

No description available.