Constant-utility index numbers of real wages /

Date on Bibliographic Data Sheet: June 1976.

Submodular Optimization and Data Processing

Thesis (Ph.D.)--University of Washington, 2016-06

Length of Stay, Discharge Destination, and Functional Improvement: Utility of the Australian National Subacute and Nonacute Patient Casemix Classification

Background and Purpose - Although implemented in 1998, no research has examined how well the Australian National Subacute and Nonacute Patient (AN-SNAP) Casemix Classification predicts length of stay (LOS), discharge destination, and functional improvement in public hospital stroke rehabilitation units in Australia. Methods - 406 consecutive admissions to 3 stroke rehabilitation units in Queensland, Australia were studied. Sociode-mographic, clinical, and functional data were collected. General linear modeling and logistic regression were used to assess the ability of AN-SNAP to predict outcomes. Results - AN-SNAP significantly predicted each outcome. There were clear relationships between the outcomes of longer LOS, poorer functional improvement and discharge into care, and the AN-SNAP classes that reflected poorer functional ability and older age. Other predictors included living situation, acute LOS, comorbidity, and stroke type. Conclusions - AN-SNAP is a consistent predictor of LOS, functional change and discharge destination, and has utility in assisting clinicians to set rehabilitation goals and plan discharge.

Utility of signs and symptoms of chronic cough in predicting specific cause in children

Background: Paediatricians rely on cough descriptors to direct them to the level of investigations needed for a child presenting with chronic cough, yet there is a lack of published data to support this approach. A study was undertaken to evaluate ( 1) whether historical cough pointers can predict which children have a specific cause for their cough and ( 2) the usefulness of chest radiography and spirometry as standard investigations in children with chronic cough. Methods: This was a prospective cohort study of children referred to a tertiary hospital with a cough lasting 3 weeks between June 2002 and July 2004. All included children completed a detailed history and examination using a standardised data collection sheet and followed a pathway of investigation until a diagnosis was made. Results: In 100 consecutively recruited children of median age 2.8 years, the best predictor of specific cough observed was a moist cough at the time of consultation with an odds ratio ( OR) of 9.34 (95% CI 3.49 to 25.03). Chest examination or chest radiographic abnormalities were also predictive with OR 3.60 ( 95% CI 1.31 to 9.90) and 3.16 (95% CI 1.32 to 7.62), respectively. The most significant historical pointer for predicting a specific cause of the cough was a parental history of moist cough ( sensitivity 96%, specificity 26%, positive predictive value 74%). Conclusions: The most useful clinical marker in predicting specific cough is the presence of a daily moist cough. Both chest examination and chest radiographic abnormalities are also useful in predicting whether children have a specific cause of their cough.

Mapping structural parameters and species composition of riparian vegetation using IKONOS and Landsat ETM+ data in Australian tropical savannahs

Government agencies responsible for riparian environments are assessing the utility of remote sensing for mapping and monitoring vegetation structural parameters. The objective of this work was to evaluate Ikonos and Landsat-7 ETM+ imagery for mapping structural parameters and species composition of riparian vegetation in Australian tropical savannahs for a section of Keelbottom Creek, Queensland, Australia. Vegetation indices and image texture from Ikonos data were used for estimating leaf area index (R-2 = 0.13) and canopy percentage foliage cover (R-2 = 0.86). Pan-sharpened Ikonos data were used to map riparian species composition (overall accuracy = 55 percent) and riparian zone width (accuracy within +/- 3 m). Tree crowns could not be automatically delineated due to the lack of contrast between canopies and adjacent grass cover. The ETM+ imagery was suited for mapping the extent of riparian zones. Results presented demonstrate the capabilities of high and moderate spatial resolution imagery for mapping properties of riparian zones.

Mapping indicators of riparian vegetation health using IKONOS and Landsat-7 ETM+ image data in Australian tropical savannas

Government agencies responsible for riparian environments are assessing the utility of remote sensing for mapping and monitoring environmental health indicators. The objective of this work was to evaluate IKONOS and Landsat-7 ETM+ imagery for mapping riparian vegetation health indicators in tropical savannas for a section of Keelbottom Creek, Queensland, Australia. Vegetation indices and image texture from IKONOS data were used for estimating percentage canopy cover (r2=0.86). Pan-sharpened IKONOS data were used to map riparian species composition (overall accuracy=55%) and riparian zone width (accuracy within 4 m). Tree crowns could not be automatically delineated due to the lack of contrast between canopies and adjacent grass cover. The ETM+ imagery was suited for mapping the extent of riparian zones. Results presented demonstrate the capabilities of high and moderate spatial resolution imagery for mapping properties of riparian zones, which may be used as riparian environmental health indicators

The utility of aminoterminal pro-B-type natriuretic peptide for the detection of preclinical systolic and diastolic dysfunction in the community

Background: There is scant data regarding methods to identify subjects in the community with preclinical left ventricular (LV) systolic and diastolic dysfunction. Methods: A population-based sample of 1229 older adults underwent examination with transthoracic echocardiography and measurement of circulating aminoterminal pro-Btype natriuretic peptide (N-BNP) levels. Heart failure status was ascertained according to past history and clinical examination. The ability of N-BNP to detect preclinical LV ejection fraction (EF)

An introduction to analysis of variance (ANOVA) with special reference to data from clinical experiments in optometry

This article is aimed primarily at eye care practitioners who are undertaking advanced clinical research, and who wish to apply analysis of variance (ANOVA) to their data. ANOVA is a data analysis method of great utility and flexibility. This article describes why and how ANOVA was developed, the basic logic which underlies the method and the assumptions that the method makes for it to be validly applied to data from clinical experiments in optometry. The application of the method to the analysis of a simple data set is then described. In addition, the methods available for making planned comparisons between treatment means and for making post hoc tests are evaluated. The problem of determining the number of replicates or patients required in a given experimental situation is also discussed. Copyright (C) 2000 The College of Optometrists.

Classification and contextual enhancement of remotely sensed data

The aims of the project were twofold: 1) To investigate classification procedures for remotely sensed digital data, in order to develop modifications to existing algorithms and propose novel classification procedures; and 2) To investigate and develop algorithms for contextual enhancement of classified imagery in order to increase classification accuracy. The following classifiers were examined: box, decision tree, minimum distance, maximum likelihood. In addition to these the following algorithms were developed during the course of the research: deviant distance, look up table and an automated decision tree classifier using expert systems technology. Clustering techniques for unsupervised classification were also investigated. Contextual enhancements investigated were: mode filters, small area replacement and Wharton's CONAN algorithm. Additionally methods for noise and edge based declassification and contextual reclassification, non-probabilitic relaxation and relaxation based on Markov chain theory were developed. The advantages of per-field classifiers and Geographical Information Systems were investigated. The conclusions presented suggest suitable combinations of classifier and contextual enhancement, given user accuracy requirements and time constraints. These were then tested for validity using a different data set. A brief examination of the utility of the recommended contextual algorithms for reducing the effects of data noise was also carried out.

Consuming linked data in supply chains:enabling data visibility via linked pedigrees

The performance of a supply chain depends critically on the coordinating actions and decisions undertaken by the trading partners. The sharing of product and process information plays a central role in the coordination and is a key driver for the success of the supply chain. In this paper we propose the concept of "Linked pedigrees" - linked datasets, that enable the sharing of traceability information of products as they move along the supply chain. We present a distributed and decentralised, linked data driven architecture that consumes real time supply chain linked data to generate linked pedigrees. We then present a communication protocol to enable the exchange of linked pedigrees among trading partners. We exemplify the utility of linked pedigrees by illustrating examples from the perishable goods logistics supply chain.

Analysing Conjoint Analysis Data by a Random Coefficient Regression Model

2000 Mathematics Subject Classification: 62J12, 62K15, 91B42, 62H99.

Connection design without accurate LV feeder load data:an argument for LV monitoring

Since privatisation, maintenance of DNO LV feeder maximum demand information has gradually demised in some Utility Areas, and it is postulated that lack of knowledge about 11kV and LV electrical networks is resulting in a less economical and energy efficient Network as a whole. In an attempt to quantify the negative impact, this paper examines ten postulated new connection scenarios for a set of real LV load readings, in order to find the difference in design solutions when LV load readings were and were not known. The load profiles of the substations were examined in order to explore the utilisation profile. It was found that in 70% of the scenarios explored, significant cost differences were found. These cost differences varied by an average of 1000%, between schemes designed with and without load readings. Obviously, over designing a system and therefore operating more, underutilised transformers becomes less financially beneficial and less energy efficient. The paper concludes that new connection design is improved in terms of cost when carried out based on known LV load information and enhances the case for regular maximum feeder demand information and/or metering of LV feeders. © 2013 IEEE.

Analyzing evacuation decisions using multi-attribute utility theory (MAUT)

Emergency managers are faced with critical evacuation decisions. These decisions must balance conflicting objectives as well as high levels of uncertainty. Multi-Attribute Utility Theory (MAUT) provides a framework through which objective trade-offs can be analyzed to make optimal evacuation decisions. This paper is the result of data gathered during the European Commission Project, Evacuation Responsiveness by Government Organizations (ERGO) and outlines a preliminary decision model for the evacuation decision. The illustrative model identifies levels of risk at which point evacuation actions should be taken by emergency managers in a storm surge scenario with forecasts at 12 and 9 hour intervals. The results illustrate how differences in forecast precision affect the optimal evacuation decision. Additional uses for this decision model are also discussed along with improvements to the model through future ERGO data-gathering.

Examination of the impact of personality, social, and cognitive factors on the co-occurrence of health risk behaviors among multi-problem youth: The utility of an integrative framework

Research has identified a number of putative risk factors that places adolescents at incrementally higher risk for involvement in alcohol and other drug (AOD) use and sexual risk behaviors (SRBs). Such factors include personality characteristics such as sensation-seeking, cognitive factors such as positive expectancies and inhibition conflict as well as peer norm processes. The current study was guided by a conceptual perspective that support the notion that an integrative framework that includes multi-level factors has significant explanatory value for understanding processes associated with the co-occurrence of AOD use and sexual risk behavior outcomes. This study evaluated simultaneously the mediating role of AOD-sex related expectancies and inhibition conflict on antecedents of AOD use and SRBs including sexual sensation-seeking and peer norms for condom use.^ The sample was drawn from the Enhancing My Personal Options While Evaluating Risk (EMPOWER: Jonathan Tubman, PI), data set (N = 396; aged 12-18 years). Measures used in the study included Sexual Sensation-Seeking Scale, Inhibition Conflict for Condom Use, Risky Sex Scale. All relevant measures had well-documented psychometric properties. A global assessment of alcohol, drug use and sexual risk behaviors was used.^ Results demonstrated that AOD-sex related expectancies mediated the influence of sexual sensation-seeking on the co-occurrence of alcohol and other drug use and sexual risk behaviors. The evaluation of the integrative model also revealed that sexual sensation-seeking was positively associated with peer norms for condom use. Also, peer norms predicted inhibition conflict among this sample of multi-problem youth. ^ This dissertation research identified mechanisms of risk and protection associated with the co-occurrence of AOD use and SRBs among a multi-problem sample of adolescents receiving treatment for alcohol or drug use and related problems. This study is informative for adolescent-serving programs that address those individual and contextual characteristics that enhance treatment efficacy and effectiveness among adolescents receiving substance use and related problems services.^

Automated detection of hematological abnormalities through classification of flow cytometric data patterns

Flow Cytometry analyzers have become trusted companions due to their ability to perform fast and accurate analyses of human blood. The aim of these analyses is to determine the possible existence of abnormalities in the blood that have been correlated with serious disease states, such as infectious mononucleosis, leukemia, and various cancers. Though these analyzers provide important feedback, it is always desired to improve the accuracy of the results. This is evidenced by the occurrences of misclassifications reported by some users of these devices. It is advantageous to provide a pattern interpretation framework that is able to provide better classification ability than is currently available. Toward this end, the purpose of this dissertation was to establish a feature extraction and pattern classification framework capable of providing improved accuracy for detecting specific hematological abnormalities in flow cytometric blood data. ^ This involved extracting a unique and powerful set of shift-invariant statistical features from the multi-dimensional flow cytometry data and then using these features as inputs to a pattern classification engine composed of an artificial neural network (ANN). The contribution of this method consisted of developing a descriptor matrix that can be used to reliably assess if a donor’s blood pattern exhibits a clinically abnormal level of variant lymphocytes, which are blood cells that are potentially indicative of disorders such as leukemia and infectious mononucleosis. ^ This study showed that the set of shift-and-rotation-invariant statistical features extracted from the eigensystem of the flow cytometric data pattern performs better than other commonly-used features in this type of disease detection, exhibiting an accuracy of 80.7%, a sensitivity of 72.3%, and a specificity of 89.2%. This performance represents a major improvement for this type of hematological classifier, which has historically been plagued by poor performance, with accuracies as low as 60% in some cases. This research ultimately shows that an improved feature space was developed that can deliver improved performance for the detection of variant lymphocytes in human blood, thus providing significant utility in the realm of suspect flagging algorithms for the detection of blood-related diseases.^