Submodular Optimization and Data Processing

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

Operations and Planning in Sustainable Power Systems

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

Single-subject research designs and data analyses for assessing elite athletes' conditioning

Research in conditioning (all the processes of preparation for competition) has used group research designs, where multiple athletes are observed at one or more points in time. However, empirical reports of large inter-individual differences in response to conditioning regimens suggest that applied conditioning research would greatly benefit from single-subject research designs. Single-subject research designs allow us to find out the extent to which a specific conditioning regimen works for a specific athlete, as opposed to the average athlete, who is the focal point of group research designs. The aim of the following review is to outline the strategies and procedures of single-subject research as they pertain to.. the assessment of conditioning for individual athletes. The four main experimental designs in single-subject research are: the AB design, reversal (withdrawal) designs and their extensions, multiple baseline designs and alternating treatment designs. Visual and statistical analyses commonly used to analyse single-subject data, and advantages and limitations are discussed. Modelling of multivariate single-subject data using techniques such as dynamic factor analysis and structural equation modelling may identify individualised models of conditioning leading to better prediction of performance. Despite problems associated with data analyses in single-subject research (e.g. serial dependency), sports scientists should use single-subject research designs in applied conditioning research to understand how well an intervention (e.g. a training method) works and to predict performance for a particular athlete.

Role of smoking in global and regional cancer epidemiology: current patterns and data needs

Although smoking is widely recognized as a major cause of cancer, there is little information on how it contributes to the global and regional burden of cancers in combination with other risk factors that affect background cancer mortality patterns. We used data from the American Cancer Society's Cancer Prevention Study II (CPS-II) and the WHO and IARC cancer mortality databases to estimate deaths from 8 clusters of site-specific cancers caused by smoking, for 14 epidemiologic subregions of the world, by age and sex. We used lung cancer mortality as an indirect marker for accumulated smoking hazard. CPS-II hazards were adjusted for important covariates. In the year 2000, an estimated 1.42 (95% CI 1.27-1.57) million cancer deaths in the world, 21% of total global cancer deaths, were caused by smoking. Of these, 1.18 million deaths were among men and 0.24 million among women; 625,000 (95% CI 485,000-749,000) smoking-caused cancer deaths occurred in the developing world and 794,000 (95% CI 749,000-840,000) in industrialized regions. Lung cancer accounted for 60% of smoking-attributable cancer mortality, followed by cancers of the upper aerodigestive tract (20%). Based on available data, more than one in every 5 cancer deaths in the world in the year 2000 were caused by smoking, making it possibly the single largest preventable cause of cancer mortality. There was significant variability across regions in the role of smoking as a cause of the different site-specific cancers. This variability illustrates the importance of coupling research and surveillance of smoking with that for other risk factors for more effective cancer prevention. (C) 2005 Wiley-Liss, Inc.

Understanding service experience in non-profit performing arts: Implications for operations and service management

Recent research in the non-profit performing arts has shown that marketing efforts designed to increase revenue from ticket sales are not achieving the results required to sustain the performing arts. This paper applies operations management analytical techniques to the non-profit performing arts to increase understanding of operational issues and inform service management strategy. The paper takes a two-study idiographic approach. Implementing a modified version of service transaction analysis (STA), Study One describes a performing arts service from provider and customer perspectives, identifies service gaps and develops an elaborated service description incorporating both perspectives. In Study Two, building on the elaborated service description and extant research, in-depth interviews are conducted to gather thick descriptions of predictors of satisfaction, value and service quality as they relate to repurchase intention (RI). Technical, functional and critical factors required to improve organizational performance are identified. Implications for operational strategy, service design and service management theory for this context are discussed. (c) 2005 Published by Elsevier B.V.

E-Government, targeting and data profiling: Policy and ethical issues of differential treatment

Networked information and communication technologies are rapidly advancing the capacities of governments to target and separately manage specific sub-populations, groups and individuals. Targeting uses data profiling to calculate the differential probabilities of outcomes associated with various personal characteristics. This knowledge is used to classify and sort people for differentiated levels of treatment. Targeting is often used to efficiently and effectively target government resources to the most disadvantaged. Although having many benefits, targeting raises several policy and ethical issues. This paper discusses these issues and the policy responses governments may take to maximise the benefits of targeting while ameliorating the negative aspects.

Modelling pre-clearing vegetation distribution using GIS-integrated statistical, ecological and data models: A case study from the wet tropics of Northeastern Australia

Traditional vegetation mapping methods use high cost, labour-intensive aerial photography interpretation. This approach can be subjective and is limited by factors such as the extent of remnant vegetation, and the differing scale and quality of aerial photography over time. An alternative approach is proposed which integrates a data model, a statistical model and an ecological model using sophisticated Geographic Information Systems (GIS) techniques and rule-based systems to support fine-scale vegetation community modelling. This approach is based on a more realistic representation of vegetation patterns with transitional gradients from one vegetation community to another. Arbitrary, though often unrealistic, sharp boundaries can be imposed on the model by the application of statistical methods. This GIS-integrated multivariate approach is applied to the problem of vegetation mapping in the complex vegetation communities of the Innisfail Lowlands in the Wet Tropics bioregion of Northeastern Australia. The paper presents the full cycle of this vegetation modelling approach including sampling sites, variable selection, model selection, model implementation, internal model assessment, model prediction assessments, models integration of discrete vegetation community models to generate a composite pre-clearing vegetation map, independent data set model validation and model prediction's scale assessments. An accurate pre-clearing vegetation map of the Innisfail Lowlands was generated (0.83r(2)) through GIS integration of 28 separate statistical models. This modelling approach has good potential for wider application, including provision of. vital information for conservation planning and management; a scientific basis for rehabilitation of disturbed and cleared areas; a viable method for the production of adequate vegetation maps for conservation and forestry planning of poorly-studied areas. (c) 2006 Elsevier B.V. All rights reserved.

Load profiling and data mining techniques in electricity deregulated market

This paper presents load profiles of electricity customers, using the knowledge discovery in databases (KDD) procedure, a data mining technique, to determine the load profiles for different types of customers. In this paper, the current load profiling methods are compared using data mining techniques, by analysing and evaluating these classification techniques. The objective of this study is to determine the best load profiling methods and data mining techniques to classify, detect and predict non-technical losses in the distribution sector, due to faulty metering and billing errors, as well as to gather knowledge on customer behaviour and preferences so as to gain a competitive advantage in the deregulated market. This paper focuses mainly on the comparative analysis of the classification techniques selected; a forthcoming paper will focus on the detection and prediction methods.