Uncertainty visualization in 3D scalar data

One problem in most three-dimensional (3D) scalar data visualization techniques is that they often overlook to depict uncertainty that comes with the 3D scalar data and thus fail to faithfully present the 3D scalar data and have risks which may mislead users’ interpretations, conclusions or even decisions. Therefore this thesis focuses on the study of uncertainty visualization in 3D scalar data and we seek to create better uncertainty visualization techniques, as well as to find out the advantages/disadvantages of those state-of-the-art uncertainty visualization techniques. To do this, we address three specific hypotheses: (1) the proposed Texture uncertainty visualization technique enables users to better identify scalar/error data, and provides reduced visual overload and more appropriate brightness than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (2) The proposed Linked Views and Interactive Specification (LVIS) uncertainty visualization technique enables users to better search max/min scalar and error data than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (3) The proposed Probabilistic Query uncertainty visualization technique, in comparison to traditional Direct Volume Rendering (DVR) methods, enables radiologists/physicians to better identify possible alternative renderings relevant to a diagnosis and the classification probabilities associated to the materials appeared on these renderings; this leads to improved decision support for diagnosis, as demonstrated in the domain of medical imaging. For each hypothesis, we test it by following/implementing a unified framework that consists of three main steps: the first main step is uncertainty data modeling, which clearly defines and generates certainty types of uncertainty associated to given 3D scalar data. The second main step is uncertainty visualization, which transforms the 3D scalar data and their associated uncertainty generated from the first main step into two-dimensional (2D) images for insight, interpretation or communication. The third main step is evaluation, which transforms the 2D images generated from the second main step into quantitative scores according to specific user tasks, and statistically analyzes the scores. As a result, the quality of each uncertainty visualization technique is determined.

Source apportionment of PM2.5 in Cork Harbour, Ireland using a combination of single particle mass spectrometry and quantitative semi-continuous measurements

An aerosol time-of-flight mass spectrometer (ATOFMS) was deployed for the measurement of the size resolved chemical composition of single particles at a site in Cork Harbour, Ireland for three weeks in August 2008. The ATOFMS was co-located with a suite of semi-continuous instrumentation for the measurement of particle number, elemental carbon (EC), organic carbon (OC), sulfate and particulate matter smaller than 2.5 μm in diameter (PM2.5). The temporality of the ambient ATOFMS particle classes was subsequently used in conjunction with the semi-continuous measurements to apportion PM2.5 mass using positive matrix factorisation. The synergy of the single particle classification procedure and positive matrix factorisation allowed for the identification of six factors, corresponding to vehicular traffic, marine, long-range transport, various combustion, domestic solid fuel combustion and shipping traffic with estimated contributions to the measured PM2.5 mass of 23%, 14%, 13%, 11%, 5% and 1.5% respectively. Shipping traffic was found to contribute 18% of the measured particle number (20–600 nm mobility diameter), and thus may have important implications for human health considering the size and composition of ship exhaust particles. The positive matrix factorisation procedure enabled a more refined interpretation of the single particle results by providing source contributions to PM2.5 mass, while the single particle data enabled the identification of additional factors not possible with typical semi-continuous measurements, including local shipping traffic.

Symptom burden in individuals with inflammatory bowel disease: a mixed methods study

To investigate the symptom burden experiences of individuals with inflammatory bowel disease (IBD). An explanatory sequential mixed methods study was conducted. A cross-sectional, correlational survey was first undertaken. Symptom burden was measured using a modified disease specific version of the Memorial Symptom Assessment Scale, which was administered to a consecutive sample of individuals with IBD (n = 247) at an IBD Outpatients department in one urban teaching hospital in Ireland. Disease activity was determined using clinical disease activity indices, which were completed by the consulting physician. A sequential qualitative, descriptive study was then conducted aimed at explaining noteworthy quantitative findings. A criterion-related purposeful sample of seven participants from the quantitative study was recruited. Semi-structured face to face interviews were conducted using an interview guide and data were analysed using content analysis. Findings revealed that participants experienced a median of 10 symptoms during the last week, however as many as 16 symptoms were experienced during active disease. The most burdensome symptoms were lack of energy, bowel urgency, diarrhoea, feeling bloated, flatulence and worry. Total symptom burden was found to be low with a mean score of 0.56 identified out of a possible range from 0 to 4. Participants with active disease (M = 0.81, SD = 0.48; n = 68) had almost double mean total symptom burden scores than participants with inactive disease (M = 0.46, SD = 0.43; n = 166) (p < 0.001). Mean total psychological symptom burden was found to be significantly greater than mean total physical symptom burden (rho = 0.73, n = 247, p < 0.001). Self-reported disease control, gender, number of flare ups in the last two years, and smoking status was found to be significant predictors of total symptom burden, with self-reported disease control identified as the strongest predictor. Qualitative data revealed tiredness, pain, bowel symptoms, worry and fear as being burdensome. Furthermore, symptom burden experiences were described in terms of its impact on restricting aspects of daily activities, which accumulated into restrictions on general life events. Psychological symptom burden was revealed as more problematic than physical symptom burden due to its constant nature, with physical and psychological symptoms described to occur in a cyclical manner. Participants revealed that disease control was evaluated not only in terms of symptoms, but also in terms of their abilities to control the impact of symptoms on their lives. This study highlights the considerable number of symptoms and the most burdensome symptoms experienced by individuals with IBD, both during active and inactive disease. This study has important implications on symptom assessment in terms of the need to encompass both physical and psychological symptoms. In addition, greater attention needs to be placed on psychological aspects of IBD care.

Application of semi-automated strain analysis techniques and anisotropy of magnetic susceptibility in fold and thrust belts

Quantitative analysis of penetrative deformation in sedimentary rocks of fold and thrust belts has largely been carried out using clast based strain analysis techniques. These methods analyse the geometric deviations from an original state that populations of clasts, or strain markers, have undergone. The characterisation of these geometric changes, or strain, in the early stages of rock deformation is not entirely straight forward. This is in part due to the paucity of information on the original state of the strain markers, but also the uncertainty of the relative rheological properties of the strain markers and their matrix during deformation, as well as the interaction of two competing fabrics, such as bedding and cleavage. Furthermore one of the single largest setbacks for accurate strain analysis has been associated with the methods themselves, they are traditionally time consuming, labour intensive and results can vary between users. A suite of semi-automated techniques have been tested and found to work very well, but in low strain environments the problems discussed above persist. Additionally these techniques have been compared to Anisotropy of Magnetic Susceptibility (AMS) analyses, which is a particularly sensitive tool for the characterisation of low strain in sedimentary lithologies.