No / Long glass fibre (LGF) composites are extensively used in manufacturing to produce components with enhanced mechanical properties. Long fibres with length 12 to 25mm are added to a thermoplastic matrix. However severe fibre breakage can occur in the injection moulding process resulting in shorter fibre length distribution (FLD). The majority of this breakage occurs due to the melt experiencing extreme shear stress during the preparation and injection stage. Care should be taken to ensure that the longer fibres make it through the injection moulding process without their length being significantly degraded. This study is based on commercial 12 mm long glass-fibre reinforced polypropylene (PP) and short glass fibre Nylon. Due to the semi-flexiable behaviour of long glass fibres, the fibre orientation distribution (FOD) will differ from the orientation distribution of short glass fibre in an injection molded part. In order to investigate the effect the change in fibre length has on the fibre orientation distribution or vice versa, FOD data was measured using the 2D section image analyser. The overall purpose of the research is to show how the orientation distribution chnages in an injection moulded centre gated disc and end gated plaque geometry and to compare this data against fibre orientation predictions obtained from Autodesk Moldflow Simulation Insight.

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This dissertation addresses the challenge of enabling accurate analysis of network data while ensuring the protection of network participants' privacy. This is an important problem: massive amounts of data are being collected (facebook activity, email correspondence, cell phone records), there is huge interest in analyzing the data, but the data is not being shared due to concerns about privacy. Despite much research in privacy-preserving data analysis, existing technologies fail to provide a solution because they were designed for tables, not networks, and cannot be easily adapted to handle the complexities of network data. We develop several technologies that advance us toward our goal. First, we develop a framework for assessing the risk of publishing a network that has been "anonymized." Using this framework, we show that only a small amount of background knowledge about local network structure is needed to re-identify an "anonymous" individual. This motivates our second contribution: an algorithm that transforms the structure of the network to provably lower re-identification risk. In comparison with other algorithms, we show that our approach more accurately preserves important features of the network topology. Finally, we consider an alternative paradigm, in which the analyst can analyze private data through a carefully controlled query interface. We show that the degree sequence of a network can be accurately estimated under strong guarantees of privacy.

1980's. A virtual harbor containing validated numerical currents, bathymetric data, and realistic vessel response allows pilots with local knowledge to test channel modifications in a no-risk environment. Current analyzes rely heavily on subject matter experts (pilots) to inform recommendations and does not analyze data output from the ship simulator. The Data Analysis Tool for Ship Simulation (DATSS) allows the user to process raw data from the ship simulator, generate summary information, compare simulations directly, and produce figures by using a rapid, semi-standardized method. This study features a case study of Mobile Harbor which presents three different possible applications of the DATSS: grounding analysis, identifying simulator errors, and supporting sponsors requests. Through the DATSS, data becomes accessible, safety is improved, conclusions are fortified, and manpower is reduced.

Background and objectives: Prediction models are useful tools in clinical practise by providing predictive estimates of outcome probabilities to aid in decision making. As biomedical research advances, concerns have been raised regarding combined effectiveness (benefit) and safety (harm) outcomes in a prediction model, while typically different prediction models only focus on predictions of separate outcomes. A second issue is that, evidence also reveals poor predictive accuracy in different populations and settings for some prediction models, requiring model calibration or redevelopment. A third issue in data analyses is whether the treatment effect estimates could be influenced by competing risk bias. If other events preclude the outcomes of interest, these events would compete with the outcomes and thus fundamentally change the probability of the outcomes of interest. Failure to recognize the existence of competing risk or to account for it may result in misleading conclusions in health research. Therefore in this thesis, we explored three methodological issues in prediction models and data analyses by: (1) developing and externally validating a prediction model for patients’ individual combined benefit and harm outcomes (stroke with no major bleeding, major bleeding with no stroke, neither event, or both stroke and major bleeding) with and without warfarin therapy for atrial fibrillation; (2) constructing a prediction model for hospital mortality in medical-surgical critically ill patients; and (3) performing a competing risk analysis to assess the efficacy of the low molecular weight heparin dalteparin versus unfractionated heparin in venous thromboembolism in medical-surgical critically ill patients. Methods: Project 1: Using the Kaiser Permanente Colorado (KPCO) anticoagulation management cohort in the Denver-Boulder metropolitan area of Colorado in the United States to include patients with AF who were and were not prescribed warfarin therapy, we used a new approach to build a prediction model of individual combined benefit and harm outcomes related to warfarin therapy (stroke with no major bleeding, major bleeding with no stroke, neither event, or both stroke and major bleeding) in patients with AF. We utilized a polytomous logistic regression (PLR) model to identify risk factors and then construct the new prediction model. Model performances and validation were evaluated systematically in the study. Project 2: We used data from a multicenter randomized controlled trial named Prophylaxis for Thromboembolism in Critical Care Trial (PROTECT) to develop a new prediction model for hospital mortality in critically ill medical-surgical patients receiving heparin thromboprophylaxis. We first identified risk factors independent of APACHE (Acute Physiology and Chronic Health Evaluation) II score for hospital mortality, and then combined the identified risk factors and APACHE II score to build the new prediction model. Model performances were compared between the new prediction model and the APACHE II score. Project 3: We re-analyzed the data from PROTECT to perform a sensitivity analysis based on a competing risk analysis to investigate the efficacy of dalteparin versus unfractionated heparin in preventing venous thromboembolism in medical-surgical critically ill patients, taking all-cause death as a competing risk for venous thromboembolism. Results from the competing risk analysis were compared with findings from the cause-specific analysis. Results and Conclusions: Project 1: The PLR model could simultaneously predict risk of individual combined benefit and harm outcomes in patients with and without warfarin therapy for AF. The prediction model was a good fit, had acceptable discrimination and calibration, and was internally and externally validated. Should this approach be validated in other patient populations, it has potential advantages over existing risk stratification approaches. Project 2: The new model combining other risk factors and APACHE II score was a good fit, well calibrated and internally validated. However, the discriminative ability of the prediction model was not satisfactory. Compared with the APACHE II score alone, the new prediction model increased data collection, was more complex but did not substantially improve discriminative ability. Project 3: The competing risk analysis yielded no significant effect of dalteparin compared with unfractionated heparin on proximal leg deep vein thromboses, but a lower risk of pulmonary embolism in critically ill medical-surgical patients. Findings from the competing risk analysis were similar to results from the cause-specific analysis. / Thesis / Doctor of Philosophy (PhD)

Patients with a history of lateral ankle sprain (LAS) may experience different levels of mechanical and/or sensorimotor deficits following their injuries. Although various factors, such as structural damage, sensorimotor adaptation, perceived instability, swelling and/or pain, can develop and perpetuate the condition of chronic ankle instability (CAI), most previous CAI research on biomechanics has considered all patients with CAI as a homogeneous group. Recent research has clustered patients with CAI into six distinct movement patterns during a maximal jump-landing/cutting task. This approach could motivate clinicians to develop appropriate rehabilitation programs for each patient with CAI depending on their movement patterns. However, evaluating patients with CAI for the quality of movement and sensorimotor deficits using a 3D motion capture system and a force plate is not easily accessible in clinical settings. PURPOSE: (i) to identify subgroups within the CAI population based on their movement patterns using inertial measurement unit (IMU) devices and (ii) to characterize each subgroup's functional movement patterns during maximal jump-landing/cutting relative to the uninjured controls. METHODS: A total of 100 patients with CAI (height = 1.76 ± 0.1 m, mass = 74.0 ± 14.9 kg) were assessed according to the Foot and Ankle Ability Measure (FAAM) (ADL: 84.3 ± 7.6%, Sport: 63.6 ± 8.6%) and the Ankle Instability Instrument (AII) (6.7 ± 1.2) and were fit into clusters based on their movement strategy during the maximal jump-landing/cutting task. A total of 21 uninjured controls (height = 1.74 ± 0.1 m, mass = 70.7 ± 13.4 kg) were compared with each cluster. Seven IMU sensors were placed on the base of the lumbar spine, lower and upper legs, and feet and participants performed 5 trials of the maximal jump-landing/cutting test. Joint kinematics in the lower extremity were collected during the task using IMU sensors. Data were reduced to functional curves; kinematic data from the sagittal and frontal planes were reduced to a single representative curve for each plane. Then, representative curves were clustered using a Bayesian clustering technique. Functional analyses of variance were used to identify between-group differences for outcome measures and describe specific movement characteristics of each subgroup. Pairwise comparison functions as well as 95% confidence interval (CI) bands were plotted to determine specific differences. If 95% CI bands did not cross the zero line, we considered the difference significant. RESULTS: Four distinct clusters were identified from the sagittal- and frontal-plane kinematic data. Specific movement patterns in each cluster compared to either uninjured controls or rest of patients with CAI were also identified. CONCLUSION: The IMUs were able to distinguish 4 clusters within the CAI population based on distinct movement patterns during a maximal jump-landing/cutting task. Thus, IMUs can be effective measuring devices to distinguish and characterize multiple distinct movement patterns without relying on a traditional 3D motion capture system. Clinicians should consider utilizing IMU devices to measure and evaluate specific movement patterns in the CAI population during multiplanar demanding tests before developing appropriate treatment interventions in clinical settings.

Challenges of financial forecasting, such as a dearth of independent samples and non- stationary underlying process, limit the relevance of conventional machine learning towards financial forecasting. Meta-learning approaches alleviate some of these is- sues by allowing the model to generalize across unrelated or loosely related tasks with few observations per task. The neural process family achieves this by con- ditioning forecasts based on a supplied context set at test time. Despite promise, meta-learning approaches remain underutilized in finance. To our knowledge, ours is the first application of neural processes to realized volatility (RV) forecasting and financial forecasting in general. We propose a hybrid temporal convolutional network attentive neural process (ANP- TCN) for the purpose of financial forecasting. The ANP-TCN combines a conven- tional and performant financial time series embedding model (TCN) with an ANP objective. We found ANP-TCN variant models outperformed the base TCN for equity index realized volatility forecasting. In addition, when stack-ensembled with a tree- based model to forecast a trading signal, the ANP-TCN outperformed the baseline buy-and-hold strategy and base TCN model in out-of-sample performance. Across four liquid US equity indices (incl. S&P 500) tested over ∼15 years, the best long-short models (reported by median trajectory) resulted in the following out-of-sample (∼3 years) performance ranges: directional accuracy of 58.65% to 62.26%, compound an- nual growth rate (CAGR) of 0.2176 to 0.4534, and annualized Sharpe ratio of 2.1564 to 3.3375. All project code can be found at: https://github.com/kpa28-git/thesis-code.

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