Abstracts and classification of graduate studies.

Thesis (Ed.M.)--Boston Universit

Lung involvement at presentation predicts disease activity and permanent organ damage at 6, 12 and 24 months follow - up in ANCA - associated vasculitis.

BACKGROUND: Antineutrophilic cytoplasmic antibody (ANCA)-associated vasculitis (AAV) may present with pulmonary involvement ranging from mild to life-threatening disease such as diffuse alveolar hemorrhage. There is a paucity of information regarding morbidity outcomes for AAV subjects presenting with lung involvement. This study determines the relationship between disease activity and damage in these subjects using the Birmingham Vasculitis Activity Score v 3 (BVAS 3) and Vasculitis Damage Index (VDI) respectively. RESULTS: 151 patients with AAV were included with 59 presenting initially with pulmonary involvement. The initial BVAS scores recorded at time of diagnosis were positively correlated with the final VDI scores at 24 months (p \u3c 0.0001, rs = 0.5871). No differences between BVAS and VDI scores were seen for both groups, however in the lung-involvement group only, BVAS scores were significantly higher at 6, 12 and 24 months whilst the VDI scores were significantly higher at 12 and 24 months. Subjects presenting with pulmonary involvement had an increased likelihood for cardiovascular (OR 1.31, 95% CI 0.89, 1.54; p = 0.032) and renal (OR 1.32, 95% CI 1.22, 1.39; p = 0.005) involvement. Subjects presenting with lung involvement with granulomatosis with polyangiitis and microscopic polyangiitis had 24-month VDI scores that were significantly higher (p = 0.027, p = 0.045), and more likely to develop pulmonary fibrosis (OR 1.79, 95% CI 1.48, 2.12; p \u3c 0.001). CONCLUSION: AAV subjects with lung involvement at presentation had a higher disease activity and damage scores at 6, 12 and 24 months follow-up representing a considerable burden of disease despite improvement in overall survival due to the introduction of immunosuppressive therapy

Forecasting solar photosynthetic photon flux density under cloud cover effects: novel predictive model using convolutional neural network integrated with long short-term memory network

Forecast models of solar radiation incorporating cloud effects are useful tools to evaluate the impact of stochastic behaviour of cloud movement, real-time integration of photovoltaic energy in power grids, skin cancer and eye disease risk minimisation through solar ultraviolet (UV) index prediction and bio-photosynthetic processes through the modelling of solar photosynthetic photon flux density (PPFD). This research has developed deep learning hybrid model (i.e., CNN-LSTM) to factor in role of cloud effects integrating the merits of convolutional neural networks with long short-term memory networks to forecast near real-time (i.e., 5-min) PPFD in a sub-tropical region Queensland, Australia. The prescribed CLSTM model is trained with real-time sky images that depict stochastic cloud movements captured through a total sky imager (TSI-440) utilising advanced sky image segmentation to reveal cloud chromatic features into their statistical values, and to purposely factor in the cloud variation to optimise the CLSTM model. The model, with its competing algorithms (i.e., CNN, LSTM, deep neural network, extreme learning machine and multivariate adaptive regression spline), are trained with 17 distinct cloud cover inputs considering the chromaticity of red, blue, thin, and opaque cloud statistics, supplemented by solar zenith angle (SZA) to predict short-term PPFD. The models developed with cloud inputs yield accurate results, outperforming the SZA-based models while the best testing performance is recorded by the objective method (i.e., CLSTM) tested over a 7-day measurement period. Specifically, CLSTM yields a testing performance with correlation coefficient r = 0.92, root mean square error RMSE = 210.31 μ mol of photons m−2 s−1, mean absolute error MAE = 150.24 μ mol of photons m−2 s−1, including a relative error of RRMSE = 24.92% MAPE = 38.01%, and Nash Sutcliffe’s coefficient ENS = 0.85, and Legate and McCabe’s Index LM = 0.68 using cloud cover in addition to the SZA as an input. The study shows the importance of cloud inclusion in forecasting solar radiation and evaluating the risk with practical implications in monitoring solar energy, greenhouses and high-value agricultural operations affected by stochastic behaviour of clouds. Additional methodological refinements such as retraining the CLSTM model for hourly and seasonal time scales may aid in the promotion of agricultural crop farming and environmental risk evaluation applications such as predicting the solar UV index and direct normal solar irradiance for renewable energy monitoring systems

Security Sector Reform: Education and Training Needs

Center for Stabilization and Reconstruction Studies Workshop

NACA Research Memorandums

Report presenting an investigation of the effects of deflected inboard ailerons on the wing loads of a 45 degree sweptback wing-body combination. The model had a wing of aspect ratio 3, a taper ratio of 0.2, and NACA 65A004 airfoil sections. Results regarding chordwise pressure distributions, the effect of ailerons on wing section loadings, the effect of aileron on wing loading, and the effects of a deflected aileron on opposite-wing loading are presented

NACA Research Memorandums

Memorandum presenting the effects of deflected inboard ailerons on the wing loads of a 45 degree sweptback wing-body combination in the 16-foot transonic tunnel. The model had a wing with an aspect ratio of 3, a taper ratio of 0.2, and NACA 65A004 airfoil sections. Results regarding the chordwise pressure distributions, effect of ailerons on wing section loadings, effect of aileron on wing loading, and effects of a deflected aileron on opposite-wing loading are provided