The feasibility of medical visualization using static 3D lungs as an effective tool for endotracheal intubation was previously shown using Augmented Reality (AR) based techniques in one of the several research efforts at the Optical Diagnostics and Applications Laboratory (ODALAB). This research effort also shed light on the potential usage of coupling such medical visualization with dynamic 3D lungs. The purpose of this dissertation is to develop 3D deformable lung models, which are developed from subject-specific high resolution CT data and can be visualized using the AR based environment.

One approach for heating a target to ''Warm Dense Matter'' conditions (similar, for example, to the interiors of giant planets or certain stages in inertial confinement fusion targets), is to use intense ion beams as the heating source (see refs.[6] and [7] and references therein for motivation and accelerator concepts). By consideration of ion beam phase-space constraints, both at the injector, and at the final focus, and consideration of simple equations of state and relations for ion stopping, approximate conditions at the target foil may be calculated. Thus, target temperature and pressure may be calculated as a function of ion mass, ion energy, pulse duration, velocity tilt, and other accelerator parameters. We connect some of these basic parameters to help search the extensive parameter space including ion mass, ion energy, total charge in beam pulse, beam emittance, target thickness and density.

Abstract: "Traditional defect analyses of software modules have focused on either identifying error prone modules or predicting the number of faults in a module, based on a set of module attributes such as complexity, lines of code, etc. In contrast to these metrics-based modeling studies, this paper explores the relationship of the number of faults per module to the prior history of the module. Specifically, we examine the relationship between (a) the faults discovered during development of a product release and those escaped to the field, and (b) faults in the current release and faults in previous releases. Based on the actual data from four releases of a commercial application product consisting of several thousand modules, we show that: modules with more defects in development have a higher probability of failure in the field. there is a way to assess the relative quality of software releases without detailed information on the exact release content or code size. it is sufficient to consider just the previous release for predicting the number of defects during development or field. the distribution of the defects by modules is consistent with a negative binomial distribution. These results can be used to improve the prediction of quality at the module level of future releases based on the past history."