Linear covariance analysis of atmospheric entry for sample return mission

Linear covariance analysis is an uncertainty analysis tool comparable to Monte Carlo analysis; both provide similar statistical information about the performance and uncertainties of a dynamic system. Linear covariance analysis linearizes the models of a dynamic system and propagates the state uncertainties alongside a reference trajectory. These uncertainties are similar to those computed from post processing a Monte Carlo analysis and require potentially significantly fewer computational resources. A comparison of the two analyses is performed on an example sample return atmospheric entry mission. The example mission is an unguided entry vehicle similar to the Stardust Sample Return Mission. Flight dynamics for entry are modeled with the three-degree-of-freedom translational equations of motion. Uncertainty in vehicle, environmental, and mission design parameters are included to determine expected flight performance. In the analysis, linear covariance results match Monte Carlo within 4% in determining the state uncertainties over the trajectory,while requiring only 0.48% the computational effort relative to Monte Carlo analysis. Further analysis using linear covariance shows that uncertainty in position is the largest contributor to state dispersions. The final state dispersion is highly sensitive to uncertainty in initial position. Comparatively uncertainty in initial velocity contributes much less to the final state dispersion and is insensitive. Varying the initial position dispersion by ±50% results in the largest changes in the 3−σ uncertainty in the altitude at parachute deploy which ranges from 995 m to 2076 m compared to the nominal 1495 m uncertainty

A study of novel exploratory tools, digital technologies, and central nervous system biomarkers to characterize unipolar depression

Background: Digital technologies have the potential to provide objective and precise tools to detect depression-related symptoms. Deployment of digital technologies in clinical research can enable collection of large volumes of clinically relevant data that may not be captured using conventional psychometric questionnaires and patient-reported outcomes. Rigorous methodology studies to develop novel digital endpoints in depression are warranted. Objective: We conducted an exploratory, cross-sectional study to evaluate several digital technologies in subjects with major depressive disorder (MDD) and persistent depressive disorder (PDD), and healthy controls. The study aimed at assessing utility and accuracy of the digital technologies as potential diagnostic tools for unipolar depression, as well as correlating digital biomarkers to clinically validated psychometric questionnaires in depression. Methods: A cross-sectional, non-interventional study of 20 participants with unipolar depression (MDD and PDD/dysthymia) and 20 healthy controls was conducted at the Centre for Human Drug Research (CHDR), the Netherlands. Eligible participants attended three in-clinic visits (days 1, 7, and 14), at which they underwent a series of assessments, including conventional clinical psychometric questionnaires and digital technologies. Between the visits, there was at-home collection of data through mobile applications. In all, seven digital technologies were evaluated in this study. Three technologies were administered via mobile applications: an interactive tool for the self-assessment of mood, and a cognitive test; a passive behavioral monitor to assess social interactions and global mobility; and a platform to perform voice recordings and obtain vocal biomarkers. Four technologies were evaluated in the clinic: a neuropsychological test battery; an eye motor tracking system; a standard high-density electroencephalogram (EEG)-based technology to analyze the brain network activity during cognitive testing; and a task quantifying bias in emotion perception. Results: Our data analysis was organized by technology – to better understand individual features of various technologies. In many cases, we obtained simple, parsimonious models that have reasonably high diagnostic accuracy and potential to predict standard clinical outcome in depression. Conclusion: This study generated many useful insights for future methodology studies of digital technologies and proof-of-concept clinical trials in depression and possibly other indications