A summary of NASTRAN fluid/structure interaction capabilities

A summary of fluid/structure interaction capabilities for the NASTRAN computer program is presented. Indirect applications of the program towards solving this class of problem were concentrated on. For completeness and comparitive purposes, direct usage of NASTRAN is briefly discussed. The solution technology addresses both steady state and transient dynamic response problems

A multi-spacecraft reanalysis of the atmosphere of Mars

We have conducted a nine-Mars Year (MY) consistent reanalysis of the martian atmosphere covering the period MY 24–32 and making use of data from three different spacecraft. Remotely-sensed measurements of temperature, dust opacity, water ice and ozone from NASA’s Mars Global Surveyor (MGS) and Mars Recconaisance Orbiter (MRO) and ESA’s Mars Express (MEx) were assimilated [1] into a single model simulation, sampled two-hourly over the whole period. This forms a large, regular reanalysis dataset that is being made publicly available as an output of the EU UPWARDS project. The same analysis technique, with an improved model and higher resolution will be conducted with ESA Trace Gas Orbiter (TGO) data as it becomes available

Trace gas assimilation of Mars orbiter observations

Ozone, water vapour and argon are minor constituents in the Martian atmosphere, observations of which can be of use in constraining atmospheric dynamical and physical processes. This is especially true in the winter season of each hemisphere, when the bulk of the main constituent in the atmosphere (CO2 ) condenses in the polar regions shifting the balance of atmospheric composition to a more trace gas rich air mass. Current Mars Global Circulation Models (MGCMs) are able to represent the photochemistry occuring in the atmosphere, with constraints being imposed by comparisons with observations. However, a long term comparison using data assimilation provides a more robust constraint on the model. We aim to provide a technique for trace gas data assimilation for the analysis of observations from current and future satellite missions (such as ExoMars) which observe the spatial and temporal distribution of trace gases on Mars

Measuring access: how accurate are patient-reported waiting times?

Introduction: A national audit of waiting times in England’s genitourinary medicine clinics measures patient access. Data are collected by patient questionnaires, which rely upon patients’ recollection of first contact with health services, often several days previously. The aim of this study was to assess the accuracy of patient-reported waiting times. Methods: Data on true waiting times were collected at the time of patient booking over a three-week period and compared with patient-reported data collected upon clinic attendance. Factors contributing to patient inaccuracy were explored. Results: Of 341 patients providing initial data, 255 attended; 207 as appointments and 48 ‘walk-in’. The accuracy of patient-reported waiting times overall was 52% (133/255). 85% of patients (216/255) correctly identified themselves as seen within or outside of 48 hours. 17% of patients (17/103) seen within 48 hours reported a longer waiting period, whereas 20% of patients (22/108) reporting waits under 48 hours were seen outside that period. Men were more likely to overestimate their waiting time (10.4% versus 3.1% p<0.02). The sensitivity of patient-completed questionnaires as a tool for assessing waiting times of less than 48 hours was 83.5%. The specificity and positive predictive value were 85.5% and 79.6%, respectively. Conclusion: The overall accuracy of patient reported waiting times was poor. Although nearly one in six patients misclassified themselves as being seen within or outside of 48 hours, given the under and overreporting rates observed, the overall impact on Health Protection Agency waiting time data is likely to be limited

Investigating microstructural variation in the human hippocampus using non-negative matrix factorization

In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses

First ozone reanalysis on Mars using SPICAM data

To further our understanding of important photochemical processes in the Martian atmosphere, a synthesis can be used to investigate the temporal and spatial agreement between model and observations and determine any possible causes of identified differences. In this study [1], we have assimilated, for the first time, total ozone into a Mars Global Circulation model (GCM) to study the ozone cycle