Hypersonic laminar boundary layers around slender bodies

Compressible laminar boundary layer equations considered for hypersonic flow around slender bodie

Knowledge-based vision for space station object motion detection, recognition, and tracking

Computer vision, especially color image analysis and understanding, has much to offer in the area of the automation of Space Station tasks such as construction, satellite servicing, rendezvous and proximity operations, inspection, experiment monitoring, data management and training. Knowledge-based techniques improve the performance of vision algorithms for unstructured environments because of their ability to deal with imprecise a priori information or inaccurately estimated feature data and still produce useful results. Conventional techniques using statistical and purely model-based approaches lack flexibility in dealing with the variabilities anticipated in the unstructured viewing environment of space. Algorithms developed under NASA sponsorship for Space Station applications to demonstrate the value of a hypothesized architecture for a Video Image Processor (VIP) are presented. Approaches to the enhancement of the performance of these algorithms with knowledge-based techniques and the potential for deployment of highly-parallel multi-processor systems for these algorithms are discussed

Integration of tools for the Design and Assessment of High-Performance, Highly Reliable Computing Systems (DAHPHRS), phase 1

Systems for Space Defense Initiative (SDI) space applications typically require both high performance and very high reliability. These requirements present the systems engineer evaluating such systems with the extremely difficult problem of conducting performance and reliability trade-offs over large design spaces. A controlled development process supported by appropriate automated tools must be used to assure that the system will meet design objectives. This report describes an investigation of methods, tools, and techniques necessary to support performance and reliability modeling for SDI systems development. Models of the JPL Hypercubes, the Encore Multimax, and the C.S. Draper Lab Fault-Tolerant Parallel Processor (FTPP) parallel-computing architectures using candidate SDI weapons-to-target assignment algorithms as workloads were built and analyzed as a means of identifying the necessary system models, how the models interact, and what experiments and analyses should be performed. As a result of this effort, weaknesses in the existing methods and tools were revealed and capabilities that will be required for both individual tools and an integrated toolset were identified

The architecture of a video image processor for the space station

The architecture of a video image processor for space station applications is described. The architecture was derived from a study of the requirements of algorithms that are necessary to produce the desired functionality of many of these applications. Architectural options were selected based on a simulation of the execution of these algorithms on various architectural organizations. A great deal of emphasis was placed on the ability of the system to evolve and grow over the lifetime of the space station. The result is a hierarchical parallel architecture that is characterized by high level language programmability, modularity, extensibility and can meet the required performance goals

Obstacles and Challenges to Implementing Multi-departmental QI at a Large, Academic Training Center-Lessons Learned from a HCV Screening Program

Objectives: We aimed to double the HCV screening rate of ‘baby-boomers’ admitted to the medicine teaching service at Methodist Hospital over the course of 6 months and demonstrate improved linkage to care for HCV RNA+ individuals. Initial efforts were a collaboration between Emergency Medicine, where faculty had experience implementing an HIV screening program, and Gastroenterology, a key stakeholder in linkage to care. Our pilot period coincided with new state regulations mandating that hospitals implement HCV screening for inpatients. These new regulations dramatically altered the scope and goals of the project.https://jdc.jefferson.edu/patientsafetyposters/1030/thumbnail.jp

Soil permittivity estimation over croplands using full and compact polarimetric SAR data

Soil permittivity estimation using Polarimetric Synthetic Aperture Radar (PolSAR) data has been an extensively researched area. Nonetheless, it provides ample scope for further improvements. The vegetation cover over the soil surface leads to a complex interaction of the incident polarized wave with the canopy and subsequently with the underlying soil surface. This paper introduces a novel methodology to estimate soil permittivity over croplands with vegetation cover using the full and compact polarimetric modes. The proposed method utilizes the full and compact polarimetric scattering-type parameters, θ FP and θ CP , respectively. These scattering type parameters are a function of the soil permittivity and the Barakat degree of polarization. The method considers the X-Bragg scattering model for the soil surface. In particular, these scattering-type parameters explicitly account for the depolarizing structure of the scattered wave while characterizing targets. Thus, the depolarization information in terms of surface roughness in the X-Bragg model gets inherent importance while using θ FP and θ CP , unlike existing scattering-type parameters. Therefore, the proposed technique enhances the expected value of the inversion accuracies. This study validated the major phenology stages of four crops using the UAVSAR full-pol and simulated compact pol SAR data and the ground truth data collected during the SMAPVEX12 campaign over Manitoba, Canada. The proposed method estimated permittivity with an RMSE of 2.2 to 4.69 for FP and 3.28 to 5.45 for CP SAR data along with a Pearson coefficient, r ≥ 0.62.Peer ReviewedPostprint (author's final draft

A national survey assessing public readiness for digital health strategies against COVID-19 within the United Kingdom

There is concern that digital public health initiatives used in the management of COVID-19 may marginalise certain population groups. There is an overlap between the demographics of groups at risk of digital exclusion (older, lower social grade, low educational attainment and ethnic minorities) and those who are vulnerable to poorer health outcomes from SARS-CoV-2. In this national survey study (n=2040), we assessed how the UK population; particularly these overlapping groups, reported their preparedness for digital health strategies. We report, with respect to using digital information to make health decisions, that those over 60 are less comfortable (net comfort: 57%) than those between 18-39 (net comfort: 78%) and lower social grades are less comfortable (net comfort: 63%) than higher social grades (net comfort: 75%). With respect to a preference for digital over non-digital sources in seeking COVID-19 health information, those over 60 (net preference: 21%) are less inclined than those between 18-39 (net preference: 60%) and those of low educational attainment (net preference: 30%) are less inclined than those of high educational attainment (net preference: 52%). Lastly, with respect to distinguishing reliable digital COVID-19 information, lower social grades (net confidence: 55%) are less confident than higher social grades (net confidence: 68%) and those of low educational attainment (net confidence: 51%) are less confident than those of high educational attainment (net confidence: 71%). All reported differences are statistically significant (p<0.01) following multivariate regression modelling. This study suggests that digital public health approaches to COVID-19 have the potential to marginalise groups who are concurrently at risk of digital exclusion and poor health outcomes from SARS-CoV-2

Quantifying gene network connectivity in silico: Scalability and accuracy of a modular approach

Large, complex data sets that are generated from microarray experiments, create a need for systematic analysis techniques to unravel the underlying connectivity of gene regulatory networks. A modular approach, previously proposed by Kholodenko and co-workers, helps to scale down the network complexity into more computationally manageable entities called modules. A functional module includes a gene\u27s mRNA, promoter and resulting products, thus encompassing a large set of interacting states. The essential elements of this approach are described in detail for a three-gene model network and later extended to a ten-gene model network, demonstrating scalability. The network architecture is identified by analysing in silico steady-state changes in the activities of only the module outputs, communicating intermediates, that result from specific perturbations applied to the network modules one at a time. These steady-state changes form the system response matrix, which is used to compute the network connectivity or network interaction map. By employing a known biochemical network, the accuracy of the modular approach and its sensitivity to key assumptions are evaluated

Crop biophysical parameter retrieval from Sentinel-1 SAR data with a multi-target inversion of Water Cloud Model

Estimation of bio-and geophysical parameters from Earth observation (EO) data is essential for developing applications on crop growth monitoring. High spatio-temporal resolution and wide spatial coverage provided by EO satellite data are key inputs for operational crop monitoring. In Synthetic Aperture Radar (SAR) applications, a semi-empirical model (viz., Water Cloud Model (WCM)) is often used to estimate vegetation descriptors individually. However, a simultaneous estimation of these vegetation descriptors would be logical given their inherent correlation, which is seldom preserved in the estimation of individual descriptors by separate inversion models. This functional relationship between biophysical parameters is essential for crop yield models, given that their variations often follow different distribution throughout crop development stages. However, estimating individual parameters with independent inversion models presume a simple relationship (potentially linear) between the biophysical parameters. Alternatively, a multi-target inversion approach would be more effective for this aspect of model inversion compared to an individual estimation approach. In the present research, the multi-output support vector regression (MSVR) technique is used for inversion of the WCM from C-band dual-pol Sentinel-1 SAR data. Plant Area Index (PAI, m2 m−2) and wet biomass (W, kg m−2) are used as the vegetation descriptors in the WCM. The performance of the inversion approach is evaluated with in-situ measurements collected over the test site in Manitoba (Canada), which is a super-site in the Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR inter-comparison experiment network. The validation results indicate a good correlation with acceptable error estimates (normalized root mean square error–nRMSE and mean absolute error–MAE) for both PAI and wet biomass for the MSVR approach and a better estimation with MSVR than single-target models (support vector regression–SVR). Furthermore, the correlation between PAI and wet biomass is assessed using the MSVR and SVR model. Contrary to the single output SVR, the correlation between biophysical parameters is adequately taken into account in MSVR based simultaneous inversion technique. Finally, the spatio-temporal maps for PAI and W at different growth stages indicate their variability with crop development over the test site.This research was supported in part by Shastri Indo-Candian Institute, New Delhi, India and the Spanish Ministry of Economy, Industry and Competitiveness, in part by the State Agency of Research (AEI), in part by the European Funds for Regional Development under project TEC2017-85244-C2-1-P