The NASA-IGES geometry data exchange standard

Described here are the data exchange efforts and plans supported by the NASA Steering Committee for Surface Modeling and Grid Generation. Current methods for geometry data exchange between computer aided design (CAD) systems and NASA computational fluid dynamics (CFD) analysis systems are tedious and induce errors. A Geometry Data Exchange Standard is proposed, utilizing a subset of an existing national standard titled Initial Graphic Exchange Standard (IGES). Future plans for data exchange standardization include all aspects of CFD data. Software systems to utilize this NASA-IGES Geometry Data Exchange Specification are under development

NASA geometry data exchange specification for computational fluid dynamics (NASA IGES)

This document specifies a subset of an existing product data exchange specification that is widely used in industry and government. The existing document is called the Initial Graphics Exchange Specification. This document, a subset of IGES, is intended for engineers analyzing product performance using tools such as computational fluid dynamics (CFD) software. This document specifies how to define mathematically and exchange the geometric model of an object. The geometry is represented utilizing nonuniform rational B-splines (NURBS) curves and surfaces. Only surface models are represented; no solid model representation is included. This specification does not include most of the other types of product information available in IGES (e.g., no material properties or surface finish properties) and does not provide all the specific file format details of IGES. The data exchange protocol specified in this document is fully conforming to the American National Standard (ANSI) IGES 5.2

Validity of self-reported measures of workplace sitting time and breaks in sitting time

CLARK, B. K., A. A. THORP, E. A. H. WINKLER, P. A. GARDINER, G. N. HEALY, N. OWEN, and D. W. DUNSTAN. Validity of Self-Reported Measures of Workplace Sitting Time and Breaks in Sitting Time. Med. Sci. Sports Exerc., Vol. 43, No. 10, pp. 1907-1912, 2011. Purpose: To understand the prevalence and potential health effect of prolonged workplace sedentary (sitting) time, valid measures are required. Here, we examined the criterion validity of a brief self-reported measure of workplace sitting time and breaks in sitting time. Methods: An interviewer-administered questionnaire was used to assess workplace sitting time (h.d(-1)) and breaks from sitting per hour at work in a convenience sample of 121 full-time workers (36% men, mean age = 37 yr, 53% office based). These self-reported measures were compared with accelerometer-derived sedentary time (hours per day, = 100 counts per minute) during work hours. Results: Self-reported sitting time was significantly correlated with accelerometer-derived sedentary time (Pearson r = 0.39, 95% confidence interval = 0.22-0.53), with an average sitting time 0.45 h.d(-1) higher than average sedentary time. Bland-Altman plots and regression analysis showed positive associations between the difference in sitting and sedentary time and the average of sitting and sedentary time (mean difference = -2.75 h + 0.47 x average sitting and sedentary time; limits of agreement = +/- 2.25 h.d(-1)). The correlation of self-reported breaks per sitting hour with accelerometer-derived breaks per sedentary hour was also statistically significant (Spearman r(s) = 0.26, 95% confidence interval = 0.11-0.44). Conclusions: This study is the first to examine the criterion validity of an interviewer-administered questionnaire measure of workplace sitting time and breaks in sitting time using objective criterion measures. The workplace sitting measure has acceptable properties for use in observational studies concerned with sedentary behavior in groups of workers; however, the wide limits of agreement suggest caution in estimating individuals' sitting time with high precision. Using self-reported measures to capture patterns of workplace sitting (such as breaks in sitting time) requires further development

Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics

Field-based plant phenomics requires robust crop sensing platforms and data analysis tools to successfully identify cultivars that exhibit phenotypes with high agronomic and economic importance. Such efforts will lead to genetic improvements that maintain high crop yield with concomitant tolerance to environmental stresses. The objectives of this study were to investigate proximal hyperspectral sensing with a field spectroradiometer and to compare data analysis approaches for estimating four cotton phenotypes: leaf water content (Cw), specific leaf mass (Cm), leaf chlorophyll a+b content (Cab), and leaf area index (LAI). Field studies tested 25 Pima cotton cultivars grown under well-watered and water-limited conditions in central Arizona from 2010 to 2012. Several vegetation indices, including the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the physiological (or photochemical) reflectance index (PRI) were compared with partial least squares regression (PLSR) approaches to estimate the four phenotypes. Additionally, inversion of the PROSAIL plant canopy reflectance model was investigated to estimate phenotypes based on 3.68 billion PROSAIL simulations on a supercomputer. Phenotypic estimates from each approach were compared with field measurements, and hierarchical linear mixed modeling was used to identify differences in the estimates among the cultivars and water levels. The PLSR approach performed best and estimated Cw,Cm,Cab, and LAI with root mean squared errors (RMSEs) between measured and modeled values of 6.8%, 10.9%, 13.1%, and 18.5%, respectively. Using linear regression with the vegetation indices, no index estimated Cw,Cm,Cab, and LAI with RMSEs better than 9.6%, 16.9%, 14.2%, and 28.8%, respectively. PROSAIL model inversion could estimate Cab and LAI with RMSEs of about 16% and 29%, depending on the objective function. However, the RMSEs for Cw and Cm from PROSAIL model inversion were greater than 30%. Compared to PLSR, advantages to the physically-based PROSAIL model include its ability to simulate the canopy's bidirectional reflectance distribution function (BRDF) and to estimate phenotypes from canopy spectral reflectance without a training data set. All proximal hyperspectral approaches were able to identify differences in phenotypic estimates among the cultivars and irrigation regimes tested during the field studies. Improvements to these proximal hyperspectral sensing approaches could be realized with a high-throughput phenotyping platform able to rapidly collect canopy spectral reflectance data from multiple view angles

CRITICAL FIELD FOR SUPERCONDUCTIVITY AND LOW-TEMPERATURE NORMAL-STATE HEAT CAPACITY OF TUNGSTEN

We have measured the critical magnetic field for superconductivity In tungsten from 5.5 to 15 mK using a {gamma}-ray anisotropy thermometer, and we have measured the heat capacity between 0.35 and 25 K. Analysis of the data gives H{sub 0} = 1.237 Oe for the 0 K critical field, T{sub c} = 16.0 mK for the critical temperature, {gamma} = 1.008 mJ/mole K{sup 2} for the coefficient of the electronic heat capacity, and {Theta}{sub 0} = 383 K for the 0 K Debye temperature. The measured values of the critical field H{sub c} are consistently higher than those reported by Black, Johnson and Wheatley (BJW) on the CMN temperature scale, but the temperature dependence is similar. This discrepancy and the temperature dependence of H{sub c} suggest that both sets of H{sub c} data are affected by magnetic impurities. Use of the calorimetric {gamma} value permits an improved test of the CMN temperature scale with the very low temperature H{sub c} data obtained by BJW

Associations of sitting accumulation patterns with cardio-metabolic risk biomarkers in Australian adults

Backgroun

Development and application of process-based simulation models for cotton production: a review of past, present, and future directions

The development and application of cropping system simulation models for cotton production has a long and rich history, beginning in the southeastern United States in the 1960's and now expanded to major cotton production regions globally. This paper briefly reviews the history of cotton simulation models, examines applications of the models since the turn of the century, and identifies opportunities for improving models and their use in cotton research and decision support. Cotton models reviewed include those specific to cotton (GOSSYM, Cotton2K, COTCO2, OZCOT, and CROPGRO-Cotton) and generic crop models that have been applied to cotton production (EPIC, WOFOST, SUCROS, GRAMI, CropSyst, and AquaCrop). Model application areas included crop water use and irrigation water management, nitrogen dynamics and fertilizer management, genetics and crop improvement, climatology, global climate change, precision agriculture, model integration with sensor data, economics, and classroom instruction. Generally, the literature demonstrated increased emphasis on cotton model development in the previous century and on cotton model application in the current century. Although efforts to develop cotton models have a 40-year history, no comparisons among cotton models were reported. Such efforts would be advisable as an initial step to evaluate current cotton simulation strategies. Increasingly, cotton simulation models are being applied by non-traditional crop modelers, who are not trained agronomists but wish to use the models for broad economic or life cycle analyses. While this trend demonstrates the growing interest in the models and their potential utility for a variety of applications, it necessitates the development of models with appropriate complexity and ease-of-use for a given application, and improved documentation and teaching materials are needed to educate potential model users. Spatial scaling issues are also increasingly prominent, as models originally developed for use at the field scale are being implemented for regional simulations over large geographic areas. Research steadily progresses toward the advanced goal of model integration with variable-rate control systems, which use real-time crop status and environmental information to spatially and temporally optimize applications of crop inputs, while also considering potential environmental impacts, resource limitations, and climate forecasts. Overall, the review demonstrates a languished effort in cotton simulation model development, but the application of existing models in a variety of research areas remains strong and continues to grow

Deleterious Associations of Sitting Time and Television Viewing Time With Cardiometabolic Risk Biomarkers: Australian Diabetes, Obesity and Lifestyle (AusDiab) study 2004–2005

OBJECTIVE - We examined the associations Of Sitting time and television (TV) Viewing time with continuously measured biomarkers of cardio-metabolic risk in Australian adults