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

Ecology of Urban Bees: A Review of Current Knowledge and Directions for Future Study

Urban bee ecology is an emerging field that holds promise for advancing knowledge of bee community dynamics and promoting bee conservation. Published studies of bee communities in urban and suburban habitats are fewer than those documenting bees in agricultural and wildland settings. As land lost to urbanization is predicted to increase in coming years the necessity of studying urban bee populations is growing. We reviewed 59 publications on urban bee ecology with the following goals, to assess current knowledge, to highlight areas in need of further research, and to suggest applications of study findings to bee conservation. Methodological variation between studies was discussed in the context of data interpretation. Identified trends in urban areas included the following, negative correlation between bee species richness and urban development, cavity-nesters increase in abundance in urban habitats, and floral specialists are scarce. Future directions for studying urban bee ecology include incorporation of landscape-scale assessments, conducting manipulative experiments and actively designing urban bee habitats. We include descriptions of plant and habitat management techniques derived from our research in northern and southern California urban habitats to promote development of bee-friendly habitats

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