The required aerodynamic simulation fidelity to usefully support a gas turbine digital twin for manufacturing

With the imminent digitalisation of the manufacturing processes of gas turbine components, a large volume of geometric data of as-manufactured parts is being generated. This geometric data can be used in aerodynamic simulations to predict component performance. Both the cost and accuracy of these simulations increase with their fidelity. To efficiently exploit Digital Twin technology, one must therefore understand how realistic the aerodynamic simulations need to be to give useful performance predictions. This paper considers this issue for a sample of scrapped high-pressure turbine rotor blades from a civil aero engine. The measured geometric data was used to build aerodynamic models of varying degrees of realism, ranging from quasi-three-dimensional blade sections for an Euler solver to three-dimensional, multi-passage and multi-stage Reynolds-Averaged-Navier-Stokes models. The flow near the tip of these shrouded blades is sensitive to manufacturing variability and can switch between two quasi-stable horseshoe vortex modes. In general, capacity and exit flow angle can be adequately predicted by three-dimensional, single-passage calcula-tions: averaging single-passage calculations gives a good prediction of the multi-passage behaviour. For efficiency and stage loading, the approach of averaging single-passage calculations is less accurate as the multi-passage behaviour requires an accurate prediction of the horseshoe vortex modes

Comparison of methods for analyzing environmental mixtures effects on survival outcomes and application to a population-based cohort study

The estimation of the effect of environmental exposures and overall mixtures on a survival time outcome is common in environmental epidemiological studies. While advanced statistical methods are increasingly being used for mixture analyses, their applicability and performance for survival outcomes has yet to be explored. We identified readily available methods for analyzing an environmental mixture's effect on a survival outcome and assessed their performance via simulations replicating various real-life scenarios. Using prespecified criteria, we selected Bayesian Additive Regression Trees (BART), Cox Elastic Net, Cox Proportional Hazards (PH) with and without penalized splines, Gaussian Process Regression (GPR) and Multivariate Adaptive Regression Splines (MARS) to compare the bias and efficiency produced when estimating individual exposure, overall mixture, and interaction effects on a survival outcome. We illustrate the selected methods in a real-world data application. We estimated the effects of arsenic, cadmium, molybdenum, selenium, tungsten, and zinc on incidence of cardiovascular disease in American Indians using data from the Strong Heart Study (SHS). In the simulation study, there was a consistent bias-variance trade off. The more flexible models (BART, GPR and MARS) were found to be most advantageous in the presence of nonproportional hazards, where the Cox models often did not capture the true effects due to their higher bias and lower variance. In the SHS, estimates of the effect of selenium and the overall mixture indicated negative effects, but the magnitudes of the estimated effects varied across methods. In practice, we recommend evaluating if findings are consistent across methods

Effects of Prenatal Social Stress and Maternal Dietary Fatty Acid Ratio on Infant Temperament: Does Race Matter?

Background: Infant temperament predicts a range of developmental and behavioral outcomes throughout childhood. Both maternal fatty acid intake and psychosocial stress exposures during pregnancy may influence infant temperament. Furthermore, maternal race may modify prenatal diet and stress effects. The goals of this study are to examine the joint effects of prenatal diet and stress and the modifying effects of race on infant behavior. Methods: Analyses included N=255 mother-infant dyads, primarily minorities (21% Blacks; 42% Hispanics), enrolled in an urban pregnancy cohort. Maternal prenatal stress was indexed by a negative life events (NLEs) score on the Crisis in Family Systems-Revised survey. Prenatal total daily intakes of polyunsaturated fatty acids (PUFAs) (n3, n6) were estimated from a food frequency questionnaire; n3:n6 ratios were calculated. Mothers completed the Infant Behavior Questionnaire-Revised (IBQ-R), a measure of infant temperament, when the children were 6 months old. Three commonly used dimensions were derived: Orienting & Regulation, Extraversion, and Negative Affectivity. Associations among prenatal stress, maternal n3:n6 ratio, and race/ethnicity on infant temperament, controlling for maternal education and age and child sex, were examined. Results: Among Blacks, prenatal stress effects on infant Orienting & Regulation scores were modified by maternal n3:n6 ratios (p=0.03): As NLEs increased, lower n3:n6 ratios predicted lower infant Orienting & Regulation scores, whereas higher n3:n6 ratios attenuated the effect of prenatal stress. There were no main or interaction effects predicting Extraversion or Negative Affectivity. Conclusions: An optimal PUFA ratio may protect the fetus from stress effects on infant behavior, particularly among Blacks. These findings may have implications for later neurodevelopment and social functioning predicted by early temperamental characteristics

The effects of socioeconomic status and indices of physical environment on reduced birth weight and preterm births in Eastern Massachusetts

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: Air pollution and social characteristics have been shown to affect indicators of health. While use of spatial methods to estimate exposure to air pollution has increased the power to detect effects, questions have been raised about potential for confounding by social factors.Methods: A study of singleton births in Eastern Massachusetts was conducted between 1996 and 2002 to examine the association between indicators of traffic, land use, individual and area-based socioeconomic measures (SEM), and birth outcomes ( birth weight, small for gestational age and preterm births), in a two-level hierarchical model.Results: We found effects of both individual ( education, race, prenatal care index) and area-based ( median household income) SEM with all birth outcomes. The associations for traffic and land use variables were mainly seen with birth weight, with an exception for an effect of cumulative traffic density on small for gestational age. Race/ethnicity of mother was an important predictor of birth outcomes and a strong confounder for both area-based SEM and indices of physical environment. The effects of traffic and land use differed by level of education and median household income.Conclusion: Overall, the findings of the study suggested greater likelihood of reduced birth weight and preterm births among the more socially disadvantaged, and a greater risk of reduced birth weight associated with traffic exposures. Results revealed the importance of controlling simultaneously for SEM and environmental exposures as the way to better understand determinants of health.This work is supported by the Harvard Environmental Protection Agency (EPA) Center, Grants R827353 and R-832416, and National Institute for Environmental Health Science (NIEHS) ES-0002

Ambient Air Pollution and Depressive Symptoms in Older Adults: Results from the MOBILIZE Boston Study

Background: Exposure to ambient air pollution, particularly from traffic, has been associated with adverse cognitive outcomes, but the association with depressive symptoms remains unclear. Objectives: We investigated the association between exposure to ambient air and traffic pollution and the presence of depressive symptoms among 732 Boston-area adults ≥ 65 years of age (78.1 ± 5.5 years, mean ± SD). Methods: We assessed depressive symptoms during home interviews using the Revised Center for Epidemiological Studies Depression Scale (CESD-R). We estimated residential distance to the nearest major roadway as a marker of long-term exposure to traffic pollution and assessed short-term exposure to ambient fine particulate matter (PM2.5), sulfates, black carbon (BC), ultrafine particles, and gaseous pollutants, averaged over the 2 weeks preceding each assessment. We used generalized estimating equations to estimate the odds ratio (OR) of a CESD-R score ≥ 16 associated with exposure, adjusting for potential confounders. In sensitivity analyses, we considered CESD-R score as a continuous outcome and mean annual residential BC as an alternate marker of long-term exposure to traffic pollution. Results: We found no evidence of a positive association between depressive symptoms and long-term exposure to traffic pollution or short-term changes in pollutant levels. For example, we found an OR of CESD-R score ≥ 16 of 0.67 (95% CI: 0.46, 0.98) per interquartile range (3.4 μg/m3) increase in PM2.5 over the 2 weeks preceding assessment. Conclusions: We found no evidence suggesting that ambient air pollution is associated with depressive symptoms among older adults living in a metropolitan area in attainment of current U.S. regulatory standards. Citation: Wang Y, Eliot MN, Koutrakis P, Gryparis A, Schwartz JD, Coull BA, Mittleman MA, Milberg WP, Lipsitz LA, Wellenius GA. 2014. Ambient air pollution and depressive symptoms in older adults: results from the MOBILIZE Boston Study. Environ Health Perspect 122:553–558; http://dx.doi.org/10.1289/ehp.120590

The upgrading of fire safety in historic buildings

There is a seemingly continual erosion of our cultural heritage due to fires in historic buildings. Some of these fires result in partial loss of the asset, some result in total loss – in all cases irreplaceable historic fabric is destroyed. Accurate recording for fires in historic buildings is problematic, but such data as has been collated indicates that the level of loss is high. One of the key factors in achieving robust fire safety in historic buildings is the upgrading of physical fire protection measures. It has been suggested that we should assume a fire event is probable, and together with a context in which outside help might be some time in arriving, such measures are considered crucial in containing the fire and raising the alarm as quickly as possible. This article considers passive and active fire protection measures, using case study material to provide illustrative examples. Where it might be expected that conservation requirements, aiming to avoid negative impact to character and significance, might hinder disruptive physical interventions to improve fire protection, in fact a great deal can be achieved. Such a pragmatic approach is arguably necessary for the safety and preservation of built heritage, when the alternative might otherwise be yet another burnt-out shell

Ready ... Go: Amplitude of the fMRI Signal Encodes Expectation of Cue Arrival Time

What happens when the brain awaits a signal of uncertain arrival time, as when a sprinter waits for the starting pistol? And what happens just after the starting pistol fires? Using functional magnetic resonance imaging (fMRI), we have discovered a novel correlate of temporal expectations in several brain regions, most prominently in the supplementary motor area (SMA). Contrary to expectations, we found little fMRI activity during the waiting period; however, a large signal appears after the “go” signal, the amplitude of which reflects learned expectations about the distribution of possible waiting times. Specifically, the amplitude of the fMRI signal appears to encode a cumulative conditional probability, also known as the cumulative hazard function. The fMRI signal loses its dependence on waiting time in a “countdown” condition in which the arrival time of the go cue is known in advance, suggesting that the signal encodes temporal probabilities rather than simply elapsed time. The dependence of the signal on temporal expectation is present in “no-go” conditions, demonstrating that the effect is not a consequence of motor output. Finally, the encoding is not dependent on modality, operating in the same manner with auditory or visual signals. This finding extends our understanding of the relationship between temporal expectancy and measurable neural signals

Salinity drives meiofaunal community structure dynamics across the Baltic ecosystem

Coastal benthic biodiversity is under increased pressure from climate change, eutrophication, hypoxia, and changes in salinity due to increase in river runoff. The Baltic Sea is a large brackish system characterized by steep environmental gradients that experiences all of the mentioned stressors. As such it provides an ideal model system for studying the impact of on‐going and future climate change on biodiversity and function of benthic ecosystems. Meiofauna (animals < 1 mm) are abundant in sediment and are still largely unexplored even though they are known to regulate organic matter degradation and nutrient cycling. In this study, benthic meiofaunal community structure was analysed along a salinity gradient in the Baltic Sea proper using high‐throughput sequencing. Our results demonstrate that areas with higher salinity have a higher biodiversity, and salinity is probably the main driver influencing meiofauna diversity and community composition. Furthermore, in the more diverse and saline environments a larger amount of nematode genera classified as predators prevailed, and meiofauna‐macrofauna associations were more prominent. These findings show that in the Baltic Sea, a decrease in salinity resulting from accelerated climate change will probably lead to decreased benthic biodiversity, and cause profound changes in benthic communities, with potential consequences for ecosystem stability, functions and services