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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Determining the response of sea level to atmospheric pressure forcing using TOPEX/POSEIDON data

The static response of sea level to the forcing of atmospheric pressure, the so-called inverted barometer (IB) effect, is investigated using TOPEX/POSEIDON data. This response, characterized by the rise and fall of sea level to compensate for the change of atmospheric pressure at a rate of -1 cm/mbar, is not associated with any ocean currents and hence is normally treated as an error to be removed from sea level observation. Linear regression and spectral transfer function analyses are applied to sea level and pressure to examine the validity of the IB effect. In regions outside the tropics, the regression coefficient is found to be consistently close to the theoretical value except for the regions of western boundary currents, where the mesoscale variability interferes with the IB effect. The spectral transfer function shows near IB response at periods of 30 degrees is -0.84 +/- 0.29 cm/mbar (1 standard deviation). The deviation from = 1 cm /mbar is shown to be caused primarily by the effect of wind forcing on sea level, based on multivariate linear regression model involving both pressure and wind forcing. The regression coefficient for pressure resulting from the multivariate analysis is -0.96 +/- 0.32 cm/mbar. In the tropics the multivariate analysis fails because sea level in the tropics is primarily responding to remote wind forcing. However, after removing from the data the wind-forced sea level estimated by a dynamic model of the tropical Pacific, the pressure regression coefficient improves from -1.22 +/- 0.69 cm/mbar to -0.99 +/- 0.46 cm/mbar, clearly revealing an IB response. The result of the study suggests that with a proper removal of the effect of wind forcing the IB effect is valid in most of the open ocean at periods longer than 20 days and spatial scales larger than 500 km.

Fu, Lee-Lueng↗
NASA NTRS1984

A comparative look at sunspot cycles

On the basis of cycles 8 through 20, spanning about 143 years, observations of sunspot number, smoothed sunspot number, and their temporal properties were used to compute means, standard deviations, ranges, and frequency of occurrence histograms for a number of sunspot cycle parameters. The resultant schematic sunspot cycle was contrasted with the mean sunspot cycle, obtained by averaging smoothed sunspot number as a function of time, tying all cycles (8 through 20) to their minimum occurence date. A relatively good approximation of the time variation of smoothed sunspot number for a given cycle is possible if sunspot cycles are regarded in terms of being either HIGH- or LOW-R(MAX) cycles or LONG- or SHORT-PERIOD cycles, especially the latter. Linear regression analyses were performed comparing late cycle parameters with early cycle parameters and solar cycle number. The early occurring cycle parameters can be used to estimate later occurring cycle parameters with relatively good success, based on cycle 21 as an example. The sunspot cycle record clearly shows that the trend for both R(MIN) and R(MAX) was toward decreasing value between cycles 8 through 14 and toward increasing value between cycles 14 through 20. Linear regression equations were also obtained for several measures of solar activity.

Wilson, R. M.↗
NASA NTRS2023

Satellite Remote-Sensing Capability to Assess Tropospheric-Column Ratios of Formaldehyde and Nitrogen Dioxide: Case Study During the Long Island Sound Tropospheric Ozone Study 2018 (LISTOS 2018) Field Campaign

Satellite retrievals of tropospheric-column formaldehyde (HCHO) and nitrogen dioxide (NO 2 ) are frequently used to investigate the sensitivity of ozone (O 3 ) production to emissions of nitrogen oxides and volatile organic carbon compounds. This study inter-compared the systematic biases and uncertainties in retrievals of NO 2 and HCHO, as well as resulting HCHO–NO 2 ratios (FNRs), from two commonly applied satellite sensors to investigate O 3 production sensitivities (Ozone Monitoring Instrument, OMI, and TROPOspheric Monitoring Instrument, TROPOMI) using airborne remote-sensing data taken during the Long Island Sound Tropospheric Ozone Study 2018 between 25 June and 6 September 2018. Compared to aircraft-based HCHO and NO 2 observations, the accuracy of OMI and TROPOMI were magnitude-dependent with high biases in clean environments and a tendency towards more accurate comparisons to even low biases in moderately polluted to polluted regions. OMI and TROPOMI NO 2 systematic biases were similar in magnitude (normalized median bias, NMB = 5 %–6 %; linear regression slope ≈ 0.5–0.6), with OMI having a high median bias and TROPOMI resulting in small low biases. Campaign-averaged uncertainties in the three satellite retrievals (NASA OMI; Quality Assurance for Essential Climate Variables, QA4ECV OMI; and TROPOMI) of NO 2 were generally similar, with TROPOMI retrievals having slightly less spread in the data compared to OMI. The three satellite products differed more when evaluating HCHO retrievals. Campaign-averaged tropospheric HCHO retrievals all had linear regression slopes ∼0.5 and NMBs of 39 %, 17 %, 13 %, and 23 % for NASA OMI, QA4ECV OMI, and TROPOMI at finer (0.05° x 0.05°) and coarser (0.15° x 0.15°) spatial resolution, respectively. Campaign-averaged uncertainty values (root mean square error, RMSE) in NASA and QA4ECV OMI HCHO retrievals were ~9.0 x 10 15 molecules cm –2 (∼ 50 %–55 % of mean column abundance), and the higher-spatial-resolution retrievals from TROPOMI resulted in RMSE values ∼30 % lower. Spatially averaging TROPOMI tropospheric-column HCHO, along with NO 2 and FNRs, to resolutions similar to the OMI reduced the uncertainty in these retrievals. Systematic biases in OMI and TROPOMI NO 2 and HCHO retrievals tended to cancel out, resulting in all three satellite products comparing well to observed FNRs. However, while satellite-derived FNRs had minimal campaign-averaged median biases, unresolved errors in the indicator species did not cancel out in FNR calculations, resulting in large RMSE values compared to observations. Uncertainties in HCHO retrievals were determined to drive the unresolved biases in FNR retrievals.

Matthew S. Johnson↗
DOE OSTI2026

The Q 10 of in situ microbial soil respiration varies with mean annual temperature, precipitation, pH, and plant cover: a meta-analysis and spatial prediction of Q 10

The temperature sensitivity of soil microbial respiration, commonly quantified using the Q 10 coefficient, is a key parameter in carbon cycle models. Uncovering how environmental factors affect in situ Q 10 values can therefore provide critical insight into potential shifts in global carbon stocks under climate change. We collected data from previously published field experiments that measured soil microbial respiration across a range of temperatures. We hypothesized that the Q 10 coefficient of in situ soil microbial respiration would vary based on environmental factors including mean annual temperature (MAT), mean annual precipitation (MAP), plant cover type, pH, soil C:N, and latitude. Linear regression revealed that Q 10 correlates negatively with MAT and MAP and positively with pH and absolute latitude. Additionally, average Q 10 varied significantly across different plant cover types; it was highest in mountain grasslands and lowest in tropical moist forests. Variation in microbial Q 10 across environmental factors may arise from underlying mechanisms such as enzyme kinetics, substrate availability and complexity, and microbial adaptation. To capture patterns in Q 10 more comprehensively, we developed a multiple linear regression model of Q 10 based on the most individually significant environmental drivers and applied it to public datasets to generate a global map of predicted Q 10 . Q 10 was higher in high-latitude and high-altitude regions, where large permafrost carbon stores are vulnerable to thawing and decomposition. We also compared fits between the Q 10 equation and a model produced from macromolecular rate theory (MMRT). We found that the MMRT model had the superior fit and may be better suited to model temperature sensitivity of complex biological reactions. Overall, our results emphasize that relationships between microbial Q 10 and environmental variables should be accounted for in climate models. Incorporating these variations in the Q 10 parameter, rather than using a fixed value, will help predict whether CO 2 emissions will be buffered or exacerbated by soil microbial respiration under climate change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Sources of variability in satellite-derived estimates of phytoplankton production in the eastern tropical Pacific

As a proxy for satellite CZCS observations and concurrent measurements of primary production rates, data from 138 stations occupied seasonally during 1967-1968 in the offshore eastern tropical Pacific were analyzed in terms of six temporal groups and our current regimes. Multiple linear regressions on column production Pt show that simulated satellite pigment is generally weakly correlated, but sometimes not correlated with Pt, and that incident irradiance, sea surface temperature, nitrate, transparency, and depths of mixed layer or nitracline assume little or no importance. After a proxy for the light-saturated chlorophyll-specific photosynthetic rate P(max) is added, the coefficient of determination ranges from 0.55 to 0.91 (median of 0.85) for the 10 cases. In stepwise multiple linear regressions the P(max) proxy is the best predictor for Pt.

Banse, Karl↗
NASA NTRS2000

A Method for Assessing the Quality of Model-Based Estimates of Ground Temperature and Atmospheric Moisture Using Satellite Data

A method is developed for validating model-based estimates of atmospheric moisture and ground temperature using satellite data. The approach relates errors in estimates of clear-sky longwave fluxes at the top of the Earth-atmosphere system to errors in geophysical parameters. The fluxes include clear-sky outgoing longwave radiation (CLR) and radiative flux in the window region between 8 and 12 microns (RadWn). The approach capitalizes on the availability of satellite estimates of CLR and RadWn and other auxiliary satellite data, and multiple global four-dimensional data assimilation (4-DDA) products. The basic methodology employs off-line forward radiative transfer calculations to generate synthetic clear-sky longwave fluxes from two different 4-DDA data sets. Simple linear regression is used to relate the clear-sky longwave flux discrepancies to discrepancies in ground temperature ((delta)T(sub g)) and broad-layer integrated atmospheric precipitable water ((delta)pw). The slopes of the regression lines define sensitivity parameters which can be exploited to help interpret mismatches between satellite observations and model-based estimates of clear-sky longwave fluxes. For illustration we analyze the discrepancies in the clear-sky longwave fluxes between an early implementation of the Goddard Earth Observing System Data Assimilation System (GEOS2) and a recent operational version of the European Centre for Medium-Range Weather Forecasts data assimilation system. The analysis of the synthetic clear-sky flux data shows that simple linear regression employing (delta)T(sub g)) and broad layer (delta)pw provides a good approximation to the full radiative transfer calculations, typically explaining more thin 90% of the 6 hourly variance in the flux differences. These simple regression relations can be inverted to "retrieve" the errors in the geophysical parameters, Uncertainties (normalized by standard deviation) in the monthly mean retrieved parameters range from 7% for (delta)T(sub g) to approx. 20% for the lower tropospheric moisture between 500 hPa and surface. The regression relationships developed from the synthetic flux data, together with CLR and RadWn observed with the Clouds and Earth Radiant Energy System instrument, ire used to assess the quality of the GEOS2 T(sub g) and pw. Results showed that the GEOS2 T(sub g) is too cold over land, and pw in upper layers is too high over the tropical oceans and too low in the lower atmosphere.

Wu, Man Li C.↗
NASA NTRS2000

Estimates of Ground Temperature and Atmospheric Moisture from CERES Observations

A method is developed to retrieve surface ground temperature (T(sub g)) and atmospheric moisture using clear sky fluxes (CSF) from CERES-TRMM observations. In general, the clear sky outgoing longwave radiation (CLR) is sensitive to upper level moisture (q(sub l)) over wet regions and (T(sub g)) over dry regions The clear sky window flux from 800 to 1200/cm (RadWn) is sensitive to low level moisture (q(sub t)) and T(sub g). Combining these two measurements (CLR and RadWn), Tg and q(sub h) can be estimated over land, while q(sub h) and q(sub l) can be estimated over the oceans. The approach capitalizes on the availability of satellite estimates of CLR and RadWn and other auxiliary satellite data. The basic methodology employs off-line forward radiative transfer calculations to generate synthetic CSF data from two different global 4-dimensional data assimilation products. Simple linear regression is used to relate discrepancies in CSF to discrepancies in T(sub g), q(sub h) and q(sub l). The slopes of the regression lines define sensitivity parameters that can be exploited to help interpret mismatches between satellite observations and model-based estimates of CSF. For illustration, we analyze the discrepancies in the CSF between an early implementation of the Goddard Earth Observing System Data Assimilation System (GEOS-DAS) and a recent operational version of the European Center for Medium-Range Weather Prediction data assimilation system. In particular, our analysis of synthetic total and window region SCF differences (computed from two different assimilated data sets) shows that simple linear regression employing Delta(T(sub g)) and broad layer Delta(q(sub l) from .500 hPa to surface and Delta(q(sub h)) from 200 to .300 hPa provides a good approximation to the full radiative transfer calculations. typically explaining more than 90% of the 6-hourly variance in the flux differences. These simple regression relations can be inverted to "retrieve" the errors in the geophysical parameters. Uncertainties (normalized by standard deviation) in the monthly mean retrieved parameters range from 7% for Delta(T(sub g)) to about 20% for Delta(q(sub l)). Our initial application of the methodology employed an early CERES-TRMM data set (CLR and Radwn) to assess the quality of the GEOS2 data. The results showed that over the tropical and subtropical oceans GEOS2 is, in general, too wet in the upper troposphere (mean bias of 0.99 mm) and too dry in the lower troposphere (mean bias of -4.7 min). We note that these errors, as well as a cold bias in the T(sub g). have largely been corrected in the current version of GEOS-2 with the introduction of a land surface model, a moist turbulence scheme and the assimilation of SSM/I total precipitable water.

Wu, Man Li C.↗
NASA NTRS2000

Estimates of Ground Temperature and Atmospheric Moisture from CERES Observations

A method is developed to retrieve surface ground temperature (Tg) and atmospheric moisture using clear sky fluxes (CSF) from CERES-TRMM observations. In general, the clear sky outgoing long-wave radiation (CLR) is sensitive to upper level moisture (q(sub h)) over wet regions and Tg over dry regions The clear sky window flux from 800 to 1200 /cm (RadWn) is sensitive to low level moisture (q(sub j)) and Tg. Combining these two measurements (CLR and RadWn), Tg and q(sub h) can be estimated over land, while q(sub h) and q(sub t) can be estimated over the oceans. The approach capitalizes on the availability of satellite estimates of CLR and RadWn and other auxiliary satellite data. The basic methodology employs off-line forward radiative transfer calculations to generate synthetic CSF data from two different global 4-dimensional data assimilation products. Simple linear regression is used to relate discrepancies in CSF to discrepancies in Tg, q(sub h) and q(sub t). The slopes of the regression lines define sensitivity parameters that can be exploited to help interpret mismatches between satellite observations and model-based estimates of CSF. For illustration, we analyze the discrepancies in the CSF between an early implementation of the Goddard Earth Observing System Data Assimilation System (GEOS-DAS) and a recent operational version of the European Center for Medium-Range Weather Prediction data assimilation system. In particular, our analysis of synthetic total and window region SCF differences (computed from two different assimilated data sets) shows that simple linear regression employing (Delta)Tg and broad layer (Delta)q(sub l) from 500 hPa to surface and (Delta)q(sub h) from 200 to 500 hPa provides a good approximation to the full radiative transfer calculations, typically explaining more than 90% of the 6-hourly variance in the flux differences. These simple regression relations can be inverted to "retrieve" the errors in the geophysical parameters. Uncertainties (normalized by standard deviation) in the monthly mean retrieved parameters range from 7% for (Delta)T to about 20% for (Delta)q(sub t). Our initial application of the methodology employed an early CERES-TRMM data set (CLR and Radwn) to assess the quality of the GEOS2 data. The results showed that over the tropical and subtropical oceans GEOS2 is, in general, too wet in the upper troposphere (mean bias of 0.99 mm) and too dry in the lower troposphere (mean bias of -4.7 mm). We note that these errors, as well as a cold bias in the Tg, have largely been corrected in the current version of GEOS-2 with the introduction of a land surface model, a moist turbulence scheme and the assimilation of SSTM/I total precipitable water.

Wu, Man Li C.↗
NASA NTRS1983

The application of parameter estimation to flight measurements to obtain lateral-directional stability derivatives of an augmented jet-flap STOL airplane

Flight experiments with an augmented jet flap STOL aircraft provided data from which the lateral directional stability and control derivatives were calculated by applying a linear regression parameter estimation procedure. The tests, which were conducted with the jet flaps set at a 65 deg deflection, covered a large range of angles of attack and engine power settings. The effect of changing the angle of the jet thrust vector was also investigated. Test results are compared with stability derivatives that had been predicted. The roll damping derived from the tests was significantly larger than had been predicted, whereas the other derivatives were generally in agreement with the predictions. Results obtained using a maximum likelihood estimation procedure are compared with those from the linear regression solutions.

Stephenson, J. D.↗
NASA NTRS2014

Relationship Between Column-Density and Surface Mixing Ratio: Statistical Analysis of O3 and NO2 Data from the July 2011 Maryland DISCOVER-AQ Mission

To investigate the ability of column (or partial column) information to represent surface air quality, results of linear regression analyses between surface mixing ratio data and column abundances for O3 and NO2 are presented for the July 2011 Maryland deployment of the DISCOVER-AQ mission. Data collected by the P-3B aircraft, ground-based Pandora spectrometers, Aura/OMI satellite instrument, and simulations for July 2011 from the CMAQ air quality model during this deployment provide a large and varied data set, allowing this problem to be approached from multiple perspectives. O3 columns typically exhibited a statistically significant and high degree of correlation with surface data (R(sup 2) > 0.64) in the P- 3B data set, a moderate degree of correlation (0.16 < R(sup 2) < 0.64) in the CMAQ data set, and a low degree of correlation (R(sup 2) < 0.16) in the Pandora and OMI data sets. NO2 columns typically exhibited a low to moderate degree of correlation with surface data in each data set. The results of linear regression analyses for O3 exhibited smaller errors relative to the observations than NO2 regressions. These results suggest that O3 partial column observations from future satellite instruments with sufficient sensitivity to the lower troposphere can be meaningful for surface air quality analysis.

nitrogen dioxide↗
NASA NTRS2010

Peak Wind Tool for General Forecasting

The expected peak wind speed of the day is an important forecast element in the 45th Weather Squadron's (45 WS) daily 24-Hour and Weekly Planning Forecasts. The forecasts are used for ground and space launch operations at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45 WS also issues wind advisories for KSC/CCAFS when they expect wind gusts to meet or exceed 25 kt, 35 kt and 50 kt thresholds at any level from the surface to 300 ft. The 45 WS forecasters have indicated peak wind speeds are challenging to forecast, particularly in the cool season months of October - April. In Phase I of this task, the Applied Meteorology Unit (AMU) developed a tool to help the 45 WS forecast non-convective winds at KSC/CCAFS for the 24-hour period of 0800 to 0800 local time. The tool was delivered as a Microsoft Excel graphical user interface (GUI). The GUI displayed the forecast of peak wind speed, 5-minute average wind speed at the time of the peak wind, timing of the peak wind and probability the peak speed would meet or exceed 25 kt, 35 kt and 50 kt. For the current task (Phase II ), the 45 WS requested additional observations be used for the creation of the forecast equations by expanding the period of record (POR). Additional parameters were evaluated as predictors, including wind speeds between 500 ft and 3000 ft, static stability classification, Bulk Richardson Number, mixing depth, vertical wind shear, temperature inversion strength and depth and wind direction. Using a verification data set, the AMU compared the performance of the Phase I and II prediction methods. Just as in Phase I, the tool was delivered as a Microsoft Excel GUI. The 45 WS requested the tool also be available in the Meteorological Interactive Data Display System (MIDDS). The AMU first expanded the POR by two years by adding tower observations, surface observations and CCAFS (XMR) soundings for the cool season months of March 2007 to April 2009. The POR was expanded again by six years, from October 1996 to April 2002, by interpolating 1000-ft sounding data to 100-ft increments. The Phase II developmental data set included observations for the cool season months of October 1996 to February 2007. The AMU calculated 68 candidate predictors from the XMR soundings, to include 19 stability parameters, 48 wind speed parameters and one wind shear parameter. Each day in the data set was stratified by synoptic weather pattern, low-level wind direction, precipitation and Richardson Number, for a total of 60 stratification methods. Linear regression equations, using the 68 predictors and 60 stratification methods, were created for the tool's three forecast parameters: the highest peak wind speed of the day (PWSD), 5-minute average speed at the same time (A WSD), and timing of the PWSD. For PWSD and A WSD, 30 Phase II methods were selected for evaluation in the verification data set. For timing of the PWSD, 12 Phase\I methods were selected for evaluation. The verification data set contained observations for the cool season months of March 2007 to April 2009. The data set was used to compare the Phase I and II forecast methods to climatology, model forecast winds and wind advisories issued by the 45 WS. The model forecast winds were derived from the 0000 and 1200 UTC runs of the 12-km North American Mesoscale (MesoNAM) model. The forecast methods that performed the best in the verification data set were selected for the Phase II version of the tool. For PWSD and A WSD, linear regression equations based on MesoNAM forecasts performed significantly better than the Phase I and II methods. For timing of the PWSD, none of the methods performed significantly bener than climatology. The AMU then developed the Microsoft Excel and MIDDS GUls. The GUIs display the forecasts for PWSD, AWSD and the probability the PWSD will meet or exceed 25 kt, 35 kt and 50 kt. Since none of the prediction methods for timing of the PWSD performed significantly better thanlimatology, the tool no longer displays this predictand. The Excel and MIDDS GUIs display forecasts for Day-I to Day-3 and Day-I to Day-5, respectively. The Excel GUI uses MesoNAM forecasts as input, while the MIDDS GUI uses input from the MesoNAM and Global Forecast System model. Based on feedback from the 45 WS, the AMU added the daily average wind speed from 30 ft to 60 ft to the tool, which is one of the parameters in the 24-Hour and Weekly Planning Forecasts issued by the 45 WS. In addition, the AMU expanded the MIDDS GUI to include forecasts out to Day-7.

Barrett, Joe H., III↗
DOE OSTI2025

Computer-aided design of stability enhanced nicotinamide cofactor biomimetics for cell-free biocatalysis

Cell-free biocatalysis (CFB) is an efficient and environmentally friendly method to synthesize molecules such as pharmaceuticals, biochemicals, and biofuels through the in vitro use of enzyme cascades. These enzymes often require redox cofactors to drive chemical reactions. Natural redox cofactors (NAD(P)H) are expensive to isolate, motivating synthetic nicotinamide cofactor biomimetics (NCBs) as a cost-effective solution. A select handful of NCBs have been identified as potential NAD(P)H alternatives with comparable or improved redox capabilities, however, they display a tendency to degrade in common buffers. In this study, a library of 132 NCB candidates is systematically generated, over 85% of which have not been characterized in the literature, to expand the diversity of currently explored NCBs. The decomposition mechanism of NCBs in phosphate is evaluated using density functional theory (DFT), revealing protonation at the nicotinamide C5 position as a reporter of cofactor stability. Based on this result, we trained a linear regression model on DFT calculated descriptors to predict NCB stability in phosphate buffer, achieving mean absolute error (MAE) and root mean squared error (RMSE) values within computational accuracy. Analysis of key atomic descriptors and qualitative trends in our dataset informed the design of novel NCB candidates we propose with optimized stability. This work enables researchers to predict the relative stability of NCBs before synthesis, thereby streamlining the process to make CFB more affordable and viable at industry scales.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Data for Propagation Method and Planting Density Influence Canopy Developmental Transition and Biomass Productivity in Miscanthus × giganteus

Understanding how establishment practices influence the mechanisms underlying Miscanthus × giganteus (miscanthus) productivity and canopy development is critical for optimizing management. Data was collected during the juvenile (2011–2013) and mature (2024) phases of a long-term field experiment established in Urbana, Illinois, to evaluate the effects of propagation method (plug propagation [PP] and rhizome propagation [RP]), planting density (1.0, 0.75, and 0.25 plants m⁻²), and nitrogen application (0 and 67 kg N ha⁻¹) on end-of-season biomass yield, tiller mass, tiller density, and tiller height. Linear regression models identified the dominant predictors of yield across stand ages and management regimes. Planting density, nitrogen (N) application, and propagation method significantly influenced early yield and canopy development. During the juvenile phase, biomass yield was driven by tiller density due to canopy expansion; in the mature phase, yield became driven by tiller mass. The PP plots produced higher tiller density than the RP plots, resulting in faster canopy closure and higher juvenile-phase yields. Rhizome-propagated (RP) plots produced lower tiller density, but individual tillers were 3.3–6.4 g tiller−1 heavier than PP tillers. After the canopy reached equilibrium, the PP and RP yields were similar because greater RP tiller mass compensated for its lower tiller density. Higher planting density resulted in greater yield and tiller density during the second year (2012), but this effect was absent from the third year (2013) onward. In the juvenile phase, N fertilization enhanced yield by 1.6–3.4 Mg ha−1. Initiating fertilization in 2013 on unfertilized plots produced biomass similar to that in fertilized plots, suggesting yield recovery in the mature phase. These findings revealed that establishment strategies, including propagation method and planting density, influence juvenile miscanthus canopy development and productivity, transitioning from tiller-density- to mass-dominated yields, but not mature phase productivity.

Miscanthus↗
NASA NTRS2010

High Resolution ECG for Evaluation of Heart Function During Exposure to Subacute Hypobaric Hypoxia

High altitude climbing presents a wide spectrum of health risks, including exposure to hypobaric hypoxia. Risks are also typically exacerbated by the difficulty in appropriately monitoring for early signs of organ dysfunction in remote areas. We investigated whether high resolution advanced ECG analysis might be helpful as a non-invasive and easy-to-use tool (e.g., instead of Doppler echocardiography) for evaluating early signs of heart overload in hypobaric hypoxia. Nine non-acclimatized healthy trained alpine rescuers (age 43.7 plus or minus 7.3 years) climbed in four days to the altitude of 4,200 m on Mount Ararat. Five-minute high-resolution 12-lead electrocardiograms (ECGs) were recorded (Cardiosoft) in each subject at rest in the supine position on different days but at the same time of day at four different altitudes: 400 m (reference altitude), 1,700 m, 3,200 m and 4,200 m. Changes in conventional and advanced resting ECG parameters, including in beat-to-beat QT and RR variability, waveform complexity, signal-averaged, high-frequency and spatial/spatiotemporal ECG was estimated by calculation of the regression coefficients in independent linear regression models. A p-value of less than 0.05 was adopted as statistically significant. As expected, the RR interval and its variability both decreased with increasing altitude, with trends k = -96 ms/1000 m with p = 0.000 and k = -9 ms/1000 m with p = 0.001, respectively. Significant changes were found in P-wave amplitude, which nearly doubled from the lowest to the highest altitude (k = 41.6 microvolt/1000 m with p = 0.000), and nearly significant changes in P-wave duration (k = 2.9 ms/1000 m with p = 0.059). Changes were less significant or non-significant in other studied parameters including those of waveform complexity, signal-averaged, high-frequency and spatial/spatiotemporal ECG. High resolution ECG analysis, particularly of the P wave, shows promise as a tool for monitoring early changes in heart function due to exposure to high altitude.

Zupet, Petra↗
NASA NTRSDate not supplied

Tropical Tropospheric Ozone Trends (1990 to 2022): A Re-evaluation Based on SHADOZ and IAGOS Profiles and TOMS/OMI Columns

Changes in tropical tropospheric ozone (TTO) are of importance because this region spans roughly a third of the Earth and portions of it are experiencing variability in trends of ozone precursors (CO, NO x , CH 4 and nonmethane hydrocarbons) associated with economic growth and fires. In addition to ozone changes affecting radiative forcing, tropical ozone is an important source of the OH radical and thus, the oxidizing capacity of the planet (Thompson, 1992). Recent studies examining TTO trends satellite and in-situ observations over the past ~25 years include: Thompson et al., JGR, 2021; Gaudel et al., ACP, 2023; Stauffer et al., ACP, 2023. The results show considerable regional and seasonal variability in TTO trends and sensitivity to data selection, frequency, and statistical method used. The satellite data vary most widely in method, time period and reliability. Here we revisit trends for the 1990-2022 period with the best-characterized buv-based satellite products that span that period (derived from TOMS and OMI, Ziemke et al., 2019). The satellite-based trends are compared to trends based on in-situ data from the Southern Hemisphere Additional Ozonesondes (SHADOZ) network (1998-2022), measurements from selected pre-SHADOZ and IAGOS commercial aircraft data (Gaudel et al., 2023). Among sensitivities examined are the dependence of ozone trends on start and end years, impacts of ENSO events and the COVID-19 perturbation to emissions. Two statistical methods are used, quantile regression (QR) and multiple linear regression (MLR). Trends of total TTO, ozone segments in the boundary layer (to ~700 hPa), and free troposphere (700-300 hPa) are compared.

ozone, OMI, tropospheric ozone, SHADOZ↗
DOE OSTI2025

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

Krantz, Joshua↗
DOE OSTI2024

Predicting Damages to Remainder Parcels in Right-of-Way Acquisitions for Expanding Transportation Infrastructure: Using a Truncated Finite-Mixture Model

Right-of-way acquisition is a critical component of transportation infrastructure development. Transportation infrastructure projects cannot proceed without proper right-of-way acquisition or may face significant delays. State Departments of Transportation frequently acquire parcels of land for roadway expansion projects. A majority of these acquisitions can be partial takings, referring to a portion of a parcel that is acquired. The remainder of the property usually suffers economic changes due to the partial acquisition, which can be calculated as damage percentages. The damage percentage represents the extent to which the remaining land or property value has been diminished due to the acquisition. It reflects the remaining property value percentage that may have been lost or compromised due to the acquisition. Here, this study aims to provide a robust model to estimate damage percentages to the remainder parcels that may help state Departments of Transportation appraisers make early predictions about the damages in cases involving partial takings. The research uses 509 appraisal reports from the Tennessee Department of Transportation to identify the key parcel attributes that influence the percentage of damages. Three regression models are developed: a linear regression model, a finite-mixture model (FMM), and a truncated FMM with two latent classes. The modeling results show that the truncated FMM with two classes outperforms the other models. To validate the models, actual sales data is collected and analyzed for 59 properties, and the results suggest that the model predictions are fairly accurate. A predictive tool is developed based on the models to help appraisers anticipate right-of-way damages under different scenarios and can provide early predictions about the damages.

42 ENGINEERING↗
DOE OSTI2026

Meteoric 10Be Flux Calibration Data for the East River Watershed, Colorado, USA

This data package contains tabular and geospatial data used to quantify and model meteoric beryllium-10 fluxes in the East River watershed, Colorado, USA. The tabular component includes calibration-site data from five glacial moraine sites and includes environmental variables used to evaluate spatial controls on meteoric 10Be delivery, including elevation, mean annual precipitation (MAP), mean snow depth, and mean snow water equivalent (SWE). These site-level data were used to compare observed fluxes with environmental gradients across the watershed and to evaluate the effects of erosion correction on flux estimates. The package also includes supporting slope and curvature values used to assess topographic inputs to the erosion analysis. A second component of the data package contains updated manuscript tables and regression outputs used to summarize the relationships between meteoric 10Be flux and environmental predictors. These tables include meteoric 10Be sample information and AMS results, site-level environmental values, site-level meteoric 10Be inventory and flux values, watershed-averaged predicted fluxes, soil bulk density measurements, fine-fraction values, soil pH measurements, and regression statistics including slope, intercept, coefficient of determination, and p-value. The regression products include both standard linear regressions and regressions in which the intercept is constrained to pass through zero, and they support the analyses presented in the companion manuscript. Together, these tabular files provide the numerical basis for the manuscript tables and the regression-based interpretation of meteoric 10Be flux variability in a snow-dominated mountain watershed. The geospatial component of the package consists of GeoTIFF raster files used to generate the map products presented in Figures 2 and 6 of the companion manuscript. These rasters represent watershed-scale spatial layers for environmental variables and regression-based predictions of meteoric 10Be flux. This dataset contains comma-separated values files (.csv), Microsoft Excel files (.xlsx), GeoTIFF raster files (.tif), and upporting metadata files, including CSV data dictionaries and readme text files (.csv, .txt). The tabular files can be opened with standard spreadsheet software, and the raster files can be viewed and analyzed in GIS software such as ArcGIS Pro or QGIS. Together, these files document the numerical and spatial datasets used to calibrate and predict meteoric 10Be delivery in the East River watershed.

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