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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Projected changes in the terrestrial and oceanic regulators of climate variability across sub-Saharan Africa

Future changes in the sign and intensity of ocean–land–atmosphere interactions have been insufficiently studied, despite implications for regional climate change projections, extreme event statistics, and seasonal climate predictability. In response to this deficiency, the present study focuses on projected responses to the enhanced greenhouse effect in: (1) the mean state of the atmosphere and land surface; (2) oceanic and terrestrial drivers of sub-Saharan climate variability; and (3) total seasonal climate predictability of sub-Saharan Africa, a region known for its pronounced land–atmosphere coupling. Analysis focuses on output from 23 Earth System Models in the Coupled Model Intercomparison Project Phase Five for the late twentieth and twenty-first centuries. It is projected that the greatest warming across sub-Saharan Africa will occur over the Sahel, the monsoon season will become more persistent into late summer and autumn, short rains in the Horn of Africa (HOA) will intensify, and leaf area index will increase across the HOA. Stepwise Generalized Equilibrium Feedback Assessment, i.e. a multivariate statistical approach, is applied to the model output over sub-Saharan Africa in order to explore the oceanic and terrestrial drivers of regional climate. The models indicate that the study region’s climate variability is dominated by oceanic drivers, with secondary contributions from soil moisture and very modest impacts from vegetation. Overall, the general model consensus of future projections indicates a concerning diminished seasonal predictability of sub-Saharan African regional climate based on key oceanic and terrestrial predictors and an elevated role of the land surface (associated with soil moisture anomalies) compared to oceanic drivers in regulating regional climate variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Towards understanding the robust strengthening of ENSO and more frequent extreme El Niño events in CMIP6 global warming simulations

The El Niño Southern Oscillation (ENSO) has profound impacts on weather patterns across the globe, yet there is no consensus on its response to global warming. Several modelling studies find a stronger ENSO in global warming scenarios, while other studies suggest ENSO weakening. Using a broad range of models from the Coupled Model Intercomparison Project phase 6 (CMIP6) and four types of warming experiments, here we show that the majority of the models predict a stronger ENSO by century-end in Shared Social Pathway (SSP) experiments, and in idealized 1pctCO 2 and abrupt 4xCO 2 experiments. Several models, however, do predict no change or ENSO weakening, especially in the idealized experiments. Critically, the strongest forcing (abrupt-4xCO 2 ) does not induce the strongest ENSO response, while differences between the models are much greater than those between warming scenarios. For the long-term response (over 1000 years) the models disagree even on the sign of change. Furthermore, changes in ENSO sea surface temperature (SST) variability are only modestly correlated with the tropical Pacific mean state change. The highest correlation for ENSO SST amplitude is found with the mean zonal SST gradient in the SSP5-8.5 experiment (R = –0.58). In contrast, changes in ENSO rainfall variability correlate well with changes in the mean state, as well as with changes in ENSO SST variability. When evaluating the Bjerknes Stability Index for a subset of models, we find that it is not a reliable predictor of ENSO strengthening, as this index tends to predict greater stability with warming. We argue that the enhanced ENSO stability is offset by increases in atmospheric noise or/and potential nonlinear effects. However, a robust inter-model mechanism that can explain a stronger ENSO simulated with global warming is still lacking. Therefore, caution should be exercised when considering ENSO changes based on a single model or warming scenario.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Climate models project increasing precipitation in the US Southwest in 2024–2100

We analyzed the measured precipitation in the southwestern US and found that, from 1900 to 2024, precipitation decreased at an average rate of − 1.2 cm per year per 100 years. The most significant precipitation decreases occurred in the three-month period from February to April. At the same time, precipitation during the southwestern monsoon season (July, August, and September) remained relatively stable. The ensemble mean of all CMIP6 (Coupled Model Intercomparison Project phase 6) climate models, along with the regression model incorporating anthropogenic aerosol (AER) and Pacific Decadal Oscillation (PDO) as predictors, projects precipitation to increase from 2024 to 2100. This projected precipitation increase relies on the anticipated decrease in emissions of anthropogenic aerosols, which is associated with the transition from fossil fuel burning to renewable and nuclear energy sources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Modeling regional precipitation over the Indus River basin of Pakistan using statistical downscaling

Complex processes govern spatiotemporal distribution of precipitation within the high-mountainous headwater regions (commonly known as the upper Indus basin (UIB)), of the Indus River basin of Pakistan. Reliable precipitation simulations particularly over the UIB present a major scientific challenge due to regional complexity and inadequate observational coverage. Here, we present a statistical downscaling approach to model observed precipitation of the entire Indus basin, with a focus on UIB within available data constraints. Taking advantage of recent high altitude (HA) observatories, we perform precipitation regionalization using K-means cluster analysis to demonstrate effectiveness of low-altitude stations to provide useful precipitation inferences over more uncertain and hydrologically important HA of the UIB. We further employ generalized linear models (GLM) with gamma and Tweedie distributions to identify major dynamic and thermodynamic drivers from a reanalysis dataset within a robust cross-validation framework that explain observed spatiotemporal precipitation patterns across the Indus basin. Final statistical models demonstrate higher predictability to resolve precipitation variability over wetter southern Himalayans and different lower Indus regions, by mainly using different dynamic predictors. The modeling framework also shows an adequate performance over more complex and uncertain trans-Himalayans and the northwestern regions of the UIB, particularly during the seasons dominated by the westerly circulations. However, the cryosphere-dominated trans-Himalayan regions, which largely govern the basin hydrology, require relatively complex models that contain dynamic and thermodynamic circulations. Furthermore, we also analyzed relevant atmospheric circulations during precipitation anomalies over the UIB, to evaluate physical consistency of the statistical models, as an additional measure of reliability. Overall, our results suggest that such circulation-based statistical downscaling has the potential to improve our understanding towards distinct features of the regional-scale precipitation across the upper and lower Indus basin. Additionally, such understanding should help to assess the response of this complex, data-scarce, and climate-sensitive river basin amid future climatic changes, to serve communal and scientific interests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Integrating Reservoirs into the Dissolved Organic Matter Versus Primary Production Paradigm: How Does Chlorophyll-$a$ Change Across Dissolved Organic Carbon Concentrations in Reservoirs?

Primary production in freshwater ecosystems is largely a function of light and nutrient availability, both of which have been changing in many lakes and reservoirs in response to anthropogenic pressures. Recent studies focusing on natural lakes have found a hump-shaped response of primary production (sometimes measured as chlorophyll-$a$) to dissolved organic matter (DOM, measured as dissolved organic carbon, DOC), which has both light-absorbing chromophoric properties and DOM-bound nutrients. We used the United States National Lakes Assessment dataset to integrate reservoirs into this paradigm in comparison with natural lakes and assessed the relative differences in the predicted response’s model structure, regression parameter values, and drivers of the chlorophyll-$a$ residuals. We found that chlorophyll-$a$ in reservoirs exhibited a hump-shaped response to DOC, while natural lakes from this dataset were better fit with a linear response, differing from previous studies focused on boreal lakes. Despite this, reservoirs had a greater maximum chlorophyll-a response compared to natural lakes in this study (45.5 versus 33.8 μg L -1 ), which occurred at a lower DOC concentration threshold (18.3 versus 26.4 mg L -1 ) when compared using quadratic models. Reservoirs had lower median light:nutrient values compared to natural lakes, and greater median surface area and total phosphorus (TP), that can all influence the light environment and the peak chlorophyll-a responses. In both reservoirs and natural lakes, chlorophyll-$a$ residuals were most strongly influenced by TP, where TP < 25-30 µg L -1 suppressed chlorophyll-a residuals and higher TP amplified them. Light:nutrient values were somewhat important predictors, and patterns with chlorophyll-$a$ residuals supported previous work showing low light:nutrient values amplified chlorophyll-$a$ responses and higher values suppressed them. In conclusion, quantifying the shape of the response of primary production to DOM quantity and quality as well as the drivers of the residuals, namely TP for lakes and reservoirs in this dataset, will be important for understanding the effects that changes in water quality may have on primary production and freshwater ecosystem processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Seasonal changes in soil respiration linked to soil moisture and phosphorus availability along a tropical rainfall gradient

Humid tropical forests contain some of the largest soil carbon (C) stocks on Earth, yet there is uncertainty about how carbon dioxide (CO 2 ) fluxes will respond to climate change in this biome. The magnitude of change in soil respiration over seasonal wetting and drying cycles can provide insight to how CO 2 fluxes might respond to precipitation changes. We measured soil respiration in 15 distinct forests across rainfall and soil fertility gradients in lowland Panama to assess spatial variation in seasonal patterns. We predicted that seasonal changes in soil respiration would be related to soil moisture, and that the ratio of wet to dry season CO 2 fluxes across landscape gradients would be regulated by soil organic C and nutrient availability. We found that soil respiration during the dry season was relatively stable across the rainfall gradient, averaging 4.3 ± 0.3 lmol CO 2 m -2 s -1 . In contrast, wet season respiration varied markedly, ranging among sites from a decline of 19% to an increase of 360% in comparison to dry season values. Soil moisture, air temperature, and rainfall were the best predictors of instantaneous soil respiration (R 2 = 0.18). Meanwhile, ratios of wet to dry season CO 2 fluxes were best predicted by sitescale resin-extractable phosphorus (P; R 2 = 0.48). That is, soil respiration in P-rich sites increased more during the wet season relative to respiration in P-poor sites. Also, sites with Wet:Dry season CO 2 flux ratios\1 were all poor in soil P, defined as resin P concentrations < 2 mg P kg -1 . Although soil organic C was not related to instantaneous soil respiration rates, forest floor biomass accumulation during the dry season was positively correlated with soil respiration during the wet season (R 2 = 0.26), indicating the contribution of microbial decomposition to wet season soil respiration. Altogether, nutrient availability regulated soil respiration responses to increased moisture during the wet season, while low soil moisture uniformly suppressed soil respiration across sites during the dry season. Phosphorus availability might therefore regulate feedbacks to climate change among humid tropical forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evidence linking calcium to increased organo-mineral association in soils

Geochemical indicators are emerging as important predictors of soil organic carbon (SOC) dynamics, but evidence concerning the role of calcium (Ca) is scarce. This study investigates the role of Ca prevalence in SOC accumulation by comparing otherwise similar sites with (CaCO 3 -bearing) or without carbonates (CaCO 3 -free). We measured the SOC content and indicators of organic matter quality (C stable isotope composition, expressed as δ 13 C values, and thermal stability) in bulk soil samples. We then used sequential sonication and density fractionation (DF) to separate two occluded pools from free and mineral-associated SOC. The SOC content, mass, and δ 13 C values were determined in all the fractions. X-ray photoelectron spectroscopy was used to investigate the surface chemistry of selected fractions. Our hypothesis was that occlusion would be more prevalent at the CaCO 3 -bearing site due to the influence of Ca on aggregation, inhibiting oxidative transformation, and preserving lower δ 13 C values. Bulk SOC content was twice as high in the CaCO 3 -bearing profiles, which also had lower bulk δ 13 C values, and more occluded SOC. Yet, contrary to our hypothesis, occlusion only accounted for a small proportion of total SOC (< 10%). Instead, it was the heavy fraction (HF), containing mineral-associated organic C, which accounted for the majority of total SOC and for the lower bulk δ 13 C values. Overall, an increased Ca prevalence was associated with a near-doubling of mineral-associated SOC content. Future investigations should now aim to isolate Ca-mediated complexation processes that increase organo-mineral association and preserve organic matter with lower δ 13 C values.

58 GEOSCIENCES↗
DOE OSTI2024

Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds

Abstract Quantifying the relative influence of factors and processes controlling riverine ecosystem function is essential to predicting future conditions under global change. Dissolved organic matter (DOM) is a fundamental component of riverine ecosystems that fuels microbial food webs, influences nutrient and light availability, and represents a significant carbon flux globally. The heterogeneous nature of DOM molecular composition and its propensity for interaction (i.e., functional diversity) can characterize riverine ecosystem function across spatiotemporal scales. To investigate fundamental drivers of DOM diversity, we collected seasonal water samples from 42 nested locations within five watersheds spanning multiple watershed sizes (~ 5 to 30,000 km 2 ) across the United States. Patterns in DOM molecular richness, aromaticity, relative abundance of N-containing formulas, and putative biochemical transformations derived from high-resolution mass spectrometry were assessed across gradients of explanatory variables associated with watershed characteristics (e.g., watershed area, water residence time, land cover). We found that putative biochemical transformations were more strongly related to explanatory variables across watersheds than common bulk DOM parameters and that watershed area, surface water residence time and derived Damköhler numbers representing DOM reactivity timescales were strong predictors of DOM diversity. The data also indicate that catchment-specific land cover factors can significantly influence DOM diversity in diverging directions. Overall, the results highlight the importance of considering water residence time and land cover when interpreting longitudinal patterns in DOM chemistry and the continued challenge of identifying generalizable drivers that are transferable across watershed and regional scales for application in Earth system models. This work also introduces a Findable Accessible Interoperable Reusable (FAIR) dataset (> 300 samples) to the community for future syntheses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Large-scale harmonic balance simulations with Krylov subspace and preconditioner recycling

The multi-harmonic balance method combined with numerical continuation provides an efficient framework to compute a family of time-periodic solutions, or response curves, for large-scale, nonlinear mechanical systems. The predictor and corrector steps repeatedly solve a sequence of linear systems that scale by the model size and number of harmonics in the assumed Fourier series approximation. In this paper, a novel Newton–Krylov iterative method is embedded within the multi-harmonic balance and continuation algorithm to efficiently compute the approximate solutions from the sequence of linear systems that arise during the prediction and correction steps. Further, the method recycles, or reuses, both the preconditioner and the Krylov subspace generated by previous linear systems in the solution sequence. A delayed frequency preconditioner refactorizes the preconditioner only when the performance of the iterative solver deteriorates. The GCRO-DR iterative solver recycles a subset of harmonic Ritz vectors to initialize the solution subspace for the next linear system in the sequence. The performance of the iterative solver is demonstrated on two exemplars with contact-type nonlinearities and benchmarked against a direct solver with traditional Newton–Raphson iterations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Storage and persistence of organic carbon in the upper three meters of soil under arable and native prairie land use

Aims: Land use change from native grasslands to arable lands globally impacts soil ecosystem functions, including the storage of soil organic carbon (SOC). Understanding the factors affecting SOC changes in topsoil and subsoil due to land use is crucial for effective mitigation strategies. We determined SOC storage and persistence as affected by land use change from native prairies to arable lands. Methods: Here we examined SOC stocks, soil δ 13 C and Δ 14 C signatures, microbial communities (bacteria and fungi), and soil mineral characteristics under native prairies and long-term arable lands (i.e., > 40 years) down to 3 m in the U.S. Midwest. Results: Native prairie soils had higher SOC stocks in the A horizon and 0–50 cm depth increment than arable soils. For both land use types, the δ 13 C and Δ 14 C values significantly decreased with depth, with the latter pointing towards highly stabilized SOC, especially in the B- and C-horizons. Analysis of the microbial communities indicated that the diversity of bacteria and fungi decreased with increasing soil depth. The content of oxalate soluble Al appeared to be the single most important predictor of SOC across horizons and land use types. Conclusion: Our data suggest that most SOC gains and losses and transformation and translocation processes seem to be restricted to the uppermost 50 cm. Increasing SOC retention in the A and B horizons within the 0–50 cm depth would enhance organic material serving as substrate and nutrients for microbes and plants (A horizon) and facilitate long-term SOC storage in the subsoil (B horizon).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Impact of graph structures for QAOA on MaxCut

The quantum approximate optimization algorithm (QAOA) is a promising method of solving combinatorial optimization problems using quantum computing. QAOA on the MaxCut problem has been studied extensively on graphs with specific structure; however, little is known about the general performance of the algorithm on arbitrary graphs. Here, we investigate how different graph characteristics correlate with QAOA performance at depths at most three on the MaxCut problem for all connected non-isomorphic graphs with at most eight vertices. Some good predictors of QAOA success relate to graph symmetries, odd cycles, and density. For example, on eight vertex graphs, the average probability for selecting an optimal solution for graphs that contain no odd cycles after three iterations of QAOA is 60.6% compared to 48.2% for those that do. The data generated from these studies are shared in a publicly accessible database to serve as a benchmark for QAOA calculations and experiments. Knowing the relationship between structure and performance can be used to identify classes of combinatorial problems that are likely to exhibit a quantum advantage.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

High Speed Impact Testing of UHMWPE Composite Using Orthogonal Arrays

Abstract Background Ultra high molecular weight polyethylene composites are fiber based composites used in armor applications. While some characterization has been conducted experimentally, this study varies multiple parameters simultaneously to investigate material response under a wide range of conditions. Objective This work focuses on characterizing the response of Dyneema ® HB26 hard laminate composites under high-speed impacts to examine the influence of plate diameter, clamping pressure, and plate spacing on target performance. Additionally, micro Computer Tomography scans are used to nondestructively evaluate the damage evolution in the targets. Methods These scan results are used in concert with more traditional armor performance metrics to evaluate the effect of various parameters using the method of orthogonal array analysis. This technique allows for multiple variables to be investigated in the same test series, saving time and budget while still providing quality results across a range of variables and variable values. Results We conclude that of the parameters investigated, the plate spacing parameter has the largest effect on performance, followed by the plate diameter. Bolt torque was found to not have a significant impact on results, indicating that an edge clamping pressure is not critical to material response. Additionally, by examining the high resolution scans, we can quantify the damage with an effective damage angle and that this angle is a good predictor of performance. Conclusion Finally a damage theory involving the effective bending strength of the plates is discussed as an explanation for all of the results observed in this test series.

Hannah, T. (ORCID:0000000230261362)↗
DOE OSTI2021

Observation of Fundamental Mechanisms in Compression-Induced Phase Transformations Using Ultrafast X-ray Diffraction

As theoretically hypothesized for several decades in group IV transition metals, we have discovered a dynamically stabilized body-centered cubic (bcc) intermediate state in Zr under uniaxial loading at sub-nanosecond timescales. Under ultrafast shock wave compression, rather than the transformation from α-Zr to the more disordered hex-3 equilibrium ω-Zr phase, in its place we find the formation of a previously unobserved nonequilibrium bcc metastable intermediate. We probe the compression-induced phase transition pathway in zirconium using time-resolved sub-picosecond x-ray diffraction analysis at the Linac Coherent Light Source. We also present molecular dynamics simulations using a potential derived from first-principles methods which independently predict this intermediate phase under ultrafast shock conditions. In contrast with experiments on longer timescale (> 10 ns) where the phase diagram alone is an adequate predictor of the crystalline structure of a material, our recent study highlights the importance of metastability and time dependence in the kinetics of phase transformations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Total Blood Mercury Predicts Methylmercury Exposure in Fish and Shellfish Consumers

Abstract Many studies evaluating methylmercury (MeHg) toxicity rely on whole blood total mercury (THg) measurements to estimate MeHg exposure. However, whole blood THg includes other forms of mercury (Hg), such as inorganic Hg, which have different exposure sources and toxicological effects than MeHg. Therefore, estimating the whole blood MeHg/THg ratio is critical to predicting MeHg exposure and, subsequently, efforts to establish an exposure–response relationship for use in risk assessment. A large, representative dataset (National Health and Nutrition Examination Survey (NHANES) 2011–2016) was used to determine the whole blood MeHg/THg ratio among (a) self-reported fish and shellfish consumers, ≥ 15 years of age (the “full adult” population ( N = 5268 training dataset; N = 2336 test dataset)) and (b) female fish and shellfish consumers, 15–44 years of age (the “women of reproductive age” population ( N = 1285 training dataset; N = 560 test dataset)). Unadjusted and adjusted linear and spline models with direct measurements for both THg and MeHg were evaluated. The mean whole blood MeHg/THg ratio was 0.75 (95% confidence interval (CI): 0.74, 0.75). This ratio was significantly higher among those with higher THg concentrations. All models exhibited excellent fit (adjusted R 2 from 0.957 to 0.982). Performance was slightly improved in spline versus linear models. For the full adult population and women of reproductive age, the unadjusted spline model predicted whole blood MeHg concentrations of 5.65 µg/L and 5.55 µg/L, respectively, when the THg concentration was 5.80 µg/L. These results suggest that whole blood THg is a good predictor of whole blood MeHg among fish and shellfish consumers.

Wells, Ellen M.↗
DOE OSTI2019

Reliability of the {sup 123}I-mIBG heart/mediastinum ratio: Results of a multicenter test–retest reproducibility study

A quantitative measurement, the Heart-to-Mediastinum (H/M) ratio of counts derived from a planar acquisition approximately 4 hours after injection of {sup 123}I-mIBG, is a strong predictor of outcomes in patients with stable class II-III heart failure and LVEF ≤ 35%. This study assessed the test–retest reproducibility of the H/M ratio in such patients. 47 subjects with class II-III systolic heart failure and LVEF ≤ 35% were tested at two time intervals separated by 5 to 14 days. Subjects were imaged twice on the same camera using the same radionuclide dose. Images were sent to a core analysis lab, where three nuclear technologists independently determined the H/M ratios. The primary endpoint was test–retest H/M ratio reproducibility calculated as the absolute difference in mean value determined by the three readers. Mean subject age was 65 ± 12 years, 85% were male, and mean BMI was 29 ± 6 kg/m{sup 2}. Mean injected activity was 10.18 ± 0.43 mCi for first dose and 10.09 ± 0.52 mCi for the second dose. The mean and SD values for first and repeat studies were almost identical: the 95% confidence interval of the mean test–retest difference was 0.055 to 0.076. Bland–Altman plots showed no systematic effect of the H/M ratio on the magnitude of the difference between replicate measurements. Inter-reader measurements were nearly identical. There were no serious adverse events despite exposure to {sup 123}I-mIBG on 2 occasions in a short time period. The Heart-to-Mediastinum ratio of {sup 123}I-mIBG is a consistent and highly reproducible measurement in stable Class II to III heart failure patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning

Background: We developed machine-learning (ML) models to estimate a patient’s risk of cardiac death based on adenosine myocardial perfusion SPECT (MPS) and associated clinical data, and compared their performance to baseline logistic regression (LR). We demonstrated an approach to visually convey the reasoning behind a patient’s risk to provide insight to clinicians beyond that of a “black box.” Methods: We trained multiple models using 122 potential clinical predictors (features) for 8321 patients, including 551 cases of subsequent cardiac death. Accuracy was measured by area under the ROC curve (AUC), computed within a cross-validation framework. We developed a method to display the model’s rationale to facilitate clinical interpretation. Results: The baseline LR (AUC = 0.76; 14 features) was outperformed by all other methods. A least absolute shrinkage and selection operator (LASSO) model (AUC = 0.77; p = .045; 6 features) required the fewest features. A support vector machine (SVM) model (AUC = 0.83; p < .0001; 49 features) provided the highest accuracy. Conclusions: LASSO outperformed LR in both accuracy and simplicity (number of features), with SVM yielding best AUC for prediction of cardiac death in patients undergoing MPS. Combined with presenting the reasoning behind the risk scores, our results suggest that ML can be more effective than LR for this application.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Cross-national analysis of food security drivers: comparing results based on the Food Insecurity Experience Scale and Global Food Security Index

Abstract The second UN Sustainable Development Goal establishes food security as a priority for governments, multilateral organizations, and NGOs. These institutions track national-level food security performance with an array of metrics and weigh intervention options considering the leverage of many possible drivers. We studied the relationships between several candidate drivers and two response variables based on prominent measures of national food security: the 2019 Global Food Security Index (GFSI) and the Food Insecurity Experience Scale’s (FIES) estimate of the percentage of a nation’s population experiencing food security or mild food insecurity (FI ). We compared the contributions of explanatory variables in regressions predicting both response variables, and we further tested the stability of our results to changes in explanatory variable selection and in the countries included in regression model training and testing. At the cross-national level, the quantity and quality of a nation’s agricultural land were not predictive of either food security metric. We found mixed evidence that per-capita cereal production, per-hectare cereal yield, an aggregate governance metric, logistics performance, and extent of paid employment work were predictive of national food security. Household spending as measured by per-capita final consumption expenditure (HFCE) was consistently the strongest driver among those studied, alone explaining a median of 92% and 70% of variation (based on out-of-sample R 2 ) in GFSI and FI , respectively. The relative strength of HFCE as a predictor was observed for both response variables and was independent of the countries used for model training, the transformations applied to the explanatory variables prior to model training, and the variable selection technique used to specify multivariate regressions. The results of this cross-national analysis reinforce previous research supportive of a causal mechanism where, in the absence of exceptional local factors, an increase in income drives increase in food security. However, the strength of this effect varies depending on the countries included in regression model fitting. We demonstrate that using multiple response metrics, repeated random sampling of input data, and iterative variable selection facilitates a convergence of evidence approach to analyzing food security drivers.

42 ENGINEERING↗
DOE OSTI2024

CMPLE: Correlation Modeling to Decode Photosynthesis Using the Minorize–Maximize Algorithm

In plant genomic experiments, correlations among various biological traits (phenotypes) give new insights into how genetic diversity may have tuned biological processes to enhance fitness under diverse conditions. Consequently, knowing how the correlations are affected by genetic (G) and environmental (E) factors helps develop climate-resilient plants. However, the current literature lacks any method for assessing the effect of predictors on pairwise correlations among multiple phenotypes together with easily interpretable model parameters. To address this need, we propose to model pairwise correlations directly in terms of G and E and develop a computationally efficient inference procedure. Two major novelties in our methodology are (1) the use of a composite pairwise likelihood method to avoid the positive definiteness restriction on the correlation matrix and (2) the use of a novel Minorize–Maximize (MM) algorithm for the efficient estimation of a large number of parameters. The proposed method shows excellent numerical performance on synthetic datasets. Here, the analysis of the motivating data on cowpea reveals that the rates of solar energy storage by photosynthesis (the aggregate trait) are differentially affected by different genetic loci through two distinct processes: “photoinhibition” which results from photodamage caused by excess light, and “photoprotection” which protects plants from photodamage but also results in energy loss.

Correlation modeling↗