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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

On-chip probabilistic inference for charged-particle tracking at the sensor edge

Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection efficiency by deciding which information to save for further analysis. Particle tracking detectors at the Large Hadron Collider exemplify this challenge: pixelated silicon sensors generate rich spatiotemporal ionization patterns, yet most of this information is discarded due to data-rate limitations. Concurrently, advancements in co-design tools provide rapid turn-around for incorporating machine learning into application-specific integrated circuits, motivating designs for particle detectors with new integrated technologies. We demonstrate that neural networks embedded in the front-end electronics can infer charged-particle kinematic parameters from a single silicon layer. We regress hit positions and incident angles with calibrated uncertainties, while satisfying stringent constraints on numerical precision, latency, and silicon area. Our results establish a path toward probabilistic inference directly at the edge, opening new opportunities for intelligent sensing in high-rate scientific instruments.

Das, Arghya Ranjan [Purdue U.] (ORCID:000000018451↗
DOE OSTI2025

Groundwater flowpath characteristics drive variability in per- and polyfluoroalkyl substances (PFAS) loading across a stream-wetland system

Groundwater dependent ecosystems in areas with industrial and military land use are at risk of direct exposure to a wide range of contaminants, including PFAS chemicals. Glaciated terrain often has mixed high and low permeability sediments coupled with groundwater flow-through lake features. These hydrogeologic attributes create highly complex ‘source to seep’ dynamics that make spatiotemporal contaminant transport patterns difficult to predict. We investigated one such system in detail using a suite of heat-tracing and chemical methods. Numerous (n=57) preferential groundwater discharge zones (vertical flux rates ranging 0.2 to 3.2 m/d) were identified across the upper Quashnet River stream-wetland system in Mashpee, MA, USA, adjacent to an Air Force Base with several known PFAS source areas. Surface-water and groundwater samples were collected and analyzed (n=145) for precursors and terminal PFAS compounds between March and September 2022. Samples were collected at identified seeps along the Quashnet River (n=59), from wells upgradient from the stream-wetland system (n= 44), from contributing flow-through kettle lakes (n=8), and at multiple locations along the Quashnet River (n=34). Samples from seeps and wells had measured PFAS concentrations ranging from non-detect to approximate 3,500 ng/L (mean= 1,650 ng/L), and a range of deuterium excess values (3.2 to 15.9 per mil) indicative of varying degrees of groundwater-lake interaction prior to emergence at the discharge zones. Groundwater-lake interaction along flowpaths that sourced the sampled seeps was farther supported by significant correlations (p < 0.01) between deuterium excess and %PFAS precursors, and between %PFAS precursors and multiple terminal PFAS compounds (e.g., PFPeS, PFBS, PFHxS). However, some sampled seeps contributing groundwater to the stream-wetland system had much higher total PFAS concentrations (>1000 ng/L) than the upgradient kettle lakes, despite showing lake (evaporative) isotopic signatures, indicating the potential for groundwater flowpath convergence at wetland discharge zones and the influence of lakebed PFAS precursor reactions. PFAS compounds and water isotopic composition at sampled multilevel groundwater wells, rivers, lakes, and seeps suggest that a complex mixture of source groundwater and flowpath characteristics are responsible for diverse observed PFAS mixtures at preferential discharge zones across the stream-wetland system. Further, total PFAS loading patterns to the Quashnet River via groundwater discharge remained remarkably similar from winter to summer to fall conditions, despite a regional dry period in late summer 2022 with the upper river channel completely drying. This work addresses gaps in the existing PFAS literature by demonstrating the importance of subsurface fate and transport on PFAS compound concentrations in controlling contaminant mass loading in preferential groundwater discharge zones and presents a transferrable field toolkit for efficient characterization of spatially preferential PFAS transport dynamics.

Contaminant transport↗
DOE OSTI2021

Two promoters integrate multiple enhancer inputs to drive wild-type knirps expression in the Drosophila melanogaster embryo

Abstract Proper development depends on precise spatiotemporal gene expression patterns. Most developmental genes are regulated by multiple enhancers and often by multiple core promoters that generate similar transcripts. We hypothesize that multiple promoters may be required either because enhancers prefer a specific promoter or because multiple promoters serve as a redundancy mechanism. To test these hypotheses, we studied the expression of the knirps locus in the early Drosophila melanogaster embryo, which is mediated by multiple enhancers and core promoters. We found that one of these promoters resembles a typical “sharp” developmental promoter, while the other resembles a “broad” promoter usually associated with housekeeping genes. Using synthetic reporter constructs, we found that some, but not all, enhancers in the locus show a preference for one promoter, indicating that promoters provide both redundancy and specificity. By analyzing the reporter dynamics, we identified specific burst properties during the transcription process, namely burst size and frequency, that are most strongly tuned by the combination of promoter and enhancer. Using locus-sized reporters, we discovered that enhancers with no promoter preference in a synthetic setting have a preference in the locus context. Our results suggest that the presence of multiple promoters in a locus is due both to enhancer preference and a need for redundancy and that “broad” promoters with dispersed transcription start sites are common among developmental genes. They also imply that it can be difficult to extrapolate expression measurements from synthetic reporters to the locus context, where other variables shape a gene’s overall expression pattern.

Li, Lily (ORCID:0000000301986521)↗
DOE OSTI2020

Shock-induced kinetics and cellular structures of liquid nitromethane detonation

Using a combination of high-speed diagnostics, optical pyrometry, velocimetry and video photography, we examine the spatiotemporal reaction kinetics of a prototypical high-energy explosive, liquid nitromethane (NM), as we drive this high explosive into detonation. The detonations were initiated by powerful shock waves whose durations (4 ns) were shorter than the characteristic time associated with the reaction (7 ns). Simple optical spectroscopy alone cannot characterize the kinetics with high time resolution because the reactants and products are flowing at a high velocity of ~6 km/s (6 µm/ns). Additionally, every detection volume behind the shock front contains material initiated over a range of times. High spatial resolution can be obtained by probing interfaces where the shock enters or breaks out of the NM. In addition, we obtain cellular patterns imprinted on the luminous shock front by the two-stage explosion in NM. These spatiotemporal patterns arise naturally as a result of the asymmetry produced by the moving shock front. In this way the shock front serves as a thin moving intrinsic optical gauge that reports and characterizes the two-stage NM explosion behind the front.

42 ENGINEERING↗
DOE OSTI2020

Leveraging Fine-Grained Occupancy Estimation Patterns for Effective HVAC Control

As occupancy sensing technologies become mature, various occupancy sensors are increasingly deployed in commercial buildings for pervasive occupancy monitoring. These sensors provide occupant-count data, which contains rich spatiotemporal information about occupancy patterns. With long-term occupant-count data collected from a commercial building, we design three different predictive models that capture the occupancy dynamics and examine how a model predictive control of the HVAC system benefits from actual occupancy count prediction. Our analysis reveals that mispredictions of occupancy states, especially false positives and false negatives, may introduce inefficient control that leads to energy waste or user discomfort. To address this issue, we take a step further to design an adaptive model predictive controller that minimizes inefficient control actions according to misprediction types and distributions. A comprehensive evaluation is performed in OpenBuild and EnergyPlus simulators to study the effectiveness of the proposed end-to-end control strategy. The evaluation shows that the proposed solution reduces energy consumption by 29.5% while improving the average weighted occupants comfort by 86.7% in Predicted Mean Vote (PMV) over the fixed schedule strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Self-supervised learning of spatiotemporal thermal signatures in additive manufacturing using reduced order physics models and transformers

Microstructure control via additive manufacturing has enormous potential as manufacturers, materials scientists, and designers alike seek to exploit novel fabrication technologies to improve component performance. Recent works have demonstrated the feasibility of producing materials with controlled microstructures across various length scales. However, the experimental approach towards exploring the process-structure space can be laborious and costly. This is particularly true if also considering scan pattern optimization which is well suited for processes such as powder bed fusion electron beam melting. In this work we propose an approach for encoding additive manufacturing layer-wise thermal response signatures using self-supervised representation learning. Thermal simulations from a reduced order model are utilized to estimate the spatiotemporal response during printing. A machine learning framework, using video-transformers, is utilized to efficiently distill spatiotemporal patterns into a compact latent space representation. This latent state representation encodes the relevant physics which is then utilized to establish a data-driven process-structure model for an additively manufactured Ni-based superalloy. In conclusion, the proposed methodology could potentially be used towards in-situ process monitoring, scan pattern experimental design, and component qualification.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2022

Soil Moisture Estimation in South Asia via Assimilation of SMAP Retrievals

A soil moisture retrieval assimilation framework is implemented across South Asia in an attempt to improve regional soil moisture estimation as well as to provide a consistent regional soil moisture dataset. This study aims to improve the spatiotemporal variability of soil moisture estimates by assimilating Soil Moisture Active Passive (SMAP) near-surface soil moisture retrievals into a land surface model. The Noah-MP (v4.0.1) land surface model is run within the NASA Land Information System software framework to model regional land surface processes. NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA2) and Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals (IMERG) provide the meteorological boundary conditions to the land surface model. Assimilation is carried out using both cumulative distribution function (CDF)-corrected (DA-CDF) and uncorrected SMAP retrievals (DA-NoCDF). CDF matching is applied to correct the statistical moments of the SMAP soil moisture retrieval relative to the land surface model. Comparison of assimilated and model-only soil moisture estimates with publicly available in situ measurements highlights the relative improvement in soil moisture estimates by assimilating SMAP retrievals. Across the Tibetan Plateau, DA-NoCDF reduced the mean bias and RMSE by 8.4 % and 9.4 %, even though assimilation only occurred during less than 10 % of the study period due to frozen (or partially frozen) soil conditions. The best goodness-of-fit statistics were achieved for the IMERG DA-NoCDF soil moisture experiment. The general lack of publicly available in situ measurements across irrigated areas limited a domain-wide direct model validation. However, comparison with regional irrigation patterns suggested correction of biases associated with an unmodeled hydrologic phenomenon (i.e., anthropogenic influence via irrigation) as a result of SMAP soil moisture retrieval assimilation. The greatest sensitivity to assimilation was observed in cropland areas. Improvements in soil moisture potentially translate into improved spatiotemporal patterns of modeled evapotranspiration, although limited influence from soil moisture assimilation was observed on modeled processes within the carbon cycle such as gross primary production. Improvement in fine-scale modeled estimates by assimilating coarse-scale retrievals highlights the potential of this approach for soil moisture estimation over data-scarce regions.

Jawairia Ahmad↗
DOE OSTI2023

A General Spatiotemporal Imputation Framework for Missing Sensor Data

Many applications from precision agriculture, environmental monitoring and transportation networks rely on data collected across space and time over a large geographic area. Missing data poses a significant challenge for any data-driven inference and control tasks. Data imputation or the estimation of missing data can help fill these gaps by utilizing inherent spatial relationships and temporal patterns. A variety of spatiotemporal imputation models have been developed to address missing data in spatiotemporal datasets. However, these classical methods rely on the assumption that the underlying data follows a smooth trend and fail to provide accurate estimates when there is a large number of missing points in the data. Even though there are machine learning driven tensor completion approaches such as convolutional neural network based tensor completion (CoSTCo) that capture the non-linear relationships in the dataset, the transductive nature makes the algorithm less scalable. Thus, existing approaches for estimating the missing information do not effectively capture all dimensions of the spatiotemporal data structure, resulting in erroneous predictions and poor performance. The main contributions of this paper are: (1) We propose a novel inductive framework (G-LSTM) for missing data imputation that integrates a graph neural network with LSTMs to effectively capture both spatial and temporal dependencies. (2) Experimental results on a traffic dataset demonstrate that the proposed GNN integrated with an LSTM framework achieves improved imputation and maintains steady performance even when there are extreme missing conditions in comparison with the state-of-the-art imputation framework (i.e, CoSTCo). (3) The simulation results on a traffic network show up to 69% reduction in mean absolute error and 61% reduction in root mean square error when compared to CoSTCo.

Tharzeen, Aabila↗
DOE OSTI2023

Dislocation Patterning in Deforming Crystals: Theory, Computational Predictions and Validation (Final Technical Report)

This project was awarded for an initial period of three years, followed by a no-cost extension for one year and a funded one-year renewal. During the first four years, the project focused on investigating the role of dislocation reactions and dislocation correlation in dislocation patterning in FCC metals. During the fifth year, the scope of research was expanded to investigate the effects of composition inhomogeneity on the mesoscale plastic response of Body-Centered Cubic (BCC) alloys. In addition to its impact on metal hardening during deformation, dislocation patterning provides the microstructure information required to understand phenomena like fracture and recrystallization in metals. The Continuum Dislocation Dynamics (CDD) framework was the methodology used during the first four years, followed by the use of Discrete Dislocation Dynamics (DDD) during the fifth year. CDD is a density-based formalism of dislocation dynamics in which the plastic deformation of crystals is predicted together with the mesoscale dislocation patterns by tracking the space-time evolution of dislocations as driven by the applied stress, short-range and long-range interactions of dislocations, and cross slip. CDD is a crystal mechanics approach in which the plastic constitutive part is replaced with the equations of dislocation dynamics, which is driven by the internal stress via a mobility laws, while the evolution of the density gives the eigenstrain required to update the internal stress itself. The governing equations are thus those of crystal mechanics cast as an eigenstrain problem and those of dislocation transport and reactions. Our investigations during the first four years were driven by the hypothesis that that dislocation patterning is triggered by spatiotemporal dislocation density and internal stress fluctuations, and that such fluctuations influence the collective dislocation dynamic through their effects on the short-range reaction rates and the collective dislocation mobility. This hypothesis was tested by modeling the influence of the dislocation reactions and dislocation correlations on collective dislocation dynamics. The main research components during the first four years were: 1) Reformulation of the CDD framework to integrate the dislocation correlations and dislocation reactions. 2) Simulation of the dislocation correlations and quantifying their contribution to the long-range stress of complex dislocation systems. 3) Computational implementation of the updated CDD model for FCC single crystals and development of an efficient CDD code. 4) Investigation of dislocation patterning in FCC crystals based on the updated CDD model. Some key findings from these investigations were: 1) The patterning of dislocations is initiated by cross slip and internal stress fluctuations, and the refinement of the pattern results from junction formation. 2) Patterning is more prominent under high stress. 3) The correlation stress was found to be a significant part of the mean field stress in continuum representations of dislocation dynamics. During the last year of the project, we have established a method for performing dislocation dynamics in inhomogeneous alloys and demonstrated the impact of composition inhomogeneity on the characteristics of initial yielding and dislocation character in BCC alloys. We also tested this method using an irradiated ferritic alloy in which the composition and dislocation loops effects are both present. In doing so, we have considered two aspects of the role of inhomogeneity, the creation of internal coherency stress due to the dependence of the lattice parameter on the local composition, and the dependence of the dislocation mobility on the local composition. These two aspects resulted in an unexpected behavior of the dislocation system, and, in turn, the yielding behavior of BCC alloys. Key findings include: 1) The composition inhomogeneity in BCC alloys alters the well-known role of screw dislocations by forcing a level of waviness on the average dislocation character, thus destroying the screw character dominance in the yielding observed in pure or homogeneous BCC metals. 2) In the case of irradiated alloys, while composition inhomogeneity by itself and dislocation loops by themselves result in some hardening, the superposition of the two mechanisms does not result in the sum of the two contributions via any known rule. The latter result contradicts the classical works related hardening via multiple mechanisms, which state that various hardening mechanisms can be added linearly or using some Pythagorean additive rule. We were able to rationalize this unexpected result by a closer loop at the origin of the hardening mechanisms themselves, which both involved long-range elastic interactions with dislocations. We reached the conclusion that the hardening resulting from the total stress associated with these two mechanisms (current work) differs from the sum of the effects of the individual stress fields (classical literature). We have also found a major flaw in the classical theory of spinodal hardening. The latter theory never considered the impact of composition undulation on the solute hardening. We have built a new general theory accounting for the effect of solute friction together with the composition undulations on the dislocation configuration at Critical Resolved Shear Stress (CRSS), which is yielding a new definition of the spinodal hardening and new different results. This work is the first to recognize the lack of the role of solute in spinodal hardening theory of alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Identifying Climate Patterns Using Clustering Autoencoder Techniques

Abstract The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k -means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k -means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.

Kurihana, Takuya↗
DOE OSTI2021

Spatiotemporal control of liquid crystal structure and dynamics through activity patterning

Active materials are capable of converting free energy into mechanical work to produce autonomous motion, and exhibit striking collective dynamics that biology relies on for essential functions. Controlling those dynamics and transport in synthetic systems has been particularly challenging. Here, we introduce the concept of spatially structured activity as a means of controlling and manipulating transport in active nematic liquid crystals consisting of actin filaments and light-sensitive myosin motors. In this work, simulations and experiments are used to demonstrate that topological defects can be generated at will and then constrained to move along specified trajectories by inducing local stresses in an otherwise passive material. These results provide a foundation for the design of autonomous and reconfigurable microfluidic systems where transport is controlled by modulating activity with light.

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

Explainable tokamak-agnostic forecasting of fusion plasma instability via megahertz turbulent fluctuations

Scientific applications of artificial intelligence (AI) often remain limited by device-specific training and unexplained “black-box” approaches, creating fundamental barriers to cross-system generalization. This challenge is critical for nuclear fusion, where future reactors will have limited operational data for AI training. Here, we demonstrate that our neural network, trained solely on megahertz-scale turbulence measurements from one machine (DIII-D), forecasts Type-I edge localized mode (ELM) onsets in a different tokamak (KSTAR) through zero-shot weight transfer following physics-consistent preprocessing without device-specific retraining. Through an explainable AI framework combining gradient-weighted class activation mapping with physics validation, we reveal that our network can internalize physics relationships governing the ELM instabilities rather than memorizing device-specific patterns. The network perceives spatiotemporal features that correlate consistently with independently calculated instability growth rates, magnetohydrodynamic stability limits, and pedestal structure dynamics. Statistical analyses of dimensionally-reduced saliency features reveal the identical triangular features between the saliency representations, instability growth rates, and prediction probability across tokamaks, providing evidence that our forecasting system can show tokamak-agnostic generalization. This work contributes to a foundation for explainable scientific AI systems, where cross-system developments are essential for transcending traditional domain-specific constraints.

AI↗
DOE OSTI2026

An On-Demand Electric Transit Case Study of New Rochelle, New York

Here, this article characterizes the performance and ridership patterns of an on-demand transit (ODT) service utilizing lightweight electric vehicles (EVs) in New Rochelle, New York. Ridership sociodemographics, travel patterns (both temporal and spatiotemporal), and energy use from the service were explored using travel and survey data from September 2019 through December 2023. The ODT service was found to be used more by women (nearly 60%) and younger demographics (>65% under the age of 42), with peak use in the middle of the day and a grocery store as a top origin and destination. The service was utilized primarily for short trips (86% under 2 mi), with approximately one-third of riders using the ODT service to connect to a train or bus. The costs associated with fueling/charging were compared for different types of fleet vehicles, and the small, right-sized EVs were found to have annual charging costs that were roughly half of the refueling costs for conventional hybrid vans, and 24 times lower than a fleet of diesel buses. Evaluating the vehicle fleet and mapping current socio-spatial travel demand can inform system performance, guide service area development, and support future planning such as expansion to nearby communities and transit hubs. The findings in this case study suggest that on-demand electric transit may be a significant and growing space for advancing highly valued public mobility services. Public transport interventions that consider right-sized, electric, on-demand vehicles can help improve mobility access and reduce energy use and refueling costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2022

Lightning Over Central Canada: Skill Assessment for Various Land-Atmosphere Model Configurations and Lightning Indices Over a Boreal Study Area

Current lightning predictions are uncertain because they rely on empirical diagnostic relationships and often use coarse-scale climate scenario simulations in which deep convection is parameterized. Previous studies demonstrated that simulations with convection permitting resolutions improve lightning predictions compared to coarser-grid simulations using convection parameterizations for different geographical locations but not over the boreal zone. In this study, lightning simulations with the NASA Unified-Weather Research and Forecasting (NU-WRF) model are evaluated over a domain including the Great Slave Lake in Canada, for six lightning seasons. The simulations are performed at convection parameterized (9 km) and convection-permitting (3 km) resolution using the Goddard 4ICE and the Thompson microphysics schemes. Four lightning indices are evaluated against observations from the Canadian Lightning Detection Network, in terms of spatiotemporal frequency distribution, spatial pattern, daily climatology, and an event-based overall skill assessment. The Thompson scheme is, regardless of the spatial resolution, superior to the Goddard 4ICE scheme in predicting daily climatology but worse in predicting the spatial patterns of lightning occurrence. Results indicate that lightning estimation benefits from modeling at convection-permitting resolution, in particular for the ice based lightning indices. In contrast, the product of convective available potential energy and precipitation rate proved to be the most robust index that was largely invariant to varying spatial resolution. Finally, this study reveals issues of the models to reproduce the observed spatial pattern of lightning well, which might be related to an insufficient representation of land surface heterogeneity, including peatlands, in the study area.

Jonas Mortelmans↗
DOE OSTI2024

Analyzing inference workloads for spatiotemporal modeling

Ensuring power grid resiliency, forecasting climate conditions, and optimization of transportation infrastructure are some of the many application areas where data is collected in both space and time. Spatiotemporal modeling is about modeling those patterns for forecasting future trends and carrying out critical decision-making by leveraging machine learning/deep learning. Once trained offline, field deployment of trained models for near real-time inference could be challenging because performance can vary significantly depending on the environment, available compute resources and tolerance to ambiguity in results. Users deploying spatiotemporal models for solving complex problems can benefit from analytical studies considering a plethora of system adaptations to understand the associated performance-quality trade-offs. To facilitate the co-design of next-generation hardware architectures for field deployment of trained models, it is critical to characterize the workloads of these deep learning (DL) applications during inference and assess their computational patterns at different levels of the execution stack. In this paper, we develop several variants of deep learning applications that use spatiotemporal data from dynamical systems. We study the associated computational patterns for inference workloads at different levels, considering relevant models (Long short-term Memory, Convolutional Neural Network and Spatio-Temporal Graph Convolution Network), DL frameworks (Tensorflow and PyTorch), precision (FP16, FP32, AMP, INT16 and INT8), inference runtime (ONNX and AI Template), post-training quantization (TensorRT) and platforms (Nvidia DGX A100 and Sambanova SN10 RDU). Overall, our findings indicate that although there is potential in mixed-precision models and post-training quantization for spatiotemporal modeling, extracting efficiency from contemporary GPU systems might be challenging. Instead, co-designing custom accelerators by leveraging optimized High Level Synthesis frameworks (such as SODA High-Level Synthesizer for customized FPGA/ASIC targets) can make workload-specific adjustments to enhance the efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Quantifying microbial control of soil organic matter dynamics at macrosystem scales

Soil organic matter (SOM) stocks, decomposition and persistence are largely the product of controls that act locally. Yet the controls are shaped and interact at multiple spatiotemporal scales, from which macrosystem patterns in SOM emerge. Theory on SOM turnover recognizes the resulting spatial and temporal conditionality in the effect sizes of controls that play out across macrosystems, and couples them through evolutionary and community assembly processes. For example, climate history shapes plant functional traits, which in turn interact with contemporary climate to influence SOM dynamics. Selection and assembly also shape the functional traits of soil decomposer communities, but it is less clear how in turn these traits influence temporal macrosystem patterns in SOM turnover. Here, we review evidence that establishes the expectation that selection and assembly should generate decomposer communities across macrosystems that have distinct functional effects on SOM dynamics. Representation of this knowledge in soil biogeochemical models affects the magnitude and direction of projected SOM responses under global change. Yet there is high uncertainty and low confidence in these projections. To address these issues, we make the case that a coordinated set of empirical practices are required which necessitate (1) greater use of statistical approaches in biogeochemistry that are suited to causative inference; (2) long-term, macrosystem-scale, observational and experimental networks to reveal conditionality in effect sizes, and embedded correlation, in controls on SOM turnover; and (3) use of multiple measurement grains to capture local- and macroscale variation in controls and outcomes, to avoid obscuring causative understanding through data aggregation. Here, when employed together, along with process-based models to synthesize knowledge and guide further empirical work, we believe these practices will rapidly advance understanding of microbial controls on SOM and improve carbon cycle projections that guide policies on climate adaptation and mitigation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Stable Methane Isotopologues From Northern Lakes Suggest That Ebullition Is Dominated by Sub‐Lake Scale Processes

Abstract Stable isotopes have emerged as popular study targets when investigating emission of methane (CH 4 ) from lakes. Yet little is known on how isotopic patterns conform to variations in emission magnitudes—a highly relevant question. Here, we present a large multiyear data set on stable isotopes of CH 4 ebullition (bubbling) from three small adjacent subarctic lakes. The δ 13 C‐CH 4 and δD‐CH 4 range from −78.4‰ to −53.1‰ and from −369.8‰ to −218.8‰, respectively, and vary greatly among the lakes. The signatures suggest dominant hydrogenotrophic methanogenesis, particularly in the deep zones, but there are also signals of seemingly acetoclastic production in some high fluxing shallow areas, possibly fueled by in situ vegetation, but in‐sediment anaerobic CH 4 oxidation cannot be ruled out as an alternative cause. The observed patterns, however, are not consistent across the lakes. Neither do they correspond to the spatiotemporal variations in the measured bubble CH 4 fluxes. Patterns of acetoclastic and hydrogenotrophic production plus oxidation demonstrate that gains and losses of sediment CH 4 are dominated by sub‐lake scale processes. The δD‐CH 4 in the bubbles was significantly different depending on measurement month, likely due to evaporation effects. On a larger scale, our isotopic data, combined with those from other lakes, show a significant difference in bubble δD‐CH 4 between postglacial and thermokarst lakes, an important result for emission inventories. Although this characteristic theoretically assists in source partitioning studies, most hypothetical future shifts in δD‐CH 4 due to high‐latitude lake area or production pathway are too small to lead to atmospheric changes detectable with current technology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The emergence and transmission dynamics of HIV-1 CRF07_BC in Mainland China

A total of 1155 partial pol gene sequences of human immunodeficiency virus (HIV)-1 CRF07_BC were sampled between 1997 and 2015, spanning 13 provinces in Mainland China and risk groups [heterosexual, injecting drug users (IDU), and men who have sex with men (MSM)] to investigate the evolution, adaptation, spatiotemporal and risk group dynamics, migration patterns, and protein structure of HIV-1 CRF07_BC. Due to the unequal distribution of sequences across time, location, and risk group in the complete dataset (‘full1155’), subsampling methods were used. Maximum-likelihood and Bayesian phylogenetic analysis as well as discrete trait analysis of geographical location and risk group were carried out. To study mutations of a cluster of HIV-1 CRF07_BC (CRF07-1), we performed a comparative analysis of this cluster to the other CRF07_BC sequences (‘backbone_295’) and mapped the mutations observed in the respective protein structure. Our findings showed that HIV-1 CRF07_BC most likely originated among IDU in Yunnan Province between October 1992 to July 1993 [95 per cent hightest posterior density (HPD): May 1989–August 1995] and that IDU in Yunnan Province and MSM in Guangdong Province likely served as the viral sources during the early and more recent spread in Mainland China. We also revealed that HIV-1 CRF07-1 has been spreading for roughly 20 years and continues to cause local transmission in Mainland China and worldwide. Overall, our study sheds light on the dynamics of HIV-1 CRF07_BC distribution patterns in Mainland China. Our research may also be useful in formulating public health policies aimed at controlling acquired immune deficiency syndrome in Mainland China and globally.

60 APPLIED LIFE SCIENCES↗