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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Short-term Interventions for Long-term Change: Spreading Stable Green Norms in Networks

Strong empirical evidence suggests that people infer prevailing pro-environmental norms based on the behavior of people they encounter and engage with. These norms seem to be adopted in response to both internal motivation and social pressure. To formalize such behavior, the economic literature has introduced theoretical models that include moral and social drivers. We complement this theoretical literature by analyzing the adoption of green behavior in presence of social networks. Leveraging insights from the network-science literature, we extend an existing model of socially contingent moral motivation to include characteristics of human social behavior that have been shown, empirically, to matter for green behavior, but which have been neglected by most theoretical models. Our network moral-motivation model leads naturally to spatial-heterogeneity in environmental norms. Consistent with non-network models, we show that temporary subsidy can lead to stable equilibria with positive adoption, even when the subsidy is discontinued. In our model, however, regulators can achieve significant savings by targeting subsidies. With our computational exercises, using small semi-realistic networks, we quantify the gains of targeting subsidies, or social interventions, towards optimal seed groups. These gains may be large compared to widespread subsidies, or random selection of seed groups, and depend on the society’s structural characteristics. Furthermore, considering social networks may change radically the performance of initiatives aimed at promoting the adoption of green behavior.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Radionuclide Inventory Calculations for the Calcined Solid Storage Facility Performance Assessment - 19292

Originally named the Idaho Chemical Processing Plant when it was constructed in the early 1950's, the Idaho Nuclear Technology and Engineering Center reprocessed SNF to recover highly enriched uranium and other nuclear-related products for the U.S. DOE. Reprocessing SNF generated millions of gallons of aqueous radioactive waste. Calcination was a process developed at the INL Site to convert liquid waste into a solid, granular form from 1963 to 2000. The calcination process produced a safer product for storage while reducing the volume of stored liquid waste by an average factor of seven. Calcine is currently stored in the Calcined Solids Storage Facility (CSSF). The CSSFs are often called 'bin sets' because each CSSF (CSSFs 1 through 6) contains several stainless-steel storage bins housed in a concrete vault. The CSSF is an interim waste storage facility. In the future, calcine will be retrieved, treated (if necessary), and sent to an off-Site facility for storage or disposal. To close the CSSF, the bulk of the calcine will be removed and stored or disposed of elsewhere. The bins and vaults will then be placed in a configuration that is protective of human health and the environment. For purposes of the performance assessment, the depth of the residual calcine at closure was assumed to be 5-cm in each bin. The 5-cm of residual calcine in each bin is assumed to be mixed in grout during closure. The activities of the residual radionuclides are the inventory used for the dose assessment calculations for the CSSF performance assessment. The objective of this analysis was to calculate the radionuclide activity in the residual calcine remaining in the CSSF after closure. The purpose of this paper is to present the approach used to calculate the radionuclide inventory of the calcine currently stored in CSSF and the residual calcine in the CSSF after closure using data from the calcined waste Historical Processing Model (HPM). Limited sampling and analysis of the calcine in the CSSF bins was conducted in the past. Future similar calcine sampling and analysis efforts would be costly, generate additional mixed waste, and may not provide the desired detailed calcine composition data. However, during the later years of calcining, an alternative to calcine sampling and analysis was developed and the information was captured in the HPM. The HPM uses multiple sources to provide definitive calcine volume, mass, and composition, including: compositions and volumes of the liquid wastes that were calcined, ORIGEN2 - and ORIGEN-ARP-based radioactivity calculations, calciner and CSSF operating data, and process knowledge of INTEC fuel and waste-processing systems. HPM calcine composition was used to develop an inventory of radionuclides for use in the performance assessment evaluation for closure of CSSF. There are 43 bins that contain calcine, with six to twelve segments in each bin for a total of 337 bin segments for which the HPM calculates the average calcine composition. The HPM calculates the composition of each of the nearly 7,000 batches of material (startup bed, nonradioactive material, and radioactive waste) charged to the calciners. The calculations use the CSSF dimensions, including the CSSF number of bins, heights, outside diameters, annular air space diameter (if necessary), and volume of calcine currently in the bins. For each of the CSSFs, for a 5-cm residual, the percent of total calcine was calculated along with the residual activity for the radionuclides. Residual activity was estimated for 148 different radionuclides. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

WE-AB-202-08: Feasibility of Single-Inhalation/Single-Energy Xenon CT for High-Resolution Imaging of Regional Lung Ventilation in Humans

Purpose: To demonstrate the efficacy of a novel functional lung imaging method that utilizes single-inhalation, single-energy xenon CT (Xe-CT) lung ventilation scans, and to compare it against the current clinical standard, ventilation single-photon emission CT (V-SPECT). Methods: In an IRB-approved clinical study, 14 patients undergoing thoracic radiotherapy received two successive single inhalation, single energy (80keV) CT images of the entire lung using 100% oxygen and a 70%/30% xenon-oxygen mixture. A subset of ten patients also received concurrent SPECT ventilation scans. Anatomic reproducibility between the two scans was achieved using a custom video biofeedback apparatus. The CT images were registered to each other by deformable registration, and a calculated difference image served as surrogate xenon ventilation map. Both lungs were partitioned into twelve sectors, and a sector-wise correlation was performed between the xenon and V-SPECT scans. A linear regression model was developed with forced expiratory volume (FEV) as a predictor and the coefficient of variation (CoV) as the outcome. Results: The ventilation comparison for five of the patients had either moderate to strong Pearson correlation coefficients (0.47 to 0.69, p<0.05). Of these, four also had moderate to strong Spearman correlation coefficients (0.46 to 0.80, p<0.03). The patients with the strongest correlation had clear regional ventilation deficits. The patient comparisons with the weakest correlations had more homogeneous ventilation distributions, and those patients also had diminished lung function as assessed by spirometry. Analysis of the relationship between CoV and FEV yielded a non-significant trend toward negative correlation (Pearson coefficient −0.60, p<0.15). Conclusion: Significant correlations were found between the Xe-CT and V-SPECT ventilation imagery. The results from this small cohort of patients indicate that single inhalation, single energy Xe-CT has the potential to quantify regional lung ventilation volumetrically with high resolution using widely accessible radiologic equipment. Bill Loo and Peter Maxim are founders of TibaRay, Inc. Bill Loo is also a board member. Bill Loo and Peter Maxim have received research grants from Varian Medical Systems, Inc. and RaySearch Laboratory.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Punishment diminishes the benefits of network reciprocity in social dilemma experiments

Network reciprocity has been widely advertised in theoretical studies as one of the basic cooperation-promoting mechanisms, but experimental evidence favoring this type of reciprocity was published only recently. When organized in an unchanging network of social contacts, human subjects cooperate provided the following strict condition is satisfied: The benefit of cooperation must outweigh the total cost of cooperating with all neighbors. In an attempt to relax this condition, we perform social dilemma experiments wherein network reciprocity is aided with another theoretically hypothesized cooperation-promoting mechanism—costly punishment. The results reveal how networks promote and stabilize cooperation. This stabilizing effect is stronger in a smaller-size neighborhood, as expected from theory and experiments. Contrary to expectations, punishment diminishes the benefits of network reciprocity by lowering assortment, payoff per round, and award for cooperative behavior. This diminishing effect is stronger in a larger-size neighborhood. An immediate implication is that the psychological effects of enduring punishment override the rational response anticipated in quantitative models of cooperation in networks.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Selective Equatorial Sclera Crosslinking in the Orbit Using a Metal-Coated Polymer Waveguide

Purpose: Photochemical crosslinking of the sclera is an emerging technique that may prevent excessive eye elongation in pathologic myopia by stiffening the scleral tissue. To overcome the challenge of uniform light delivery in an anatomically restricted space, we previously introduced the use of flexible polymer waveguides. We presently demonstrate advanced waveguides that are optimized to deliver light selectively to equatorial sclera in the intact orbit. Methods: Our waveguides consist of a polydimethylsiloxane cladding and a polyurethane core, coupled to an optical fiber. A reflective silver coating deposited on the top and side surfaces of the waveguide prevents light leakage to nontarget, periorbital tissue. Postmortem rabbits were used to test the feasibility of in situ equatorial sclera crosslinking. Tensometry measurements were performed on ex vivo rabbit eyes to confirm a biomechanical stiffening effect. Results: Metal-coated waveguides enabled efficient light delivery to the entire circumference of the equatorial sclera with minimal light leakage to the periorbital tissues. Blue light was delivered to the intact orbit with a coefficient of variation in intensity of 22%, resulting in a 45 ± 11% bleaching of riboflavin fluorescence. A 2-fold increase in the Young's modulus at 5% strain (increase of 92% P < 0.05, at 25 J/cm 2 ) was achieved for ex vivo crosslinked eyes. Conclusions: Flexible polymer waveguides with reflective, biocompatible surfaces are useful for sclera crosslinking to achieve targeted light delivery. We anticipate that our demonstrated procedure will be applicable to sclera crosslinking in live animal models and, potentially, humans in vivo.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A METHODOLOGY TO SUPPORT THE DEVELOPMENT OF A NEW STATE VISION FOR THE UNITED STATES NUCLEAR INDUSTRY

A new strategy in the way in which the United States nuclear power plants (NPPs) are operated, maintained, and supported is needed. One such strategy is to transform the NPP operating model through a business-driven approach that leverages technology to enable new capabilities that improve performance and reduce cost. This paper presents a methodology for developing an achievable yet transformative new state vision that ensures continued safe and efficient operations of the United States NPP fleet. This work builds on existing guidance and leverages previous research to comprehensively address both bottom-up (i.e., utility needs) and top-down (i.e., first principles) considerations important for developing a new state vision. The proposed methodology is intended to provide industry-wide guidance for developing a new state vision that leverages both the selected vendor’s capabilities in a way that meets the utility’s modernization goals while ensuring state-of-the-art systems engineering and human factors engineering principles are applied that promote overall plant safety, performance, and efficiency.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Free Energy and Flexibility Analysis of Autoinhibited Human BRAF

The RAF serine/threonine protein kinases function as direct effectors of RAS in the intracellular transmission of extracellular growth signals, and they are key targets for drug discovery, given the high incidence of oncogenic mutations in RAF and other components of this signaling pathway. In its inactive state, RAF is held in an autoinhibited conformation in the cytosol through a combination of intramolecular interactions and binding to a regulatory 14−3−3 protein dimer. Activation of RAF is initiated by its interaction with membrane-localized GTP-bound RAS, which induces conformational changes that release RAF from its autoinhibited state. However, the molecular mechanisms governing RAF activation remain incomplete, largely due to the challenges in experimentally capturing the intermediate conformational states in this process. To address this gap, we developed a comprehensive all-atom model of BRAF based on existing cryo-EM structures. Using this model, we performed extensive molecular dynamics simulations to evaluate the stability and free energy landscape of autoinhibited BRAF in solution. Our analysis reveals conformational flexibility within the autoinhibited complex, suggesting that this dynamic behavior may play a role in facilitating BRAF activation upon engagement with the membrane-bound RAS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

NASA’s first ground-based Galactic Cosmic Ray Simulator: Enabling a new era in space radiobiology research

With exciting new NASA plans for a sustainable return to the moon, astronauts will once again leave Earth’s protective magnetosphere only to endure higher levels of radiation from galactic cosmic radiation (GCR) and the possibility of a large solar particle event (SPE). Gateway, lunar landers, and surface habitats will be designed to protect crew against SPEs with vehicle optimization, storm shelter concepts, and/or active dosimetry; however, the ever penetrating GCR will continue to pose the most significant health risks especially as lunar missions increase in duration and as NASA sets its aspirations on Mars. The primary risks of concern include carcinogenesis, central nervous system (CNS) effects resulting in potential in-mission cognitive or behavioral impairment and/or late neurological disorders, degenerative tissue effects including circulatory and heart disease, as well as potential immune system decrements impacting multiple aspects of crew health. Characterization and mitigation of these risks requires a significant reduction in the large biological uncertainties of chronic (low-dose rate) heavy-ion exposures and the validation of countermeasures in a relevant space environment. Historically, most research on understanding space radiation-induced health risks has been performed using acute exposures of monoenergetic single-ion beams. However, the space radiation environment consists of a wide variety of ion species over a broad energy range. Using the fast beam switching and controls systems technology recently developed at the NASA Space Radiation Laboratory (NSRL) at Brookhaven National Laboratory, a new era in radiobiological research is possible. NASA has developed the “GCR Simulator” to generate a spectrum of ion beams that approximates the primary and secondary GCR field experienced at human organ locations within a deep-space vehicle. The majority of the dose is delivered from protons (approximately 65%–75%) and helium ions (approximately 10%–20%) with heavier ions (Z ≥ 3) contributing the remainder. The GCR simulator exposes state-of-the art cellular and animal model systems to 33 sequential beams including 4 proton energies plus degrader, 4 helium energies plus degrader, and the 5 heavy ions of C, O, Si, Ti, and Fe. A polyethylene degrader system is used with the 100 MeV/n H and He beams to provide a nearly continuous distribution of low-energy particles. A 500 mGy exposure, delivering doses from each of the 33 beams, requires approximately 75 minutes. To more closely simulate the low-dose rates found in space, sequential field exposures can be divided into daily fractions over 2 to 6 weeks, with individual beam fractions as low as 0.1 to 0.2 mGy. In the large beam configuration (60 × 60 cm 2 ), 54 special housing cages can accommodate 2 to 3 mice each for an approximately 75 min duration or 15 individually housed rats. On June 15, 2018, the NSRL made a significant achievement by completing the first operational run using the new GCR simulator. This paper discusses NASA’s innovative technology solution for a ground-based GCR simulator at the NSRL to accelerate our understanding and mitigation of health risks faced by astronauts. Ultimately, the GCR simulator will require validation across multiple radiogenic risks, endpoints, doses, and dose rates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Demonstration and Evaluation of Explainable and Trustworthy Predictive Technology for Condition-based Maintenance

The domestic nuclear power plant (NPP) fleet has historically relied on labor-intensive and time-consuming predictive maintenance (PdM) programs, thus driving up operation and maintenance (O&M) costs to achieve high-capacity factors. Artificial intelligence (AI) and machine-learning (ML) can help simplify complex problems such as diagnosing equipment degradation to enable more effective decision-making efforts. The benefits of AI will be felt through more efficient plant O&M, improved work processes, and better integration of people and technology. Together, these benefits hold the promise to make nuclear power more sustainable by reducing O&M costs while improving employee engagement. While AI and ML technologies hold significant promise for the nuclear industry, there are challenges or barriers to their adoption. Explainability and trustworthiness of AI are two salient challenges that need to be addressed for wider deployment of these technologies in NPPs. This research focuses specifically on addressing the explainability and trustworthiness of AI technologies to advance the human, technical, and organization (HTO) readiness levels in adopting a risk-informed PdM strategy at commercial NPPs. In addition, this approach can be adapted to enhance the acceptability of AI in other nuclear applications with a few application-specific modifications. The technical approach ensuring wider adoption of AI technologies was developed by Idaho National Laboratory (INL)—in collaboration with Public Service Enterprise Group (PSEG), Nuclear, LLC—by utilizing the circulating water system (CWS) at two PSEG-owned plant sites for demonstration. Focused user studies were performed in collaboration with subject matter experts (SMEs) from PSEG and other nuclear domains to enhance human and organization readiness by building trust in AI-informed technologies. VIsualization for PrEdictive maintenance Recommendation (VIPER)—a Battelle Energy Alliance, LLC, copyrighted software—was developed and expanded to provide a user-centric visualization by incorporating inputs from the collaborating utility, human factors engineering guidelines, and data analysts. The VIPER software enables users, who may be unfamiliar with ML in general, to be interactively engaged by asking technical questions about PdM, work orders, diagnosis results and their confidence levels, the kind of data being used, and the types of ML algorithms employed. This interactive engagement enhances explainability and builds trust. One of the enabling accomplishments was the integration of large language models (LLMs), both text-based and vision-based, in the VIPER software.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

A whole wilderness of warning: Assessing economic consequences of nuclear releases - 15494

The consequences of the Fukushima Dai-ichi disaster may provide a useful analogue for assessing the consequences of an accident involving the transport of spent nuclear fuel in the United States. An unexamined, to date, aspect of the Fukushima Dai-ichi disaster is the wide disagreement about the economic consequences of the release. There exist a variety of different estimates and different models and methods for performing these estimates, but not a systematic way to address the range of issues such a disaster generated. This paper examines the sources, methods, data, and assumptions used to prepare economic consequence estimates of the disaster. This paper compares and contrasts these estimates and evaluates them for their reliability. The wide dispersion of consequence assessments as defined by various media and agency impact reports in the post-Fukushima era are evidence that such variability does not serve the industry, regulators, government or researchers well. While a protocol exists to gauge the intensity of an incident (INES Scale) no pre-existing methodology exists to measure the various socioeconomic impacts associated with a radiological disaster, be it induced by a natural disaster incident or human initiated. This paper argues that an improved method will be necessary and suggests some parameters for such a method. Nevada has started the dialogue on such a comprehensive methodology (Ballard 2012, Ballard 2014). Despite this effort by a stakeholder, the regulatory agencies involved in nuclear waste shipments in the United States appear reluctant to begin a dialogue about how to address the full range and scope of issues. Resolution of this problem will be a necessary prerequisite to any large scale shipping program. It will be necessary to adopt a standardized methodology to assess consequences of a radiological emergency and incorporate this into the environmental impact statement process for the shipping program. This analytical deficiency should be addressed by these agencies/regulators and in preparation for the potential movement of nuclear waste in the quantities necessary to service the nuclear power industry and to address the existing spent nuclear fuel (SNF) stockpiles across the United States. It will be necessary to demonstrate to stakeholders that the benefits of shipping radioactive substances this dangerous outweigh the costs of a possible accident or terrorist incident. The nuclear energy industry should support funding for these assessment studies in order to provide a basis for discussions about the costs and benefits of the shipments. The studies would provide stakeholders with clarity about the possible consequences of the program. The studies will also assist Japan in its efforts to understand what their disaster means for that country. This paper will begin the dialogue on how such analytical protocols could be constructed to pre-stage the research necessary to assess such consequences. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Apolipoprotein A-I modulates HDL particle size in the absence of apolipoprotein A-II

Human high-density lipoproteins (HDLs) are a complex mixture of structurally related nanoparticles that perform distinct physiological functions. We previously showed that human HDL containing apolipoprotein A-I (APOA1) but not apolipoprotein A-II (APOA2), designated LpA-I, is composed primarily of two discretely sized populations. Here, we isolated these particles directly from human plasma by antibody affinity chromatography, separated them by high-resolution size-exclusion chromatography and performed a deep molecular characterization of each species. The large and small LpA-I populations were spherical with mean diameters of 109 Å and 91 Å, respectively. Unexpectedly, isotope dilution MS/MS with [ 15 N]-APOA1 in concert with quantitation of particle concentration by calibrated ion mobility analysis demonstrated that the large particles contained fewer APOA1 molecules than the small particles; the stoichiometries were 3.0 and 3.7 molecules of APOA1 per particle, respectively. MS/MS experiments showed that the protein cargo of large LpA-I particles was more diverse. Human HDL and isolated particles containing both APOA1 and APOA2 exhibit a much wider range and variation of particle sizes than LpA-I, indicating that APOA2 is likely the major contributor to HDL size heterogeneity. We propose a ratchet model based on the trefoil structure of APOA1 whereby the helical cage maintaining particle structure has two “settings”—large and small—that accounts for these findings. This understanding of the determinants of HDL particle size and protein cargo distribution serves as a basis for determining the roles of HDL subpopulations in metabolism and disease states.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Distribution Locational Real-Time Pricing Based Smart Building Control and Management

This paper proposes an real-virtual parallel computing scheme for smart building operations aiming at augmenting overall social welfare. The University of Denver's campus power grid and Ritchie fitness center is used for demonstrating the proposed approach. An artificial virtual system is built in parallel to the real physical system to evaluate the overall social cost of the building operation based on the social science based working productivity model, numerical experiment based building energy consumption model and the power system based real-time pricing mechanism. Through interactive feedback exchanged between the real and virtual system, enlarged social welfare, including monetary cost reduction and energy saving, as well as working productivity improvements, can be achieved.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Migration-inducing gene-7 independently predicts poor prognosis of human osteosarcoma and is associated with vasculogenic mimicry

Highlights: • MIG-7 expression is associated with VM formation. • MIG-7 is an independent indicator of both overall and metastasis-free survival. • MIG-7 knockdown reduces osteosarcoma cells migration, invasion and VM formation. • MIG-7 knockdown didn’t affect osteosarcoma cell apoptosis or proliferation. • MIG-7 knockdown exhibits potent antimetastasis and anti-VM effects in vivo. Vasculogenic mimicry (VM) is a special type of vascular channel formed by tumor cells without endothelial cell participation. Migration-inducing gene 7 (MIG-7) plays an important role in regulating VM. In this study, immunohistochemical staining was used to detect MIG-7 in tissue specimens from 141 primary osteosarcoma patients, and the relationship between MIG-7 and VM was examined. Survival analysis were performed to evaluate the prognoses. MIG-7 knockdown osteosarcoma cells were used for cell proliferation, apoptosis, migration, invasiveness and VM formation assays. A spontaneously metastasizing cell line-derived orthotopic xenograft mouse model was established to evaluate the effect of MIG-7 knockdown on tumorigenesis, VM formation and lung metastasis. MIG-7 expression was associated with VM formation. There were significant differences in overall and metastasis-free survival between the MIG-7-positive and MIG-7-negative groups. The MIG-7 expression was shown to be an independent indicator of both overall and metastasis-free survival. In vitro knockdown of MIG-7 dramatically reduced migration, invasion and VM formation in osteosarcoma cells without any significant effect on cell proliferation and apoptosis. MIG-7 knockdown also exhibited potent antitumor, antimetastasis and anti-VM effects in the orthotopic mouse model of 143B osteosarcoma. Therefore, MIG-7 serves as an independent unfavorable prognostic indicator in osteosarcoma patients and MIG-7 is an important mediator of osteosarcoma VM formation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N<sub>2</sub>O emission using data from mesocosm experiments

Abstract. Agricultural nitrous oxide (N2O) emission accounts for a non-trivial fraction of global greenhouse gas (GHG) budget. To date, estimating N2O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or uncertainties of model parameters, and due to leverage recent advances in machine learning (ML) a new method is needed to unlock the “black box” to overcome its limitations such as low interpretability, out-of-sample failure and massive data demand. In this study, we developed a first-of-its-kind knowledge-guided machine learning model for agroecosystems (KGML-ag) by incorporating biogeophysical and chemical domain knowledge from an advanced PB model, ecosys, and tested it by comparing simulating daily N2O fluxes with real observed data from mesocosm experiments. The gated recurrent unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including (1) using initial values of intermediate variables (IMVs) instead of time series as model input to reduce data demand; (2) building hierarchical structures to explicitly estimate IMVs for further N2O prediction; (3) using multi-task learning to balance the simultaneous training on multiple variables; and (4) pre-training with millions of synthetic data generated from ecosys and fine-tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosm N2O fluxes (overall r2=0.81, and RMSE=3.6 mgNm-2d-1 from cross validation). Importantly, KGML-ag always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML-ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Advanced Analytics Studies Applied to US Department of Veterans Affairs' Corporate Data Warehouse (Initial Draft)

In this report, we describe our experience of applying several advanced analytics algorithms to the US Department of Veterans Affairs’ (VA’s) Corporate Data Warehouse (CDW) electronic health records datasets during FY2017-18. While various algorithms were applied to the CDW data, the main goal of this effort was to provide useful insights into the operational aspects (as opposed to the purely clinical aspect) of this specific implementation. Since there are not many reports in the public literature on advanced analytics applied to this data, this report provides unique insight in this regard. We focused on two machine learning (ML) applications: (1) clinical pathway inference (patterns dominating clinical pathways applied to a male dataset of Stable Ischemic Heart Disease patients as use case) and (2) medical-concept representation learning’s capability to inform and refine cohort membership based on probable patient outcomes. During our study on clinical pathway inference, we used two main modeling techniques: (a) topic probabilistic modeling (latent Dirichlet allocation [LDA]) and (b) feature imbedding. We observed that the applicability of LDA to the pathway inference remains in question. LDA provides results that are intuitive and easy for humans to comprehend; however, when applied to the pathway inference, it also generates pathway patterns that consist of a few dominant pathway components. In addition, LDA provides no information regarding the relationships among components in the same pathway. The outputs of LDA are also found to categorize patients based on their trace data of clinical procedures. However, the results suggest that LDA is biased toward statistically dominant components. This makes it particularly hard to discriminate pathway subbranches. To address the issue, we designed a new pathway inference methodology that integrates temporal ordering of pathway components into LDA results, as existing feature embedding tools such as word2vec are not readily applicable to our task. In addition, we developed our own feature embedding tool customized for patient traces. We conclude this study by suggesting two approaches to use embedding representation of a component into LDA and briefly list our lessons learned and recommendations for future work. For our study on medical-concept representation learning ability to inform and refine cohort membership based on probable patient outcomes, during our empirical evaluation we compared methods from medical-concept learning to standard one of a kind (one-of-K) encoding to evaluate the change in effectiveness as done in Choi, Schuetz, Stewart, and Sun (2016). We performed two primary empirical evaluations. The first evaluation was on a curated collection of 60,000 patients with no more than 1 year of medical history included. The second was a collection of patients with no restriction to the amount of medical history included. The hypothesis across both evaluations is that representation learning is broadly useful independent of downstream processing models; thus, for each experiment, we trained three models—a logistic regression model, a two-layer neural network model, and a nearest-neighbor model—and then averaged the results. Each model was trained using fivefold cross validation. We provided evidence that medical-representation learning improves predictions of the primary diagnosis category of a short patient history through 12 experiments. In addition, we provided evidence that medical-representation learning fails to improve prediction of the primary diagnosis category of an arbitrarily long patient history through 12 experiments. We conclude this study with a brief list of our lessons learned and recommendations for future work. We conclude the report with a discussion of recent technology trends as they relate to our artificial intelligence and, more specifically, to our ML research and approaches as described previously.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Ethyl 3-ethoxybutyrate, a new component of the transportation renewable fuel portfolio

The vast majority of energy that powers our global economy is from combustion of fossil fuels with the unintended consequence of increased deposition of carbon dioxide in the atmosphere and oceans. The scientific and technical challenges for the energy sector are to develop renewable energy sources that are sufficient to meet human energy consumption, are economically viable, and are ecologically sustainable. We investigated ethyl 3-ethoxybutyrate (EEB) as a fuel oxygenate in ultra low sulfur diesel (ULSD) with a bench-scale research engine and determined its economic potential as a renewable fuel with technoeconomic modeling using wastewater treatment plant biosolids as the feedstock for poly-3-hydroxyalkanoates (PHB), a bacterial storage polymer from which EEB can be synthesized. EEB blended well with ULSD, and cetane values of 10% and 20% v/v EEB-ULSD blends exceeded 40. A diesel internal combustion engine fueled with 5%, 10%, and 20% EEB-ULSD blends met or exceeded all tested transportation diesel fuel emissions criteria. Inedible organic feedstocks may be used to produce PHB; and thus, EEB might contribute to carbon reductions without compromising performance or air pollutant emissions. However, further research is needed to determine its role in the overall fuel portfolio. (C) 2015 Elsevier Ltd. All rights reserved.

09 BIOMASS FUELS↗
DOE OSTI2023

Implicit learning of convective organization explains precipitation stochasticity

Accurate prediction of precipitation intensity is crucial for both human and natural systems, especially in a warming climate more prone to extreme precipitation. Yet, climate models fail to accurately predict precipitation intensity, particularly extremes. One missing piece of information in traditional climate model parameterizations is subgrid-scale cloud structure and organization, which affects precipitation intensity and stochasticity at coarse resolution. Here, using global storm-resolving simulations and machine learning, we show that, by implicitly learning subgrid organization, we can accurately predict precipitation variability and stochasticity with a low-dimensional set of latent variables. Using a neural network to parameterize coarse-grained precipitation, we find that the overall behavior of precipitation is reasonably predictable using large-scale quantities only; however, the neural network cannot predict the variability of precipitation (R 2 ~ 0.45) and underestimates precipitation extremes. The performance is significantly improved when the network is informed by our organization metric, correctly predicting precipitation extremes and spatial variability (R 2 ~ 0.9). The organization metric is implicitly learned by training the algorithm on a high-resolution precipitable water field, encoding the degree of subgrid organization. The organization metric shows large hysteresis, emphasizing the role of memory created by subgrid-scale structures. We demonstrate that this organization metric can be predicted as a simple memory process from information available at the previous time steps. These findings stress the role of organization and memory in accurate prediction of precipitation intensity and extremes and the necessity of parameterizing subgrid-scale convective organization in climate models to better project future changes of water cycle and extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Advanced Computing is at the Forefront of a New “Moonshot” Revolutionizing the North American Power Grid

In the 50+ years since the first humans landed on the moon, computing has grown at breakneck speed. We are faced with another challenge that is just as daunting, and just as important to overcome-modernizing the North American electric power grid-and high-performance computing (HPC) systems with specialized software will be an important element in rising to this challenge. We describe at a high level how software developed in the ExaSGD project addresses this "moonshot" goal by utilizing exascale computing and a novel high performance solver software stack to support the mission of decarbonizing power grid operations in an environment of uncertain weather and climate. To reach the exascale benchmark the team has made a number of first-of-their-kind innovations, including novel method for stochastic optimization, fine grained parallel methods for modeling power systems, and GPU resident sparse numerical linear solvers.

17 WIND ENERGY↗