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DOE OSTI2022

A new approach to zip–lignin: 3,4–dihydroxybenzoate is compatible with lignification

Renewed interests in the development of bioenergy, biochemicals, and biomaterials have elicited new strategies for engineering the lignin of biomass feedstock plants. Furthermore, this study shows, for the first time, that 3,4-dihydroxybenzoate (DHB) is compatible with the radical coupling reactions that assemble polymeric lignin in plants. We introduced a bacterial 3-dehydroshikimate dehydratase into hybrid poplar (Populus alba × grandidentata) to divert carbon flux away from the shikimate pathway, which lies upstream of lignin biosynthesis. Transgenic poplar wood had up to 33% less lignin with p-hydroxyphenyl units comprising as much as 10% of the lignin. Mild alkaline hydrolysis of transgenic wood released fewer ester-linked p-hydroxybenzoate groups than control trees, and revealed the novel incorporation of cell-wall-bound DHB, as well as glycosides of 3,4-dihydroxybenzoic acid (DHBA). Two-dimensional nuclear magnetic resonance (2D-NMR) analysis uncovered DHBA-derived benzodioxane structures suggesting that DHB moieties were integrated into the lignin polymer backbone. In addition, up to 40% more glucose was released from transgenic wood following ionic liquid pretreatment and enzymatic hydrolysis. This work highlights the potential of diverting carbon flux from the shikimate pathway for lignin engineering and describes a new type of ‘zip-lignin’ derived from the incorporation of DHB into poplar lignin.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2026

Benchmarking Bayesian Optimization Frameworks and Acquisition Strategies for Materials Discovery and Autonomous Laboratories

Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.

Bayesian optimization↗

NASA NTRS2012

Characterization of Aerodynamic Performance of Boundary-Layer-Ingesting Inlet Under Crosswind

NASA has been studying future transport concepts, envisioned to be technically realizable in the timeframe of 2020-2030, to meet environmental and performance goals. One concept receiving considerable interest involves a propulsion system embedded into a hybrid wing-body aircraft. While offering significant advantages in fuel savings and noise reduction by this concept, there are several technical challenges that are not encountered in the current fleet and must be overcome so as to deliver target performance and operability. One of these challenges is associated with an inlet system that ingests a significantly thick boundary layer, developing along the wing-body surface, into a serpentine diffuser before the flow meeting fan blades. The flow is subject to considerable total pressure loss and distorted at the fan face, much more significantly than in the inlet system of conventional aircraft. In our previous studies [1, 2], we have shown that through innovative design changes on the airframe surface, it is possible to simultaneously increase total pressure recovery and decrease distortion in the flow, without resorting to conventional penalty-ridden flow control concepts, such as vortex generator or boundary layer bleeding/suction. In the current study, we are interested in understanding the following issues: how the embedded propulsion system performs under a crosswind condition by studying in detail the flow characteristics of two inlets, the baseline and another optimized previously under the cruise condition. With the insight, it is hoped that it can help in the follow-on study by devising effective strategies to minimize flow distortion arising from the integration of an embedded-engine system into an airframe to the level acceptable to the operation and fuel consumption before 2030. To achieve these demanding goals, non-conventional concepts are called for; but technology gap is too big that it requires evolutionary approach by focusing various concepts and technologies needed in the next three generations of aircraft, respectively named as N+1, N+2, and N+3. Noticeably, considerable reduction in each category of 1 is required in N+2 (relative to Boeing 777-200 and GE90 engines) and N+3 (relative to Boeing 737-800 and CFM56-7B engines). In this study, concepts for N+2 is our interest. A concept that has potential to achieve these metrics and has been under intensive study is the hybrid wing body (HWB) airframe with a tightly integrated propulsion system, see Fig. 1. The inlet is non-circular at the entrance and the entering flow, no longer uniform or free of disturbances, and is now carrying with it a boundary layer developing along the fuselage; the inlet is thus known as boundary-layer-ingesting (BLI) inlet.

Liou, Meng-Sing↗

NASA NTRS2015

Software Testbed for Developing and Evaluating Integrated Autonomous Subsystems

To implement fault tolerant autonomy in future space systems, it will be necessary to integrate planning, adaptive control, and state estimation subsystems. However, integrating these subsystems is difficult, time-consuming, and error-prone. This paper describes Intelliface/ADAPT, a software testbed that helps researchers develop and test alternative strategies for integrating planning, execution, and diagnosis subsystems more quickly and easily. The testbed's architecture, graphical data displays, and implementations of the integrated subsystems support easy plug and play of alternate components to support research and development in fault-tolerant control of autonomous vehicles and operations support systems. Intelliface/ADAPT controls NASA's Advanced Diagnostics and Prognostics Testbed (ADAPT), which comprises batteries, electrical loads (fans, pumps, and lights), relays, circuit breakers, invertors, and sensors. During plan execution, an experimentor can inject faults into the ADAPT testbed by tripping circuit breakers, changing fan speed settings, and closing valves to restrict fluid flow. The diagnostic subsystem, based on NASA's Hybrid Diagnosis Engine (HyDE), detects and isolates these faults to determine the new state of the plant, ADAPT. Intelliface/ADAPT then updates its model of the ADAPT system's resources and determines whether the current plan can be executed using the reduced resources. If not, the planning subsystem generates a new plan that reschedules tasks, reconfigures ADAPT, and reassigns the use of ADAPT resources as needed to work around the fault. The resource model, planning domain model, and planning goals are expressed using NASA's Action Notation Modeling Language (ANML). Parts of the ANML model are generated automatically, and other parts are constructed by hand using the Planning Model Integrated Development Environment, a visual Eclipse-based IDE that accelerates ANML model development. Because native ANML planners are currently under development and not yet sufficiently capable, the ANML model is translated into the New Domain Definition Language (NDDL) and sent to NASA's EUROPA planning system for plan generation. The adaptive controller executes the new plan, using augmented, hierarchical finite state machines to select and sequence actions based on the state of the ADAPT system. Real-time sensor data, commands, and plans are displayed in information-dense arrays of timelines and graphs that zoom and scroll in unison. A dynamic schematic display uses color to show the real-time fault state and utilization of the system components and resources. An execution manager coordinates the activities of the other subsystems. The subsystems are integrated using the Internet Communications Engine (ICE). an object-oriented toolkit for building distributed applications.

Testbed↗

DOE OSTI2026

Localized Heterogeneous Nucleation for Vapor‐Assisted Sequential Deposition of Metal Halide Perovskites

Vapor-assisted hybrid two-step deposition, which combines thermally evaporated inorganic layers with solution-processed organic halides to form halide perovskites, has emerged as a scalable and industry-compatible route for textured tandem photovoltaics. However, this process is often hindered by reaction-limited phase formation, particularly when compact, non-porous, and highly crystalline inorganic layers formed by thermal evaporation restrict subsequent conversion, resulting in incomplete reaction and pronounced depth-dependent heterogeneity. In this study, we introduce a strategy to regulate the inorganic precursor layer by incorporating localized heterogeneous nucleation sites. Sparsely distributed hydrophilic metal oxide species serve as effective nucleation centers during vapor deposition, enabling effective control over film morphology and crystal orientation from the early stages of growth. This tailored inorganic framework facilitates the subsequent incorporation of organic halides, alleviating reaction limitations and suppressing residual unreacted precursors. Consequently, the perovskite films exhibit improved stoichiometric uniformity and enhanced optoelectronic quality, enabling wide-bandgap perovskite solar cells with markedly improved performance and operational stability. This work provides important mechanistic insight into crystal growth engineering of vapor-deposited perovskite thin films.

nucleation↗

DOE OSTI2021

Develop Wake Mitigation Strategy (CRADA CRD-17-00693 Final Report)

Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. In an autonomous wind farm, enabling cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving an autonomous wind farm is to develop algorithms that provide necessary information to ensure reliable, robust, and efficient operation of wind turbines in a wind plant using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. In this work consensus control is applied in a hybrid analysis to data from an existing wind farm to demonstrate the benefit of consensus control.

17 WIND ENERGY↗

NASA NTRS2020

Control Demonstration of Multiple Doubly Fed Induction Motors for Hybrid Electric Propulsion

The Convergent Aeronautics Solutions (CAS) High Voltage Hybrid Electric Propulsion (HVHEP) activity was formulated to support the move into future hybrid-electric aircraft. The goal of this high-voltage activity is to develop a new alternating current (AC) power architecture to support the needs of higher efficiency and lower emissions aircraft. This proposed architecture will adopt the use of the doubly fed induction machine (DFIM) for propulsor drive motor application. The DFIMs are attractive for several reasons, including but not limited to, the ability to self-start, the ability to operate subsynchronously and supersynchronously, and the requirement for power converters rated at a fraction of those required in a corresponding direct current system, dependent on the range of operation. The focus of this report is based specifically on the presentation and analysis of a novel strategy, which allows for independent operation of multiple doubly fed induction motors from a common AC bus

David J Sadey↗

DOE OSTI2021

Hardware-in-the-Loop Investigation of Emissions Challenges in Hybrid Medium- and Heavy-Duty Powertrains Using a Pre-Production Diesel-Electric Parallel Hybrid System With and Without Stop-Start Operation

Hybrid electric powertrains are a growing market in medium- and heavy-duty applications. There is a lack of available information to understand the challenges in the integration of engine platforms into electrified powertrains, such as cold-start, restart, and load-reduction effects on emissions and emission control devices. Results from the Heavy Heavy-Duty Diesel Truck (HHDDT) cycle using a conventional medium-duty diesel engine were compared with those of a parallel hybrid architecture. Oak Ridge National Laboratory in collaboration with the US Department of Energy and Odyne Systems, LLC developed a powertrain in a hardware-in-the-loop environment, integrating the Odyne Systems, LLC medium-duty parallel hybrid system, which was used for the hybrid portion of this study. Experiments under the HHDDT cycle showed increasing improvements in fuel consumption and engine-out emissions with the integration of stop/start, hybrid, and hybrid with stop/start. However, the effects of load reduction and exhaust temperature on the thermal management strategy have shown an increase in fueling in the second part of the HHDDT cycle. Four configurations of medium-duty electrification were studied and contributed to building a unique data set containing combustion, emissions, and system integration data. Each electrification level was compared with the conventional baseline. The calibration of the conventional engine was not altered for this study. Opportunities to tailor the combustion process were identified with the stop/start strategy.

Lerin, Chloe↗

DOE OSTI2026

Alleviating prior dependencies for DESI DR1 clustering fits through reparameterization

Bayesian analyses of the full-shape clustering of Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) exhibit prior-volume projection effects, whereby weakly constrained nuisance parameters of the Effective Field Theory of Large Scale Structure (EFTofLSS) shift marginalized cosmological posteriors away from the posterior maximum. We reanalyze DESI DR1 power spectrum multipoles using two complementary mitigation strategies: (i) nonlinear orthogonalization to decorrelate nuisance and cosmological parameter priors, and (ii) a fully reparameterization-invariant Jeffreys prior over all EFTofLSS coefficients, evaluated on-the-fly via closed-form Jacobians. Including data from DESI, Big-Bang Nuclesynthesis and a constraint on $n_{\mathrm{s}}$, baseline priors lead to multi-$σ$ projection in the Hubble parameter $H_{0}$ and dark energy equation of state parameters $w_{0}$ and $w_{a}$; the Jeffreys prior successfully recenters these posteriors to enclose the maximum a posteriori estimate within the 68% credible regions, demonstrating clear mitigation of projection effects for these late-time expansion parameters. A hybrid Jeffreys+baseline-Gaussian configuration controls residual over-broad tails in the physical cold dark matter density $ω_{\mathrm{c}}$ while preserving the volume correction, and is our favoured approach. We compare the credible intervals derived using our methodology to those obtained using Halo Occupation Distribution (HOD)-informed priors and to confidence intervals derived using frequentist profile likelihood analyses, finding agreement in both central values and degeneracy directions in the $w_{0}$--$w_{a}$ plane. This demonstrates that, once projection effects are properly controlled, we can make robust inferences about the late-time cosmological expansion independent of the statistical framework adopted.

Bonici, M. [Waterloo U.; Perimeter Inst. Theor. Ph↗

NASA NTRS1987

Stability and rheology of dispersions of silicon nitride and silicon carbide

The relationship between the surface and colloid chemistry of commercial ultra-fine silicon carbide and silicon nitride powders was examined by a variety of standard characterization techniques and by methodologies especially developed for ceramic dispersions. These include electrokinetic measurement, surface titration, and surface spectroscopies. The effects of powder pretreatment and modification strategies, which can be utilized to augment control of processing characteristics, were monitored with these technologies. Both silicon carbide and nitride were found to exhibit silica-like surface chemistries, but silicon nitride powders possess an additional amine surface functionality. Colloidal characteristics of the various nitride powders in aqueous suspension is believed to be highly dependent on the relative amounts of the two types of surface groups, which in turn is determined by the powder synthesis route. The differences in the apparent colloidal characteristics for silicon nitride powders cannot be attributed to the specific absorption of ammonium ions. Development of a model for the prediction of double-layer characteristics of materials with a hybrid site interface facilitated understanding and prediction of the behavior of both surface charge and surface potential for these materials. The utility of the model in application to silicon nitride powders was demonstrated.

Feke, Donald L.↗

DOE OSTI2025

Template‐Directed Growth of Palladium Nanoparticles in Holey Single‐Layer Graphene for High‐Performance Room‐Temperature Hydrogen Sensing

Holey graphene (HG), formed by introducing nanoscale perforations into graphene sheets, combines the structural advantages of continuous sp 2 conjugation with the beneficial effects of high surface area and enhanced chemical reactivity associated with nanoscopic holes. Conventional HG fabrication methods often rely on harsh oxidative treatments that compromise graphene's intrinsic electronic properties by disrupting its sp 2 conjugation. A new approach is introduced to fabricate HG directly from single-layer graphene (SLG), using two-dimensional covalent organic frameworks (COFs) as a template followed by controlled oxygen plasma etching. This method preserves the integrity of the SLG's sp 2 network while producing well-defined holes with an average diameter of 2.7 nm. These hole edges act as reactive sites that facilitate the confined, autoreductive growth of palladium nanoparticles (PdNPs) without external reducing agents. The confined hole geometry prevents NP agglomeration and ensures a uniform size distribution. The resulting Pd@HG hybrid exhibits exceptional chemiresistive hydrogen sensing characteristics, including ultrahigh sensitivity, low detection limits, rapid response and recovery, and long-term stability under both dry and humid conditions. Mechanistic investigations reveal a two-step sensing process involving surface redox interactions and hydrogen absorption into the PdNP lattices. This strategy presents a scalable platform for integrating metal NPs within conductive carbon frameworks for advanced sensing applications.

chemical vapor deposition↗

DOE OSTI2026

Influence of the drive design on ablation front hydrodynamic instability growth in a capsule implosion at the National Ignition Facility

A critical aspect of inertial confinement fusion experiments lies in the control of ablation front instabilities during the implosion process. The growth of Rayleigh–Taylor and Richtmyer–Meshkov instabilities, seeded by target defects, can significantly degrade the performance of the implosion. Here, this study explores the influence of drive design on ablation front stability using the hydro-growth radiography platform at the National Ignition Facility. This platform allows the measurement of the ablation front hydrodynamic instability growth. Because these measurements are restricted to early convergence, their primary role is to constrain the initial instability growth and benchmark the simulations used to infer shell integrity and performance at peak velocity and ignition. Three ignition designs, Hybrid-E (HyE), SQ-n (“S” for scaling and “Q” for quality), and High temperature, High thickness (HiT) were analyzed. The results demonstrated a high dependence of the growth factor on the choice of design, capsule scale as well as the hohlraum conditions, mainly the radiative temperature and the gold M-band emission. HyE and SQ-n display a similar growth factor range, but their evolution is different due to the design differences in hohlraum conditions. HiT has the lowest growth factor of the studied designs, which is a result of a higher radiative temperature. These results highlight the importance of design choices in controlling instability dynamics. These insights inform future drive design strategies to enhance stability and efficiency in fusion ignition experiments.

Physics↗

DOE OSTI2026

A Co-Simulation Framework for Steady-State Analyses of Multiple Droop-based MTdc Grids in Continental-Scale Systems

The growing scale and complexity of planning continental hybrid ac and multi-terminal dc (MTdc) systems require scalable steady-state modeling and analysis approaches not currently available in commercial tools. This paper presents a comprehensive multi-fidelity model-conversion framework that enables the efficient transition of MTdc grid models from production cost modeling (PCM) and approximated ac power flow to detailed ac–MTdc power flow for large-scale planning studies. The core of this framework is a scalable co-simulation approach that, for the first time, enables power flow analysis in continental-scale ac–MTdc systems. It seamlessly couples commercial ac solvers with a detailed MTdc grid model that incorporates droop-based control and current-limiting strategies of multiple meshed MTdc grids. Leveraging this capability, an evaluation framework to systematically assess and compare different MTdc power redispatch strategies under ac and dc contingencies is introduced. The proposed framework and algorithm are evaluated using a combined Western and Eastern Interconnection system with 11 MTdc grids of various sizes, showing a coherent transition from PCM to detailed ac–MTdc power flow and improved system performance in voltage regulation and line overload mitigation following typical contingencies.

Nguyen, Quan H.↗

DOE OSTI2022

LANL Activities on Mechanistic Approach to Analyzing and Improving Unconventional Hydrocarbon Production

Hydrocarbon production from shale reservoirs is inherently inefficient and challenging since these are low permeability plays. In addition, there is a limited understanding of the fundamentals and the controlling mechanisms, further complicating how to optimize these plays. Herein, we summarize our experimental and computational efforts fully and partially supported by the fundamental shale portfolio to reveal unconventional shale fundamentals and devise development strategies to enhance extraction efficiency with a minimal environmental footprint. Integrating these fundamentals with machine learning, we outline a pathway to improve the predictive power of our models, which enhances the forecast quality of production, thereby improving the economics of operations in unconventional reservoirs. For instance, we have developed science informed workflows and platforms for optimizing pressure-drawdown at a site, which allow operators to make reservoir-management decisions that optimize recovery in consideration of future production. Recently, our work relies on the hybridization of physics-based prediction and machine learning, whereby accurate synthetic data (combined with available site data) can enable the application of machine learning methods for rapid forecasting and optimization. Consequently, the workflow and platform are readily extendable to operations at other sites, plays, and basins.

04 OIL SHALES AND TAR SANDS↗

NASA NTRS1997

Hybrid Electric Transit Bus

A government, industry, and university cooperative is developing an advanced hybrid electric city transit bus. Goals of this effort include doubling the fuel economy compared to current buses and reducing emissions to one-tenth of current EPA standards. Unique aspects of the vehicle's power system include the use of ultra-capacitors as an energy storage system, and a planned natural gas fueled turbogenerator developed from a small jet engine. Power from both the generator and energy storage system is provided to a variable speed electric motor attached to the rear axle. At over 15000 kg gross weight, this is the largest vehicle of its kind ever built using ultra-capacitor energy storage. This paper describes the overall power system architecture, the evolution of the control strategy, and its performance over industry standard drive cycles.

Viterna, Larry A.↗

DOE OSTI2026

Upgrading Biogas through in situ Conversion of Carbon Dioxide to Biomethane in Anaerobic Digesters

Organic waste streams generated by wastewater treatment plants, agricultural operations, and food processing industries represent an important yet underutilized opportunity for renewable energy production in the United States. Through anaerobic digestion, these waste streams can produce biogas, a mixture primarily composed of methane (CH4) and carbon dioxide (CO2), that can be upgraded to pipeline-quality natural gas. However, most existing upgrading technologies remove CO2 from biogas rather than utilizing it, leaving a significant portion of the potential energy unused. This project investigates a novel biological upgrading approach that converts CO2 into additional CH4 by supplying hydrogen (H2) to specialized microorganisms capable of performing hydrogenotrophic methanation. The main challenges associated with biological biogas upgrading are related to hydrogen supply, gas-liquid mass transfer, and process stability. First, due to the high cost of hydrogen gas, it is preferable that H2 be produced on-site using renewable energy sources such as wind or solar power. Second, hydrogen has low solubility in liquids, which limits its availability to microorganisms and requires strategies to improve gas dissolution and transfer within the reactor. Third, process inhibition may occur as a result of increased pH caused by CO2 consumption or elevated H2 partial pressure, both of which can negatively affect methanogenic activity. Although research in these areas has advanced during the course of this project, these challenges have not yet been fully resolved. To date, the biological systems that have achieved the highest methane concentrations are typically ex-situ reactors, where operational conditions can be more easily controlled. For this reason, the findings of the present project remain highly relevant. The project goal was to develop an innovative system that can accomplish biogas upgrading via biological conversion of CO2 to CH4, in a novel hybrid approach that combines the advantages of both in-situ and ex-situ systems. The proposed system employs a three-phase upflow anaerobic bioreactor with H2 delivery through a gas-permeable membrane, enabling efficient hydrogen transfer and microbial conversion. Under optimized operating conditions, the system achieved 99% H2 consumption and 90% CO2 conversion. A subsequent gas cleaning stage was implemented to further improve gas quality and meet target purity standards. The upgraded gas composition reached 97.7% CH4, 2.2% CO2, and 0.97% O2, while H2S concentrations remained below detection limits. In addition, a flue gas-driven inorganic thermoelectric generator (TEG) system was designed and experimentally validated as a potential source of electricity for H2 production. The system consisted of six TEG modules connected in series and achieved an open-circuit voltage of 4.5 V and a maximum power output of 224 mW at a temperature difference of approximately 53.5 °C, demonstrating effective conversion of waste heat into electrical power under simulated flue gas conditions. Finally, a comprehensive techno-economic analysis was completed to evaluate the capital and operating costs associated with the proposed system. The results provide important insights to guide future scale-up, optimization, and potential deployment of integrated biological biogas upgrading technologies.

09 BIOMASS FUELS↗

DOE OSTI2023

Innovative Strategies for Long-Term Monitoring of Complex Groundwater Plumes at DOE’s Legacy Sites (Workshop Report)

Most remaining Department of Energy (DOE) sites will require extended periods of institutional control, especially at complex groundwater sites where attenuation-based strategies have been implemented to facilitate closure. The current practice of monitoring—obtaining and analyzing contaminant concentration in groundwater samples at numerous wells—will account for a large portion of the projected life-cycle at these DOE sites unless a new approach is adopted. State-of-the-art technologies are being developed, including in situ sensors, geophysics, radiation mapping, numerical modeling and AI/ML. These technologies can optimize monitoring strategies in space and time, provide spatially extensive information at vulnerable regions and/or provide more continuous monitoring at lower cost. As part of DOE’s Office of Environmental Management (DOE-EM’s) efforts to advance long-term monitoring systems, an in-person/virtual hybrid workshop was hosted by Savannah River National Laboratory (SRNL) on January 24 and 25, 2023, in Augusta, Georgia. Because DOE-EM’s complex sites will eventually be transferred to DOE’s Office of Legacy Management (DOE-LM), representatives of DOE-LM were important participants in the workshop. The purpose of the workshop was to identify challenges and opportunities for deploying advanced technologies for long-term monitoring at DOE sites. The key questions during the workshop were: 1) the regulatory acceptance of replacing a process that traditionally has used laboratory sampling and analysis of groundwater samples, and 2) the application of this strategy to the southwestern arid sites that include many of the remaining DOE-EM and DOE-LM complex groundwater plumes. Characteristics common to most arid sites present both limitations and opportunities for advanced technologies. DOE-EM has funded a National Laboratory team from SRNL, Lawrence Berkeley National Laboratory (LBNL), and Pacific Northwest National Laboratory (PNNL) to establish the overarching framework of long-term monitoring by systematically combining advanced hardware and software technologies. This project is titled “Advanced Long-Term Environmental Monitoring Systems (ALTEMIS)” and is sponsored by the DOE-EM Technology Development Program. The multi-laboratory team is currently developing and testing innovative monitoring strategies, including the use of in situ groundwater sensors, geophysics, drone/satellite-based remote sensing, reactive transport modeling, and artificial intelligence/machine learning (AI/ML). The project’s demonstration testbed is at the Savannah River Site (SRS) F-Area Seepage Basins, where a well-characterized complex groundwater plume composed of uranium and other radionuclides is in the latter stages of remediation. The workshop included more than 70 participants, presentations, a field visit to F-Area, breakout working groups, and large group discussion. Participants developed recommendations on five topics: in situ sensors, spatially integrative tools, challenges to regulatory acceptance, AI/ML strategies, and transitioning sites to DOE-LM.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Deep-learning↗