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DOE OSTI2020

Optimizing the Design of Diatom Biosilica-Targeted Fusion Proteins in Biosensor Construction for Bacillus anthracis Detection

In vivo functionalization of diatom biosilica frustules by genetic manipulation requires careful consideration of the overall structure and function of complex fusion proteins. Although we previously had transformed Thalassiosira pseudonana with constructs containing a single domain antibody (sdAb) raised against the Bacillus anthracis Sterne strain, which detected an epitope of the surface layer protein EA1 accessible in lysed spores, we initially were unsuccessful with constructs encoding a similar sdAb that detected an epitope of EA1 accessible in intact spores and vegetative cells. This discrepancy limited the usefulness of the system as an environmental biosensor for B. anthracis. We surmised that to create functional biosilica-localized biosensors with certain constructs, the biosilica targeting and protein trafficking functions of the biosilica-targeting peptide Sil3T8 had to be uncoupled. We found that retaining the ER trafficking sequence at the N-terminus and relocating the Sil3T8 targeting peptide to the C-terminus of the fusion protein resulted in successful detection of EA1 with both sdAbs. Homology modeling of antigen binding by the two sdAbs supported the hypothesis that the rescue of antigen binding in the previously dysfunctional sdAb was due to removal of steric hindrances between the antigen binding loops and the diatom biosilica for that particular sdAb.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

High Strength and Fracture Resistance of Reduced-Activity W-Ta-Ti-V-Zr High-Entropy Alloy for Fusion Energy Applications

Refractory high-entropy alloys (HEAs) are promising candidates for next-generation nuclear applications, particularly fusion reactors, due to their excellent high-temperature mechanical properties and irradiation resistance. Here, the microstructure and mechanical behavior were investigated for an equimolar WTaTiVZr HEA, designed from a palette of low-activation elements. The as-cast alloy exhibited a dendritic microstructure composed of W-Ta rich dendrites and Zr-Ti-V rich inter-dendritic regions, both possessing a body-centered cubic (BCC) crystal structure. Room temperature bulk compression tests showed ultra-high strength of around 1.6 GPa and plastic strain ~6%, with fracture surfaces showing cleavage facets. The alloy also demonstrated excellent high-temperature strength of ~650 MPa at 500 °C. Scratch-based fracture toughness was ~38 MPa√m for the as-cast WTaTiVZr HEA compared to ~25 MPa√m for commercially used pure tungsten. This higher value of fracture toughness indicates superior damage tolerance relative to commercially used pure tungsten. These results highlight the alloy’s potential as a low-activation structural material for high-temperature plasma-facing components (PFCs) in fusion reactors.

Physics↗

DOE OSTI2024

Mapping Geothermal Indicator Minerals Using Fusion of Target Detection Algorithms

Mineral mapping from satellite images provides valuable insights into subsurface mineral alteration for geothermal exploration. In previous studies, eight fundamental algorithms were used for mineral mapping utilizing USGS spectra, a collection of reflectance spectra containing samples of minerals, rocks, and soils created by the USGS. We used an ASD FieldSpec 4 Hi-RES NG portable spectrometer to collect spectra for analyzing ASTER images of the Coso Geothermal Field. Then, we established the ground-truth information and the spectral library by analyzing 97 samples. Samples collected from the field were analyzed using the CSIRO TSG (The Spectral Geologist of the Commonwealth Scientific and Industrial Research Organization). Based on the mineralogy study, multiple high-purity spectra of geothermal alteration minerals were selected from collected data, including alunite, chalcedony, hematite, kaolinite, and opal. Eight mineral spectral target detection algorithms were applied to the preprocessed satellite data with a proposed local spectral library. We measured the highest overall accuracy of 87% for alunite, 95% for opal, 83% for chalcedony, 60% for hematite, and 96% for kaolinite out of these eight algorithms. Three, four, five, and eight algorithms were fused to extract mineral alteration with the obtained target detection results. The results prove that the fusion of algorithms gives better results than using individual ones. In conclusion, this paper discusses the significance of evaluating different mapping algorithms. It proposes a robust fusion approach to extract mineral maps as an indicator for geothermal exploration.

Cavur, Mahmut↗

DOE OSTI2022

Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach

False alerts due to misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to severe economic and operational damage. However, research using deep learning to reduce false alerts often requires the physical and cyber sensor data to be trustworthy. Implicit trust is a major problem for artificial intelligence or machine learning (AI/ML) in cyber-physical system (CPS) security, because when these solutions are most urgently needed is also when they are most at risk (e.g., during an attack). To address this, the Inter-Domain Evidence theoretic Approach for Inference (IDEA-I) is proposed that reframes the detection problem as how to make good decisions given uncertainty. Specifically, an evidence theoretic approach leveraging Dempster–Shafer (DS) combination rules and their variants is proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from supervised-learning classifiers. Using this model, a location-cum-domain-based fusion framework is proposed to evaluate the detector’s performance using disjunctive, conjunctive, and cautious conjunctive rules. The approach is demonstrated in a cyber-physical power system testbed, and the classifiers are trained with datasets from Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, we consider plausibility, belief, pignistic, and general Bayesian theorem-based metrics as decision functions. To improve the performance, a multi-objective-based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. Finally, we present a software application to evaluate the DS fusion approaches with different parameters and architectures.

42 ENGINEERING↗

DOE OSTI2025

Effect of radial pressure corrugations and profile shearing on turbulence in Fusion plasmas

Microturbulence can produce stationary fine-scale radial corrugations on the plasma density and temperature gradients in magnetic confinement fusion devices. We show that these structures play a significant role in regulating turbulent transport. We focus on the pedestal, studying electron-temperature-gradient (ETG) mode destabilisation and saturation in the presence of radial corrugations on the electron temperature gradient that could result from microtearing turbulence. A linear dispersion relation is derived for a shearless slab case, which indicates that in the presence of a sinusoidal background corrugation, each ETG mode splits into three distinct eigenvalues, with one being the original, one being more unstable and one being less unstable. However, despite the presence of more unstable linear modes, nonlinear gyrokinetic simulations of ETG with corrugated background electron temperature show a reduction of fluxes. Our investigation reveals a radial variation of the phase velocity of the modes that is proportional to the diamagnetic drift velocity and the local pressure gradient. The associated profile shearing breaks the turbulent eddies apart, reducing the transport level. This profile shearing resulting from fine-scale pressure corrugations could be a ubiquitous turbulence saturation mechanism not just in Fusion plasmas, but in Astrophysics and other areas.

FOS: Physical sciences↗

DOE OSTI2023

Global nitrogen and sulfur deposition mapping using a measurement–model fusion approach

Abstract. Global reactive nitrogen (N) deposition has more than tripled since 1860 and is expected to remain high due to food production and fossil fuel consumption. Global sulfur emissions have been decreasing worldwide over the last 30 years, but many regions are still experiencing unhealthily high levels of deposition. We update the 2010 global deposition budget for reactive nitrogen and sulfur components with new regional wet deposition measurements from Asia, improving the ensemble results of 11 global chemistry transport models from the second phase of the United Nations Economic Commission for Europe's Task Force on Hemispheric Transport of Air Pollution (HTAP II). The observationally adjusted global N deposition budget is 114.5 Tg N, representing a minor increase of 1 % from the model-only derived values, and the adjusted global sulfur deposition budget is 88.9 Tg S, representing a 6.5 % increase from the modeled values, using an interpolation distance of 2.5∘. Regionally, deposition adjustments can be up to ∼ 73 % for nitrogen and 112 % for sulfur. Our study demonstrates that a global measurement–model fusion approach can improve N and S deposition model estimates at a regional scale, with sufficient availability of observations; however, in large parts of the world, alternative approaches need to be explored. The analysis presented here represents a step forward toward the World Meteorological Organization's goal of global fusion products for accurately mapping harmful air pollution deposition.

Rubin, Hannah J. (ORCID:0000000182226924)↗

DOE OSTI2024

Production rate calibration for cosmogenic 10 Be in pyroxene by applying a rapid fusion method to 10 Be-saturated samples from the Transantarctic Mountains, Antarctica

Measurements of multiple cosmogenic nuclides in a single sample are valuable for various applications of cosmogenic nuclide exposure dating and allow for correcting exposure ages for surface weathering and erosion and establishing exposure–burial history. Here we provide advances in the measurement of cosmogenic 10 Be in pyroxene and constraints on the production rate that provide new opportunities for measurements of multi-nuclide systems, such as 10 Be/ 3 He, in pyroxene-bearing samples. We extracted and measured cosmogenic 10 Be in pyroxene from two sets of Ferrar Dolerite samples collected from the Transantarctic Mountains in Antarctica. One set of samples has 10 Be concentrations close to saturation, which allows for the production rate calibration of 10 Be in pyroxene by assuming production–decay equilibrium. The other set of samples, which has a more recent exposure history, is used to determine if a rapid fusion method can be successfully applied to samples with Holocene to Last Glacial Maximum exposure ages. From measured 10 Be concentrations in the near-saturation sample set we find the production rate of 10 Be in pyroxene to be 3.74 ± 0.10 atoms g -1 yr -1 , which is consistent with 10 Be/ 3 He paired nuclide ratios from samples assumed to have simple exposure. Given the high 10 Be concentration measured in this sample set, a sample mass of ~ 0.5 g of pyroxene is sufficient for the extraction of cosmogenic 10 Be from pyroxene using a rapid fusion method. However, for the set of samples that have low 10 Be concentrations, measured concentrations were higher than expected. We attribute spuriously high 10 Be concentrations to failure in removing all meteoric 10 Be and/or a highly variable and poorly quantified procedural blank background correction.

58 GEOSCIENCES↗

DOE OSTI2022

A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion

Abstract. Rivers and river habitats around the world are under sustained pressure from human activities and the changing global environment. Our ability to quantify and manage the river states in a timely manner is critical for protecting the public safety and natural resources. In recent years, vector-based river network models have enabled modeling of large river basins at increasingly fine resolutions, but are computationally demanding. This work presents a multistage, physics-guided, graph neural network (GNN) approach for basin-scale river network learning and streamflow forecasting. During training, we train a GNN model to approximate outputs of a high-resolution vector-based river network model; we then fine-tune the pretrained GNN model with streamflow observations. We further apply a graph-based, data-fusion step to correct prediction biases. The GNN-based framework is first demonstrated over a snow-dominated watershed in the western United States. A series of experiments are performed to test different training and imputation strategies. Results show that the trained GNN model can effectively serve as a surrogate of the process-based model with high accuracy, with median Kling–Gupta efficiency (KGE) greater than 0.97. Application of the graph-based data fusion further reduces mismatch between the GNN model and observations, with as much as 50 % KGE improvement over some cross-validation gages. To improve scalability, a graph-coarsening procedure is introduced and is demonstrated over a much larger basin. Results show that graph coarsening achieves comparable prediction skills at only a fraction of training cost, thus providing important insights into the degree of physical realism needed for developing large-scale GNN-based river network models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Classification of Dissolution Events Using Fusion of Effluents Measurements and Classifiers

Classifiers for dissolution events at a radiochemical processing facility are studied using gamma spectra measurements of effluents collected by a high purity germanium detector located at its off-gas stack. Data sets collected at the Oak Ridge National Laboratory’s Radiochemical Engineering Development Center under a Pu dissolution campaign spanning a three months period are utilized. Features corresponding to the activity levels of 15 radionuclides, including isotopes of iodine, krypton, and xenon, that are indicated by the target decay chains, are computed from the spectra at 1 hour intervals. A conceptualization diagram is developed to reflect the steps from the source to measurement to feature computation that depend on fission products indicated by decay chains, chemical processing, and effluents transport to the off-gas stack. A diverse set of eight classifiers based on different design principles are trained using the ground truth data for this campaign, and the outputs of top three classifiers, namely, classification trees, Ensemble of Trees (EOT), and k-nearest neighbor, are combined using EOT classifier-fuser. Our results show that for 5-fold cross validation, features associated with isotopes of xenon provide the lowest classification error among the different elements across the classifiers; the classification error is furthered improved when all 15 isotope features are used by each classifier, and it is again improved by the fusion of three classifiers. Further reduction in classification error is achieved by using a measurement window of 1-2 days which is identified based on half-life time estimates of the isotopes; it is long enough for the stabilization of feature estimates while being short enough not to be affected by the follow on dissolution events. As a net result of feature and classifier fusion, combined with the incorporation of decay chain and isotope half-life information, this approach achieves 98% detection rate while maintaining a false alarm rate under 2% for this data set.

Rao, Nageswara↗

DOE OSTI2021

Local and global decision fusion for cyber-physical system abnormality detection

Monitoring nodes may generate a series of current monitoring node values over time representing current operation of a cyber-physical system. A decision fusion computer platform may receive, from a local status determination module, an indication of whether each node has an initial local status of “normal”/“abnormal” and a local certainty score (with higher values of the local certainty score representing greater likelihood of abnormality). The computer platform may also receive, from a global status determination module, an indication of whether the system has an initial global status of “normal”/“abnormal” and a global certainty score. The computer platform may output, for each node, a fused local status of “normal” or “abnormal,” at least one fused local status being based on the initial global status. The decision fusion computer platform may also output a fused global status of “normal” or “abnormal” based on at least one initial local status.

Abbaszadeh, Masoud↗

DOE OSTI2021

Fusion Conference Awards 2020

Since its inception, ISIF has promoted a high-quality technical program at FUSION. One way to encourage this excellence is to promote the paper awards program. Accordingly, each year the conference includes recognition of the best regular papers and the best student papers. Student papers are those for which the lead author is a full-time graduate (or undergraduate) student at an accredited university. As mandated by the ISIF Board of Directors, the best paper receives the Jean Pierre Le Cadre Award. The best student paper receives the Tammy L. Blair Award. These awards honor the efforts and commitment of both Jean-Pierre and Tammy to the international fusion community over many years.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Measurement of the Higgs boson production via vector boson fusion and its decay into bottom quarks in proton-proton collisions at $\sqrt{s}$ = 13 TeV

A measurement of the Higgs boson (H) production via vector boson fusion (VBF) and its decay into a bottom quark-antiquark pair ($\mathrm{b\bar{b}}$) is presented using proton-proton collision data recorded by the CMS experiment at $\sqrt{s}$ = 13 TeV and corresponding to an integrated luminosity of 90.8 fb$^{-1}$. Treating the gluon-gluon fusion process as a background and constraining its rate to the value expected in the standard model (SM) within uncertainties, the signal strength of the VBF process, defined as the ratio of the observed signal rate to that predicted by the SM, is measured to be $\mu^\text{qqH}_\mathrm{Hb\bar{b}}$ = 1.01$^{+0.55}_{-0.46}$. The VBF signal is observed with a significance of 2.4 standard deviations relative to the background prediction, while the expected significance is 2.7 standard deviations. Considering inclusive Higgs boson production and decay into bottom quarks, the signal strength is measured to be $\mu^\text{incl.}_\mathrm{Hb\bar{b}}$ = 0.99$^{+0.48}_{-0.41}$, corresponding to an observed (expected) significance of 2.6 (2.9) standard deviations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

A LASER POWDER BED FUSION ADDITIVE MANUFACTURING CHARACTERIZATION APPROACH AND DATASET HIGHLIGHTING IMPLICATIONS FOR CROSS-PLATFORM PERFORMANCE CONSISTENCY

Additive Manufacturing (AM) has remained a rapidly growing technology over the past decade, which affords many benefits. However, there is one main hinderance to widespread adoption: Products produced on one Laser Powder Bed Fusion (LPBF) AM system can vary in mechanical performance from those produced on another system. This is due to the fact that while LPBF AM system is producing a product’s shape, it is also producing the product’s material. Due to the complex and dynamic nature of LPBF, items fabricated on one system can be different in terms of dimensions, microstructure and quality compared to items fabricated on a different make or model system. This lack of cross-platform performance consistency makes widespread adoption challenging. This work was created to help understand the variation between different Laser Powder Bed Fusion (LPBF) Additive Manufacturing (AM) systems across different manufacturers, models, and users, and to provide insight into how to ensure product consistency regardless of system selection.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Design considerations for colliders using co-moving beams for fusion cross section research

Collisions between beams moving in the same direction but with different velocities allow for low center of mass energy collisions with higher energy beams. These types of systems lend themselves to fusion interactions since they require low center of mass kinetic energies but can benefit from higher energies in the constituent beams. Here we cover the design considerations for a machine that could study these types of interactions. Furthermore, we especially highlight reactants that can be stored and collided in a single ring, using a collision timing system known as gear-changing, which could allow for a proof-of-concept experiment in an existing machine.