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DOE OSTI2015

Complement activation as a bioequivalence issue relevant to the development of generic liposomes and other nanoparticulate drugs

Liposomes are known to activate the complement (C) system, which can lead in vivo to a hypersensitivity syndrome called C activation-related pseudoallergy (CARPA). CARPA has been getting increasing attention as a safety risk of i.v. therapy with liposomes, whose testing is now recommended in bioequivalence evaluations of generic liposomal drug candidates. This review highlights the adverse consequences of C activation, the unique symptoms of CARPA triggered by essentially all i.v. administered liposomal drugs, and the various features of vesicles influencing this adverse immune effect. For the case of Doxil, we also address the mechanism of C activation and the opsonization vs. long circulation (stealth) paradox. In reviewing the methods of assessing C activation and CARPA, we delineate the most sensitive porcine model and an algorithm for stepwise evaluation of the CARPA risk of i.v. liposomes, which are proposed for standardization for preclinical toxicology evaluation of liposomal and other nanoparticulate drug candidates. - Highlights: • Outlining of difficulties in generic development of liposomal drugs. • New regulatory requirements to evaluate CARPA in preclinical studies. • Review of complement activation by liposomes and its adverse consequences (CARPA). • Assays of C activation in vitro and CARPA in vivo, with the porcine test in focus. • Decision tree how to handle the risk of CARPA assessed by a battery of tests.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Scalable methodology for large scale building energy improvement: Relevance of calibration in model-based retrofit analysis

The increasing interest in retrofitting of existing buildings is motivated by the need to make a major contribution to enhancing building energy efficiency and reducing energy consumption and CO2 emission by the built environment. This paper examines the relevance of calibration in model-based analysis to support decision-making for energy and carbon efficiency retrofits of individual buildings and portfolios of buildings. The authors formulate a set of real retrofit decision-making situations and evaluate the role of calibration by using a case study that compares predictions and decisions from an uncalibrated model with those of a calibrated model. The case study illustrates both the mechanics and outcomes of a practical alternative to the expert- and time-intense application of dynamic energy simulation models for large-scale retrofit decision-making under uncertainty.

Heo, Yeonsook↗

DOE OSTI2025

Non-stationary precipitation design standards for stormwater infrastructure modernization at USAF installations

The resilience of defense infrastructure systems to a changing climate is critical for national security. Climate induced recurrent flooding is already impacting over 20 U.S. Air Force installations, underscoring the urgency of revisiting precipitation standards and stormwater infrastructure design. Despite growing scientific knowledge and an expanding set of tools for updating outdated precipitation standards based on the assumption of climate stationarity, the adoption of climate informed analyses remain limited in practice. This study utilizes an existing framework to update Intensity (or Depth)-Duration-Frequency (DDF) curves using an ensemble of future climate projections. Change factors in precipitation estimates are derived and applied to six USAF installations across the U.S. The analysis is further extended to evaluate the implications of climate-informed DDFs on stormwater infrastructure performance and flood analysis at Tyndall AFB. Results indicate that the current design precipitation estimates are likely to become obsolete in all six USAF bases by the end of the century. The wide range of change factors across 32 GCM ensembles highlights the need to integrate uncertainty and evolving scientific data into infrastructure planning. The study also finds that the impacts of a changing climate vary spatially and temporally, emphasizing the value of localized analysis for infrastructure decision-making. The work advances ongoing DoD and societal efforts to implement adaptation strategies aimed at enhancing infrastructure resilience.

Intensity-duration-frequency curves↗

DOE OSTI2024

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

Integrating monitoring data to efficiently update reservoir pressure and CO 2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse modeling techniques are commonly used to fuse observational data and refine reservoir model parameters, thereby improving state variable forecasts. However, these techniques often rely on linear or Gaussian assumptions, which can limit their effectiveness in accurately predicting state variables. Moreover, simulating large-scale three-dimensional (3D) GCS problems is computationally expensive, making iterative runs in inverse problems prohibitive. To address these challenges, we propose a conditional generative model utilizing the score-based diffusion method for real-time 3D pressure and saturation field distribution predictions. Our approach involves solving the score function with a mini-batch-based Monte Carlo estimator to generate labeled data. This data is subsequently employed to train a fully connected neural network, enabling it to learn the conditional sample generator within a supervised learning framework. This method enables the rapid generation of a large ensemble of predictions, facilitating comprehensive uncertainty quantification of state variables. Here we applied our method to forecast the dynamic 3D distributions of pressure and saturation fields over a 30-year injection period. The statistical assessment with low root mean square error (RMSE) values demonstrates that our method can accurately predict the spatiotemporal distributions of both pressure and saturation fields. Moreover, the developed conditional generative model shows high computational efficiency by generating 100 ensemble forecasts of 3D state variables in less than 10 min. The consistency between ensemble averages and ground truth values further illustrates the model’s capability to capture state variable dynamics during the CO 2 plume injection process. Notably, the ground truth values fall within the ensemble forecasts, indicating that our uncertainty quantification effectively captures variability and potential noise in the observations. Thus, the developed conditional generative model proves to be a more efficient, accurate, and practical tool for GCS applications, facilitating timely risk analysis and informed decision-making.

58 GEOSCIENCES↗

DOE OSTI2014

In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches

Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure–toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data, optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R{sup 2}) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R{sup 2} and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals. - Graphical abstract: Importance of input variables in DTB and DTF classification models for (a) two-category, and (b) four-category toxicity intervals in T. pyriformis data. Generalization and predictive abilities of the constructed (c) DTB and (d) DTF regression models to predict the T. pyriformis toxicity of diverse chemicals. - Highlights: • Ensemble learning (EL) based models constructed for toxicity prediction of chemicals • Predictive models used a few simple non-quantum mechanical molecular descriptors. • EL-based DTB/DTF models successfully discriminated toxic and non-toxic chemicals. • DTB/DTF regression models precisely predicted toxicity of chemicals in multi-species. • Proposed EL based models can be used as tool to predict toxicity of new chemicals.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Attractor comparisons based on density

Recognizing a chaotic attractor can be seen as a problem in pattern recognition. Some feature vector must be extracted from the attractor and used to compare to other attractors. The field of machine learning has many methods for extracting feature vectors, including clustering methods, decision trees, support vector machines, and many others. In this work, feature vectors are created by representing the attractor as a density in phase space and creating polynomials based on this density. Density is useful in itself because it is a one dimensional function of phase space position, but representing an attractor as a density is also a way to reduce the size of a large data set before analyzing it with graph theory methods, which can be computationally intensive. The density computation in this paper is also fast to execute. In this paper, as a demonstration of the usefulness of density, the density is used directly to construct phase space polynomials for comparing attractors. Comparisons between attractors could be useful for tracking changes in an experiment when the underlying equations are too complicated for vector field modeling.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

A method of building of decision trees based on data from wearable device during a rehabilitation of patients with tibia fractures

Nowadays technologies which are used in traumatology are a combination of mechanical, electronic, calculating and programming tools. Relevance of development of mobile applications for an expeditious data processing which are received from medical devices (in particular, wearable devices), and formulation of management decisions increases. Using of a mathematical method of building of decision trees for an assessment of a patient’s health condition using data from a wearable device considers in this article.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning

In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a method to photometrically classify type Ia supernovae based on machine learning with redshifts that are derived from the SN light-curves. This method is implemented on real data from the SNLS deferred pipeline, a purely photometric pipeline that identifies SNe Ia at high-redshifts (0.2 < z < 1.1). Our method consists of two stages: feature extraction (obtaining the SN redshift from photometry and estimating light-curve shape parameters) and machine learning classification. We study the performance of different algorithms such as Random Forest and Boosted Decision Trees. We evaluate the performance using SN simulations and real data from the first 3 years of the Supernova Legacy Survey (SNLS), which contains large spectroscopically and photometrically classified type Ia samples. Using the Area Under the Curve (AUC) metric, where perfect classification is given by 1, we find that our best-performing classifier (Extreme Gradient Boosting Decision Tree) has an AUC of 0.98.We show that it is possible to obtain a large photometrically selected type Ia SN sample with an estimated contamination of less than 5%. When applied to data from the first three years of SNLS, we obtain 529 events. We investigate the differences between classifying simulated SNe, and real SN survey data. In particular, we find that applying a thorough set of selection cuts to the SN sample is essential for good classification. This work demonstrates for the first time the feasibility of machine learning classification in a high- z SN survey with application to real SN data.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Measuring the electron Yukawa coupling via resonant s-channel Higgs production at FCC-ee

The Future Circular Collider (FCC-ee) offers the unique opportunity of studying the Higgs Yukawa coupling to the electron, \(y_\mathrm {e}\) , via resonant s -channel production, \(\mathrm {e^+e^-}\rightarrow \mathrm {H}\) , in a dedicated run at \(\sqrt{s} = m_\mathrm {H}\) . The signature for direct Higgs production is a small rise in the cross sections for particular final states, consistent with Higgs decays, over the expectations for their occurrence due to Standard Model (SM) background processes involving \(\mathrm {Z}^*\) , \(\gamma ^*\) , or t -channel exchanges alone. Performing such a measurement is remarkably challenging for four main reasons. First, the low value of the e \(^\pm \) mass leads to a tiny \(y_\mathrm {e}\) coupling and correspondingly small cross section: \(\sigma _\mathrm {ee\rightarrow H} \propto m_\mathrm {e}^2 = 0.57\) fb accounting for initial-state \(\gamma \) radiation. Second, the \(\mathrm {e^+e^-}\) beams must be monochromatized such that the spread of their centre-of-mass (c.m.) energy is commensurate with the narrow width of the SM Higgs boson, \(\varGamma _\mathrm {H} = 4.1\) MeV, while keeping large beam luminosities. Third, the Higgs mass must also be known beforehand with a few-MeV accuracy in order to operate the collider at the resonance peak, \(\sqrt{s} = m_\mathrm {H}\) . Last but not least, the cross sections of the background processes are many orders-of-magnitude larger than those of the Higgs decay signals. A preliminary generator-level study of 11 Higgs decay channels using a multivariate analysis, which exploits boosted decision trees to discriminate signal and background events, identifies two final states as the most promising ones in terms of statistical significance: \(\mathrm {H}\rightarrow gg\) and \(\mathrm {H}\rightarrow \mathrm {W}\mathrm {W}^*\!\rightarrow \ell \nu \) + 2 jets. For a benchmark monochromatization with 4.1-MeV c.m. energy spread (leading to \(\sigma _\mathrm {ee\rightarrow H} = 0.28\) fb) and 10 ab \(^{-1}\) of integrated luminosity, a \(1.3\sigma \) signal significance can be reached, corresponding to an upper limit on the e \(^\pm \) Yukawa coupling at 1.6 times the SM value: |y e | < 1.6|y e SM | at 95% confidence level, per FCC-ee interaction point per year. Directions for future improvements of the study are outlined.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2014

Reduced Order Modeling for Prediction and Control of Large-Scale Systems.

This report describes work performed from June 2012 through May 2014 as a part of a Sandia Early Career Laboratory Directed Research and Development (LDRD) project led by the first author. The objective of the project is to investigate methods for building stable and efficient proper orthogonal decomposition (POD)/Galerkin reduced order models (ROMs): models derived from a sequence of high-fidelity simulations but having a much lower computational cost. Since they are, by construction, small and fast, ROMs can enable real-time simulations of complex systems for onthe- spot analysis, control and decision-making in the presence of uncertainty. Of particular interest to Sandia is the use of ROMs for the quantification of the compressible captive-carry environment, simulated for the design and qualification of nuclear weapons systems. It is an unfortunate reality that many ROM techniques are computationally intractable or lack an a priori stability guarantee for compressible flows. For this reason, this LDRD project focuses on the development of techniques for building provably stable projection-based ROMs. Model reduction approaches based on continuous as well as discrete projection are considered. In the first part of this report, an approach for building energy-stable Galerkin ROMs for linear hyperbolic or incompletely parabolic systems of partial differential equations (PDEs) using continuous projection is developed. The key idea is to apply a transformation induced by the Lyapunov function for the system, and to build the ROM in the transformed variables. It is shown that, for many PDE systems including the linearized compressible Euler and linearized compressible Navier-Stokes equations, the desired transformation is induced by a special inner product, termed the “symmetry inner product”. Attention is then turned to nonlinear conservation laws. A new transformation and corresponding energy-based inner product for the full nonlinear compressible Navier-Stokes equations is derived, and it is demonstrated that if a Galerkin ROM is constructed in this inner product, the ROM system energy will be bounded in a way that is consistent with the behavior of the exact solution to these PDEs, i.e., the ROM will be energy-stable. The viability of the linear as well as nonlinear continuous projection model reduction approaches developed as a part of this project is evaluated on several test cases, including the cavity configuration of interest in the targeted application area. In the second part of this report, some POD/Galerkin approaches for building stable ROMs using discrete projection are explored. It is shown that, for generic linear time-invariant (LTI) systems, a discrete counterpart of the continuous symmetry inner product is a weighted L2 inner product obtained by solving a Lyapunov equation. This inner product was first proposed by Rowley et al., and is termed herein the “Lyapunov inner product“. Comparisons between the symmetry inner product and the Lyapunov inner product are made, and the performance of ROMs constructed using these inner products is evaluated on several benchmark test cases. Also in the second part of this report, a new ROM stabilization approach, termed “ROM stabilization via optimization-based eigenvalue reassignment“, is developed for generic LTI systems. At the heart of this method is a constrained nonlinear least-squares optimization problem that is formulated and solved numerically to ensure accuracy of the stabilized ROM. Numerical studies reveal that the optimization problem is computationally inexpensive to solve, and that the new stabilization approach delivers ROMs that are stable as well as accurate. Summaries of “lessons learned“ and perspectives for future work motivated by this LDRD project are provided at the end of each of the two main chapters.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Enhancing Site Screening for Underground Hydrogen Storage: Qualitative Site Quality Assessment - SHASTA: Subsurface Hydrogen Assessment, Storage, and Technology Acceleration Project

The global shift towards renewable energy sources to mitigate fossil fuel dependency and meet carbon emission targets by 2050 has underscored the importance of innovative solutions to address energy supply-demand imbalances. Underground Hydrogen Storage (UHS) has emerged as a promising strategy to store excess renewable energy in subsurface formations for future retrieval and utilization. This report focuses on enhancing the site screening process for UHS facilities. By drawing on insights from prior research to identify key criteria influencing site suitability, our objective is to present a comprehensive set of sixteen specific criteria essential for refining the selection of UHS sites. These criteria cover various aspects such as reservoir performance, legal access, regulatory compliance, economic viability, public acceptance, and safety and security considerations. Our proposed methodology allows users to evaluate potential sites using binary responses, transitioning from isolated factors to broader considerations that facilitate a qualitative assessment of site suitability. This method enables comparative analysis and informed decision-making, supporting stakeholders in the site selection process. Our approach is designed as a guiding framework rather than a rigid template for site screening, emphasizing the importance of customizing approaches to individual circumstances. As UHS site development project evolves, adopting a comprehensive selection methodology that integrates holistic evaluations will be essential for ensuring efficient and effective site screening processes tailored to specific project needs.

08 HYDROGEN↗

DOE OSTI2024

Enhancing Site Screening for Underground Hydrogen Storage: Qualitative Site Quality Assessment - SHASTA: Subsurface Hydrogen Assessment, Storage, and Technology Acceleration Project

08 HYDROGEN↗

DOE OSTI2020

Mean Estimate Distances for Galaxies with Multiple Estimates in NED-D

Numerous research topics rely on an improved cosmic distance scale (e.g., cosmology, gravitational waves) and the NASA/IPAC Extragalactic Database of Distances (NED-D) supports those efforts by tabulating multiple redshift-independent distances for 12,000 galaxies (e.g., Large Magellanic Cloud (LMC) zero-point). Six methods for securing a mean estimate distance (MED) from the data are presented (e.g., indicator and Decision Tree). All six MEDs yield surprisingly consistent distances for the cases examined, including for the key benchmark LMC and M106 galaxies. The results underscore the utility of the NED-D MEDs in bolstering the cosmic distance scale and facilitating the identification of systematic trends.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1972

Data Transmission Signal Design and Analysis

The error performances of several digital signaling methods are determined as a function of a specified signal-to-noise ratio. Results are obtained for Gaussian noise and impulse noise. Performance of a receiver for differentially encoded biphase signaling is obtained by extending the results of differential phase shift keying. The analysis presented obtains a closed-form answer through the use of some simplifying assumptions. The results give an insight into the analysis problem, however, the actual error performance may show a degradation because of the assumptions made in the analysis. Bipolar signaling decision-threshold selection is investigated. The optimum threshold depends on the signal-to-noise ratio and requires the use of an adaptive receiver.

Moore, J. D.↗

NASA NTRS1975

Summary and recommendations

An economic analysis using econometric and cost benefit analysis techniques was performed to determine the feasibility of space processing of certain products. The overall objectives of the analysis were (1) to determine specific products or processes uniquely connected with space manufacturing, (2) to select a specific product or process from each of the areas of semiconductors, metals, and biochemicals, and (3) to determine the overall price/cost structure of each product or process considered. The economic elements of the analysis involved a generalized decision making format for analyzing space manufacturing, a comparative cost study of the selected processes in space vs. earth manufacturing, and a supply and demand study of the economic relationships of one of the manufacturing processes. Space processing concepts were explored. The first involved the use of the shuttle as the factory with all operations performed during individual flights. The second concept involved a permanent unmanned space factory which would be launched separately. The shuttle in this case would be used only for maintenance and refurbishment. Finally, some consideration was given to a permanent manned space factory.

Source record↗

NASA NTRS1976

TCM aircraft piston engine emission reduction program

The technology necessary to safely reduce general aviation piston engine exhaust emissions to meet the EPA 1980 Emission Standards with minimum adverse effects on cost, weight, fuel economy, and performance was demonstrated. A screening and assessment of promising emission reduction concepts was provided, and the preliminary design and development of those concepts was established. A system analysis study and a decision making procedure were used by TCM to evaluate, trade off, and rank the candidate concepts from a list of 14 alternatives. Cost, emissions, and 13 other design criteria considerations were defined and traded off against each candidate concept to establish its merit and emission reduction usefulness. A computer program was used to aid the evaluators in making the final choice of three concepts.

Rezy, B. J.↗

NASA NTRS1987

Advanced probabilistic method of development

Advanced structural reliability methods are utilized on the Probabilistic Structural Analysis Methods (PSAM) project to provide a tool for analysis and design of space propulsion system hardware. The role of the effort at the University of Arizona is to provide reliability technology support to this project. PSAM computer programs will provide a design tool for analyzing uncertainty associated with thermal and mechanical loading, material behavior, geometry, and the analysis methods used. Specifically, reliability methods are employed to perform sensitivity analyses, to establish the distribution of a critical response variable (e.g., stress, deflection), to perform reliability assessment, and ultimately to produce a design which will minimize cost and/or weight. Uncertainties in the design factors of space propulsion hardware are described by probability models constructed using statistical analysis of data. Statistical methods are employed to produce a probability model, i.e., a statistical synthesis or summary of each design variable in a format suitable for reliability analysis and ultimately, design decisions.

Wirsching, P. H.↗

NASA NTRS1992

Hurricane risk assessment to rollback or ride out a cost versus loss decision making approach

The potential exists that a hurricane striking the Kennedy Space Center while a Space Shuttle is on the pad. Winds in excess of 74.5 knots could cause the failure of the holddown bolts bringing about the catastrophic loss of the entire vehicle. Current plans call for the rollback of the shuttle when winds of that magnitude are forecast to strike the center. As this is costly, a new objective method for making rollback/rideout decisions based upon Bayesian Analysis and economic cost versus loss is presented.

Wohlman, Richard A.↗