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DOE OSTI2021

Memory-Related Frontal Brainwaves Predict Transition to Mild Cognitive Impairment in Healthy Older Individuals Five Years Before Diagnosis

Background: Early prognosis of high-risk older adults for amnestic mild cognitive impairment (aMCI), using noninvasive and sensitive neuromarkers, is key for early prevention of Alzheimer’s disease. We have developed individualized measures in electrophysiological brain signals during working memory that distinguish patients with aMCI from age-matched cognitively intact older individuals. Objective: Here we test longitudinally the prognosis of the baseline neuromarkers for aMCI risk. We hypothesized that the older individuals diagnosed with incident aMCI already have aMCI-like brain signatures years before diagnosis. Methods: Electroencephalogram (EEG) and memory performance were recorded during a working memory task at baseline. The individualized baseline neuromarkers, annual cognitive status, and longitudinal changes in memory recall scores up to 10 years were analyzed. Results: Seven of the 19 cognitively normal older adults were diagnosed with incident aMCI for a median 5.2 years later. The seven converters’ frontal brainwaves were statistically identical to those patients with diagnosed aMCI (n = 14) at baseline. Importantly, the converters’ baseline memory-related brainwaves (reduced mean frontal responses to memory targets) were significantly different from those who remained normal. Furthermore, differentiation pattern of left frontal memory-related responses (targets versus nontargets) was associated with an increased risk hazard of aMCI (HR = 1.47, 95% CI 1.03, 2.08). Conclusion: The memory-related neuromarkers detect MCI-like brain signatures about five years before diagnosis. The individualized frontal neuromarkers index increased MCI risk at baseline. These noninvasive neuromarkers during our Bluegrass memory task have great potential to be used repeatedly for individualized prognosis of MCI risk and progression before clinical diagnosis.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Low Cost (CAPEX and variable): Tool design for cell and module fabrication with thin, free-standing silicon wafers

This project aimed to develop technologies that can potentially enable free-standing thin (<80 μm) wafer in today’s manufacturing lines with high production yield, and thereby reduce capex barriers of silicon photovoltaics cells and modules. One of the major benefits is that thin wafer dramatically reduces the amount of polysilicon required. As a result, it can lead to reduction in the capital expenditures associated with polysilicon refining and wafer fabrication, which together are more than half of the total capital expenditure to manufacture Si PV module. We focused our efforts on developing the tools needed to enable high yield fabrication of wafer, cell, and module with thin silicon wafers. However, as the wafer thickness reduces, the major challenge is that wafer breakage increases significantly. Three technological areas were explored in this project to improve the production yield of the silicon wafer, namely detection of edge cracks via dark-field near-infrared (NIR) scattering; (2) wafer handling using controlled temperature profiles; (3) manufacturable low-stress cell interconnection for multiwire. First, the formation of wafer cracks in submillimeter length is one of the reasons that causes wafer breakages. Crack detection tools are needed to enable us to locate and track the wafer crack during manufacturing, so that we can improve the process to reduce initialization. The state-of-the-art crack detection technique cannot fulfill the need for measuring submillimeter edge cracks detrimental for thin wafers. The prototype developed in this project demonstrates the scanning of microcracks near wafer edges. With a semi-automatic laboratory setup, the submillimeter cracks were reliably detected near the edges in multi-Si wafers. The smallest detectable crack is 200 µm in length in slow scans; and submillimeter cracks are detected in high-throughput scans at the scan speed of >0.5 m/s, which is compatible with the inline detection of a manufacturing line at least 1 sec/wafer. This detection limit is a significant advancement in comparison to the benchmarked industrial tool. Second, wafer handling with the well-controlled temperature profile was thought to be the solution to reduce crack initiation and propagation during the manufacturing. However, without crack detection being widely adopted in production line, we did not find a strong industrial pull toward this technology. We did an initial literature survey and then diverted our efforts to the other tasks. Third, the innovation on low-stress multi-wire interconnection tackles a fundamental problem in the standard interconnection scheme. The standard over-under “zig-zag” interconnection induces a significant amount of stress into the soldering point on the cell whenever PV module is under stress, e.g., thermal cycling, transportation, and installation. Therefore, the interconnection process was re-designed in this project to allow for significant movement between adjacent solar cells, e.g., due to thermal expansion, without building up stresses on the solar cells or solder joints. The new interconnection method with the cross-connect wire also simplifies the tabbing and stringing process by replacing the standard over-under method with an off-cell interconnect from top to bottom. A manual tabbing and stringing tool for this new process was prototyped in the lab to demonstrate the fabrication of this new interconnection design. During the test with brass sheets as our “testing cells”, it was found that the mechanical cycling test only broke the interconnection after more than 50,000 cycles, which is equivalent to more than 130 years of the day-and-night thermal cycles. Lastly, throughout the project, we continuously analyzed the PV market with techno-economic analysis to identify the opportunity for thin Si adoption. Even though the drastic cost reduction has already happened in the past five years, our analysis results indicated that we can still save quite significantly in both manufacturing cost and factory capex, Particularly, in order to grow the PV manufacturing capacity to multi-terawatt level, reducing the thickness of silicon wafer is one of the most effective ways to quickly reduce factory capex, and sustain the high growth rate. The technologies developed in this project are readily available to provide some assistances in tackling the production yield problem.

14 SOLAR ENERGY↗

DOE OSTI2018

Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks

Accurate segmentation of specific organ from computed tomography (CT) scans is a basic and crucial task for accurate diagnosis and treatment. To avoid time-consuming manual optimization and to help physicians distinguish diseases, an automatic organ segmentation framework is presented. The framework utilized convolution neural networks (CNN) to classify pixels. To reduce the redundant inputs, the simple linear iterative clustering (SLIC) of super-pixels and the support vector machine (SVM) classifier are introduced. To establish the perfect boundary of organs in one-pixel-level, the pixels need to be classified step-by-step. First, the SLIC is used to cut an image into grids and extract respective digital signatures. Next, the signature is classified by the SVM, and the rough edges are acquired. Finally, a precise boundary is obtained by the CNN, which is based on patches around each pixel-point. The framework is applied to abdominal CT scans of livers and high-resolution computed tomography (HRCT) scans of lungs. The experimental CT scans are derived from two public datasets (Sliver 07 and a Chinese local dataset). Experimental results show that the proposed method can precisely and efficiently detect the organs. This method consumes 38 s/slice for liver segmentation. The Dice coefficient of the liver segmentation results reaches to 97.43%. For lung segmentation, the Dice coefficient is 97.93%. This finding demonstrates that the proposed framework is a favorable method for lung segmentation of HRCT scans.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2025

Neural Active Manifolds: Nonlinear Dimensionality Reduction for Uncertainty Quantification

We present a new approach for nonlinear dimensionality reduction, specifically designed for computationally expensive mathematical models. We leverage autoencoders to discover a one-dimensional neural active manifold (NeurAM) capturing the model output variability, through the aid of a simultaneously learnt surrogate model with inputs on this manifold. Our method only relies on model evaluations and does not require the knowledge of gradients. The proposed dimensionality reduction framework can then be applied to assist outer loop many-query tasks in scientific computing, like sensitivity analysis and multifidelity uncertainty propagation. In particular, we prove, both theoretically under idealized conditions, and numerically in challenging test cases, how NeurAM can be used to obtain multifidelity sampling estimators with reduced variance by sampling the models on the discovered low-dimensional and shared manifold among models. Several numerical examples illustrate the main features of the proposed dimensionality reduction strategy and highlight its advantages with respect to existing approaches in the literature.

Autoencoders↗

DOE OSTI2022

Detection of Forced Change Within Combined Climate Fields Using Explainable Neural Networks

Abstract Assessing forced climate change requires the extraction of the forced signal from the background of climate noise. Traditionally, tools for extracting forced climate change signals have focused on one atmospheric variable at a time, however, using multiple variables can reduce noise and allow for easier detection of the forced response. Following previous work, we train artificial neural networks to predict the year of single‐ and multi‐variable maps from forced climate model simulations. To perform this task, the neural networks learn patterns that allow them to discriminate between maps from different years—that is, the neural networks learn the patterns of the forced signal amidst the shroud of internal variability and climate model disagreement. When presented with combined input fields (multiple seasons, variables, or both), the neural networks are able to detect the signal of forced change earlier than when given single fields alone by utilizing complex, nonlinear relationships between multiple variables and seasons. We use layer‐wise relevance propagation, a neural network explainability tool, to identify the multivariate patterns learned by the neural networks that serve as reliable indicators of the forced response. These “indicator patterns” vary in time and between climate models, providing a template for investigating inter‐model differences in the time evolution of the forced response. This work demonstrates how neural networks and their explainability tools can be harnessed to identify patterns of the forced signal within combined fields.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

On-the-fly closed-loop materials discovery via Bayesian active learning

Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Parametrizing linear generalized Langevin dynamics from explicit molecular dynamics simulations

Fundamental understanding of complex dynamics in many-particle systems on the atomistic level is of utmost importance. Often the systems of interest are of macroscopic size but can be partitioned into a few important degrees of freedom which are treated most accurately and others which constitute a thermal bath. Particular attention in this respect attracts the linear generalized Langevin equation, which can be rigorously derived by means of a linear projection technique. Within this framework, a complicated interaction with the bath can be reduced to a single memory kernel. This memory kernel in turn is parametrized for a particular system studied, usually by means of time-domain methods based on explicit molecular dynamics data. Here, we discuss that this task is more naturally achieved in frequency domain and develop a Fourier-based parametrization method that outperforms its time-domain analogues. Very surprisingly, the widely used rigid bond method turns out to be inappropriate in general. Importantly, we show that the rigid bond approach leads to a systematic overestimation of relaxation times, unless the system under study consists of a harmonic bath bi-linearly coupled to the relevant degrees of freedom.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

A Block-Based Triangle Counting Algorithm on Heterogeneous Environments

Triangle counting is a fundamental building block in graph algorithms. In this article, we propose a block-based triangle counting algorithm to reduce data movement during both sequential and parallel execution. Our block-based formulation makes the algorithm naturally suitable for heterogeneous architectures. The problem of partitioning the adjacency matrix of a graph is well-studied. Our task decomposition goes one step further: it partitions the set of triangles in the graph. By streaming these small tasks to compute resources, we can solve problems that do not fit on a device. We demonstrate the effectiveness of our approach by providing an implementation on a compute node with multiple sockets, cores and GPUs. The current state-of-the-art in triangle enumeration processes the Friendster graph in 2.1 seconds, not including data copy time between CPU and GPU. Using that metric, our approach is 20 percent faster. When copy times are included, our algorithm takes 3.2 seconds. This is 5.6 times faster than the fastest published CPU-only time.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Measurement of time delays in gated radiotherapy for realistic respiratory motions

Purpose: Gated radiotherapy is used to reduce internal motion margins, escalate target dose, and limit normal tissue dose; however, its temporal accuracy is limited. Beam-on and beam-off time delays can lead to treatment inefficiencies and/or geographic misses; therefore, AAPM Task Group 142 recommends verifying the temporal accuracy of gating systems. Many groups use sinusoidal phantom motion for this, under the tacit assumption that use of sinusoidal motion for determining time delays produces negligible error. The authors test this assumption by measuring gating time delays for several realistic motion shapes with increasing degrees of irregularity. Methods: Time delays were measured on a linear accelerator with a real-time position management system (Varian TrueBeam with RPM system version 1.7.5) for seven motion shapes: regular sinusoidal; regular realistic-shape; large (40%) and small (10%) variations in amplitude; large (40%) variations in period; small (10%) variations in both amplitude and period; and baseline drift (30%). Film streaks of radiation exposure were generated for each motion shape using a programmable motion phantom. Beam-on and beam-off time delays were determined from the difference between the expected and observed streak length. Results: For the system investigated, all sine, regular realistic-shape, and slightly irregular amplitude variation motions had beam-off and beam-on time delays within the AAPM recommended limit of less than 100 ms. In phase-based gating, even small variations in period resulted in some time delays greater than 100 ms. Considerable time delays over 1 s were observed with highly irregular motion. Conclusions: Sinusoidal motion shapes can be considered a reasonable approximation to the more complex and slightly irregular shapes of realistic motion. When using phase-based gating with predictive filters even small variations in period can result in time delays over 100 ms. Clinical use of these systems for patients with highly irregular patterns of motion is not advised due to large beam-on and beam-off time delays.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-J-52: Decreasing Frequency of Performing TG-142 Imaging QA – 5 Year Experience

Purpose This study is an update to check if the frequency of imaging QA suggested by AAPM Task Group Report 142 (TG142) is necessary with our 5 year experience. TG142 presents recommendations for QA criteria of IGRT treatment. ACR has adopted it to be the requirements for any radiatiotherapy practices; however, we propose to reduce the frequency on image quality QA according to this 5 year study.Method and Materials: This study uses VarianIX2100 and Siemens Artiste Linacs to perform QAs on KV, MV, CBCT modalities. The QA was designed following under the recommendations of TG142. This study reports the daily imaging positioning/repositioning and imaging and treatment coordinate coincidence. QA results on kV, MV and CBCT from 4/7/2010∼3/11/15 are analyzed. KV, MV, CBCT images are taken with the Varian isocube localized at the isocenter. Digital graticule is used in the software to verify the isocenter position. CBCT images are taken with the cube placed at 1cm superior, lateral and anterior of the isocenter. In-line fusion software is used to verify the contrived shift. Digital ruler provided at the on-board-imaging software or adaptive-targeting software was used to measure the position differences. The position differences were recorded at AP,LR,SI directions. Results 5 year records on kV, MV, CBCT show the shifts in all three directions are within the tolerance of 1mm suggested in TG142 for stereotactic radiation treatment(SRS/SRT). There is no occasion where shifts are outside 1mm tolerance. Conclusions The daily imaging QA suggested in TG142 is useful in ensuring the accuracy needed for SRS/SRT in IGRT. 5 year measurements presented suggest that decreasing the frequency of imaging QA may be acceptable, in particular for institutions reporting no violation of tolerance over periods of few years.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

LLaMP v0.1.0

Reducing hallucination of Large Language Models (LLMs) is imperative for use in the sciences, where reliability and reproducibility are crucial. However, LLMs inherently lack long-term memory, making it a nontrivial, ad hoc, and inevitably biased task to fine-tune them on domain-specific literature and data. LLaMP is a multimodal retrieval-augmented generation (RAG) framework of hierarchical reasoning and acting (ReAct) agents that can dynamically and recursively interact with Materials Project to ground large language models on high-fidelity materials informatics.

Riebesell, Janosh [Lawrence Berkeley National Labo↗

DOE OSTI2016

Formulation and preparation of Hanford Waste Treatment Plant direct feed low activity waste Effluent Management Facility core simulant

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

Evaporation Of Hanford Waste Treatment Plant Direct Feed Low Activity Waste Effluent Management Facility Core Simulant

The Hanford Waste Treatment and Immobilization Plant (WTP) Low Activity Waste (LAW) vitrification facility will generate an aqueous condensate recycle stream (LAW Melter Off-Gas Condensate, LMOGC) from the off-gas system. The baseline plan for disposition of this stream during full WTP operations is to send it to the WTP Pretreatment Facility, where it will be blended with LAW, concentrated by evaporation, and recycled to the LAW vitrification facility. However, during the Direct Feed LAW (DFLAW) scenario, planned disposition of this stream is to evaporate it in a new evaporator, in the Effluent Management Facility (EMF), and then return it to the LAW melter. It is important to understand the composition of the effluents from the melter and new evaporator, so that the disposition of these streams can be accurately planned and accommodated. Furthermore, alternate disposition of the LMOGC stream would eliminate recycling of problematic components, and would reduce the need for closely integrated operation of the LAW melter and the Pretreatment Facilities. Long-term implementation of this option after WTP start-up would decrease the LAW vitrification mission duration and quantity of glass waste, amongst the other operational complexities such a recycle stream presents. In order to accurately plan for the disposition path, it is key to experimentally determine the fate of contaminants. To do this, testing is needed to accurately account for the buffering chemistry of the components, determine the achievable evaporation end point, identify insoluble solids that form, and determine the distribution of key regulatory-impacting constituents. The LAW Melter Off-Gas Condensate stream will contain components that are volatile at melter temperatures, have limited solubility in the glass waste form, and represent a materials corrosion concern, such as halides and sulfate. Because this stream will recycle within WTP, these components will accumulate in the Melter Condensate stream, exacerbating their impact on the number of LAW glass containers that must be produced. Diverting the stream reduces the halides and sulfates in the recycled Condensate and is a key outcome of this work. This overall program examines the potential treatment and immobilization of this stream to enable alternative disposal. The objective of this task was to demonstrate evaporation of a simulant of the LAW Melter Off-gas Condensate expected during DFLAW operations, in order to predict the composition of the effluents from the EMF evaporator to aid in planning for their disposition. This document describes the results of that test using the core simulant. This simulant formulation is designated as the “core simulant”; other additives will be included for specific testing, such as volatiles for evaporation or hazardous metals for measuring leaching properties of waste forms. The results indicate that the simulant can easily be concentrated via evaporation. During that the pH adjustment step in simulant preparation, ammonium is quickly converted to ammonia, and most of the ammonia was stripped from the simulated waste and partitioned to the condensate. Additionally, it was found that after concentrating (>12x) and cooling that a small amount of LiF and Na 3 (SO 4 )F precipitate out of solution. With the exception of ammonia, analysis of the condensate indicated very low to below detectable levels of many of the constituents in the simulant, yielding very high decontamination factors (DF).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

A Block-Based Triangle Counting Algorithm on Heterogeneous Environments

Triangle counting is a fundamental building block in graph algorithms. In this paper, we propose a block-based triangle counting algorithm to reduce data movement during both sequential and parallel execution. Our block-based formulation makes the algorithm naturally suitable for heterogeneous architectures. The problem of partitioning the adjacency matrix of a graph is well-studied. Our task decomposition goes one step further: it partitions the set of triangles in the graph. By streaming these small tasks to compute resources, we can solve problems that do not fit on a device. We demonstrate the effectiveness of our approach by providing an implementation on a compute node with multiple sockets, cores and GPUs. The current state-of-the-art in triangle enumeration processes the Friendster graph in 2.1 seconds, not including data copy time between CPU and GPU. Using that metric, our approach is 20 percent faster. When copy times are included, our algorithm takes 3.2 seconds. This is 5.6 times faster than the fastest published CPU-only time.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1986

The SIMRAND methodology: Theory and application for the simulation of research and development projects

A research and development (R&D) project often involves a number of decisions that must be made concerning which subset of systems or tasks are to be undertaken to achieve the goal of the R&D project. To help in this decision making, SIMRAND (SIMulation of Research ANd Development Projects) is a methodology for the selection of the optimal subset of systems or tasks to be undertaken on an R&D project. Using alternative networks, the SIMRAND methodology models the alternative subsets of systems or tasks under consideration. Each path through an alternative network represents one way of satisfying the project goals. Equations are developed that relate the system or task variables to the measure of reference. Uncertainty is incorporated by treating the variables of the equations probabilistically as random variables, with cumulative distribution functions assessed by technical experts. Analytical techniques of probability theory are used to reduce the complexity of the alternative networks. Cardinal utility functions over the measure of preference are assessed for the decision makers. A run of the SIMRAND Computer I Program combines, in a Monte Carlo simulation model, the network structure, the equations, the cumulative distribution functions, and the utility functions.

Miles, R. F., Jr.↗

NASA NTRS1985

KARL: A Knowledge-Assisted Retrieval Language

Data classification and storage are tasks typically performed by application specialists. In contrast, information users are primarily non-computer specialists who use information in their decision-making and other activities. Interaction efficiency between such users and the computer is often reduced by machine requirements and resulting user reluctance to use the system. This thesis examines the problems associated with information retrieval for non-computer specialist users, and proposes a method for communicating in restricted English that uses knowledge of the entities involved, relationships between entities, and basic English language syntax and semantics to translate the user requests into formal queries. The proposed method includes an intelligent dictionary, syntax and semantic verifiers, and a formal query generator. In addition, the proposed system has a learning capability that can improve portability and performance. With the increasing demand for efficient human-machine communication, the significance of this thesis becomes apparent. As human resources become more valuable, software systems that will assist in improving the human-machine interface will be needed and research addressing new solutions will be of utmost importance. This thesis presents an initial design and implementation as a foundation for further research and development into the emerging field of natural language database query systems.

Dominick, Wayne D.↗

NASA NTRS1988

The Flight Telerobotic Servicer Program

The Flight Telerobotic Servicer (FTS) Program, initiated by NASA to provide new capabilities for task accomplishment in the space environment, is described. The FTS Program includes the following: a series of test flights, a ground-based supporting infrastructure, and a technology utilization and transfer process. The FTS Program strives to reduce Space Station dependence on crew EVA, improve crew safety, enhance crew utilization, provide remote servicing capabilities for platforms, and accelerate technology transfer from research to U.S. industry.

Townsend, John W., Jr.↗

NASA NTRS1992

Extravehicular mobility unit subcritical liquid oxygen storage and supply system

The storage of life support oxygen in the Extravehicular Mobility Unit in the liquid state offers some advantages over the current method of storing the oxygen as a high pressure gas. Storage volume is reduced because of the increased density associated with liquid. The lower storage and operating pressures also reduce the potential for leakage or bursting of the storage tank. The potential for combustion resulting from adiabatic combustion of the gas within lines and components is substantially reduced. Design constraints on components are also relaxed due to the lower system pressures. A design study was performed to determine the requirements for a liquid storage system and prepare a conceptual design. The study involved four tasks. The first was to identify system operating requirements that influence or direct the design of the system. The second was to define candidate storage system concepts that could possibly satisfy the requirements. An evaluation and comparison of the candidate concepts was conducted in the third task. The fourth task was devoted to preparing a conceptual design of the recommended storage system and to evaluate concerns with integration of the concept into the EMU. The results are presented.

Anderson, John↗