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DOE OSTI2026

Juvenile Salmon and Their Habitats in the Columbia River Estuary: A Review and Synthesis of Knowledge Development 2000–2025

[This is a 90% discussion draft.] This is the third Synthesis Memorandum funded by the U.S. Army Corps of Engineers and developed for the Columbia Estuary Ecosystem Restoration Program (CEERP) on the topic of habitat restoration in the Columbia River Estuary (CRE) from Bonneville Dam to the river mouth. While the first two were developed by PNNL and NOAA without the benefit of stakeholder participation, for the current memo, two key activities were initiated: (1) review, by the Expert Regional Technical Group (ERTG), of status and trends monitoring and action effectiveness monitoring funded by CEERP, and (2) a workshop including representatives of the Bonneville Power Administration and the U.S. Army Corps of Engineers (the action agencies [AAs]), the National Oceanic and Atmospheric Administration (NOAA), major research agencies contributing to CEERP, and sponsors who implement CEERP restoration actions. A systematic literature review was conducted using ClarivateTM Web of ScienceTM database. The topics of interest for CRE relevant research included salmon ecology, physical processes, and wetland habitats, and therefore required the use of broad search terms. Our final search criteria included a combination of Boolean operators and an approach to combine different sets of search terms. The final search result yielded 669 records. The records were classified by groups and assigned to the relevant disciplinary expert for review. The review identified substantive advances in understanding the provision of salmon habitat functions through spatiotemporally dynamic physical and ecological processes, and the use of CRE habitats by numerous stocks of juvenile salmon. It also uncovered heretofore unincorporated historical documentation of riparian habitats across the CRE. The characterization of the structural components of floodplain habitat including plant associations and channel networks has advanced considerably, together with the understanding of seasonal changes and long-term trends. The relative influence of salmon-habitat location in the CRE as compared with temporal factors, mainly season, has been well described, which affects the prioritization of restoration. Stressors on the ecosystem and fish, and the drivers of these stressors, have been more carefully elucidated and predictive models are in various stages of development. The vision, aims, and design of restoration projects have advanced together with methods of data collection, analysis, and modeling that have seen substantial improvements. Experiments intended to inform the design of restoration projects are underway or have been completed. An important outstanding area of research that has lagged behind the advances in fundamental understanding of the ecosystem and salmon habitat functions remains the peer-reviewed documentation of the outcomes of restoration for both habitats and fish functions.

estuary↗

NASA NTRS2003

Compact Interconnection Networks Based on Quantum Dots

Architectures that would exploit the distinct characteristics of quantum-dot cellular automata (QCA) have been proposed for digital communication networks that connect advanced digital computing circuits. In comparison with networks of wires in conventional very-large-scale integrated (VLSI) circuitry, the networks according to the proposed architectures would be more compact. The proposed architectures would make it possible to implement complex interconnection schemes that are required for some advanced parallel-computing algorithms and that are difficult (and in many cases impractical) to implement in VLSI circuitry. The difficulty of implementation in VLSI and the major potential advantage afforded by QCA were described previously in Implementing Permutation Matrices by Use of Quantum Dots (NPO-20801), NASA Tech Briefs, Vol. 25, No. 10 (October 2001), page 42. To recapitulate: Wherever two wires in a conventional VLSI circuit cross each other and are required not to be in electrical contact with each other, there must be a layer of electrical insulation between them. This, in turn, makes it necessary to resort to a noncoplanar and possibly a multilayer design, which can be complex, expensive, and even impractical. As a result, much of the cost of designing VLSI circuits is associated with minimization of data routing and assignment of layers to minimize crossing of wires. Heretofore, these considerations have impeded the development of VLSI circuitry to implement complex, advanced interconnection schemes. On the other hand, with suitable design and under suitable operating conditions, QCA-based signal paths can be allowed to cross each other in the same plane without adverse effect. In principle, this characteristic could be exploited to design compact, coplanar, simple (relative to VLSI) QCA-based networks to implement complex, advanced interconnection schemes. The proposed architectures require two advances in QCA-based circuitry beyond basic QCA-based binary-signal wires described in the cited prior article. One of these advances would be the development of QCA-based wires capable of bidirectional transmission of signals. The other advance would be the development of QCA circuits capable of high-impedance state outputs. The high-impedance states would be utilized along with the 0- and 1-state outputs of QCA.

Fijany, Amir↗

NASA NTRS1990

Advanced data management system architectures testbed

The objective of the Architecture and Tools Testbed is to provide a working, experimental focus to the evolving automation applications for the Space Station Freedom data management system. Emphasis is on defining and refining real-world applications including the following: the validation of user needs; understanding system requirements and capabilities; and extending capabilities. The approach is to provide an open, distributed system of high performance workstations representing both the standard data processors and networks and advanced RISC-based processors and multiprocessor systems. The system provides a base from which to develop and evaluate new performance and risk management concepts and for sharing the results. Participants are given a common view of requirements and capability via: remote login to the testbed; standard, natural user interfaces to simulations and emulations; special attention to user manuals for all software tools; and E-mail communication. The testbed elements which instantiate the approach are briefly described including the workstations, the software simulation and monitoring tools, and performance and fault tolerance experiments.

Grant, Terry↗

DOE OSTI2015

LOCALIZATION OF SHORT DURATION GRAVITATIONAL-WAVE TRANSIENTS WITH THE EARLY ADVANCED LIGO AND VIRGO DETECTORS

The Laser Interferometer Gravitational wave Observatory (LIGO) and Virgo advanced ground-based gravitational-wave detectors will begin collecting science data in 2015. With first detections expected to follow, it is important to quantify how well generic gravitational-wave transients can be localized on the sky. This is crucial for correctly identifying electromagnetic counterparts as well as understanding gravitational-wave physics and source populations. We present a study of sky localization capabilities for two search and parameter estimation algorithms: coherent WaveBurst, a constrained likelihood algorithm operating in close to real-time, and LALInferenceBurst, a Markov chain Monte Carlo parameter estimation algorithm developed to recover generic transient signals with latency of a few hours. Furthermore, we focus on the first few years of the advanced detector era, when we expect to only have two (2015) and later three (2016) operational detectors, all below design sensitivity. These detector configurations can produce significantly different sky localizations, which we quantify in detail. We observe a clear improvement in localization of the average detected signal when progressing from two-detector to three-detector networks, as expected. Although localization depends on the waveform morphology, approximately 50% of detected signals would be imaged after observing 100-200 deg{sup 2} in 2015 and 60-110 deg{sup 2} in 2016, although knowledge of the waveform can reduce this to as little as 22 deg{sup 2}. This is the first comprehensive study on sky localization capabilities for generic transients of the early network of advanced LIGO and Virgo detectors, including the early LIGO-only two-detector configuration.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Isotopomer-selective spectra of a single intact H 2 O molecule in the Cs + (D 2 O) 5 H 2 O isotopologue: Going beyond pattern recognition to harvest the structural information encoded in vibrational spectra

We report the vibrational signatures of a single H 2 O water molecule occupying distinct sites of the hydration network in the Cs + (H 2 O) 6 cluster. This is accomplished using isotopomer selective IR-IR hole-burning on the Cs + (D 2 O) 5 (H 2 O) clusters formed by gas-phase exchange of a single, intact H 2 O molecule for D 2 O in the Cs + (D 2 O) 6 ion. The OH stretching pattern of the Cs + (H 2 O) 6 isotopologue is accurately recovered by superposition of the isotopomer spectra, thus establishing that the H 2 O incorporation is random and that the OH stretching manifold is largely due to contributions from decoupled water molecules. This behavior enables a powerful new way to extract structural information from vibrational spectra of size-selected clusters by explicitly identifying the local environments responsible for specific infrared features. The Cs + (H 2 O) 6 structure was unambiguously assigned to the 4.1.1 isomer (a homodromic water tetramer with two additional flanking water molecules) from the fact that its computed IR spectrum matches the observed overall pattern and recovers the embedded correlations in the two OH stretching bands of the water molecule in the Cs + (D 2 O) 5 (H 2 O) isotopomers. The 4.1.1 isomer is the lowest in energy among other candidate networks at advanced (e.g., CCSD(T)) levels of theoretical treatment after corrections for (anharmonic) zero-point energy (ZPE). With the structure in hand, we then explore the mechanical origin of the various band locations using a local electric field formalism. Furthermore, this approach promises to provide a transferrable scheme for the prediction of the OH stretching fundamentals displayed by water networks in close proximity to solute ions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Tribal Colleges and Universities/Advanced Manufacturing Network Initiative (Q3 FY2020 Progress Report)

The National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) to 1) align investments in a university capacity and workforce development with the NNSA mission to develop the needed skills and talent for NNSA's enduring technical workforce at the laboratories and production plants, and 2) to enhance research and education at under-represented colleges and universities. Out of this effort, MSIPP launched a new consortium in early FY17 focused on Tribal Colleges and Universities (TCUs) known as the Advanced Manufacturing Network Initiative (AMNI). This consortium has been extended for FY20 and FY21. The following report summarizes the status update during this quarter.

42 ENGINEERING↗

DOE OSTI2020

Tribal Colleges and Universities/Advanced Manufacturing Network Initiative (Q4 FY2020 Progress Report)

The National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) to 1) align investments in a university capacity and workforce development with the NNSA mission to develop the needed skills and talent for NNSA’s enduring technical workforce at the laboratories and production plants, and 2) to enhance research and education at under-represented colleges and universities. Out of this effort, MSIPP launched a new consortium in early FY17 focused on Tribal Colleges and Universities (TCUs) known as the Advanced Manufacturing Network Initiative (AMNI). This consortium has been extended for FY20 and FY21. The following report summarizes the status update during this quarter.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2024

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve the understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainties with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of the classification of winter precipitation type and regression of surface-layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. To encourage broader adoption of evidential deep learning, we have developed a new Python package, Machine Integration and Learning for Earth Systems (MILES) group Generalized Uncertainty for Earth System Science (GUESS) (MILES-GUESS) ( https://github.com/ai2es/miles-guess ), that enables users to train and evaluate both evidential and ensemble deep learning. Significance Statement This study demonstrates a new technique, evidential deep learning, for robust and computationally efficient uncertainty quantification in modeling the Earth system. The method integrates probabilistic principles into deep neural networks, enabling the estimation of both aleatoric uncertainty from noisy data and epistemic uncertainty from model limitations using a single model. Our analyses reveal how decomposing these uncertainties provides valuable insights into reliability, accuracy, and model shortcomings. We show that the approach can rival standard methods in classification and regression tasks within atmospheric science while offering practical advantages such as computational efficiency. With further advances, evidential networks have the potential to enhance risk assessment and decision-making across meteorology by improving uncertainty quantification, a longstanding challenge. This work establishes a strong foundation and motivation for the broader adoption of evidential learning, where properly quantifying uncertainties is critical yet lacking.

Schreck, John S.↗

DOE OSTI2021

Tribal Colleges and Universities/Advanced Manufacturing Network Initiative (Phase II) (Q1 FY2021 Progress Report)

The National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) to 1) align investments in a university capacity and workforce development with the NNSA mission to develop the needed skills and talent for NNSA’s enduring technical workforce at the laboratories and production plants, and 2) to enhance research and education at under-represented colleges and universities. Out of this effort, MSIPP launched a new consortium in early FY17 focused on Tribal Colleges and Universities (TCUs) known as the Advanced Manufacturing Network Initiative (AMNI). This consortium has been extended for FY20 and FY21. The following report summarizes the status update during this quarter.

42 ENGINEERING↗

DOE OSTI2021

Tribal Colleges and Universities/Advanced Manufacturing Network Initiative Phase II Sandia Technical Assistance (Q2 FY2021 Progress Report)

The National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) to 1) align investments in a university capacity and workforce development with the NNSA mission to develop the needed skills and talent for NNSA’s enduring technical workforce at the laboratories and production plants, and 2) to enhance research and education at under-represented colleges and universities. Out of this effort, MSIPP launched a new consortium in early FY17 focused on Tribal Colleges and Universities (TCUs) known as the Advanced Manufacturing Network Initiative (AMNI). This consortium has been extended for FY20 and FY21. The following report summarizes the status update during this quarter.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Tribal Colleges and Universities/Advanced Manufacturing Network Initiative Phase II Sandia Technical Assistance FY21 Q3 Progress Report

The National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) to 1) align investments in a university capacity and workforce development with the NNSA mission to develop the needed skills and talent for NNSA’s enduring technical workforce at the laboratories and production plants, and 2) to enhance research and education at under-represented colleges and universities. Out of this effort, MSIPP launched a new consortium in early FY17 focused on Tribal Colleges and Universities (TCUs) known as the Advanced Manufacturing Network Initiative (AMNI). This consortium has been extended for FY20 and FY21. The following report summarizes the status update during this quarter.

42 ENGINEERING↗

DOE OSTI2021

Tribal Colleges and Universities/Advanced Manufacturing Network Initiative: Phase II Sandia Technical Assistance (FY21 Q4 Progress Report)

The National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) 1) to align investments in a university capacity and workforce development with the NNSA mission to develop the needed skills and talent for NNSA’s enduring technical workforce at the laboratories and production plants, and 2) to enhance research and education at under-represented colleges and universities. Out of this effort, MSIPP launched a new consortium in early FY17 focused on Tribal Colleges and Universities (TCUs) known as the Advanced Manufacturing Network Initiative (AMNI). This consortium has been extended for FY20 and FY21. The following report summarizes the status update during this quarter.

42 ENGINEERING↗

DOE OSTI2015

A Probabilistic Risk Mitigation Model for Cyber-Attacks to PMU Networks

The power grid is becoming more dependent on information and communication technologies. Complex networks of advanced sensors such as phasor measurement units (PMUs) are used to collect real time data to improve the observability of the power system. Recent studies have shown that the power grid has significant cyber vulnerabilities which could increase when PMUs are used extensively. Therefore, recognizing and responding to vulnerabilities are critical to the security of the power grid. This paper proposes a risk mitigation model for optimal response to cyber-attacks to PMU networks. We model the optimal response action as a mixed integer linear programming (MILP) problem to prevent propagation of the cyber-attacks and maintain the observability of the power system.

Cyber-attack↗

DOE OSTI2022

NNSA Minority Serving Institute Partnership Program (MSIPP)— Advanced Synergistic Program for Indigenous Research in Engineering (ASPIRE) (FY22 Q1 Progress Report)

In 2016, the National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) targeting Tribal Colleges and Universities (TCUs) to offer programs that will prepare students for technical careers in NNSA’s laboratories and production plants. The MSIPP consortium’s approach is as follows: 1) align investments at the college and university level to develop a curriculum and workforce needed to support NNSA’s nuclear weapon enterprise mission, and 2) to enhance research and education at under-represented colleges and universities. The first TCU consortium that MSIPP launched was known as the Advanced Manufacturing Network Initiative (AMNI) whose purpose was to develop additive manufacturing (AM) learning opportunities. The AMNI consortium consisted of Bay Mills Community College, Cankdeska Cikana Community College, Navajo Tech University, Salish Kootenai Community College, Turtle Mountain Community College, and United Tribes Technical College. In 2016, the American Indian Higher Education Consortium (AIHEC), the AMNI consortium and the Southwestern Indian Polytechnic Institute (SIPI), in collaboration with Sandia National Labs, using a grant by NNSA hosted the first TCU Advanced Manufacturing Technology Summer Institute (TCU AMTSI). The AMNI consortium will officially end Sept. 2022. However, building on the successes of AMNI, in FY22 NNSA’s MSIPP launched three additional consortiums: (1) the Indigenous Mutual Partnership to Advanced Cybersecurity Technology (IMPACT), which focuses on STEM and cybersecurity, (2) the Advanced Synergistic Program for Indigenous Research in Engineering (ASPIRE), which focuses on STEM and the electrical and mechanical engineering skills set needed for renewable and distributed energy systems, and (3) the Partnership for Advanced Manufacturing Education and Research (PAMER), which focuses on developing and maintaining a sustainable pathway for a highly trained, next-generation additive manufacturing workforce and a corresponding community of subject matter experts for NNSA enterprises. The following report summarizes the status update during this quarter for the ASPIRE program.

42 ENGINEERING↗

DOE OSTI2024

Understanding the High Energy Higgs Sector with the CMS Experiment and Artificial Intelligence

This dissertation describes efforts towards understanding the Higgs boson at the highest energies humanly accessible, using the CMS experiment at the Large Hadron Collider and advances in artificial intelligence (AI) and machine learning (ML). We present searches for resonant and nonresonant Higgs-boson (H) pair production in the all-hadronic two beauty-quark and two vector boson (V) final state, using a novel strategy to measure the quartic HHVV coupling and search for new Higgs-like bosons. By targeting highly Lorentz-boosted Higgs pairs, we probe effects of potential new physics in the high energy Higgs sector, which could hold answers to fundamental mysteries of nature such as baryon asymmetry. To enable these and future searches, we introduce as well significant developments in AI/ML, including in the identification of boosted H$\rightarrow$VV decays with deep transformer networks and advances in AI-accelerated fast simulations of the CMS detector. The latter notably includes the development of the first, highly performant generative models for point-cloud data in high energy physics, which have the potential to improve CMS' computational efficiency by up to three orders of magnitude. We also highlight novel solutions to the important and challenging problems of calibrating and validating these ML techniques. Finally, we present new approaches to search for new physics in a model-agnostic manner, using physics-informed ML methods equivariant to Lorentz transformations. The quartic HHVV coupling is observed (expected) to be constrained to $[-0.04, 2.05]$ ($[0.05, 1.98]$) at the 95% confidence level relative to the standard model prediction, representing the second-most sensitive measurement of this coupling by CMS to date. Exclusion limits on the production cross section of new heavy resonances decaying to two Higgs-like bosons are expected to be as low as 0.3 fb for high resonance masses.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2010

Energy Sciences Network

ESnet is a high-speed network serving thousands of Department of Energy scientists at over 40 institutions, as well as connecting to more than 100 other networks. ESnet is a pioneer in providing high-bandwidth, reliable connections that link researchers at national laboratories, universities and other research institutions, enabling them to collaborate on some of the world's most important scientific research challenges including energy, climate science, and the origins of the universe. Funded by the DOE Office of Science, and managed and operated by the ESnet team at Lawrence Berkeley National Laboratory, ESnet provides scientists access to unique DOE research facilities and computing resources.

100 GBPS↗

NASA NTRSDate not supplied

Development and Field Test Results of Distributed Ground Sensor Fusion Based Object Tracking

Autonomous operations are a crucial aspect in the context of Advanced Air Mobility and other emerging aviation markets. In order to enable this autonomy, an accurate and detailed understanding of the positions of the various vehicles in the air is necessary. Full localization independent of on-board sensors makes the system suitable for noncooperative vehicles. This paper focuses on the object tracking part that relies on distributed ground-based RF and other sensor fusion, considering specific properties and limitations of different sensor types. Results show satisfactory performance in nominal scenarios with full coverage for some sensor types, but RF signals are challenging because of their nature. This paper includes the results from simulations as well as field tests to support the observations and conclusions.

sensor fusion↗

DOE OSTI2022

App2Net: Moving App Functions to Network & a Case Study on Low-latency Feedback

Recent advances in programmable networks enable custom processing of data at hundreds of gigabits per second. These advances can boost the performance of many distributed applications. Yet the high-level languages used by application developers are different from the data plane programming languages (such as P4 and NPL) used by network equipment. This language barrier slows innovation. Our hourglass-shaped architectural solution aims to lower this language barrier. This enables the application developer community to leverage programmable networks for achieving better performance. In this paper we propose a JSON-based intermediate representation to bridge the gap between applications and in-network computing. We demonstrate an instance of the solution in the context of a low-latency feedback application that enables SQL-based data filtering in a P4-based programmable environment. We also present a prototype compiler to convert an intermediate representation in JSON to P4 source.

Sankaran, Ganesh↗