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At least 397 records · Page 22

All sources NASA NTRS DOE OSTI

DOE OSTI2022

A Single Application of Compost Can Leave Lasting Impacts on Soil Microbial Community Structure and Alter Cross-Domain Interaction Networks

Our current understanding suggests that nutrient management strategies applied to agricultural soils over multiple years are required to cause major and stable shifts in soil microbial communities. However, some studies suggest that agricultural soils can benefit even from sporadic, single additions of organic matter. Here we investigate how single additions of high-quality organic matter can cause significant shifts in microbial soil communities over multiple cropping cycles. We grew radishes in a tropical Oxisol soil for six crop cycles after a single application of a high-nitrogen compost or urea. At planting and before biomass harvest, we sampled soils influenced by the radish rhizosphere and sequenced bacterial and archaeal 16S and fungal ITS rDNA marker genes. We measured microbial richness and diversity, community composition and structure, and constructed correlation networks to predict cross-domain microbial interactions. We found that a single application of compost, compared to urea or control, resulted in a persistent improved plant biomass response and led to sustained changes in the soil microbial community throughout the duration of the 227-day study. Compost altered the structure of both the fungal and prokaryotic microbial communities, introduced new microorganisms that persisted in the resident soil system, and altered soil microbial correlation network structure and hub taxa. In contrast, fertilization with urea did not significantly alter the structure of soil microbial communities compared to the control but reduced network complexity and altered hub taxa. This study highlights the significant impacts that high-quality organic matter fertilization can exert on agricultural soil microbiomes and adds to the growing body of knowledge on using organic fertilizers as a way to steer the soil microbiome toward a healthier soil.

Heisey, Steven↗

DOE OSTI2021

Topological data analysis of task-based fMRI data from experiments on schizophrenia

We use methods from computational algebraic topology to study functional brain networks, in which nodes represent brain regions and weighted edges represent similarity of fMRI time series from each region. With these tools, which allow one to characterize topological invariants such as loops in high-dimensional data, we are able to gain understanding into low-dimensional structures in networks in a way that complements traditional approaches based on pairwise interactions. In the present paper, we analyze networks constructed from task-based fMRI data from schizophrenia patients, healthy controls, and healthy siblings of schizophrenia patients using persistent homology, which allows us to explore the persistence of topological structures such as loops at different scales in the networks. We use persistence landscapes, persistence images, and Betti curves to create output summaries from our persistent-homology calculations, and we study the persistence landscapes and images using k-means clustering and community detection. Based on our analysis of persistence landscapes, we find that the members of the sibling cohort have topological features (specifically, their 1-dimensional loops) that are distinct from the other two cohorts. From the persistence images, we are able to distinguish all three subject groups and to determine the brain regions in the loops (with four or more edges) that allow us to make these distinctions.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Day-ahead continuous double auction-based peer-to-peer energy trading platform incorporating trading losses and network utilisation fee

Integration of distributed energy resources, such as photovoltaic solar (PV), introduces new opportunities to establish local energy market frameworks to improve renewable energy utilisation in residential sectors. Such peer-to-peer (P2P) energy trading refers to a local market structure where customers (and prosumers) interact to share excess PV generation to enhance the individual and community social welfare. In this work, a day-ahead continuous double auction (CDA)-based P2P market structure considering network losses and network utilisation fees was designed. Day-ahead PV energy is modelled using fractional integral polynomials and the output is forecasted using an autoregressive integrated moving average model for each market interval. Based on the customer load and excess PV energy, the CDA market is cleared using a bid/ask matching mechanism. The performance of the P2P market was evaluated by computing different welfare metrics while analysing the effect of network constraints. The results show that the designed CDA-based P2P market structure increases the social welfare of all participants by an average of 17.75% compared to the baseline for the presented cases. Moreover, the impact of the forecasting error between the day-ahead and real-time market was also quantified.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1998

Network Coronal Bright Points: Coronal Heating Concentrations Found in the Solar Magnetic Network

We examine the magnetic origins of coronal heating in quiet regions by combining SOHO/EIT Fe xii coronal images and Kitt Peak magnetograms. Spatial filtering of the coronal images shows a network of enhanced structures on the scale of the magnetic network in quiet regions. Superposition of the filtered coronal images on maps of the magnetic network extracted from the magnetograms shows that the coronal network does indeed trace and stem from the magnetic network. Network coronal bright points, the brightest features in the network lanes, are found to have a highly significant coincidence with polarity dividing lines (neutral lines) in the network and are often at the feet of enhanced coronal structures that stem from the network and reach out over the cell interiors. These results indicate that, similar to the close linkage of neutral-line core fields with coronal heating in active regions (shown in previous work), low-lying core fields encasing neutral lines in the magnetic network often drive noticeable coronal heating both within themselves (the network coronal bright points) and on more extended field lines rooted around them. This behavior favors the possibility that active core fields in the network are the main drivers of the heating of the bulk of the quiet corona, on scales much larger than the network lanes and cells.

Falconer, D. A.↗

NASA NTRS1997

Network Coronal Bright Points: Coronal Heating Concentrations Found in the Solar Magnetic Network

We examine the magnetic origins of coronal heating in quiet regions by combining SOHO/EIT Fe XII coronal images and Kitt Peak magnetograms. Spatial filtering of the coronal images shows a network of enhanced structures on the scale of the magnetic network in quiet regions. Superposition of the filtered coronal images on maps of the magnetic network extracted from the magnetograms shows that the coronal network does indeed trace and stem from the magnetic network. Network coronal bright points, the brightest features in the network lanes, are found to have a highly significant (8 sigma above random chance) coincidence with polarity dividing lines (neutral lines) in the network, and are often at the feet of enhanced coronal structures that stem from the network and reach out over the cell interiors. These results indicate that, similar to the close linkage of neutral-line core fields with coronal heating in active regions, low-lying core fields encasing neutral lines in the magnetic network often drive noticeable coronal heating both within themselves (the network coronal bright points) and on more extended fields lines rooted around them. This behavior favors the possibility that active core fields in the network are the main drivers of the heating of the bulk of the quiet corona, on scales much larger than the network lanes and cells.

Falconer, D. A.↗

DOE OSTI2025

Building molecular model series from heterogeneous CryoEM structures using Gaussian mixture models and deep neural networks

Cryogenic electron microscopy (CryoEM) produces structures of macromolecules at near-atomic resolution. However, building molecular models with good stereochemical geometry from those structures can be challenging and time-consuming, especially when many structures are obtained from datasets with conformational heterogeneity. Here we present a model refinement protocol that automatically generates series of molecular models from CryoEM datasets, which describe the dynamics of the macromolecular system and have near-perfect geometry scores. This method makes it easier to interpret the movement of the protein complex from heterogeneity analysis and to compare the structural dynamics observed from CryoEM data with results from other experimental and simulation techniques.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2022

Lithium-ion Battery Strain Gauge Monitoring and Depth of Discharge Estimation

Lithium-ion battery diagnostics and prognostics rely on measurements of electrical impedance, capacity, and voltage to infer the internal state of the battery. Mechanical changes to the cell structure represent an additional measure of the battery’s state because these changes are related to the overall battery health. As lithium-ion batteries are charged and discharged, lithium ions are inserted or removed from the anode and cathode, a process called intercalation and de-intercalation. As lithium ions intercalate and de-intercalate, they can cause changes to the lattice of the electrode particles, resulting in volumetric changes. These volumetric changes cause mechanical stresses and strains on the lithium-ion battery electrodes, and subsequently, the whole cell’s thickness varies as it is charged and discharged. This paper presents a study on the use of surface-mounted strain gauges for in-situ measurement of structural changes to lithium-ion batteries, along with a characterization of the unit-to-unit differences in strain response. A neural network modeling structure is then used to predict the battery’s depth of discharge under dynamic discharge conditions.

Lithium-ion batteries, depth of discharge, strain ↗

DOE OSTI2024

Improving Property Graph Layouts by Leveraging Attribute Similarity for Structurally Equivalent Nodes

Many real-world networks contain structurally-equivalent nodes. These are defined as vertices that share the same set of neighboring nodes, making them interchangeable with a traditional graph layout approach. However, many real-world graphs also have properties associated with nodes, adding additional meaning to them. We present an approach for swapping locations of structurally-equivalent nodes in graph layout so that those with more similar properties have closer proximity to each other. This improves the usefulness of the visualization from an attribute perspective without negatively impacting the visualization from a structural perspective. We include an algorithm for finding these sets of nodes in linear time, as well as methodologies for ordering nodes based on their attribute similarity, which works for scalar, ordinal, multidimensional, and categorical data.

graph drawing, network visualization, property gra↗

NASA NTRS2016

Simulations of Li+ in Ionic Liquids: Structure, Transport, and Electrochemical Windows

Ionic liquids have been proposed as candidate electrolytes for a number of electrochemical applications. The Li+ solvation structure in these liquids is of central importance to electrolyte properties, like ionic conductivity and electrochemical stability. To this point, we employ simulations at three different size scales to better understand various aspects of the interplay between Li+ solvation structure and dynamics. The smallest systems are Li(Anion)n clusters that are treated with high-accuracy density functional theory (DFT) techniques to provide insight into solvation shell structure through energetics and comparisons to experimental IRRaman spectra. Mid-range sized liquid-phase systems (12-24 ion pairs) are treated with DFT molecular dynamics (MD) to provide temperature-dependent insight into Li+ solvation structure, diffusion, and electrochemical window. The largest systems (144-216 ion pairs) are treated with polarizable MD simulations to evaluate the influence of Li-networks on structure and provide size independent values of transport properties. We perform this procedure on three technologically important ionic liquids and comment on property correlations with solvation structure.

batteries↗

DOE OSTI2021

A multi-phase-field model of topological pattern formation during electrochemical dealloying of binary alloys

Porous metal structures produced by electrochemical dealloying have been extensively studied for use in various applications that require high surface areas. A new comprehensive multi-phase-field (MPF) model is proposed to study topological porous patterns formed by spontaneously etching a bulk binary alloy that involves electrochemical reactions, bulk and surface diffusion, ion transport, applied electrode potential, and charge conservation. The governing equations for the alloy-porous cluster-electrolyte system account for a generalized Butler-Volmer electrochemical reaction and are in accordance with the classical nucleation theory. Based on a quantitative examination of the effects of electrode potential and precursor composition, the simulation results reproduce typical phenomena including passive surface dealloying, active porosity evolution, critical potential, and characteristic length scale in two-dimension (2-D) and three-dimension (3-D). To consider more complicated dealloying systems, the evolutions of a bimodal porous metal structure, nanocomposite, and nested porous network with a structured hierarchy are investigated. Finally, the proposed model can be a useful tool for understanding and predicting the morphology evolution of diverse porous structures during electrochemical dealloying.

Hierarchical porous structure↗

NASA NTRS1978

Ku-band signal design study

Analytical tools, methods and techniques for assessing the design and performance of the space shuttle orbiter data processing system (DPS) are provided. The computer data processing network is evaluated in the key areas of queueing behavior synchronization and network reliability. The structure of the data processing network is described as well as the system operation principles and the network configuration. The characteristics of the computer systems are indicated. System reliability measures are defined and studied. System and network invulnerability measures are computed. Communication path and network failure analysis techniques are included.

Rubin, I.↗

NASA NTRS2001

Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)

The research accomplishments for the cooperative agreement 'Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)' include the following: (1) previous IFC program data collection and analysis; (2) IFC program support site (configured IFC systems support network, configured Tornado/VxWorks OS development system, made Configuration and Documentation Management Systems Internet accessible); (3) Airborne Research Test Systems (ARTS) II Hardware (developed hardware requirements specification, developing environmental testing requirements, hardware design, and hardware design development); (4) ARTS II software development laboratory unit (procurement of lab style hardware, configured lab style hardware, and designed interface module equivalent to ARTS II faceplate); (5) program support documentation (developed software development plan, configuration management plan, and software verification and validation plan); (6) LWR algorithm analysis (performed timing and profiling on algorithm); (7) pre-trained neural network analysis; (8) Dynamic Cell Structures (DCS) Neural Network Analysis (performing timing and profiling on algorithm); and (9) conducted technical interchange and quarterly meetings to define IFC research goals.

Niewoehner, Kevin R.↗

NASA NTRS1972

Satellite networks for education

Satellite based educational networking is discussed with particular attention given to the potential uses of communications satellites to help meet educational needs in the United states. Four major subject areas were covered; (1) characteristics and structure of networks, (2) definition of pressures within educational establishment that provide motivation for various types of networks, (3) examination of current educational networking status for educational radio and television, instructional television fixed services, inter- and intra-state educational communication networks, computer networks, and cable television for education, and (4) identification of possible satellite based educational telecommunication services and three alternatives for implementing educational satellite systems.

Singh, J. P.↗

DOE OSTI2022

Training neural networks using Metropolis Monte Carlo and an adaptive variant

Abstract We examine the zero-temperature Metropolis Monte Carlo (MC) algorithm as a tool for training a neural network by minimizing a loss function. We find that, as expected on theoretical grounds and shown empirically by other authors, Metropolis MC can train a neural net with an accuracy comparable to that of gradient descent (GD), if not necessarily as quickly. The Metropolis algorithm does not fail automatically when the number of parameters of a neural network is large. It can fail when a neural network’s structure or neuron activations are strongly heterogenous, and we introduce an adaptive Monte Carlo algorithm (aMC) to overcome these limitations. The intrinsic stochasticity and numerical stability of the MC method allow aMC to train deep neural networks and recurrent neural networks in which the gradient is too small or too large to allow training by GD. MC methods offer a complement to gradient-based methods for training neural networks, allowing access to a distinct set of network architectures and principles.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1993

Use of networked workstations for parallel nonlinear structural dynamic simulations of rotating bladed-disk assemblies

The principal objective of this research is to investigate, develop and demonstrate coarse-grained, parallel-processing strategies for nonlinear dynamic simulations for rotating bladed-disk assemblies. The parallel -processing strategies addressed include numerical algorithms for parallel nonlinear solutions and techniques to effect load balancing among processors. The parallel environment employed is a distributed-memory, coarse-grained one consisting of networked workstations. A parallel explicit time integration method has been implemented for transient nonlinear solutions of rotationg bladed-disk assemblies. Automatic domain partitioning techniques have been investigated for load balancing among processors. Advanced computing environments, data structures and interactive computer graphics all contribute to an integrated parallel finite element analysis system to facilitate more efficient and powerful dynamic simulations.

Hsieh, Shang-Hsien↗

DOE OSTI2026

Growth in heterogeneous, evolving macromolecular networks: toward functional, biomimetic material

This report summarizes research carried out by the Balazs and Matyjaszewski Labs at Pitt and CMU, respectively, during 2025-26, supported by the DOE grant. It has produced the following 3 research articles and 2 review publications that acknowledged grant ER45998: 1. Computational Modeling of Hyperbranched Polymers 2. Synthesis of Structurally Tailored Networks 3. Sustainable and Oxygen-Tolerant Catalysis 4. A review paper on Current Status and Outlook for ATRP 5. A review paper on Future Directions for Atom Transfer Radical Polymerization

Balazs, Anna (ORCID:0000000255552692)↗

DOE OSTI2022

Multilayered Network Models for Security: Enhancing System Security Engineering with Orchestration

Security engineering approaches can often focus on a particular domain—physical security, cyber security, or personnel security, for example. Yet, security systems engineering consistently faces challenges requiring socio-technical solutions to address evolving and dynamic complexity. While some drivers of this complexity stem from complex risk environments, innovative adversaries, and disruptive technologies, other drivers are endogenous and emerge from the interactions across security engineering approaches. In response, INCOSE's Systems Security Working Group identified the need to better coordinate “disparate security solutions [that] operate independently” as one of eleven key concepts in their IS21 FuSE Security Roadmap. From this perspective, this need for “security orchestration” aligns with the perspective that security is a property that emerges from interactions within complex systems. Current efforts at Sandia National Laboratories are developing a systems security engineering approach that describes high consequence facility (HCF) security as a multidomain set of interacting layers. The result is a multilayered network (MLN)-based approach that captures the interactions between infrastructure, physical components, digital components, and humans in nuclear security systems. This article will summarize the MLN-based approach to HCF security and describe two preliminary results demonstrating potential benefits from incorporating interactions across disparate security solutions. Here, leveraging the logical structure of networks, this MLN model-based approach provides an example of how security orchestration provides enhanced systems security engineering solutions.

42 ENGINEERING↗

DOE OSTI2022

Dispersion of modified fumed silica in elastomeric nanocomposites

In polymer nanocomposites, surface modification of silica aggregates can shield Coulombic interactions that inhibit agglomeration and formation of a network of agglomerates. Surface modification is usually achieved with silane coupling agents although carbon-coating during pyrolytic silica production is also possible. Pyrogenic silica with varying surface carbon contents were dispersed in styrene-butadiene (SBR) rubber to explore the impact on hierarchical dispersion, the emergence of meso-scale structures, and the rheological response. Pristine pyrogenic silica aggregates at concentrations above a critical value (related to the Debye screening length) display correlated meso-scale structures and poor filler network formation in rubber nanocomposites due to the presence of silanol groups on the surface. Here, in the present study, flame synthesized silica with sufficient surface carbon monolayers can mitigate the charge repulsion thereby impacting network structural emergence. The impact of the surface carbon on the van der Waals enthalpic attraction, a * , is determined. The van der Waals model for polymer nanocomposites is drawn through an analogy between thermal energy, k B T, and the accumulated strain, γ. The rheological response of the emergent meso-scale structures depends on the surface density of both carbon and silanol groups.

36 MATERIALS SCIENCE↗