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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Root cytoskeleton: its role in perception of and response to gravity

We have critically evaluated the possible functions of the plant cytoskeleton in root gravisensing and graviresponse and discussed the evidence that microtubules (MTs) and actin microfilaments (MFs) do not control differential cell growth during bending of roots. On the other hand, MF and MT networks are envisaged to participate in gravisensing because of the mechanical properties of the cytoskeletal structures that interconnect plant cell organelles with the plasma membrane. In restrained gravisensing, forces are suggested to be transmitted to membranes because large-scale gravity-dependent repositioning of organelles is effectively prevented due to the cytoskeleton-mediated anchorage of their envelopes at the plasma membrane. From the cytoskeletal point of view, we can also envisage an unrestrained gravity sensing when cytoskeletal tethers are not strong enough to preserve the tight control over distribution of organelles and the latter, if heavy enough, are allowed to sediment towards the physical bottom of cells. This situation obviously occurs in root cap statocytes because these uniquely organized cells are depleted of prominent actin MF bundles, endoplasmic MT arrays, and ER elements in their internal cytoplasm. Nevertheless, indirect evidence clearly indicates that sedimented root cap statoliths are enmeshed within fine but dynamic MF networks and that their behaviour is obviously under, at least partial, cytoskeletal control. The actomyosin-enriched domain among and around amyloplasts is proposed to increase the perception of gravity due to the grouping effect of sedimenting statoliths. Cytoskeletal links between myosin-rich statoliths, and cell peripheries well equipped with dense cortical MTs, membrane-associated cytoskeleton, as well as with ER elements, would allow efficient restrained gravisensing only at the statocyte cell cortex. As a consequence of cytoskeletal depletion in the internal statocyte cytoplasm and bulk sedimentation of large amyloplasts, restrained gravisensing is spatially restricted to the bottom of the statocyte irrespective of whether roots are vertical or horizontal. This spatial aspect allows for efficient gravisensing via amplification of gravity-induced impacts on the cellular architecture, a phenomenon which is unique to root cap statocytes.

NASA Discipline Plant Biology↗
DOE OSTI2025

Quantum optimal control of superconducting qubits based on machine-learning characterization

Implementing fast and high-fidelity quantum operations using open-loop quantum optimal control relies on having an accurate model of the quantum dynamics. Any deviations between this model and the complete dynamics of the device, such as the presence of spurious modes or pulse distortions, can degrade the performance of optimal controls in practice. Here, we propose an experimentally simple approach to realize optimal quantum controls tailored to the device parameters and environment while specifically characterizing this quantum system. Concretely, we use physics-inspired machine learning to infer an accurate model of the dynamics from experimentally available data and then optimize our experimental controls on this trained model. We show the power and feasibility of this approach by optimizing arbitrary single-qubit operations in detailed numerical simulations of a superconducting transmon qubit. Furthermore, we demonstrate that this framework produces an accurate description of the device dynamics under arbitrary controls, together with the precise pulses achieving arbitrary single-qubit gates with a high fidelity of ∼99.99%.

Artificial neural networks↗
DOE OSTI2017

An Optimization-Based State Estimation Framework for Large-Scale Natural Gas Networks

We propose an optimization-based state estimation framework to track internal spacetime flow and pressure profiles of natural gas networks during dynamic transients. We find that the estimation problem is ill-posed (because of the infinite-dimensional nature of the states) and that this leads to instability of the estimator when short estimation horizons are used. To circumvent this issue, we propose moving horizon strategies that incorporate prior information. In particular, we propose a strategy that initializes the prior using steady-state information and compare its performance against a strategy that does not initialize the prior. We find that both strategies are capable of tracking the state profiles but we also find that superior performance is obtained with steady-state prior initialization. We also find that, under the proposed framework, pressure sensor information at junctions is sufficient to track the state profiles. We also derive approximate transport models and show that some of these can be used to achieve significant computational speed-ups without sacrificing estimation performance. We show that the estimator can be easily implemented in the graph-based modeling framework Plasmo.jl and use a multipipeline network study to demonstrate the developments.

Jalving, Jordan↗
DOE OSTI2015

Rigidity and soft percolation in the glass transition of an atomistic model of ionic liquid, 1-ethyl-3-methyl imidazolium nitrate, from molecular dynamics simulations—Existence of infinite overlapping networks in a fragile ionic liquid

The typical ionic liquid, 1-ethyl-3-methyl imidazolium nitrate (EMIM-NO{sub 3}), was examined by molecular dynamics simulations of an all-atomistic model to show the characteristics of networks of cages and/or bonds in the course of vitrification of this fragile glass-former. The system shows changes of dynamics at two characteristic temperatures, T{sub B} (or T{sub c}) and the glass transition temperature T{sub g}, found in other fragile glass forming liquids [K. L. Ngai and J. Habasaki, J. Chem. Phys. 141, 114502 (2014)]. On decreasing temperature, the number of neighboring cation-anion pairs, N{sub B}, within the first minimum of the pair correlation function, g(r){sub min}, increases. On crossing T{sub B} (>T{sub g}), the system volume and diffusion coefficient both show changes in temperature dependence, and as usual at T{sub g}. The glass transition temperature, T{sub g}, is characterized by the saturation of the total number of “bonds,” N{sub B} and the corresponding decrease in degree of freedom, F = [(3N − 6) − N{sub B}], of the system consisting of N particles. Similar behavior holds for the other ion-ion pairs. Therefore, as an alternative, the dynamics of glass transition can be interpreted conceptually by rigidity percolation. Before saturation occurring at T{sub g}, the number of bonds shows a remarkable change at around T{sub B}. This temperature is associated with the disappearance of the loosely packed coordination polyhedra of anions around cation (or vice versa), related to the loss of geometrical freedom of the polyhedra, f{sub g}, of each coordination polyhedron, which can be defined by f{sub g} = [(3N{sub V} − 6) − N{sub b}]. Here, 3N{sub v} is the degree of freedom of N{sub V} vertices of the polyhedron, and N{sub b} is number of fictive bonds. The packing of polyhedra is characterized by the soft percolation of cages, which allows further changes with decreasing temperature. The power spectrum of displacement of the central ion in the cage is found to be correlated with the fluctuation of N{sub b} of cation-cation (or anion-anion) pairs in the polyhedron, although the effect from the coordination shells beyond the neighboring ions is not negligible.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Rewiring the unfolded protein response for plant growth recovery after stress

The unfolded protein response (UPR) is a highly coordinated signaling network that alleviates endoplasmic reticulum (ER) stress, a condition induced by diverse environmental challenges in plants. Over the past two decades, substantial progress has been made in elucidating the genetic and molecular mechanisms of ER stress sensing and signal transduction in plants, largely through studies in the model plant Arabidopsis thaliana . These advances have established the UPR as a central regulator of proteostasis and underscored its broader relevance to plant growth and development and crop productivity under stress conditions. Despite this progress, critical knowledge gaps remain, particularly concerning the downstream biological processes required for growth recovery once ER stress has subsided and how these processes are coordinated by UPR regulators. Recent systems-level and integrative studies have begun to reveal critical roles of UPR signaling in pathways governing growth re-establishment and homeostasis of nutrient allocation and energy metabolism. In this review, we highlight recent findings on the functional roles of the plant UPR in recovery from ER stress, with a focus on mechanisms mediated by UPR regulators and downstream biological pathways that enable the transition from stress mitigation to growth restoration. Although this research area is still emerging, accumulating evidence supports a model in which the UPR functions as a dynamic regulatory network that actively coordinates post-stress physiological recovery to support plant fitness.

ER stress↗
DOE OSTI2015

An opinion-driven behavioral dynamics model for addictive behaviors

We present a model of behavioral dynamics that combines a social network-based opinion dynamics model with behavioral mapping. The behavioral component is discrete and history-dependent to represent situations in which an individual’s behavior is initially driven by opinion and later constrained by physiological or psychological conditions that serve to maintain the behavior. Additionally, individuals are modeled as nodes in a social network connected by directed edges. Parameter sweeps illustrate model behavior and the effects of individual parameters and parameter interactions on model results. Mapping a continuous opinion variable into a discrete behavioral space induces clustering on directed networks. Clusters provide targets of opportunity for influencing the network state; however, the smaller the network the greater the stochasticity and potential variability in outcomes. Furthermore, this has implications both for behaviors that are influenced by close relationships verses those influenced by societal norms and for the effectiveness of strategies for influencing those behaviors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

PySFD: comprehensive molecular insights from significant feature differences detected among many simulated ensembles

Motivation Many modeling analyses of molecular dynamics (MD) simulations are based on a definition of states that can be (groups of) clusters of simulation frames in a feature space composed of molecular coordinates. With increasing dimension of this feature space (due to the increasing size or complexity of a simulated molecule), it becomes very difficult to cluster the underlying MD data and estimate a statistically robust model. To mitigate this “curse of dimensionality”, one can reduce the feature space, e.g., with principal component or time-lagged independent component analysis transformations, focusing the analysis on the most important modes of transitions. In practice, however, all these reduction strategies may neglect important molecular details that are susceptible to experimental verification. Results To recover such molecular details, I have developed PySFD (Significant Feature Differences analyzer for Python), a multi-processing software package that efficiently selects significantly different features of any user-defined feature type among potentially many different simulated state ensembles, such as meta-stable states of a Markov State Model (MSM). Applying PySFD on MSMs of an aggregate of 300 microseconds MD simulations recently performed on the major histocompatibility complex class II (MHCII) protein, I demonstrate how this toolkit can extract and visualize valuable mechanistic information from big MD simulation data, e.g., in form of networks of dynamic interaction changes connecting functionally relevant sites of a protein complex.

Biochemistry & Molecular Biology↗
DOE OSTI2026

Block encoding of the three-dimensional heterogeneous Poisson equation with application to fracture flow

Quantum linear system (QLS) algorithms offer the potential to solve large-scale linear systems exponentially faster than classical methods. However, applying QLS algorithms to real-world problems remains challenging due to issues such as state preparation, data loading, and efficient information extraction. In this work, we study the feasibility of applying QLS algorithms to solve discretized three-dimensional (3D) heterogeneous Poisson equations, with specific examples relating to groundwater flow through geologic fracture networks. We explicitly construct a block encoding for the 3D heterogeneous Poisson matrix by leveraging the sparse local structure of the discretized operator. While classical solvers benefit from preconditioning, we show that block encoding the system matrix and preconditioner separately does not improve the effective condition number that dominates the QLS run-time. This differs from classical approaches where the preconditioner and the system matrix can often be implemented independently. Nevertheless, due to the structure of the problem in three dimensions, the quantum algorithm achieves a run-time of 𝑂⁡(𝑁 2/3 polylog 𝑁 ⋅log (1/𝜖)), outperforming the best classical methods (with run times of 𝑂⁡(𝑁⁢log 𝑁 ⋅log (1/𝜖))) and offering exponential memory savings. These results highlight both the promise and limitations of QLS algorithms for practical scientific computing, and point to effective condition-number reduction as a key barrier in achieving quantum advantages.

58 GEOSCIENCES↗
DOE OSTI2021

Transmission-and-Distribution Dynamic Co-simulation Framework for Distributed Energy Resource Frequency Response

The rapid deployment of distributed energy resources (DERs) in distribution networks has made it challenging to balance the transmission system and stabilize frequency. DERs have the ability to provide frequency regulation services; however, existing frequency dynamic simulation tools - which were developed mainly for the transmission system - lack the capability to simulate distribution network dynamics with high penetrations of DERs. Although electromagnetic transient simulation tools can simulate distribution network dynamics, the computation efficiency limits their use for large-scale transmission-and-distribution (T&D) co-simulation. This paper presents an efficient open-source T&D dynamic co-simulation framework for DER frequency response based on the HELICS platform and off-the-shelf T&D simulators. The challenge of synchronizing the simulation time between the transmission network and the DERs in the distribution network is solved through the detailed modeling of DERs in frequency dynamic models while DER power flow models are also preserved in the distribution networks, thereby respecting local voltage constraints when dispatching DER power for frequency response. DER frequency response (primary and secondary) is simulated in case studies to validate the proposed framework. Last, the accuracy of the proposed co-simulation model is benchmarked, and a large T&D system simulation (2k transmission and 1M distribution nodes) is presented to demonstrate the efficiency and effectiveness of the overall framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

The Brachypodium ENCODE Project – from sequence to function: Predicting physiological responses in grasses to facilitate engineering of biofuel crops

The overarching goal of this proposal was to gain deep knowledge of chromatin dynamics and gene networks related to stress responses in grasses. To accomplish this goal our Brachypodium ENCODE project (1) coupled drought-focused physiological analysis with multiple layers of ENCODE ‘omics’ profiling; (2) obtained fundamental information about the Brachypodium transcriptome, epigenome, chromatin dynamics, and gene regulatory networks; and (3) elucidated the regulatory dynamics of gene networks that can be manipulated to increase energy grass crop productivity.

09 BIOMASS FUELS↗
DOE OSTI2019

Conformational Entropy as a Means to Control the Behavior of Poly(diketoenamine) Vitrimers In and Out of Equilibrium

Abstract Control of equilibrium and non‐equilibrium thermomechanical behavior of poly(diketoenamine) vitrimers is shown by incorporating linear polymer segments varying in molecular weight (MW) and conformational degrees of freedom into the dynamic covalent network. While increasing MW of linear segments yields a lower storage modulus at the rubbery plateau after softening above the glass transition (T g ), both T g and the characteristic time of stress relaxation are independently governed by the conformational entropy of the embodied linear segments. Activation energies for bond exchange in the solid state are lower for networks incorporating flexible chains; the network topology freezing temperature decreases with increasing MW of flexible linear segments but increases with increasing MW of stiff segments. Vitrimer reconfigurability is therefore influenced not only by the energetics of bond exchange for a given network density, but also the entropy of polymer chains within the network.

He, Changfei↗
DOE OSTI2010

Adaptation of cells to new environments

The evolutionary success of an organism is a testament to its inherent capacity to keep pace with environmental conditions that change over short and long periods. Mechanisms underlying adaptive processes are being investigated with renewed interest and excitement. This revival is partly fueled by powerful technologies that can probe molecular phenomena at a systems scale. Additionally, such studies provide spectacular insight into the mechanisms of adaptation, including rewiring of regulatory networks via natural selection of horizontal gene transfers, gene duplication, deletion, readjustment of kinetic parameters, and myriad other genetic reorganizational events. Here, we discuss advances in prokaryotic systems biology from the perspective of evolutionary principles that have shaped regulatory networks for dynamic adaptation to environmental change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Exploring temporal community evolution: algorithmic approaches and parallel optimization for dynamic community detection

Abstract Dynamic (temporal) graphs are a convenient mathematical abstraction for many practical complex systems including social contacts, business transactions, and computer communications. Community discovery is an extensively used graph analysis kernel with rich literature for static graphs. However, community discovery in a dynamic setting is challenging for two specific reasons. Firstly, the notion of temporal community lacks a widely accepted formalization, and only limited work exists on understanding how communities emerge over time. Secondly, the added temporal dimension along with the sheer size of modern graph data necessitates new scalable algorithms. In this paper, we investigate how communities evolve over time based on several graph metrics under a temporal formalization. We compare six different algorithmic approaches for dynamic community detection for their quality and runtime. We identify that a vertex-centric (local) optimization method works as efficiently as the classical modularity-based methods. To its advantage, such local computation allows for the efficient design of parallel algorithms without incurring a significant parallel overhead. Based on this insight, we design a shared-memory parallel algorithm DyComPar , which demonstrates between 4 and 18 fold speed-up on a multi-core machine with 20 threads, for several real-world and synthetic graphs from different domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features

Residential electricity load profiles and their diversity have become increasingly important to realize the benefits of Smart or Transactive Energy Networks (TENs). An important element of TENs will be practical, accurate, and implementable residential load forecasting techniques. While there have been many approaches to short-term load forecasting, few have included forecasting for individual households, partly because the high volatility and idiosyncrasies present in individual household load data can pose significant challenges. In this study, we develop a Convolutional Long Short-Term Memory-based neural network with Selected Autoregressive Features (termed a CLSAF model) to improve short-term household electricity load forecasting accuracy by employing three strategies: autoregressive features selection, exogenous features selection, and a “default” state to avoid overfitting at times of high load volatility. We include aggregations of apartments to floor and building level, because utilities may favor transactive approaches that rely on aggregator models, e.g., a cluster of consumers as opposed to an individual. We demonstrate that the CLSAF model, by virtue of its enhanced feature representation and modest computational resources, can accomplish load forecasting in a multi-family residential building across three spatial granularities (individual apartment/household, floor, and building levels), with an accuracy improvement of up to 25% compared to a persistence model. We propose a data screening technique to characterize time-series electricity-load data. This technique is suitable for integration into a TEN ecosystem and allows one to estimate confidence levels of the load forecasts to optimize computational resources and the risks associated with uncertain forecasts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2010

Insights into Antiparallel Microtubule Crosslinking by PRC1, a Conserved Nonmotor Microtubule Binding Protein

Formation of microtubule architectures, required for cell shape maintenance in yeast, directional cell expansion in plants and cytokinesis in eukaryotes, depends on antiparallel microtubule crosslinking by the conserved MAP65 protein family. Here, we combine structural and single molecule fluorescence methods to examine how PRC1, the human MAP65, crosslinks antiparallel microtubules. We find that PRC1's microtubule binding is mediated by a structured domain with a spectrin-fold and an unstructured Lys/Arg-rich domain. These two domains, at each end of a homodimer, are connected by a linkage that is flexible on single microtubules, but forms well-defined crossbridges between antiparallel filaments. Further, we show that PRC1 crosslinks are compliant and do not substantially resist filament sliding by motor proteins in vitro. Together, our data show how MAP65s, by combining structural flexibility and rigidity, tune microtubule associations to establish crosslinks that selectively mark antiparallel overlap in dynamic cytoskeletal networks.

59 BASIC BIOLOGICAL SCIENCES↗