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At least 1,243 records · Page 69

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Scoreboard

Emerging HPC machines have given rise to enhanced compute power that far outstrips the machine's ability to save large scale results for post-processing. To combat this, in situ data analysis techniques are slowly being adopted. With in situ data management favoring workflows composed of multiple simulations and analyses connected in transit on heterogeneous machines, scientists and engineers need a tool that enables them to create data extracts, visualizations, and interactively monitor and steer their simulations. Scoreboard Phase II is a next generation analysis software that supports composite in transit workflows on heterogeneous architectures and restores interactivity to in situ data analysis through simulation monitoring and computational steering. Scoreboard provides a simulation dashboard with graphs of metrics over time, controls for setting custom simulation steering parameters, controls for managing the set of data extracts being produced in the simulation, as well as the ability to explore data extracts, all from a web browser. Realizing the vision outlined in this project required research into making a system that integrates end to end from simulations all the way to the user. In situ tools generally suffer from complexity and excessive software dependencies. Scoreboard, by contrast, is easy to build and integrate into simulation codes and it provides first class FORTRAN support. The Scoreboard library is capable of in situ and in transit data analysis that can produce data extracts commonly needed for Computational Fluid Dynamics (CFD) analysis. Simulations can transparently stage data in transit to a Scoreboard Endpoint program, which can accept their data and produce the requested data extracts. This lets simulations return to their work while the Endpoint works on the analysis. Efficiently staging the data at scale was a topic of this research. Scoreboard provides the means to let the user manage data extracts and monitor/steer many simulations from a web browser. This area of the research focused on discovery of in transit network components to expose and control their steering parameters within an interactive browser-based user interface that includes: system topology, gathered metrics, notifications, dynamically-generated steering controls, and exploration of visualization data products.

Whitlock, BradJoseph [Intelligent Light] (00000001↗

DOE OSTI2017

Adaptive Self-Tuning of Signal Detection Parameters

The quality of automatic detections from sensor networks depends on a large number of data processing parameters that interact in complex ways. The largely manual process of identifying effective parameters is painstaking and does not guarantee that the resulting controls are the optimal configuration settings, yet achieving superior automatic detection of events is closely related to these parameters. We present an automated sensor tuning (AST) system that tunes effective parameter settings for each sensor detector to the current state of the environment by leveraging cooperation within a neighborhood of sensors. After a stabilization period, the AST algorithm can adapt in near real-time to changing conditions and automatically self-tune a signal detector to identify (detect) only signals from events of interest. The overall goal is to reduce the number of missed legitimate event detections and the number of false event detections. Our current work focuses on reducing false signal detections early in the seismic signal processing pipeline, which leads to fewer false events and has a significant impact on reducing analyst time and effort. Applicable both for existing sensor performance boosting and new sensor deployment, this system provides an important new method to automatically tune complex remote sensing systems. Systems tuned in this way will achieve better performance than is currently possible by manual tuning, and with much less time and effort devoted to the tuning process. With ground truth on detections from a seismic sensor network monitoring the Mount Erebus Volcano in Antarctica, we show that AST increases the probability of detection while decreasing false alarms.

58 GEOSCIENCES↗

DOE OSTI2013

Discovery and structural characterization of a small molecule 14-3-3 protein-protein interaction inhibitor

The 14-3-3 family of phosphoserine/threonine-recognition proteins engage multiple nodes in signaling networks that control diverse physiological and pathophysiological functions and have emerged as promising therapeutic targets for such diseases as cancer and neurodegenerative disorders. Thus, small molecule modulators of 14-3-3 are much needed agents for chemical biology investigations and therapeutic development. To analyze 14-3-3 function and modulate its activity, we conducted a chemical screen and identified 4-[(2Z)-2-[4-formyl-6-methyl-5-oxo-3-(phosphonatooxymethyl)pyridin-2-ylidene]hydrazinyl]benzoate as a 14-3-3 inhibitor, which we termed FOBISIN (FOurteen-three-three BInding Small molecule INhibitor) 101. FOBISIN101 effectively blocked the binding of 14-3-3 with Raf-1 and proline-rich AKT substrate, 40 kD{sub a} and neutralized the ability of 14-3-3 to activate exoenzyme S ADP-ribosyltransferase. To provide a mechanistic basis for 14-3-3 inhibition, the crystal structure of 14-3-3{zeta} in complex with FOBISIN101 was solved. Unexpectedly, the double bond linking the pyridoxal-phosphate and benzoate moieties was reduced by X-rays to create a covalent linkage of the pyridoxal-phosphate moiety to lysine 120 in the binding groove of 14-3-3, leading to persistent 14-3-3 inactivation. We suggest that FOBISIN101-like molecules could be developed as an entirely unique class of 14-3-3 inhibitors, which may serve as radiation-triggered therapeutic agents for the treatment of 14-3-3-mediated diseases, such as cancer.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Cyber-Physical Test Platform for Microgrids: Combining Hardware, Hardware-in-the-Loop, and Network-Simulator-in-the-Loop

This paper presents a cyber-physical testbed, developed to investigate the complex interactions between emerging microgrid technologies such as grid-interactive power sources, control systems, and a wide variety of communication platforms and bandwidths. The cyber-physical testbed consists of three major components for testing and validation: real time models of a distribution feeder model with microgrid assets that are integrated into the National Renewable Energy Laboratory's (NREL) power hardware-in-the-loop (PHIL) platform; real-time capable network-simulator-in-the-loop (NSIL) models; and physical hardware including inverters and a simple system controller. Several load profiles and microgrid configurations were tested to examine the effect on system performance with increasing channel delays and router processing delays in the network simulator. Testing demonstrated that the controller's ability to maintain a target grid import power band was severely diminished with increasing network delays and laid the foundation for future testing of more complex cyber-physical systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1989

Unsupervised learning in neurodynamics using example-interaction approach

A new concept for unsupervised learning based upon examples introduced to the neural network is proposed. Each example is considered as an interpolation node of the velocity field in the phase space. The velocities at these nodes are selected such that all the streamlines diverge to an attracting set imbedded in the subspace occupied by the cluster of examples. The synaptic interconnections are found from the minimization of the strength energy, while the node velocities play the role of constraints.

Zak, Michail↗

NASA NTRS1989

Unsupervised learning in neurodynamics using example-interaction approach

Zak, Michail↗

DOE OSTI2011

Basics of perfect communication through quantum networks

Perfect transfer of a quantum state through a one-dimensional chain is now well understood, allowing one not only to decide whether a fixed Hamiltonian achieves perfect transfer but to design a suitable one. We are particularly interested in being able to design, or understand the limitations imposed upon, Hamiltonians subject to various naturally arising constraints such as a limited coupling topology with low connectivity (specified by a graph) and type of interaction. In this paper, we characterize the necessary and sufficient conditions for transfer through a network and describe some natural consequences such as the impossibility of routing between many different recipients for a large class of Hamiltonians and the limitations on transfer rate. We also consider some of the trade-offs that arise in uniformly coupled networks (both Heisenberg and XX models) between transfer distance and the size of the network as a consequence of the derived conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

Integrated Transmission and Distribution Systems Restoration with Distributed Generation Scheduling

The self-healing smart grids enable system operators to quickly and efficiently recover transmission and distribution systems from outages and blackouts. The current restoration procedure solves the transmission and distribution system restoration problems separately without considering their impacts on each other, which may prolong the restoration process. However, with the emerging active distribution networks, distributed generators can contribute into the bottom-up restoration strategy. The interaction between the restoration of two interconnected systems should be taken into account. In this paper, a hybrid model of transmission and distribution systems restoration (HTDSR) is proposed by exchanging data of power and voltage between two separate optimization problems. The effectiveness of the HTDSR algorithm is demonstrated through case studies of the integrated IEEE 13-bus distribution network and IEEE 39-bus transmission grid. Simulation results demonstrate that the integrated restoration strategy with optimal scheduling of distributed generation sources can expedite the restoration process.

Distributed generation↗

DOE OSTI2023

Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation

Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes.

16S rRNA↗

DOE OSTI2011

Collaborative Visualization: Definition, Challenges, and Research Agenda

Collaborative visualization has emerged as a new research direction which offers the opportunity to reach new audiences and application areas for visualization tools and techniques. Technology now allows us to easily connect and collaborate with one another - in settings as diverse as over networked computers, across mobile devices, or using shared displays such as interactive walls and tabletop surfaces. Any of these collaborative settings carries a set of challenges and opportunities for visualization research. Digital information is already regularly accessed by multiple people together in order to share information, to view it together, to analyze it, or to form decisions. However, research on how to best support collaboration with and around visualizations is still in its infancy and has so far focused only on a small subset of possible application scenarios. The purpose of this article is (1) to provide a clear scope, definition, and overview of the evolving field of collaborative visualization, (2) to help pinpoint the unique focus of collaborative visualization with its specific aspects, challenges, and requirements within the intersection of general computer-supported collaborative work (CSCW) and visualization research, and (3) to draw attention to important future research questions to be addressed by the community. Thus, the goal of the paper is to discuss a research agenda for future work on collaborative visualization, including our vision for how to meet the grand challenge and to urge for a new generation of visualization tools that were designed with collaboration in mind from their very inception.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Constructing tensor network influence functionals for general quantum dynamics

Here, we describe an iterative formalism to compute influence functionals that describe the general quantum dynamics of a subsystem beyond the assumption of linear coupling to a quadratic bath. We use a space-time tensor network representation of the influence functional and investigate its approximability in terms of its bond dimension and time-like entanglement in the tensor network description. We study two numerical models, the spin-boson model and a model of interacting hard-core bosons in a 1D harmonic trap. We find that the influence functional and the intermediates involved in its construction can be efficiently approximated by low bond dimension tensor networks in certain dynamical regimes, which allows the quantum dynamics to be accurately computed for longer times than with direct time evolution methods. However, as one iteratively integrates out the bath, the correlations in the influence functional can first increase before decreasing, indicating that the final compressibility of the influence functional is achieved via non-trivial cancellation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

NASMDR: a framework for miRNA-drug resistance prediction using efficient neural architecture search and graph isomorphism networks

Abstract As a frontier field of individualized therapy, microRNA (miRNA) pharmacogenomics facilitates the understanding of different individual responses to certain drugs and provides a reasonable reference for clinical treatment. However, the known drug resistance-associated miRNAs are not yet sufficient to support precision medicine. Although existing methods are effective, they all focus on modelling miRNA-drug resistance interaction graphs, making their performance bounded by the interaction density. In this study, we propose a framework for miRNA-drug resistance prediction through efficient neural architecture search and graph isomorphism networks (NASMDR). NASMDR uses attribute information instead of the commonly used interactive graph information. In the cross-validation experiment, the proposed framework can achieve an AUC of 0.9468 on the ncDR dataset, which is 2.29% higher than the state-of-the-art method. In addition, we propose a novel sequence characterization approach, k-mer Sparse Nonnegative Matrix Factorization (KSNMF). The results show that NASMDR provides novel insights for integrating efficient neural architecture search and graph isomorphic networks into a unified framework to predict drug resistance-related miRNAs. The codes for NASMDR are available at https://github.com/kaizheng-academic/NASMDR.

Zheng, Kai↗

DOE OSTI2023

Semi-supervised graph neural networks for pileup noise removal

Abstract The high instantaneous luminosity of the CERN Large Hadron Collider leads to multiple proton–proton interactions in the same or nearby bunch crossings (pileup). Advanced pileup mitigation algorithms are designed to remove this noise from pileup particles and improve the performance of crucial physics observables. This study implements a semi-supervised graph neural network for particle-level pileup noise removal, by identifying individual particles produced from pileup. The graph neural network is firstly trained on charged particles with known labels, which can be obtained from detector measurements on data or simulation, and then inferred on neutral particles for which such labels are missing. This semi-supervised approach does not depend on the neutral particle pileup label information from simulation, and thus allows us to perform training directly on experimental data. The performance of this approach is found to be consistently better than widely-used domain algorithms and comparable to the fully-supervised training using simulation truth information. The study serves as the first attempt at applying semi-supervised learning techniques to pileup mitigation, and opens up a new direction of fully data-driven machine learning pileup mitigation studies.

43 PARTICLE ACCELERATORS↗

DOE OSTI2012

The complex metal-rich boride Ti{sub 1+x}Rh{sub 2-x+y}Ir{sub 3-y}B{sub 3} (x=0.68, y=1.06) with a new structure type containing B{sub 4} zigzag fragments: Synthesis, crystal chemistry and theoretical calculations

Polycrystalline samples and single crystals of the new complex boride Ti{sub 1+x}Rh{sub 2-x+y}Ir{sub 3-y}B{sub 3} (x=0.68; y=1.06) were synthesized by arc-melting the elements in a water-cooled copper crucible under an argon atmosphere and characterized by X-Ray diffraction as well as EDX measurements. The crystal structure was refined on the basis of single crystal data. The new phase, which represents a new structure type containing trans zigzag B{sub 4} fragments as well as isolated boron atoms crystallizes in the orthorhombic space group Pbam (Nr. 55) with the lattice parameters a=8.620(1) A, b=14.995(2) A and c=3.234(1) A. First-principles density functional theory calculations using the Vienna ab-initio simulation package (VASP) were performed on an appropriate structural model (using a supercell approach) and the experimental crystallographic data could be reproduced accurately. Based on this model, the density of states and crystal orbital Hamilton population (for bonding analysis) were calculated, using the linear muffin-tin orbital atomic sphere approximation (LMTO-ASA) method. According to these calculations, this metal-rich compound should be metallic, as expected. Furthermore, very strong boron-boron interactions are observed in the trans zigzag B{sub 4} fragment, which induce a clear differentiation of two types of metal-boron contacts with different strength. The observed three-dimensional metal-metal interaction is in good agreement with the predicted metallic behavior. - graphical abstract: The structure of Ti{sub 1.68(2)}Rh{sub 2.38(6)}Ir{sub 1.94(4)} B{sub 3}, a new structure type containing planar trans zigzag B{sub 4} units, is another example which illustrates the tendency of metal-rich borides to form B-B bonds with increasing boron content. Beside the B{sub 4} fragment it exhibits one-dimensional chains of titanium atoms and hold one-dimensional strings of face-sharing empty tetrahedral and square pyramidal clusters (see figure). Highlights: Black-Right-Pointing-Pointer Synthesis of a new metal-rich complex boride. Black-Right-Pointing-Pointer New structure type containing isolated boron and trans zigzag B{sub 4} units. Black-Right-Pointing-Pointer Crystallographic parameters and bond length well reproduced by theory. Black-Right-Pointing-Pointer Strong boron-boron and metal-boron interactions responsible for structural stability. Black-Right-Pointing-Pointer Three-dimensional metallic network responsible for metallic behavior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

SU-F-R-44: Modeling Lung SBRT Tumor Response Using Bayesian Network Averaging

Purpose: The prediction of tumor control after a patient receives lung SBRT (stereotactic body radiation therapy) has proven to be challenging, due to the complex interactions between an individual’s biology and dose-volume metrics. Many of these variables have predictive power when combined, a feature that we exploit using a graph modeling approach based on Bayesian networks. This provides a probabilistic framework that allows for accurate and visually intuitive predictive modeling. The aim of this study is to uncover possible interactions between an individual patient’s characteristics and generate a robust model capable of predicting said patient’s treatment outcome. Methods: We investigated a cohort of 32 prospective patients from multiple institutions whom had received curative SBRT to the lung. The number of patients exhibiting tumor failure was observed to be 7 (event rate of 22%). The serum concentration of 5 biomarkers previously associated with NSCLC (non-small cell lung cancer) was measured pre-treatment. A total of 21 variables were analyzed including: dose-volume metrics with BED (biologically effective dose) correction and clinical variables. A Markov Chain Monte Carlo technique estimated the posterior probability distribution of the potential graphical structures. The probability of tumor failure was then estimated by averaging the top 100 graphs and applying Baye’s rule. Results: The optimal Bayesian model generated throughout this study incorporated the PTV volume, the serum concentration of the biomarker EGFR (epidermal growth factor receptor) and prescription BED. This predictive model recorded an area under the receiver operating characteristic curve of 0.94(1), providing better performance compared to competing methods in other literature. Conclusion: The use of biomarkers in conjunction with dose-volume metrics allows for the generation of a robust predictive model. The preliminary results of this report demonstrate that it is possible to accurately model the prognosis of an individual lung SBRT patient’s treatment.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

A Dual-Lattice Discrete Element Model to Understand Hydraulic Fracturing in a Naturally Fractured System

Interaction between hydraulic fractures (HF) and natural fractures (NF) may lead to complex fracture networks as a consequence of branching and merging of the natural and hydraulically-induced/reopened fractures. Conventional simulation methods, with assumptions of homogeneous and continuous media, are not able to capture realistic fracture patterns in the presence of pre-existing natural fractures (open or healed). These natural discontinuities alter the local stress field and impact the fracturing fluid pressure distribution when hydraulically-driven fractures approach natural discontinuities. Modeling the propagation of one or more propagating hydraulic fractures in naturally fractured reservoirs should carefully consider the complex mechanical and hydraulic interactions between hydraulically-driven and natural fractures. A rigorous hydraulic fracturing model based on a coupled discrete element method (DEM) and conjugate network flow model is described in this work. The coupled DEM-network flow model conveniently accounts for both continuous poroelasticity and discrete natural fractures simultaneously and provides more realistic fracture growth patterns in low-permeability rocks. This dual lattice DEM-network flow model can capture both tension-induced rock failure and shear-induced natural discontinuity sliding and dilation. Simulation results revealed that reactivation of natural fractures becomes more difficult when the following conditions are present: larger magnitude of stress difference, higher intercepting angle between the natural fracture and the propagating hydraulic fracture (higher implies more orthogonal to the natural fracture), larger natural fracture cohesion and angle of internal friction and/or lower natural fracture permeability - leading to continued growth of the hydraulic fracture. The term stress difference was colloquially used in the preceding sentence. For clarity, we will define this as ΔS = S H,max – S h,min ). The quantitative results of numerous simulations reinforced the intuitive understanding of the underlying phenomena. Other simulations reinforced insights from laboratory experimentation and field measurements that lower injection rates and treating fluid viscosities facilitate preferential fluid intake into natural fractures. Finally, this simulation capability for predicting the nature of complex fracture networks potentially enables optimizing treatment design and completion strategy in naturally fractured reservoirs.

42 ENGINEERING↗

DOE OSTI2020

Influences of Hydrogen Bonding-Based Stabilization of Bolaamphiphile Layers on Molecular Diffusion within Organic Nanotubes Having Inner Carboxyl Groups

This paper reports molecular diffusion behavior in two bolaamphiphile-based organic nanotubes having inner carboxyl groups with different inner dimeters (10 and 20 nm) and wall structures, COOH-ONT 10nm and COOH-ONT 20nm , using imaging fluorescence correlation spectroscopy (imaging FCS). Here, the results were compared to those previously obtained in a similar nanotube with inner amine groups (NH 2 -ONT 10nm ). COOH-ONT 10nm , as with NH 2 -ONT 10nm , were formed from a rolled bolaamphiphile layer incorporating triglycine moieties, whereas COOH-ONT 20nm consisted of four stacks of triglycine-free bolaamphiphile layers. Imaging FCS measurements were carried out for anionic sulforhodamine B (SRB), zwitterionic/cationic rhodamine B (RB) and cationic rhodamine-123 (R123) diffusing within ONTs (1–9 µm long) at different pH (3.4–8.4) and ionic strengths (1.6–500 mM). Diffusion coefficients (D) of these dyes in the ONTs were very small (0.01–0.1 µm 2 /s), reflecting the significant contributions of molecule-nanotube interactions to diffusion. The D of SRB was larger at higher pH and ionic strength, indicating the essential role of electrostatic repulsion that was enhanced by the deprotonation of the inner carboxyl groups. Importantly, the D of SRB was virtually independent of nanotube inner diameter and wall structure, indicating the diffusion of the hydrophilic molecule was controlled by short timescale adsorption/desorption processes onto the inner surface. In contrast, pH effects on D were less clear for relatively hydrophobic R123 and RB, suggesting the significant contributions of non-Coulombic interactions. Interestingly, the diffusion of these molecules in COOH-ONT 20nm was slower than in COOH-ONT 10nm . Slower diffusion in COOH-ONT20nm was attributable to relatively efficient partitioning of the hydrophobic dyes into the bolaamphiphile layers, which was reduced in COOH-ONT 10nm due to the stabilization of its layer by polyglycine-II-type hydrogen bonding networks. These results show that, by tuning the bolaamphiphile structures and their intermolecular interactions, unique environments can be created within the nanospaces for enhanced molecular separations and reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

A surprisingly simple correlation between the classical and quantum structural networks in liquid water

Nuclear quantum effects in liquid water have profound implications for several of its macroscopic properties related to the structure, dynamics, spectroscopy, and transport. Although several of water’s macroscopic properties can be reproduced by classical descriptions of the nuclei using interaction potentials effectively parameterized for a narrow range of its phase diagram, a proper account of the nuclear quantum effects is required to ensure that the underlying molecular interactions are transferable across a wide temperature range covering different regions of that diagram. In this work, when performing an analysis of the hydrogen-bonded structural networks in liquid water resulting from the classical ( class ) and quantum ( qm ) descriptions of the nuclei with two interaction potentials that are at the two opposite ends of the range in describing quantum effects, namely the flexible, pair-wise additive q-TIP4P/F, and the flexible, polarizable TTM3-F, we found that the ( class ) and ( qm ) results can be superimposed over the temperature range T = 250-350 K using a surprisingly simple, linear scaling of the two temperatures according to T ( qm ) = α T ( class ) + Δ T , where α = 0.99 and Δ T = –6 K for q-TIP4P/F and α = 1.24 and Δ T = –64 K for TTM3-F. This simple relationship suggests that the structural networks resulting from the quantum and classical treatment of the nuclei with those two very different interaction potentials are essentially similar to each other over this extended temperature range once a model-dependent linear temperature scaling law is applied.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗