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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Bacteria “Read” Light To Gain a Competitive Advantage

Photosynthesis, the process of converting solar energy into stored chemical bonds, represents the primary mechanism by which biological organisms utilize photons. Light can also be used to activate a number of photosensory compounds and proteins designed to carry out tasks, such as DNA repair, gene regulation, and synchronization with the diurnal cycle. Given that sunlight is incident upon many environments, it is not farfetched to think that life may have evolved other as-yet-undetected mechanisms to profit from solar irradiation. In this issue, Maresca and coworkers detail their observations of light-enhanced growth of several nonphotosynthetic actinobacteria, as well as describe the potential photosensitizer responsible for this phenotype and discuss the regulatory networks involved (J. A. Maresca, J. L. Keffer, P. P. Hempel, S. W. Polson, et al., J Bacteriol 201:e00740-18, 2019,). This study opens the door to many intriguing questions about the use of light as information in nonphotosynthetic biological systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Draft Genome Sequence of “Candidatus Spirobacillus cienkowskii,” a Pathogen of Freshwater Daphnia Species, Reconstructed from Hemolymph Metagenomic Reads

We report here the near-complete genome sequence of “ Candidatus Spirobacillus cienkowskii,” a spiral-shaped, red-pigmented uncultivated bacterial pathogen of Daphnia spp. The genome is 2.74 Mbp in size, has a GC content of 32.1%, and contains genes associated with bacterial motility and the production of carotenoids, which could explain the distinctive red color of hosts infected with this pathogen.

Bresciani, Luana↗
DOE OSTI2016

The formation of quantum images and their transformation and super-resolution reading

Images formed by light with suppressed photon fluctuations are interesting objects for studies with the aim of increasing their limiting information capacity and quality. This light in the sub-Poisson state can be prepared in a resonator filled with a medium with Kerr nonlinearity, in which self-phase modulation takes place. Spatially and temporally multimode light beams are studied and the production of spatial frequency spectra of suppressed photon fluctuations is described. The efficient operation regimes of the system are found. A particular schematic solution is described, which allows one to realize the potential possibilities laid in the formation of the squeezed states of light to a maximum degree during self-phase modulation in a resonator for the maximal suppression of amplitude quantum noises upon two-dimensional imaging. The efficiency of using light with suppressed quantum fluctuations for computer image processing is studied. An algorithm is described for interpreting measurements for increasing the resolution with respect to the geometrical resolution. A mathematical model that characterizes the measurement scheme is constructed and the problem of the image reconstruction is solved. The algorithm for the interpretation of images is verified. Conditions are found for the efficient application of sub-Poisson light for super-resolution imaging. It is found that the image should have a low contrast and be maximally transparent.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Hyperfine Interaction and Shockley–Read–Hall Recombination in Semiconductors

Experimental and theoretical studies on optical orientation and spin-dependent recombination in a semiconductor in a magnetic field under the normal incidence of circularly polarized radiation onto the sample surface are reviewed. The experiments were carried out on GaAs{sub 1 –x}N{sub x} solid solutions, in which Ga{sup 2+} interstitial displacement defects play the role of deep paramagnetic centers responsible for spin-dependent recombination. It is established that, in the investigated materials, the hyperfine interaction of a localized electron with one nucleus of the paramagnetic center remains strong even at room temperature. The theory is compared with an experiment conducted in the steady-state excitation mode and under two-pulse pump-probe conditions. An analytical formula for spin beats in a magnetic field is derived.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

I Can’t Read All That! Improving the Usability of Semantic Models Using Concise, Ontology-Agnostic, Building-Specific Schemas

Semantic ontologies have enabled the creation of formalized, machine-readable descriptions of heterogenous building systems by providing dictionaries of well defined concepts that can be applied to model them. Within a semantic model of a particular building, a subset of an ontology's concepts may be applied in different ways to represent a particular perspective of the building's systems. How the concepts were applied can only be understood by examining the large amount of instance data within a semantic model, which leads to usability challenges. We propose a concise, ontology-agnostic method for defining building-specific schema (b-schema) graphs that summarize the structure and content of a semantic model. This approach provides a queryable and concise representation of the model's contents, separate from the instance data within a model, that can mitigate the challenges posed by the size and complexity of semantic models in processes such as visualization, querying, validation, and the use of large language models (LLMs). We validate our approach on semantic models based on the Brick and ASHRAE S223 ontologies. Results demonstrate that b-schemas significantly reduce the complexity of visual interpretation, accelerate SPARQL queries and SHACL validation, and improve LLM-based knowledge graph question answering.

Paul, Lazlo [Lawrence Berkeley National Laboratory↗
DOE OSTI2020

Who Should Read This Journal?

We are excited and pleased that from this volume (Volume 45, published in 2020), the Annual Review of Environment and Resources joins the ranks of the first handful of Annual Reviews journals that are converting to open access. The conversion uses a new model: Subscribe to Open (S2O). All articles in this volume will be permanently open access to the whole world, and so long as S2O continues, all articles in all past volumes of the Annual Review of Environment and Resources will also be kept accessible to all without a paywall.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Designing a graphics processing unit accelerated petaflop capable lattice Boltzmann solver: Read aligned data layouts and asynchronous communication

The lattice Boltzmann method is a well-established numerical approach for complex fluid flow simulations. Recently, general-purpose graphics processing units (GPUs) have become available as high-performance computing resources at large scale. We report on designing and implementing a lattice Boltzmann solver for multi-GPU systems that achieves 1.79 PFLOPS performance on 16,384 GPUs. To achieve this performance, we introduce a GPU compatible version of the so-called bundle data layout and eliminate the halo sites in order to improve data access alignment. Furthermore, we make use of the possibility to overlap data transfer between the host central processing unit and the device GPU with computing on the GPU. As a benchmark case, we simulate flow in porous media and measure both strong and weak scaling performance with the emphasis being on large-scale simulations using realistic input data.

Computer Science↗
DOE OSTI2024

NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study

Abstract Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (Fe 3 O 4 _A and Fe 3 O 4 _B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe 3 O 4 _A and Fe 3 O 4 _B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDs compounds, serving as an efficient alternative to time-consuming trial-and-error procedures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Storage and Read-Optimized Data Placement Structures for High-Performance Analysis [Book Chapter]

Here, we present state-of-art structures and methods for efficient data preparation and representation for analysis. Our intent is to introduce the data science and analytics communities to open source data placements, structures, and methods. These practices can make the foundational processes of data preparation and access dramatically more efficient than typical raw file or database representations and use more conservative storage. To illustrate this, we introduce two highly efficient data placement structures. We then present a tutorial, supported by step-by-step examples, of how to create, use, and access data, structured by Parquet or ORC (Optimized Row Columnar), using Apache Spark. Finally, we illustrate the benefits of using these structures with computational and storage volume benchmarks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

MontePy: a Python library for reading, editing, and writing MCNP input files.

The Monte Carlo N-Particle (MCNP) radiation transport code is a highly capable and accurate code with a long legacy. MCNP uses the Monte Carlo simulation process to simulate the path of particles (e.g., neutrons, photons, charged particles, etc.), and their interaction with materials. It is widely used in nuclear engineering, high-energy physics, and other fields. Its origins in the mid-twentieth century predate many modern software conventions. MCNP users provide an input file to MCNP, which it then uses to create an internal representation of the simulation problem. These input files originally had to be stored as punchcard decks, and the user manual still uses the terminology of cards and decks, despite moving beyond punchcards. MCNP predates nearly all modern human readable markup or data serialization languages, such as the extensible Markup Language (XML), the Standard Generalized Markup Language (SGML), YAML (YAML Ain’t Markup Language), and Javascript Object Notation (JSON). Due to this, MCNP uses an entirely custom defined syntax language for its input, making off-the-shelf libraries for XML, YAML, and JSON impossible to use for scripting various operations on MCNP input files (Kulesza et al., 2022).

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Need something worthwhile to read and watch? The Lab’s classified library director, senior historian’s offer recommendations

Social distancing has many of us turning even more to our streaming services and e-bookshelves in our free time. But if you’re looking for something even more compelling than Tiger King, staff from the National Security Research Center (the Lab’s classified library) have some recommendations for you. NSRC Director Riz Ali and Senior Historian Alan Carr share their top picks of books and movies on Los Alamos and atomic bomb history.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Accelerator Real-time Edge AI for Distributed Systems (READS) (Proposal)

Over the last decade, Machine Learning (ML) technologies have slowly made their way into the accelerator community. Rapid advances in recent years in deep learning, particularly reinforcement learning for control system applications and the accessibility of deep learning in embedded hardware, have generated renewed interest and spawned a number of applications. The Fermilab Accelerator Complex, shown in Fig. 1, has provided High Energy Physics (HEP) experiments with proton beams for nearly fifty years. The current focus of the laboratory is its world-class experimental program at the intensity frontier. While increasing beam intensity certainly presents its own challenges, preserving beam size while minimizing beam losses – particles lost through interactions with the beam vacuum pipe – turns out to be, in many ways, the main challenge. The accelerator is controlled via a complex system of hundreds of thousands of devices. Enabling fine tuning and real-time optimization of their parameters using ML methods and stepping beyond experience-based reasoning of human operators are key to the success of future intensity upgrades. Our objective will be to integrate ML into accelerator operations and furthermore, provide an accessible framework, which can also be used by a broad range of other accelerator systems with dynamic tuning needs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Reasons to read this book (hint: it’ll help your weapons research) Q and A with author highlights Lab’s collections, importance of early weapons work

Michael Bernardin’s retirement actually looks a little more like a second career. This one, though, as an author of books. Bernardin retired last year after 35 years as a nuclear physicist that culminated with his role as the Associate Lab Director for Weapons Physics. Since then, he’s continued accessing the collections of the National Security Research Center, which is the Lab’s classified library.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗