Individual Addressing and State Readout of Trapped Ions Utilizing Radio-Frequency Micromotion
Not Available
Lysne, Nathan K. (ORCID:0000000158491672)↗
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Results for “utilization”
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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Utilizing the deuterium-tritium fusion resonance to diagnose thermal runaway in igniting plasmas
For high-efficiency inertial confinement fusion implosions, it is predicted that a burning hot spot will successfully encompass all surrounding fuel and then transition into a thermal runaway where the internal energy increase from fusion occurs on a timescale faster than the expansion of the fuel is able to quench the fusion chain reaction after ignition occurs. Observation of this dynamic phase transition would indicate distinct burn properties and indicate an implosion's robustness. A technique for diagnosing the presence of thermal runaway from measurements of nuclear reaction history is presented. The technique is based on taking the logarithmic derivative of the nuclear reaction history, called the 𝛼 curve, and allowing a mathematical decoupling of the mass, volume, and thermal reactivity in the fusion reaction rate equation. During thermal runaway, where the thermal temperature dominates the burn dynamics, a maximum in the 𝛼 curve is found where there is a maximum in the first derivative of the thermal fusion reactivity, an effect to the deuterium-tritium (DT) fusion cross-section resonance. This provides a distinct signature related to the fundamental nature of the DT fusion nuclear resonance and signifies the transition into the fusion thermal instability. Impacts of charged particle transport on the effect are also assessed and the analytical formulas are compared and found to be in agreement with radiation hydrodynamic codes.
high-energy-density plasmas↗
Utilize Data Augmentation and Flexi Corner Block for Road Damage Detection
Not Available
Wu, Zhaohui [China Academy of Transportation Scien↗
Overview of RFID Applications Utilizing Neural Networks
As Radio Frequency Identification (RFID) methods continue to evolve to higher levels of complexity, one form of machine learning is making its appearance. The use of Neural Networks (NN) in the RFID field is steadily increasing, and in the fields of localization and activity recognition, promising results are being shown from a variety of research. RFID applications fall primarily under two types of problems including regression and classification. We analyze RIFD localization techniques which fall under regression, and activity recognition which falls under classification. Many works don’t classify themselves as activity recognition methods, but because they fall under the classification category, we still consider them as activity recognition techniques. This research overviews the Neural Network models in the localization field based on whether they can perform independently of the environment in which they were tested. For activity recognition and accessory fields, the major methods involve tag-based and tag-free approaches. In conclusion, after the models are surveyed, a comparison study is given to examine what may be the cause for increased accuracy between different Neural Network models.
42 ENGINEERING↗
A Transactive Approach for Service Restoration Utilizing Customer Load Flexibility and Grid-Edge Resources
This paper develops a transactive energy system model to restore electricity to customers in an isolated distribution system after an outage. The model is developed to engage a variety of customer types - prosumers, flexible loads, critical/noncritical customers, and distributed generators - as active participants in the restoration process. Unlike many existing transactive approaches, the proposed model is developed for service restoration and accounts for various customer types and their autonomy and privacy through an iterative approach to determine the optimal market price, while maintaining systemlevel power flow and voltage constraints. The advantages of the proposed approach are numerically validated on a modified IEEE 123-bus test system.
distributed energy resources↗
Assembly, comparative analysis, and utilization of a single haplotype reference genome for soybean
Cultivar Williams 82 has served as the reference genome for the soybean research community since 2008, but is known to have areas of genomic heterogeneity among different sub-lines. This work provides an updated assembly (version Wm82.a6) derived from a specific sub-line known as Wm82-ISU-01 (seeds available under USDA accession PI 704477). The genome was assembled using Pacific BioSciences HiFi reads and integrated into chromosomes using HiC. The 20 soybean chromosomes assembled into a genome of 1.01Gb, consisting of 36 contigs. The genome annotation identified 48 387 gene models, named in accordance with previous assembly versions Wm82.a2 and Wm82.a4. Comparisons of Wm82.a6 with other near-gapless assemblies of Williams 82 reveal large regions of genomic heterogeneity, including regions of differential introgression from the cultivar Kingwa within approximately 30 Mb and 25 Mb segments on chromosomes 03 and 07, respectively. Additionally, our analysis revealed a previously unknown large (> 20 Mb) heterogeneous region in the pericentromeric region of chromosome 12, where Wm82.a6 matches the ‘Williams’ haplotype while the other two near-gapless assemblies do not match the haplotype of either parent of Williams 82. In addition to the Wm82.a6 assembly, we also assembled the genome of ‘Fiskeby III,’ a rich resource for abiotic stress resistance genes. A genome comparison of Wm82.a6 with Fiskeby III revealed the nucleotide and structural polymorphisms between the two genomes within a QTL region for iron deficiency chlorosis resistance. The Wm82.a6 and Fiskeby III genomes described here will enhance comparative and functional genomics capacities and applications in the soybean community.
59 BASIC BIOLOGICAL SCIENCES↗
Compilation and utilization of a sorghum transcriptome compendium for gene regulatory network analysis and crop trait engineering
Sorghum bicolor (Sorghum) is a drought and heat tolerant C4 grass crop used to produce grain, forage, biofuels, and other bioproducts. Genetic improvement of sorghum hybrid crops is aided by a large and diverse germplasm, sorghum's diploid inbreeding genetics, and a relatively small genome that has facilitated genomic research. Over the past 20 years, the sorghum research community characterized the cytogenetic and recombinant landscapes of sorghum's 10 chromosomes, sequenced and annotated the sorghum genome, and used that information to identify genes/alleles that modulate flowering time, plant height, seed shattering, and other important traits. More recently, >1000 RNA-seq transcriptome profiles were collected from 15 sorghum genotypes to help understand the genetic basis of variation in growth and development of sorghum stems, tillers, roots, and leaves, and the regulation of biosynthetic pathways that produce epicuticular wax, dhurrin, and RFOs, compounds that contribute to sorghum's resilience. Transcriptome studies were designed to identify differentially expressed genes that are co-expressed during development or in response to a treatment to enable construction of gene regulatory networks. Co-expression and network analysis identified transcription factors and their cognate binding sites in target gene promoters and signaling pathways that modulate gene regulatory networks providing gene editing targets for further trait optimization. RNA-seq data from >20 experiments targeting sorghum organs, tissues, cell types, developmental stages, and responses to environmental conditions (i.e., diel, day-length, shading, water-deficit, temperature) has been compiled in a sorghum transcriptome compendium. The goal of this resource paper is to describe compendium content, accessibility, and a compendium data analysis pipeline and to illustrate the types of information that can be derived from the compendium with a focus on the elucidation of gene regulatory networks useful for guiding the improvement of sorghum traits through gene editing.
RNA-seq↗
A Nonintrusive Optical Approach to Characterize Heliostats in Utility-Scale Power Tower Plants: Camera Position Sensitivity Analysis
Optics plays a major role in the effectiveness of concentrating solar power (CSP) technologies. The nonintrusive optical (NIO) approach developed by the National Renewable Energy Laboratory uses uncrewed aircraft system (UAS)-based imaging to survey heliostats in a commercial-scale power tower CSP plant and characterize their optical errors. The image processing algorithm uses photogrammetry to calculate the camera position for each image frame, and the accuracy of the estimated optical errors is highly sensitive to the calculated camera position accuracy. In this study, we simulate a series of case studies in python to examine the impact of different parameters of the sensitivity of the camera calculation, including the number of facet corners used as control points for the photogrammetric calculation, precision error in the detected pixel locations of the facet corners in the image, and precision error of the canting and mounting positions of the facets of the heliostat. The case studies consider heliostat geometry based on three commercial designs to serve as representative examples of different possible sizes of heliostats that the NIO method could be applied to. The results show that increasing the number of control points can improve accuracy for heliostats with many facets, pixel precision has a significantly larger impact on camera calculation accuracy than facet canting and mounting errors, and the camera distance and focal length must be chosen to ensure adequate pixel accuracy on the heliostat surface depending on the size of heliostat. In conclusion, based on the results, recommendations for the allowable values of each parameter are provided to achieve the required NIO optical error estimation accuracy depending on the size of heliostat.
14 SOLAR ENERGY↗
A Digital Twin Framework Utilizing Machine Learning for Robust Predictive Maintenance: Enhancing Tire Health Monitoring
We introduce a novel digital twin (DT) framework for the predictive maintenance of long-term physical systems. Using monitoring tire health as an application, we show how the DT framework can be used to enhance automotive safety and efficiency, and how the technical challenges can be overcome using a three-step approach. First, to manage the data complexity over a long operation span, we employ data reduction techniques to concisely represent physical tires using historical performance and usage data. Relying on these data, for fast real-time prediction, we train a transformer-based model offline on our concise dataset to predict future tire health over time, represented as remaining casing potential (RCP). Based on our architecture, our model quantifies both epistemic and aleatoric uncertainties, providing reliable confidence intervals around predicted RCP. Second, to incorporate real-time data, we update the predictive model in the DT framework, ensuring its accuracy throughout its lifespan with the aid of hybrid modeling and the use of the discrepancy function. Third, to assist decision-making in predictive maintenance, we implement a tire state decision algorithm, which strategically determines the optimal timing for tire replacement based on RCP forecasted by our transformer model. This approach ensures that our DT accurately predicts system health, continually refines its digital representation, and supports predictive maintenance decisions. Furthermore, our framework effectively embodies a physical system, leveraging big data and machine learning (ML) for predictive maintenance, model updates, and decision-making.
advanced computing infrastructure↗
DoCeph: DPU-Offloaded Messaging in Ceph for Reduced Host CPU Utilization
Ceph is a widely used distributed object store, but its messenger layer imposes substantial CPU overhead on the host. To address this limitation, we propose DoCeph, a DPU-offloaded storage architecture for Ceph that disaggregates the system by offloading the communication-intensive messaging component to the DPU while retaining the storage backend on the host. The DPU efficiently manages communication, using lightweight RPC for metadata operations and DMA for data transfer. Moreover, DoCeph introduces a pipelining technique that overlaps data transmission with buffer preparation, mitigating hardware-imposed transfer size limitations. We implemented DoCeph on a Ceph cluster with NVIDIA BlueField-3 DPUs. Evaluation results indicate that DoCeph cuts host CPU usage by up to 92% while sustaining stable throughput and providing larger performance benefits for object writes over 1 MB.
Park, Kuri [Sogang University]↗
fife-utils
This is a collection of scripts related to the FIFE project at Fermilab, including utiltities for performing bulk oprations with our SAM and MetaCat (github)data handling systems. The most heavily used is the fife_launch/fife_wrap script pair, which is used to convert physics analysis executables into distributed grid jobs.
Mengel, Marc [Fermi National Accelerator Laborator↗
PUMA:POWDER UTILIZATION MODELING APPLICATION
SF-25-084 PUMA a high performance modeling framework to simulate powder processing. It provides a scalable tool for manufacturers to simulate powder pre- and post-processing. The tool can predict the distortion, residual stress, and (for reactive processes) reaction completion fraction of complex parts after curing/debinding, sintering, and infiltration processes. These predictions are key metrics industry uses to optimize these processes to produce dense, defect-free, stable components.
HU, TIANCHEN (GARY)↗
Optimizing the Potential Utility of Blue-Blocking Glasses for Sleep and Circadian Health
Not provided.
Ophthalmology↗
Understanding power and energy utilization in large scale production physics simulation codes
Power is an often-cited reason for the move to advanced architectures on the path to Exascale computing. Here, this is due to practical considerations related to delivering enough power to successfully site and operate these machines, as well as concerns about energy usage while running large simulations. Since obtaining accurate power measurements can be challenging, it may be tempting to use the processor thermal design power (TDP) as a surrogate due to its simplicity and availability. However, TDP is not indicative of typical power usage while running simulations. Using commodity and advanced technology systems at Lawrence Livermore and Sandia National Labs, we performed a series of experiments to measure power and energy usage in running simulation codes. These experiments indicate that large scale Lawrence Livermore simulation codes are significantly more efficient than a simple processor TDP model might suggest.
HPC↗
Fine-tuning TrailMap: The utility of transfer learning to improve the performance of deep learning in axon segmentation of light-sheet microscopy images
Light-sheet microscopy has made possible the 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual undertaking. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through optimizing transfer learning. We fine-tuned the existing TrailMap model using expert-labeled data from noradrenergic axonal structures in the mouse brain. By changing the cross-entropy weights and using augmentation, we demonstrate a generally improved adjusted F1-score over using the originally trained TrailMap model within our test datasets.
97 MATHEMATICS AND COMPUTING↗
Utilization of traceable standards to validate plutonium isotopic purification and separation of plutonium progeny using AG MP-1M resin for nuclear forensic investigations
Radio-chronometric studies on plutonium (Pu) materials require independent measurement of the Pu (parent) content and isotopic distribution as well as concentration and isotopic distribution of the plutonium isotopic decay products. We performed a series of experiments to demonstrate the consistency of separations using the Lewatit MP 800 macroporous anion exchange resin and the AG MP-1M resin with traceable Pu isotopic certified reference material (CRM) standards 136, 137, 138, and 126-A. Two different mesh-sizes of the AG MP-1M resin were tested and the 50–100 mesh size resin was found to work more efficiently for the separation task. Both Lewatit and AG MP-1M resins were found to perform satisfactorily for quantitatively extracting the americium (Am) and uranium (U) progeny as well as gallium (Ga) present as a tracer in the Pu material. Both resins were effective in removing isobaric interferences from the Pu fraction used in isotopic measurements by thermal ionization mass spectrometry (TIMS). To address the co-elution of uranium and gallium, Alizarin red S (ARS) was used as a colorimetric dye to determine the behavior of UO 2 2+ and Ga 3+ on AG MP-1M resin with various acidic solutions as eluents using UV–vis spectra. Poor resolution of these peaks complicated quantitative analysis by UV–vis spectroscopy, but these results were informative in planning automated separation experiments by HPLC. LA-UR-24-28919.
38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
Utilizing 3D Mechanical Earth Models for Calibration and Validation in a Large-Scale Carbon Capture and Storage Project in North Dakota
Conference paper presented at 17th International Conference on Greenhouse Gas Control Technologies (GHGT-17), Calgary, Alberta, Canada, October 20–24, 2024. Three-dimensional (3D) mechanical earth models (MEMs) are pivotal in assessing geological sites for carbon dioxide (CO 2 ) storage and mitigating potential risks associated with storage and injection. The Energy & Environmental Research Center is exploring innovative carbon storage monitoring techniques at a CO 2 storage site near Beulah, North Dakota. These methods aim to provide lower-impact, faster feedback for commercial carbon capture and storage (CCS) projects. The research includes monitoring CO 2 injection using various approaches: 1) an automated, integrated modular monitoring station; 2) time-lapse electromagnetic (EM) field surveys; 3) drone-based surveillance; 4) time-lapse seismic techniques; and 5) advanced wellbore monitoring.
02 PETROLEUM↗
Identifying Performance Advantaged Biobased Chemicals Utilizing Bioprivileged Molecules
Two technology areas were advanced; a) novel molecules with improved performance in the end use application of organic corrosion inhibitors and flame retardant nylon polymers and b) development of a systematic process for identifying biomass-derived molecules with improved performance in end use applications. In total 17 novel organic corrosion inhibitors were identified that had significantly better performance than the commercial reference organic corrosion inhibitor and 7 novel nylons were synthesized with improved flame retardant properties relative to standard nylon-6,6. While an end-to-end systematic process for identifying biomass-derived molecules with improved end use performance was not completed, important progress was made computational tools for mining chemical structures from the literature and databases as well as establishing reaction network generation algorithms to aid in the discovery of novel molecules.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗