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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Transcriptomic Analysis of the CAM Species Kalanchoë fedtschenkoi Under Low- and High-Temperature Regimes

Temperature stress is one of the major limiting environmental factors that negatively impact global crop yields. Kalanchoë fedtschenkoi is an obligate crassulacean acid metabolism (CAM) plant species, exhibiting much higher water-use efficiency and tolerance to drought and heat stresses than C 3 or C 4 plant species. Previous studies on gene expression responses to low- or high-temperature stress have been focused on C 3 and C 4 plants. There is a lack of information about the regulation of gene expression by low and high temperatures in CAM plants. To address this knowledge gap, we performed transcriptome sequencing (RNA-Seq) of leaf and root tissues of K. fedtschenkoi under cold (8 °C), normal (25 °C), and heat (37 °C) conditions at dawn (i.e., 2 h before the light period) and dusk (i.e., 2 h before the dark period). Our analysis revealed differentially expressed genes (DEGs) under cold or heat treatment in comparison to normal conditions in leaf or root tissue at each of the two time points. In particular, DEGs exhibiting either the same or opposite direction of expression change (either up-regulated or down-regulated) under cold and heat treatments were identified. In addition, we analyzed gene co-expression modules regulated by cold or heat treatment, and we performed in-depth analyses of expression regulation by temperature stresses for selected gene categories, including CAM-related genes, genes encoding heat shock factors and heat shock proteins, circadian rhythm genes, and stomatal movement genes. Our study highlights both the common and distinct molecular strategies employed by CAM and C 3 /C 4 plants in adapting to extreme temperatures, providing new insights into the molecular mechanisms underlying temperature stress responses in CAM species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Tuning Thermal and Mechanical Properties of Polydimethylsiloxane with Carbon Fibers

In order to meet the needs of constantly advancing technologies, fabricating materials with improved properties and predictable behavior has become vital. To that end, we have prepared polydimethylsiloxane (PDMS) polymer samples filled with carbon nanofibers (CFs) at 0, 0.5, 1.0, 2.0, and 4.0 CF loadings (w/w) to investigate and optimize the amount of filler needed for fabrication with improved mechanical properties. Samples were prepared using easy, cost-efficient mechanical mixing to combine the PDMS and CF filler and were then characterized by chemical (FTIR), mechanical (hardness and tension), and physical (swelling, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and coefficient of thermal expansion) analyses to determine the material properties. We found that hardness and thermal stability increased predictably, while the ultimate strength and toughness both decreased. Repeated tension caused the CF-filled PDMS samples to lose significant toughness with increasing CF loadings. The hardness and thermal degradation temperature with 4 wt.% CF loading in PDMS increased more than 40% and 25 °C, respectively, compared with the pristine PDMS sample. Additionally, dilatometer measurements showed a 20% decrease in the coefficient of thermal expansion (CTE) with a small amount of CF filler in PDMS. In this study, we were able to show the mechanical and thermal properties of PDMS can be tuned with good confidence using CFs.

degradation temperature↗
DOE OSTI2022

Material Compatibility of 3D Printed Polymer Composites with Low GWP Refrigerants

The recent development of 3D printing brings numerous opportunities to develop polymer heat exchangers with desired configurations and allows access to the new complex engineering design for better performance. To be used in the HVAC&R system, the compatibility of these materials to refrigerants needs to be thoroughly investigated and understood. This study tested the material compatibility of the printed polymer composites according to the ASHRAE Guideline 38 using metal pressure vessels. These printed parts were tested with R-134A, R-1234YF, R-1234ZE, R-1233ZD(E), and 32-3 MAF POE (10 wt.%) was used as the lubricants. The change in volume, mass, hardness, and mechanical properties was studied in detail. Different compatibility of the composites with refrigerants was observed when the composition of the composite changed. After exposure to refrigerants, the morphological and thermal properties were studied with the scanning electron microscope, thermogravimetric analysis, and differential scanning calorimetry to further reveal the influence of these refrigerants.

Li, Kai↗
DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗
DOE OSTI2019

Accuracy of Fukuoka and American Gastroenterological Association Guidelines for Predicting Advanced Neoplasia in Pancreatic Cyst Neoplasm: A Meta-Analysis

Background: A differential diagnosis of advanced pancreatic cystic neoplasms (PCNs) is critical to determine optimal treatment. The Fukuoka and American Gastroenterological Association (AGA) guidelines are the most widely accepted criteria for the management of PCNs. Objective: This study aimed to evaluate the diagnostic value of these guidelines in predicting advanced neoplasia (AN). Methods: A comprehensive electronic search of the PubMed, EMBASE, Web of Science, Cochrane Library, and Scopus databases was conducted to identify all relevant studies evaluating the Fukuoka and AGA guidelines in surgically resected and histologically confirmed PCNs. Pooled sensitivity, specificity, and diagnostic odds ratios (DORs) were calculated as compound measures of diagnostic accuracy using the random-effects model. Summary of receiver operating characteristic (SROC) curves and the area under the curve (AUC) were also performed. Results: A total of 21 studies with 3723 patients were included in this meta-analysis. Of these studies, 15, 4, and 2 evaluated the Fukuoka guidelines, the AGA guidelines, and both guidelines, respectively. For AN prediction, the Fukuoka guidelines had a pooled sensitivity of 0.67 (95% confidence interval [CI] 0.64–0.70), pooled specificity of 0.64 (95% CI 0.62–0.66), and pooled DOR of 6.28 (95% CI 4.38–9.01), with an AUC of the SROC of 0.78. AGA guidelines showed a pooled sensitivity of 0.59 (95% CI 0.52–0.65), pooled specificity of 0.77 (95% CI 0.74–0.80), and pooled DOR of 5.84 (95% CI 2.60–13.15), with an AUC of 0.79 (95% CI 0.70–0.88). Conclusion: When used alone, the Fukuoka and AGA guidelines showed similar but unsatisfactory diagnostic accuracy in the risk stratification of malignant potential of PCN. Thus, we recommend that they be applied only as a broad framework in clinical practice.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations

Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in the field of signal classification and detection; however, these methods aren't designed to handle the low signal-to-noise ratios inherent within non-vision signal processing tasks. While they are powerful, they are currently not the method of choice in the inherently noisy and dynamic critical infrastructure domain, such as smart-grid sensing, anomaly detection, and non-intrusive load monitoring. Currently, these models can be brittle, which makes them susceptible to noisy input. This also means they have sub-optimal stability of explanation outputs. Experts and technicians using these models to make decisions in real world scenarios need assurance that a model is performing as it is supposed to. The classification or prediction outputs it generates should be sound and grounded, not likely to change in the presence of shifting noise landscapes. In this work, we explore the idea of Neural Stochastic Differential Equations (NSDE's) to improve the robustness of models trained to classify time series data and the effect of NSDE's on the explainability of outputs. We then test the effectiveness of these approaches by applying them to a non-intrusive load monitoring (NILM) dataset that consists of simulated harmonic signals injected into a real building.

Brogan, Joel↗
DOE OSTI2024

Solving differential‐algebraic equations in power system dynamic analysis with quantum computing

Abstract Power system dynamics are generally modeled by high dimensional non‐linear differential‐algebraic equations (DAEs) given a large number of components forming the network. These DAEs' complexity can grow exponentially due to the increasing penetration of distributed energy resources, whereas their computation time becomes sensitive due to the increasing interconnection of the power grid with other energy systems. This paper demonstrates the use of quantum computing algorithms to solve DAEs for power system dynamic analysis. We leverage a symbolic programming framework to equivalently convert the power system's DAEs into ordinary differential equations (ODEs) using index reduction methods and then encode their data into qubits using amplitude encoding. The system non‐linearity is captured by Hamiltonian simulation with truncated Taylor expansion so that state variables can be updated by a quantum linear equation solver. Our results show that quantum computing can solve the power system's DAEs accurately with a computational complexity polynomial in the logarithm of the system dimension. We also illustrate the use of recent advanced tools in scientific machine learning for implementing complex computing concepts, that is, Taylor expansion, DAEs/ODEs transformation, and quantum computing solver with abstract representation for power engineering applications.

computational complexity↗
DOE OSTI2026

Performance evaluation of neutron noise analysis in detecting special nuclear materials

Reliable and rapid inspection techniques play a vital role in preventing illicit trafficking of special nuclear materials. Active interrogation systems using neutrons produced by portable, high-flux deuterium-deuterium or deuterium-tritium neutron generators are being actively developed as a secondary scanning tool for this purpose. In this study, a neutron noise analysis-based approach for detecting unshielded and shielded special nuclear materials by using a pulsed deuterium-tritium neutron generator was evaluated. Here, this approach analyzes the fluctuation of neutron counts. Its performance was quantified with regard to time-to-detection to achieve a minimum probability of detection of 99% and a probability of false alarm of less than 1% considering various amounts of special nuclear materials and different shielding configurations. It was demonstrated that this approach could detect 17 uranium slugs in 5 s given a neutron generator yield of 8.1 × 10 7 n/s. These slugs could be detected within a reasonable time frame (200 s) when they were shielded by 10.16 cm of high-density polyethylene. The results obtained using the neutron noise analysis approach were compared with those obtained using the commonly used differential die-away analysis technique, a sensitive technique for detecting the presence of fissile materials by utilizing the prompt fission neutrons produced when the source neutrons from a neutron generator are completely diminished. For example, the time to detect 2 unshielded uranium slugs was 2.1 s when using the differential die-away analysis technique; it increased to 93 s for the neutron noise analysis approach. Although the noise analysis-based approach exhibits an overall performance which is not as good as that of differential die-away, neutron noise provides an alternative method for effective detection of special nuclear materials.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Selection of a Pair of Experiments to Optimally Reduce Uncertainty in Targeted Nuclear Data

We propose a novel process to select a pair of differential and integral experiments that best reduce uncertainties in targeted 239 ⁢Pu nuclear data while compressing the current nuclear data pipeline from 20 to 3 years. 239⁢ Pu nuclear data are poorly understood for neutrons in the intermediate energy range due to sparsity and uncertainty in historical experiments. New experiments targeting this range will enable better understanding of these nuclear data, but choosing the ideal experiments to conduct is challenging. Beginning with a prior distribution represented by samples of nuclear data generated from theory, generalized least squares adjustments are made to incorporate data from historical experiments. To quantify potential uncertainty reduction obtainable from a pair of candidate experiments, we compute the D-optimality criterion of the posterior covariance of intermediate energy range nuclear data compared to the equivalent covariance after additional adjustment to the pair of candidate experiments. Repeating the process for each of many candidate pairs facilitates the final selection. Results support 63⁢ Cu total cross section measurements for differential experiments and alumina and alumina/graphite configurations for integral experiments. This analysis enables choosing differential and integral experiments to be executed concurrently while shortening decision times relative to the current nuclear data pipeline.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Stage-local partitioned two-step runge-kutta methods for large systems of ordinary differential equations

We introduce stage-local partitioned two-step Runge-Kutta methods are an extension of standard two-step Runge-Kutta methods, which are an alternative to the standard additive two-step Runge-Kutta methods currently existing in the literature. Furthermore, these new schemes are designed with an eye towards truly N-partitioned systems and leverage local stage approximations to make several computationally interesting approximations viable. Specifically, the focus on local stage approximations makes possible the construction of truly asynchronous schemes, in the parallel sense, possible. In addition, we show that an implicit-explicit approach to these schemes can lead to methods that require the inversion of only local nonlinear systems.

Applied Dynamical Systems↗
DOE OSTI2023

Small Angle X-Ray Scattering and Polymers

Polymers are some of the most versatile and useful materials on this planet. Properties of polymers are a result of their processing history, chemical makeup, and physical structure. Therefore, characterization is necessary to develop new polymer formulations and processes. Many established techniques for polymer characterization are destructive, and alternative methods that don’t destroy samples are in high demand. Thermal analysis techniques such as differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) yield important characteristic information for polymer samples while destroying the sample. Non-destructive strategies such as X ray diffraction (XRD) or small angle X-ray scattering (SAXS) can also find polymer characteristics while preserving a sample’s attributes. Sample preservation is desirable when investigating novel formulation and processing methods. SAXS analysis will be done on two different polymers: polyethylene, and a triblock copolymer. Using these two polymers allows for confidence between tests as polyethylene is very common, and well understood. Polyethylene analysis will be done first, and when methods have been refined so that the SAXS data matches accepted thermal analysis values for HDPE, there will be confidence to use the same analysis on block copolymers. Block copolymers remain relatively unexplored, so SAXS data will yield valuable information on their characteristics. Understanding the mechanical properties of these polymers is paramount to their application in upcycling, a process by which waste plastic can be converted into a higher value commodity. For this project, the upcycling process will be done via 3-dimensional (3d) printing.

36 MATERIALS SCIENCE↗
DOE OSTI2026

MapsTorch : automatic differentiation for X-ray fluorescence data analysis

X-ray fluorescence (XRF) is a popular spectroscopy technique for elemental analysis. Spectrum fitting and parameter tuning are at the core of XRF analysis and are conventionally manually intensive, especially for synchrotron experiments involving large amounts of diverse samples. This work introduces the automatic differentiation (AD) technique to XRF and an open-source package called MapsTorch. By transforming an analytical model of the XRF spectrum into a differentiable computation graph with AD, MapsTorch enables robust optimization of parameters and elemental intensities. We evaluate MapsTorch by conducting computational experiments on a large number of historical synchrotron XRF datasets and compare its performance with the currently practiced fitting tool NLopt. The results show that MapsTorch consistently achieves high-quality fits and often leads to better fitting quality than NLopt, particularly in tasks such as initial spectrum fitting and elemental intensity refinement. The robust performance of MapsTorch paves the way for developing automated and high-throughput XRF data analysis workflows to handle the increasing data volumes expected from next-generation synchrotron facilities.

X-ray fluorescence↗
DOE OSTI2023

Exploring the $A$-V-Sb landscape beyond $A$$\mathrm{V_3}$ $\mathrm{Sb_5}$: A case study on the $\mathrm{KV_6}$ $\mathrm{Sb_6}$ Kagome compound

Kagome compounds have garnered attention in the past few years for their intriguing magnetic properties arising from spin frustration dictated by the geometry of the Kagome sublattice. In this paper, we highlight the success of the unconventional hydride route for the fast and easy synthesis of the Kagome compound $\mathrm{KV_6}$ $\mathrm{Sb_6}$. Here, high-temperature in situ powder x-ray diffraction (PXRD) studies proved to be useful in hinting at the existence of $\mathrm{KV_6}$ $\mathrm{Sb_6}$, identifying its synthesis conditions, and understanding the reaction mechanism. The crystal structure for $\mathrm{KV_6}$ $\mathrm{Sb_6}$ was determined from high-resolution PXRD data. The compound has a layered structure [$R\overline{3}m, a = 5.5318$ (9) Å, $c = 34.23$ (3)Å 3 $Z = 3$] at room temperature] and features a Kagome bilayer of V atoms. $\mathrm{KV_6}$ $\mathrm{Sb_6}$ is isostructural to the previously reported $\mathrm{RbV_6}$ $\mathrm{Sb_6}$ and $\mathrm{CsV_6}$ $\mathrm{Sb_6}$ compounds. $\mathrm{KV_6}$ $\mathrm{Sb_6}$ is thermally stable in vacuum up to 1173 K, as evident from the high-temperature in situ PXRD and differential scanning calorimetric analysis. Investigation of magnetic properties for $\mathrm{KV_6}$ $\mathrm{Sb_6}$ between 2 and 300 K reveals temperature-independent paramagnetism and an absence of superconductivity, like the Rb and Cs analogs. Furthermore, we compare the magnetic properties of K V 3 Sb 5 , another ternary Kagome compound, synthesized via two different methods: the hydride route and the traditional route from elements. Low-temperature transport property measurements of $\mathrm{KV_6}$ $\mathrm{Sb_6}$ indicate metallic behavior and an intrinsically low thermal conductivity of $\mathrm{1.0 W K^-1}$ $\mathrm{m^-1}$ at 300 K. The layered structure of $\mathrm{KV_6}$ $\mathrm{Sb_6}$ makes it an attractive candidate for deintercalation and doping studies to tune both magnetic and transport properties, laying a foundation for further studies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Development of a deep learning based automated data analysis for step-filter x-ray spectrometers in support of high-repetition rate short-pulse laser-driven acceleration experiments

We present a deep learning based framework for real-time analysis of a differential filter based x-ray spectrometer that is common on short-pulse laser experiments. The analysis framework was trained with a large repository of synthetic data to retrieve key experimental metrics, such as slope temperature. With traditional analysis methods, these quantities would have to be extracted from data using a time-intensive and manual analysis. Furthermore, this framework was developed for a specific diagnostic, but may be applicable to a wide variety of diagnostics common to laser experiments and thus will be especially crucial to the development of high-repetition rate (HRR) diagnostics for HRR laser systems that are coming online.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Charge radii in covariant density functional theory: A global view

A systematic global investigation of differential charge radii has been performed within the CDFT framework for the first time. Theoretical results obtained with conventional covariant energy density functionals and the separable pairing interaction of Tian et al. [Phys. Lett. B 676, 44 (2009)] are compared with experimental differential charge radii in the regions of the nuclear chart in which available experimental data crosses the neutron shell closures at N=28,50,82, and 126. The analysis of absolute differential radii of different isotopic chains and their relative properties indicate clearly that such properties are reasonably well described in model calculations in the cases when the mean-field approximation is justified. However, while the observed clusterization of differential charge radii of different isotopic chains is well described above the N=50 and N=126 shell closures, it is more difficult to reproduce it above the N=28 and N=82 shell closures because of possible deficiencies in the underlying single-particle structure. The impact of the latter has been evaluated for spherical shapes and it was shown that the relative energies of the single-particle states and the patterns of their occupation with increasing neutron number have an appreciable impact on the evolution of the δ$\langle$r2$\rangle$N,N' values. These factors also limit the predictive power of model calculations in the regions of high densities of the single-particle states of different origin. It is shown that the kinks in the charge radii at neutron shell closures are due to the underlying single-particle structure and due to weakening or collapse of pairing at these closures. The regions of the nuclear chart in which the correlations beyond mean field are expected to have an impact on charge radii are indicated; the analysis shows that the assignment of a calculated excited prolate minimum to the experimental ground state allows us to understand the trends of the evolution of differential charge radii with neutron number in many cases of shape coexistence even at the mean-field level. It is usually assumed that pairing is a dominant contributor to odd-even staggering (OES) in charge radii. Our analysis paints a more complicated picture. It suggests a new mechanism in which the fragmentation of the single-particle content of the ground state in odd-mass nuclei due to particle-vibration coupling provides a significant contribution to OES in charge radii.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗