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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Insight into particle production mechanisms via angular correlations of identified particles in pp collisions at $\sqrt{\mathrm{s}}=7$ TeV

We measured two-particle angular correlations in pp collisions at √s=7 TeV for pions, kaons, protons, and lambdas, for all particle/anti-particle combinations in the pair. Data for mesons exhibit an expected peak dominated by effects associated with mini-jets and are well reproduced by general purpose Monte Carlo generators. However, for baryon–baryon and anti-baryon–anti-baryon pairs, where both particles have the same baryon number, a near-side anti-correlation structure is observed instead of a peak. This effect is interpreted in the context of baryon production mechanisms in the fragmentation process. It currently presents a challenge to Monte Carlo models and its origin remains an open question.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Implementing and evaluating a Gaussian mixture framework for identifying gene function from TnSeq data

The rapid acceleration of microbial genome sequencing increases opportunities to understand bacterial gene function. Unfortunately, only a small proportion of genes have been studied. Recently, TnSeq has been proposed as a cost-effective, highly reliable approach to predict gene functions as a response to changes in a cell’s fitness before-after genomic changes. However, major questions remain about how to best determine whether an observed quantitative change in fitness represents a meaningful change. To address the limitation, we develop a Gaussian mixture model framework for classifying gene function from TnSeq experiments. In order to implement the mixture model, we present the Expectation-Maximization algorithm and a hierarchical Bayesian model sampled using Stan’s Hamiltonian Monte-Carlo sampler. We compare these implementations against the frequentist method used in current TnSeq literature. From simulations and real data produced by E.coli TnSeq experiments, we show that the Bayesian implementation of the Gaussian mixture framework provides the most consistent classification results.

Li, Kevin↗
DOE OSTI2019

A Novel Pruning Method for Convolutional Neural Networks Based off Identifying Critical Filters

Convolutional Neural Networks (CNNs) are one of the most extensively used tools in machine learning, but they are still not well understood and in many cases they are over-parameterized, leading to slow inference and impeding their deployment on low-power devices. In the last few years, many methods for decreasing the number of parameters in a network by pruning its output channels have been suggested, but a very recent work has argued that random pruning of channels performs on-par with state-of-the-art pruning methods. While random and other pruning methods might be effectively used for lowering the number of parameters in a CNN, none of these methods can be used to gain any further understanding of the model that the CNN has built. In this work, we propose a novel method for pruning a network, that at the same time can lead to a better understanding of what the individual filters of the network learn about the data. The method proposed aims to keep only the filters that are "important" for a class. We define a filter as important for a class if its removal has the highest negative impact on the accuracy for that class. We demonstrate that our method is better than random pruning on two networks used on the EMNIST and CIFAR10 datasets. By analyzing the important filters, we find that the important filters in the pruned networks learn features which are more general across classes. We demonstrate the importance and applicability of that observation in two transfer-learning tasks.

Dimovska, Mihaela↗
DOE OSTI2019

Using Digital Trace Data to Identify Regions and Cities

A greater understanding of human dynamics as they play out in both physical space and through inter-personal communication is vital for the design and development of intelligent and resilient cities. Physical context provides insight in to the space-time distribution of population and their activity patterns, while inter-personal communication can, for the first time, be measured at scale through virtual interactions. The confluence of these two enables spatial social network analysis in the built environment for edge computing, knowledge discovery, and artificial intelligence activities. In this work, we propose a novel method to discover such dynamics. We use a dataset of 72 million tweets to develop a spatially embedded network of communication, and then use community detection algorithms to explore regional and urban delineation in the United States. We find that the broad spatial delineation of communities and community sub-structure is consistent with major United States socio-cultural regions, states, and major metropolitan areas. We describe how these methods could be extended to generate a measure of socially cohesive regions that can be consistently applied anywhere there is a sufficiently rich data source. We hope the proposed methods will generate new perspectives on smart and resilient cities.

Brelsford, Christa↗
DOE OSTI2026

Identifying Critical Electrode Metrics for Efficient, Selective CO 2 Electrochemical Conversion

Low-temperature electrochemical CO 2 reduction (CO 2 R) in zero-gap membrane electrode assembly (MEA) reactors presents a scalable route to fuels and carbon utilization. However, performance at industrially relevant current densities hinges on mesoscale catalyst layer integration, particularly at the ionomer|catalyst interface. Here, we demonstrate a generalizable in situ electrochemical impedance spectroscopy (EIS) method. We utilize this technique to decouple electrode-level parameters that are correlated to the overall MEA performance. By performing this ex situ EIS method on CO 2 -to-CO catalyst-coated membranes with systematically varied ionomer-to-catalyst (I:C) ratios, we reveal a pronounced dependence of performance, ion transport resistance, and catalyst utilization on the I:C ratio as well as the electrode conditioning. We demonstrate that an optimal I:C ratio exists at which ion transport resistance is minimized and Faradaic efficiency for CO production is maximized. Beyond the electrodes examined, here we compare ion transport resistance to MEA selectivity/Faradaic efficiency obtained in prior studies, revealing a clear correlation between the two. These results suggest that ion transport resistance within the catalyst layer may be a quantitative predictor of MEA performance which underscores the importance of mesoscale integration in achieving scalable CO 2 R technologies.

08 HYDROGEN↗
DOE OSTI2016

Utilization of Ca K-Edge X-ray Absorption Near Edge Structure to Identify Intercalation in Potential Multivalent Battery Materials

Multivalent-ion batteries have been under investigation as a technology that can provide energy densities beyond that provided by lithium ion batteries. An emerging cation of interest is calcium, as it has similar electrochemical properties and size to sodium, and systems incorporating it have high theoretical density. Common techniques used to investigate intercalation, such as NMR, are currently unable to distinguish calcium's role in energy storage materials. In this work, we investigated the ability of calcium XANES to provide fingerprint identification of intercalated species using Ca ion exchanged Na x CoO 2 and a Ca(PF 6 ) 2 -based electrolyte as a case study. Ca XANES was able to detect intercalation down to concentrations of ~1 Ca per 950 Å 3 , even with residual electrolyte on the surface. The ability to observe the structure of the intercalated ion will enable the discovery of new electrolytes and intercalation cathodes by helping to confirm theoretical predictions, allowing multivalent batteries to become a viable high energy density storage technology.

25 ENERGY STORAGE↗
DOE OSTI2022

Master State Threat Identifier (masti)

The MSE cyber sensor offers a novel approach for scalable network monitoring using heterogeneous computing. Packet data is collected, analyzed, and stored by a stand alone cyber sensor. Machine learning algorithms are run to discover patterns in the communication without the need for deep packet inspection. Tuning of the detection algorithms happens automatically, without need for an engineer to manually create detection rules.

Russell, PierceL↗
DOE OSTI2023

PINNSTRIPES (Physics-Informed Neural Network SurrogaTe for Rapidly Identifying Parameters in Energy Systems) [SWR-22-12]

Energy systems models typically take the form of complex partial differential equations which make multiple forward calculations prohibitively expensive. Fast and data-efficient construction of surrogate models is of utmost importance for applications that require parameter exploration such as design optimization and Bayesian calibration. In presence of a large number of parameters, surrogate models that capture correct dependencies may be difficult to construct with traditional techniques. The issue is addressed here with the formulation of the surrogate model constructed via Physics-Informed Neural Networks (PINN) which capture the dependence with respect to the parameters to estimate, while using a limited amount of data. Since forward evaluations of the surrogate model are cheap, parameter exploration is made inexpensive, even when considering a large number of parameters.

Hassanaly, Malik↗
DOE OSTI2022

Bloom filter de-identifier

A bloom filter is a binary data structure that is designed to be memory efficient and able to determine whether an element is present in a set. The bloom filter is a probabilistic data structure. It can tell you whether an element, or set of elements, is likely to exist within a set. It can also tell you whether an element, or set of elements, does not exist within a set. So false positives are probable, but false negatives are not. Our novel approach utilizes the bloom filter to determine how many bigrams (two letter sequence) each value has in common. The resulting metric approximates the relative similarity of two text values.

Bleeker, Amelia↗
DOE OSTI2021

Breast-Specific Molecular Clocks Comprised of ELF5 Expression and Promoter Methylation Identify Individuals Susceptible to Cancer Initiation

A robust breast cancer prevention strategy requires risk assessment biomarkers for early detection. We show that expression of ELF5, a transcription factor critical for normal mammary development, is downregulated in mammary luminal epithelia with age. DNA methylation of the ELF5 promoter is negatively correlated with expression in an age-dependent manner. Both ELF5 methylation and gene expression were used to build biological clocks to estimate chronological ages of mammary epithelia. ELF5 clock-based estimates of biological age in luminal epithelia from average-risk women were within three years of chronological age. Biological ages of breast epithelia from BRCA1 or BRCA2 mutation carriers, who were high risk for developing breast cancer, suggested they were accelerated by two decades relative to chronological age. The ELF5 DNA methylation clock had better performance at predicting biological age in luminal epithelial cells as compared with two other epigenetic clocks based on whole tissues. We propose that the changes in ELF5 expression or ELF5-proximal DNA methylation in luminal epithelia are emergent properties of at-risk breast tissue and constitute breast-specific biological clocks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Identifying Meteorological Drivers for Errors in Modeled Winds along the Northern California Coast

Abstract An accurate wind resource dataset is required for assessing the potential energy yield of floating offshore wind farms that are expected along the California outer continental shelf. The National Renewable Energy Laboratory has developed and disseminated an updated wind resource dataset offshore of California, using the Weather Research and Forecasting Model, referred to as the CA20 dataset. As compared to buoy lidar measurements that have become available recently, the CA20 dataset showed significant positive biases for 100-m wind speeds along Northern California wind energy lease areas. To investigate the meteorological drivers for the model errors, we first consider two 1-yr simulations run with two different planetary boundary layer (PBL) parameterizations: the Mellor–Yamada–Nakanishi–Niino (MYNN) PBL scheme (the chosen configuration in the CA20 dataset) and the Yonsei University PBL scheme (which significantly reduces the bias in modeled winds). By comparing the 1-yr simulations to the concurrent lidar buoy observations, we find that errors are larger with the MYNN PBL scheme in warm seasons. We then dive deeper into the analysis by running simulations for short-term (3-day) case studies to evaluate the sensitivity of initial/boundary condition forcings on model results. By analyzing the short-term simulations, we find that during synoptic-scale northerly flows driven by the North Pacific high and inland thermal low, a coastal warm bias in the MYNN simulation is mainly responsible for the modeled wind speed bias by altering the boundary layer thermodynamics. The results of our analysis will help guide the creation of an updated version of the CA20 dataset.

17 WIND ENERGY↗
DOE OSTI2015

Identify Structural Flaw Location and Type with an Inverse Algorithm of Resonance Inspection

To evaluate the fitness-for-service of a structural component and to quantify its remaining useful life, aging and service-induced structural flaws must be quantitatively determined in service or during scheduled maintenance shutdowns. Resonance inspection (RI), a non-destructive evaluation (NDE) technique, distinguishes the anomalous parts from the good parts based on changes in the natural frequency spectra. Known for its numerous advantages, i.e., low inspection cost, high testing speed, and broad applicability to complex structures, RI has been widely used in the automobile industry for quality inspection. However, compared to other contemporary direct visualization-based NDE methods, a more widespread application of RI faces a fundamental challenge because such technology is unable to quantify the flaw details, e.g. location, dimensions, and types. In this study, the applicability of a maximum correlation-based inverse RI algorithm developed by the authors is further studied for various flaw cases. It is demonstrated that a variety of common structural flaws, i.e. stiffness degradation, voids, and cracks, can be accurately retrieved by this algorithm even when multiple different types of flaws coexist. The quantitative relations between the damage identification results and the flaw characteristics are also developed to assist the evaluation of the actual state of health of the engineering structures.

non-destructive evaluation, resonance inspection, ↗
DOE OSTI2020

iPNHOT: a knowledge-based approach for identifying protein-nucleic acid interaction hot spots

The interaction between proteins and nucleic acids plays pivotal roles in various biological processes such as transcription, translation, and gene regulation. Hot spots are a small set of residues that contribute most to the binding affinity of a protein-nucleic acid interaction. Compared to the extensive studies of the hot spots on protein-protein interfaces, the hot spot residues within protein-nucleic acids interfaces remain less well-studied, in part because mutagenesis data for protein-nucleic acids interaction are not as abundant as that for protein-protein interactions. In this study, we built a new computational model, iPNHOT, to effectively predict hot spot residues on protein-nucleic acids interfaces. One training data set and an independent test set were collected from dbAMEPNI and some recent literature, respectively. To build our model, we generated 97 different sequential and structural features and used a two-step strategy to select the relevant features. The final model was built based only on 7 features using a support vector machine (SVM). The features include two unique features such as ΔSASsa 1/2 and esp3, which are newly proposed in this study. Based on the cross validation results, our model gave F1 score and AUROC as 0.725 and 0.807 on the subset collected from ProNIT, respectively, compared to 0.407 and 0.670 of mCSM-NA, a state-of-the art model to predict the thermodynamic effects of protein-nucleic acid interaction. The iPNHOT model was further tested on the independent test set, which showed that our model outperformed other methods. Here, by collecting data from a recently published database dbAMEPNI, we proposed a new model, iPNHOT, to predict hotspots on both protein-DNA and protein-RNA interfaces. The results show that our model outperforms the existing state-of-art models. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPNHOT/.

59 BASIC BIOLOGICAL SCIENCES↗