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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Electrospun PVA Fibers for Drug Delivery: A Review

Innovation in biomedical science is always a field of interest for researchers. Drug delivery, being one of the key areas of biomedical science, has gained considerable significance. The utilization of simple yet effective techniques such as electrospinning has undergone significant development in the field of drug delivery. Various polymers such as PEG (polyethylene glycol), PLGA (Poly(lactic-co-glycolic acid)), PLA(Polylactic acid), and PCA (poly(methacrylate citric acid)) have been utilized to prepare electrospinning-based drug delivery systems (DDSs). Polyvinyl alcohol (PVA) has recently gained attention because of its biocompatibility, biodegradability, non-toxicity, and ideal mechanical properties as these are the key factors in developing DDSs. Moreover, it has shown promising results in developing DDSs individually and when combined with natural and synthetic polymers such as chitosan and polycaprolactone (PCL). Considering the outstanding properties of PVA, the aim of this review paper was therefore to summarize these recent advances by highlighting the potential of electrospun PVA for drug delivery systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Predicting Short-Term Deformation in the Central Valley Using Machine Learning

Land subsidence caused by excessive groundwater pumping in Central Valley, California, is a major issue that has several negative impacts such as reduced aquifer storage and damaged infrastructures which, in turn, produce an economic loss due to the high reliance on crop production. This is why it is of utmost importance to routinely monitor and assess the surface deformation occurring. Two main goals that this paper attempts to accomplish are deformation characterization and deformation prediction. The first goal is realized through the use of Principal Component Analysis (PCA) applied to a series of Interferomtric Synthetic Aperture Radar (InSAR) images that produces eigenimages displaying the key characteristics of the subsidence. Water storage changes are also directly analyzed by the use of data from the Gravity Recovery and Climate Experiment (GRACE) twin satellites and the Global Land Data Assimilation System (GLDAS). The second goal is accomplished by building a Long Short-Term Memory (LSTM) model to predict short-term deformation after developing an InSAR time series using LiCSBAS, an open-source InSAR time series package. The model is applied to the city of Madera and produces better results than a baseline averaging model and a one dimensional convolutional neural network (CNN) based on a mean squared error metric showing the effectiveness of machine learning in deformation prediction as well as the potential for incorporation in hazard mitigation models. The model results can directly aid policy makers in determining the appropriate rate of groundwater withdrawal while maintaining the safety and well-being of the population as well as the aquifers’ integrity.

58 GEOSCIENCES↗
DOE OSTI2012

Plasmonics-based detection of H 2 and CO: discrimination between reducing gases facilitated by material control

Monitoring emissions in high-temperature-combustion applications is very important for regulating the discharge of gases such as NO 2 and CO as well as unburnt fuel into the environment. This work reports the detection of H 2 and CO gases by employing a metal–metal oxide nanocomposite (gold–yttria stabilized zirconia (Au–YSZ)) film fabricated through layer-by-layer physical vapor deposition (PVD). The change in the peak position of the localized surface plasmon resonance (LSPR) was monitored as a function of time and gas concentration. The responses of the films were preferential towards H 2 , as observed from the results of exposing the films to the gases at temperatures of 500 °C in a background of dry air. Characterization of the samples by XRD and SEM enabled the correlation of material properties with the differences in the CO- and H 2 -induced LSPR peak shifts, including the relative desensitization towards NO 2 . Sensing characteristics of films with varying support thicknesses and metal-particle diameters have been studied, and the results are presented. A comparison has been made to films fabricated through co-sputtered PVD, and the calibration curves of the sensing response show a preferential response towards H 2 . The distinction between H 2 and CO responses is also seen through the use of principal-component analysis (PCA). Such material arrangements, which can be tuned for their selectivity by changing certain parameters such as particle size, support thickness, etc., have direct applications within optical chemical sensors for turbine engines, solid-oxide fuel cells, and other high-temperature applications.

36 MATERIALS SCIENCE↗
DOE OSTI2016

AN EMPIRICAL METHOD FOR IMPROVING THE QUALITY OF RXTE HEXTE SPECTRA

We have developed a correction tool to improve the quality of Rossi X-ray Timing Explorer (RXTE) High Energy X-ray Timing Experiment (HEXTE) spectra by employing the same method we used earlier to improve the quality of RXTE Proportional Counter Array (PCA) spectra. We fit all of the hundreds of HEXTE spectra of the Crab individually to a simple power-law model, some 37 million counts in total for Cluster A and 39 million counts for Cluster B, and we create for each cluster a combined spectrum of residuals. We find that the residual spectrum of Cluster A is free of instrumental artifacts while that of Cluster B contains significant features with amplitudes ∼1%; the most prominent is in the energy range 30–50 keV, which coincides with the iodine K edge. Starting with the residual spectrum for Cluster B, via an iterative procedure we created the calibration tool hexBcorr for correcting any Cluster B spectrum of interest. We demonstrate the efficacy of the tool by applying it to Cluster B spectra of two bright black holes, which contain several million counts apiece. For these spectra, application of the tool significantly improves the goodness of fit, while affecting only slightly the broadband fit parameters. The tool may be important for the study of spectral features, such as cyclotron lines, a topic that is beyond the scope of this paper.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

X-RAY SPECTRAL ANALYSIS OF THE STEADY STATES OF GRS1915+105

We report on the X-ray spectral behavior within the steady states of GRS1915+105. Our work is based on the full data set of the source obtained using the Proportional Counter Array (PCA) on the Rossi X-ray Timing Explorer (RXTE) and 15 GHz radio data obtained using the Ryle Telescope. The steady observations within the X-ray data set naturally separated into two regions in the color–color diagram and we refer to these regions as steady-soft and steady-hard. GRS1915+105 displays significant curvature in the coronal component in both the soft and hard data within the RXTE/PCA bandpass. A majority of the steady-soft observations displays a roughly constant inner disk radius (R{sub in}), while the steady-hard observations display an evolving disk truncation which is correlated to the mass accretion rate through the disk. The disk flux and coronal flux are strongly correlated in steady-hard observations and very weakly correlated in the steady-soft observations. Within the steady-hard observations, we observe two particular circumstances when there are correlations between the coronal X-ray flux and the radio flux with log slopes η∼0.68±0.35 and η∼1.12±0.13. They are consistent with the upper and lower tracks of Gallo et al. (2012), respectively. A comparison of the model parameters to the state definitions shows that almost all of the steady-soft observations match the criteria of either a thermal or steep power-law state, while a large portion of the steady-hard observations match the hard-state criteria when the disk fraction constraint is neglected.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

A COMPREHENSIVE STATISTICAL DESCRIPTION OF RADIO-THROUGH- γ -RAY SPECTRAL ENERGY DISTRIBUTIONS OF ALL KNOWN BLAZARS

We combined multi-wavelength data for blazars from the Roma-BZCAT catalog and analyzed hundreds of X-ray spectra. We present the fluxes and spectral energy distributions (SEDs), in 12 frequency bands from radio to γ -rays, for a final sample of 2214 blazars. Using a model-independent statistical approach, we looked for systematic trends in the SEDs; the most significant trends involved the radio luminosities and X-ray spectral indices of the blazars. We used a principal component analysis (PCA) to determine the basis vectors of the blazar SEDs and, in order to maximize the size of the sample, imputed missing fluxes using the K -nearest neighbors method. Using more than an order of magnitude more data than was available when Fossati et al. first reported trends of SED shape with blazar luminosity, we confirmed the anti-correlation between radio luminosity and synchrotron peak frequency, although with greater scatter than was seen in the smaller sample. The same trend can be seen between bolometric luminosity and synchrotron peak frequency. Finally, we used all of the available blazar data to determine an empirical SED description that depends only on the radio luminosity at 1.4 GHz and the redshift. We verified that this statistically significant relation was not a result of the luminosity–luminosity correlations that are natural in flux-limited samples (i.e., where the correlation is actually caused by the redshift rather than the luminosity).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

A Principal Component Analysis of the Diffuse Interstellar Bands

We present a principal component (PC) analysis of 23 line-of-sight parameters (including the strengths of 16 diffuse interstellar bands, DIBs) for a well-chosen sample of single-cloud sightlines representing a broad range of environmental conditions. Our analysis indicates that the majority (∼93%) of the variations in the measurements can be captured by only four parameters The main driver (i.e., the first PC) is the amount of DIB-producing material in the line of sight, a quantity that is extremely well traced by the equivalent width of the λ 5797 DIB. The second PC is the amount of UV radiation, which correlates well with the λ 5797/ λ 5780 DIB strength ratio. The remaining two PCs are more difficult to interpret, but are likely related to the properties of dust in the line of sight (e.g., the gas-to-dust ratio). With our PCA results, the DIBs can then be used to estimate these line-of-sight parameters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

New Measurements of the Lyα Forest Continuum and Effective Optical Depth with LyCAN and DESI Y1 Data

Abstract We present the Ly α Continuum Analysis Network (LyCAN), a convolutional neural network that predicts the unabsorbed quasar continuum within the rest-frame wavelength range of 1040–1600 Å based on the red side of the Ly α emission line (1216–1600 Å). We developed synthetic spectra based on a Gaussian mixture model representation of nonnegative matrix factorization (NMF) coefficients. These coefficients were derived from high-resolution, low-redshift ( z < 0.2) Hubble Space Telescope/Cosmic Origins Spectrograph (COS) quasar spectra. We supplemented this COS-based synthetic sample with an equal number of DESI Year 5 mock spectra. LyCAN performs extremely well on testing sets, achieving a median error in the forest region of 1.5% on the DESI mock sample, 2.0% on the COS-based synthetic sample, and 4.1% on the original COS spectra. LyCAN outperforms principal component analysis (PCA) and NMF-based prediction methods using the same training set by 40% or more. We predict the intrinsic continua of 83,635 DESI Year 1 spectra in the redshift range of 2.1 ≤ z ≤ 4.2 and perform an absolute measurement of the evolution of the effective optical depth. This is the largest sample employed to measure the optical depth evolution to date. We fit a power law of the form τ ( z ) = τ 0 ( 1 + z ) γ to our measurements and find τ 0 = (2.46 ± 0.14) × 10 −3 and γ = 3.62 ± 0.04. Our results show particular agreement with high-resolution, ground-based observations around z = 2, indicating that LyCAN is able to predict the quasar continuum in the forest region with only spectral information outside the forest.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Balancing aggregation and smoothing errors in inverse models

Inverse models use observations of a system (observation vector) to quantify the variables driving that system (state vector) by statistical optimization. When the observation vector is large, such as with satellite data, selecting a suitable dimension for the state vector is a challenge. A state vector that is too large cannot be effectively constrained by the observations, leading to smoothing error. However, reducing the dimension of the state vector leads to aggregation error as prior relationships between state vector elements are imposed rather than optimized. Here we present a method for quantifying aggregation and smoothing errors as a function of state vector dimension, so that a suitable dimension can be selected by minimizing the combined error. Reducing the state vector within the aggregation error constraints can have the added advantage of enabling analytical solution to the inverse problem with full error characterization. We compare three methods for reducing the dimension of the state vector from its native resolution: (1) merging adjacent elements (grid coarsening), (2) clustering with principal component analysis (PCA), and (3) applying a Gaussian mixture model (GMM) with Gaussian pdfs as state vector elements on which the native-resolution state vector elements are projected using radial basis functions (RBFs). The GMM method leads to somewhat lower aggregation error than the other methods, but more importantly it retains resolution of major local features in the state vector while smoothing weak and broad features.

Turner, A. J.↗
DOE OSTI2019

Chemical composition of ultrafine aerosol particles in central Amazonia during the wet season

Central Amazonia serves as an ideal location to study atmospheric particle formation, since it often represents nearly natural, pre-industrial conditions but can also experience periods of anthropogenic influence due to the presence of emissions from large metropolitan areas like Manaus, Brazil. Ultrafine (sub-100 nm diameter) particles are often observed in this region, although new particle formation events seldom occur near the ground despite being readily observed in other forested regions with similar emissions of volatile organic compounds (VOCs). This study focuses on identifying the chemical composition of ultrafine particles as a means of determining the chemical species and mechanisms that may be responsible for new particle formation and growth in the region. These measurements were performed during the wet season as part of the Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) field campaign at a site located 70 km southwest of Manaus. A thermal desorption chemicalionization mass spectrometer (TDCIMS) characterized the most abundant compounds detected in ultrafine particles. Two time periods representing distinct influences on aerosol composition, which we label as “anthropogenic” and “background” periods, were studied as part of a larger 10 d period of analysis. Higher particle number concentrations were measured during the anthropogenic period, and modeled back-trajectory frequencies indicate transport of emissions from the Manaus metropolitan area. During the background period there were much lower number concentrations, and back-trajectory frequencies showed that air masses arrived at the site predominantly from the forested regions to the north and northeast. TDCIMS-measured constituents also show distinct differences between the two observational periods. Although bisulfate was detected in particles throughout the 10 d period, the anthropogenic period had higher levels of particulate bisulfate overall. Ammonium and trimethyl ammonium were positively correlated with bisulfate. The background period had distinct diurnal patterns of particulate cyanate and acetate, while oxalate remained relatively constant during the 10 d period. 3-Methylfuran, a thermal decomposition product of a particulate-phase isoprene epoxy diol(IEPOX), was the dominant species measured in the positive-ion mode. Principal component analysis (PCA) was performed on the TDCIMS-measured ion abundance and aerosol mass spectrometer (AMS) mass concentration data. Two different hierarchical clusters representing unique influences arise: one comprising ultrafine particulate acetate, hydrogen oxalate, cyanate, trimethyl ammonium and 3-methylfuran and another made up of ultra fine particulate bisulfate, chloride, ammonium and potassium. A third cluster separated AMS-measured species from the two TDCIMS-derived clusters, indicating different sources or processes in ultrafine aerosol particle formation compared to larger submicron-sized particles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Prediction of DIII-D Pedestal Structure from Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. An experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (ne) and electron temperature (Te) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (Ip), toroidal magnetic field (Bφ), neutral beam heating power (PNBI) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1972

Short term prediction of E greater than or equal to 10 MeV proton fluxes from solar flares

Both the anisotropic and isotropic diffusion theories can be used to extrapolate proton fluxes for E greater than or equal to 10 meV for over 50% of the particle events. The isotropic diffusion theory uses a diffusion coefficient: D = Mr sup beta. It was found that M and beta tended to be functions of flare position on the solar disk. A measurement of the interplanetary flux in near earth space gives a good indication of the polar cap fluxes. It was found that the 30 MHz absorption over the poles during a PCA is proportional to the square root of the integral proton flux E greater than or equal to 11 meV in interplanetary space, J = KA squared, with K = 8 plus or minus 2 and J in protons/sq cm-sec-ster.

Kuck, G. A.↗
NASA NTRS1972

Large-scale negative polarity magnetic fields on the sun and particle-emitting flares

Some observational facts about the large-scale patterns formed by solar negative polarity magnetic fields during the 19th and 20th cycles of solar activity are presented. The close relation of the position of occurrence of very large flares accompanied by cosmic ray and PCA events as well as other phenomena of solar activity during the declining part of the 19th cycle of the regularities in the internal structure of large scale negative polarity features are demonstrated.

Bumba, V.↗
NASA NTRS1973

Electric field and fine structure in aurora.

Simultaneous rocket measurements of electric field and optical aurora during a weak PCA event are presented. The aurora consisted of faint structures superposed on a homogeneous glow, and on-board photometers showed that the rocket passed through two discrete auroral forms. These enhanced auroral emissions coincided with variations in the dc electric field and an increase in the ac field. A detailed comparison between electric fields and optical emissions shows that the relation is quite complex. The results are compared with earlier electric field and optical auroral measurements.

Maseide, K.↗
NASA NTRS1987

Neural mechanisms by which gravitational stimuli and stress affect the secretion of renin and other hormones

The present goal is to determine by the production of discrete lesions the parts of the hypothalamus and brainstem that are involved in serotonin-mediated increases in renin secretion. A variety of stimuli which act in different ways to increase renin stimuli were developed and standardized. The experiments with p-chloroamphetamine (PCA) demonstrated that there is a serotonergic pathway which projects from the dorsal raphe nuclei to the paraventricular nuclei and the vetromedial nuclei of the hypothalamus; that projection from paraventricular nuclei to the brainstem and spinal cord may be oxytocinergic; and that the pathway from the spinal cord to the renin secreting cells is sympathetic. The demonstration that paraventicular lesions lower circulating renin substrate is important because it raises the possibility that substrate secretion is under neural control, either via the pituitary or by direct neural pathways. The discovery that lesions of the ventromedial nuclei appear to abolish the increase in renin secretion produced by many different stimuli without affecting the concentration of renin substrate in the plasma makes the position of the hypothalamus in the regulation of fluid and electrolyte balance more prominent than previously suspected.

Ganong, William F.↗
NASA NTRS1989

Data structure characterization of miltispectral data using principal component and principal factor analysis

Both principal component analysis (PCA) and principal factor analysis (PFA) were used to analyze an experimental multispectral data structure in terms of common and unique variance. Only the common variance of the multispectral data was associated with the principal factor, while higher-order principal components were associated with both common and unique variance. The unique variance was found to represent small spectral variations within each cover type as well as noise vectors, and was most abundant in the lower-order principal components. The lower-order principal components can be useful in research designed to discriminate minor physical variations within features, and to highlight localized change when using multitemporal-multispectral data. Conversely, PFA of the multispectral data provided an insight into a great potential for discriminating basic land-cover types by excluding the unique variance which was related to the noise and minor spectral variations.

Lee, Jae K.↗