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Correlation analysis in MST radar to determine the scattered power, Doppler frequency and correlation time for a noisy signal is examined. It is assumed that coherent detection was employed, with two accurately balanced quadrature receiving channels and that coherent integration is performed with a window length significantly less than the correlation time of the signal.
Hercules AS4 graphite fiber, unsized, or with EPON 828, PVA, or polysulfone sizing, was combined with three different polymer matrices. These included Hercules 3501-6 epoxy, Hercules 4001 bismaleimide, and Hexcel F155 rubber toughened epoxy. Unidirectional composites in all twelve combinations were fabricated and tested in transverse tension and axial compression. Quasi-isotropic laminates were tested in axial tension and compression, flexure, interlaminar shear, and tensile impact. All tests were conducted at both room temperature, dry and elevated temperature, and wet conditions. Single fiber pullout testing was also performed. Extensive scanning electron microphotographs of fracture surfaces are included, along with photographs of single fiber pullout failures. Analytical/experimental correlations are presented, based on the results of a finite element micromechanics analysis. Correlations between matrix type, fiber sizing, hygrothermal environment, and loading mode are presented. Results indicate that the various composite properties were only moderately influenced by the fiber sizings utilized.
We report on variability and correlation studies using multiwavelength observations of the blazar Mrk 421 during the month of February, 2010 when an extraordinary flare reaching a level of ~27 Crab Units above 1 TeV was measured in very-highenergy (VHE) γ-rays with the VERITAS observatory. This is the highest flux state for Mrk 421 ever observed in VHE γ-rays. Data are analyzed from a coordinated campaign across multiple instruments including VHE γ-ray (VERITAS, MAGIC), high-energy (HE) γ-ray (Fermi-LAT), X-ray (Swift, RXTE, MAXI), optical (including the GASP-WEBT collaboration and polarization data) and radio (Mets¨ahovi, OVRO, UMRAO). Light curves are produced spanning multiple days before and after the peak of the VHE flare, including over several flare ‘decline’ epochs. The main flare statistics allow 2-minute time bins to be constructed in both the VHE and optical bands enabling a cross-correlation analysis that shows evidence for an optical lag of ~25–55 minutes, the first time-lagged correlation between these bands reported on such short timescales. Limits on the Doppler factor (δ & 33) and the size of the emission region (δ -1RB . 3.8 × 10 13 cm) are obtained from the fast variability observed by VERITAS during the main flare. Analysis of 10-minute-binned VHE and X-ray data over the decline epochs shows an extraordinary range of behavior in the flux-flux relationship: from linear to quadratic to lack of correlation to anti-correlation. Taken together, these detailed observations of an unprecedented flare seen in Mrk 421 are difficult to explain by the classic single-zone synchrotron self-Compton model.
Highlights: • Long noncoding RNAs may be potential biomarkers to diagnose asthma. • Long noncoding RNAs may regulate the expression of cytokines in CD4+T cells in asthma. • Specific long noncoding RNAs are related with the characteristics of asthmatic patients. The aim of the study was to determine the expression profiles of message RNAs and long non-coding RNAs in CD4{sup +}T cells of asthmatic patients and to explore the clinical value and biological function. Expression profiles in CD4{sup +}T cells of asthmatic patients and healthy controls were analyzed by microarray. We found 2725 lncRNAs and 3167 mRNAs differentially expressed. The data were validated by quantitative real time polymerase chain reaction, with 3 up-regulated (ENST00000444682, ENST00000566098, ENST00000583179) and 1 down-regulated (ENST00000579468) lncRNAs found. Receiver operating characteristic curve analysis showed the area under the curve was 0.7058, 0.9026, 0.8361, 0.8316, respectively. Spearman correlation analysis showed that ENST00000566098 was positively related with IL-13 and ENST00000579468 was positively related with peak expiratory flow. Bioinformatics analyses were performed to explore the function of lncRNAs. Specific lncRNAs aberrantly expressed in CD4{sup +}T cells may take part in the development of asthma and may be used as biomarkers for diagnosis.
ABSTRACT In this paper, we investigate how massive central galaxies cease their star formation by comparing theoretical predictions from cosmological simulations: EAGLE, Illustris, and IllustrisTNG with observations of the local Universe from the Sloan Digital Sky Survey (SDSS). Our machine learning (ML) classification reveals supermassive black hole mass (MBH) as the most predictive parameter in determining whether a galaxy is star forming or quenched at redshift z = 0 in all three simulations. This predicted consequence of active galactic nucleus (AGN) quenching is reflected in the observations, where it is true for a range of indirect estimates of MBH via proxies as well as its dynamical measurements. Our partial correlation analysis shows that other galactic parameters lose their strong association with quiescence, once their correlations with MBH are accounted for. In simulations, we demonstrate that it is the integrated power output of the AGN, rather than its instantaneous activity, which causes galaxies to quench. Finally, we analyse the change in molecular gas content of galaxies from star-forming to passive populations. We find that both gas fractions (fgas) and star formation efficiencies (SFEs) decrease upon transition to quiescence in the observations but SFE is more predictive than fgas in the ML passive/star-forming classification. These trends in the SDSS are most closely recovered in IllustrisTNG and are in direct contrast with the predictions made by Illustris. We conclude that a viable AGN feedback prescription can be achieved by a combination of preventative feedback and turbulence injection which together quench star formation in central galaxies.
Purpose: To develop a clinically-deployable, automated process for detecting artifacts in routine nuclear medicine (NM) quality assurance (QA) bar phantom images. Methods: An artifact detection algorithm was created to analyze bar phantom images as part of an ongoing QA program. A low noise, high resolution reference image was acquired from an x-ray of the bar phantom with a Philips Digital Diagnost system utilizing image stitching. NM bar images, acquired for 5 million counts over a 512×512 matrix, were registered to the template image by maximizing mutual information (MI). The MI index was used as an initial test for artifacts; low values indicate an overall presence of distortions regardless of their spatial location. Images with low MI scores were further analyzed for bar linearity, periodicity, alignment, and compression to locate differences with respect to the template. Findings from each test were spatially correlated and locations failing multiple tests were flagged as potential artifacts requiring additional visual analysis. The algorithm was initially deployed for GE Discovery 670 and Infinia Hawkeye gamma cameras. Results: The algorithm successfully identified clinically relevant artifacts from both systems previously unnoticed by technologists performing the QA. Average MI indices for artifact-free images are 0.55. Images with MI indices < 0.50 have shown 100% sensitivity and specificity for artifact detection when compared with a thorough visual analysis. Correlation of geometric tests confirms the ability to spatially locate the most likely image regions containing an artifact regardless of initial phantom orientation. Conclusion: The algorithm shows the potential to detect gamma camera artifacts that may be missed by routine technologist inspections. Detection and subsequent correction of artifacts ensures maximum image quality and may help to identify failing hardware before it impacts clinical workflow. Going forward, the algorithm is being deployed to monitor data from all gamma cameras within our health system.
Background:Lignocellulose biomass is known as a recalcitrant material towards enzymatic hydrolysis, increasing the process cost in biorefinery. In nature, filamentous fungi naturally degrade lignocellulose, using an arsenal of hydrolytic and oxidative enzymes. Assessment of enzyme hydrolysis efficiency generally relies on the yield of glucose for a given biomass. To better understand the markers governing recalcitrance to enzymatic degradation, there is a need to enlarge the set of parameters followed during deconstruction. Results:Industrially-pretreated biomass feedstocks from wheat straw, miscanthus and poplar were sequentially hydrolysed following two steps. First, standard secretome from Trichoderma reesei was used to maximize cellulose hydrolysis, producing three recalcitrant lignin-enriched solid substrates. Then fungal secretomes from three basidiomycete saprotrophs ( Laetisaria arvalis , Artolenzites elegans and Trametes ljubarskyi ) displaying various hydrolytic and oxidative enzymatic profiles were applied to these recalcitrant substrates, and compared to the T. reesei secretome. As a result, most of the glucose was released after the first hydrolysis step. After the second hydrolysis step, half of the remaining glucose amount was released. Overall, glucose yield after the two sequential hydrolyses was more dependent on the biomass source than on the fungal secretomes enzymatic profile. Solid residues obtained after the two hydrolysis steps were characterized using complementary methodologies. Correlation analysis of several physico-chemical parameters showed that released glucose yield was negatively correlated with lignin content and cellulose crystallinity while positively correlated with xylose content and water sorption. Water sorption appears as a pivotal marker of the recalcitrance as it reflects chemical and structural properties of lignocellulosic biomass. Conclusions:Fungal secretomes applied to highly recalcitrant biomass samples can further extend the release of the remaining glucose. The glucose yield can be correlated to chemical and physical markers, which appear to be independent from the biomass type and secretome. Overall, correlations between these markers reveal how nano-scale properties (polymer content and organization) influence macro-scale properties (particle size and water sorption). Further systematic assessment of these markers during enzymatic degradation will foster the development of novel cocktails to unlock the degradation of lignocellulose biomass.
An experiment was conducted from 2016 to 2017 to assess the effect of kernel metabolism in development stages after organic mulching compared to control. Organic mulching significantly increased crop yields (higher 128% in 2016, higher 60% in 2017), oil content (the highest oil content was 27.6% higher than that of the control), and improved soil properties (SOC, SAN, AP, and AK). In this study, soil pH, SOC, AN, AP, and AK in 0–30 cm soil depth were measured. Results showed that the effect of mulching on soil pH was not significant at the harvesting stage. The greatest metabolic differences occurred during the period of high oil conversion (S2–S4), primarily involving 11 relevant metabolic pathways. This further verified that Camellia oleifera oil yield was improved after mulching. A total of 1,106 OTUs were detected by using 16S rRNA, and Venn diagram showed that there were 106 unique OTUs in control and 103 OTUs in the treatment, respectively. Correlation analysis showed that soil pH and soil temperature were two indicators with the most correlations with soil microbiota. The yield was significantly positively correlated with soil microbial Proteobacteria, Bacteroidetes, and soil nutrition indexes. Organic mulching improved the physicochemical properties of soils, caused differences in the relative abundance of dominant bacteria in soil bacteria, and improved the soil microbiological environment to promote plant growth, indicating that organic mulching is an effective measure to alleviate seasonal drought.
Results of a methodical compilation, reduction, and correlated analysis of spectrophotometric data obtained by various scientific groups during NASA's 1968 and 1969 Airborne Auroral Expedition are presented.
A generalized approach to reverberation mapping (RM) is presented, which is applicable to broad- and narrowband photometric data, as well as to spectroscopic observations. It is based on multivariate correlation analysis techniques and, in its present implementation, is able to identify reverberating signals across the accretion disk and the broad-line region (BLR) of active galactic nuclei (AGNs). Statistical tests are defined to assess the significance of time-delay measurements using this approach, and the limitations of the adopted formalism are discussed. It is shown how additional constraints on some of the parameters of the problem may be incorporated into the analysis thereby leading to improved results. When applied to a sample of 14 Seyfert 1 galaxies having good-quality high-cadence photometric data, accretion disk scales and BLR sizes are simultaneously determined, on a case-by-case basis, in most objects. The BLR scales deduced here are in good agreement with the findings of independent spectroscopic RM campaigns. Implications for the photometric RM of AGN interiors in the era of large surveys are discussed.
Several planet-search groups have acquired a great deal of data in the form of time-series spectra of several hundred nearby stars with time baselines of over a decade. While binary star detections are generally not the goal of these long-term monitoring efforts, the binary stars hiding in existing planet search data are precisely the type that are too close to the primary star to detect with imaging or interferometry techniques. We use a cross-correlation analysis to detect the spectral lines of a new low-mass companion to ψ{sup 1} Draconis A, which has a known roughly equal-mass companion at ∼680 AU. We measure the mass of ψ{sup 1} Draconis C as M{sub 2} = 0.70 ± 0.07M{sub ⊙}, with an orbital period of ∼20 years. This technique could be used to characterize binary companions to many stars that show large-amplitude modulation or linear trends in radial velocity data.
We present a simple x-ray phase imaging method that utilizes the sample-induced distortion of a high contrast random intensity pattern to quantitatively retrieve the two-dimensional phase map at the exit surface of a coherently illuminated sample. This reference pattern is created by placing a sheet of sandpaper in the x-ray beam, with the sample-induced distortion observed after propagation to the detector, a meter downstream. Correlation analysis comparing a single ''sample and sandpaper'' image to a reference ''sandpaper only'' image produces two sensitive differential phase contrast images, giving the sample phase gradient in vertical and horizontal directions. These images are then integrated to recover the projected phase depth of the sample. The simple experimental set-up, retention of flux, and the need for only a single sample image per reconstruction suggest that this method is of value in imaging a range of dynamic processes at both synchrotron and laboratory x-ray sources.
We target the thermal emission spectrum of the non-transiting gas giant HD 88133 b with high-resolution near-infrared spectroscopy, by treating the planet and its host star as a spectroscopic binary. For sufficiently deep summed flux observations of the star and planet across multiple epochs, it is possible to resolve the signal of the hot gas giant’s atmosphere compared to the brighter stellar spectrum, at a level consistent with the aggregate shot noise of the full data set. To do this, we first perform a principal component analysis to remove the contribution of the Earth’s atmosphere to the observed spectra. Then, we use a cross-correlation analysis to tease out the spectra of the host star and HD 88133 b to determine its orbit and identify key sources of atmospheric opacity. In total, six epochs of Keck NIRSPEC L-band observations and three epochs of Keck NIRSPEC K-band observations of the HD 88133 system were obtained. Based on an analysis of the maximum likelihood curves calculated from the multi-epoch cross-correlation of the full data set with two atmospheric models, we report the direct detection of the emission spectrum of the non-transiting exoplanet HD 88133 b and measure a radial projection of the Keplerian orbital velocity of 40 ± 15 km s{sup −1}, a true mass of 1.02{sub −0.28}{sup +0.61}M{sub J}, a nearly face-on orbital inclination of 15{sub −5}{sup +6{sup ∘}}, and an atmosphere opacity structure at high dispersion dominated by water vapor. This, combined with 11 years of radial velocity measurements of the system, provides the most up-to-date ephemeris for HD 88133.
It is pointed out that an extensive study of the causes and manifestations of geomagnetic activity has been carried out as part of the sixth Coordinated Data Analysis Workshop, CDAW 6. The present investigation has the objective to determine the coupling between the solar wind and the magnetosphere for the two selected analysis intervals, taking into account, as a basis for the study, the interplanetary field and plasma observations from ISEE 3 and IMP 8 and the geomagnetic activity indicators developed by CDAW 6 participants. The method of analysis employed is discussed, giving attention to geomagnetic indices, upstream parameters, and a cross-correlation analysis. In a description of the obtained results, the March 22, 1979 event is considered along with the March 31 to April 1, 1979 event, and an intercomparison of the events. The relationship between interplanetary indices and the resulting geomagnetic activity for the two CDAW 6 intervals is illustrated.
Swift intensive accretion disk reverberation mapping of four AGN yielded light curves sampled ∼200–350 times in 0.3–10 keV X-ray and six UV/optical bands. Uniform reduction and cross-correlation analysis of these data sets yields three main results: (1) The X-ray/UV correlations are much weaker than those within the UV/optical, posing severe problems for the lamp-post reprocessing model in which variations in a central X-ray corona drive and power those in the surrounding accretion disk. (2) The UV/optical interband lags are generally consistent with t μ l4 3 as predicted by the centrally illuminated thin accretion disk model. While the average interband lags are somewhat larger than predicted, these results alone are not inconsistent with the thin disk model given the large systematic uncertainties involved. (3) The one exception is the U band lags, which are on average a factor of ∼2.2 larger than predicted from the surrounding band data and fits. This excess appears to be due to diffuse continuum emission from the broad-line region (BLR). The precise mixing of disk and BLR components cannot be determined from these data alone. The lags in different AGN appear to scale with mass or luminosity. We also find that there are systematic differences between the uncertainties derived by JAVELIN versus more standard lag measurement techniques, with JAVELIN reporting smaller uncertainties by a factor of 2.5 on average. In order to be conservative only standard techniques were used in the analyses reported herein.
Thermal model correlation uses data from thermal balance tests to better estimate uncertain input parameter values. During the correlation process, input parameters are modified in an iterative manner which can become computationally expensive since this requires that the high-fidelity thermal model be run for each iteration. Depending on the number of thermal balance test points there can be many sets of correlation parameters that satisfy correlation criteria; and having enough data to ascertain the best set of correlation parameters to use, further increases the computational expense. Reduced-order models (ROMs) provide computationally efficient surrogates of high-fidelity models and are often built to reduce development cycle times and cost. By leveraging the speed of reduced-order models and the Correlation Analysis feature in the Veritrek software, the typical computational expense of a traditional thermal model correlation process can be significantly reduced and having access to hundreds of thousands of iteration results provides an advanced means of intelligently determining the best set of correlation parameters to use. The L’Ralph thermal team at NASA Goddard Space Flight Center explored the use of the Veritrek software for their thermal model correlation efforts. The ROM that was created allowed for the variation of 15 input parameters to match 70 temperature sensor readouts from 3 thermal balance plateus and required 125 runs of the high-fidelity Thermal Desktop® model to generate a ROM that could predict the detailed model’s results to within 0.2 K (RMS). The ROM was then used to find dozens of plausible correlation parameter values based on L’Ralph instrument test data within a few seconds. By providing several plausible correlation parameter combinations, Veritrek allowed the thermal team to explore different uncertain parameter value combinations and provided insight into how deterministic each input parameter was. This allowed for a more confident decision on the best set of correlation parameters to use, compared to traditional model correlation techniques. In this presentation, the L’Ralph thermal team will be presenting their experience with the Veritrek software and how the software was utilized to provide additional insights during the correlation process.
Thermal model correlation uses data from thermal balance tests to better estimate uncertain input parameter values. During the correlation process, input parameters are modified in an iterative manner which can become computationally expensive since this requires that the high-fidelity thermal model be run for each iteration. Depending on the number of thermal balance test points there can be many sets of correlation parameters that satisfy correlation criteria; and having enough data to ascertain the best set of correlation parameters to use, further increases the computational expense. Reduced-order models (ROMs) provide computationally efficient surrogates of high-fidelity models and are often built to reduce development cycle times and cost. By leveraging the speed of reduced-order models and the Correlation Analysis feature in the Veritrek software, the typical computational expense of a traditional thermal model correlation process can be significantly reduced and having access to hundreds of thousands of iteration results provides an advanced means of intelligently determining the best set of correlation parameters to use. The L’Ralph thermal team at NASA Goddard Space Flight Center explored the use of the Veritrek software for their thermal model correlation efforts. The ROM that was created allowed for the variation of 15 input parameters to match 70 temperature sensor readouts from 3 thermal balance plateus and required 125 runs of the high-fidelity Thermal Desktop® model to generate a ROM that could predict the detailed model’s results to within 0.2 K (RMS). The ROM was then used to find dozens of plausible correlation parameter values based on L’Ralph instrument test data within a few seconds. By providing several plausible correlation parameter combinations, Veritrek allowed the thermal team to explore different uncertain parameter value combinations and provided insight into how deterministic each input parameter was. This allowed for a more confident decision on the best set of correlation parameters to use, compared to traditional model correlation techniques. In this presentation, the L’Ralph thermal team will be presenting their experience with the Veritrek software and how the software was utilized to provide additional insights during the correlation process. "
Planck has mapped the intensity and polarization of the sky at microwave frequencies with unprecedented sensitivity. We use these data to characterize the frequency dependence of dust emission. We utilize the Planck 353 GHz I, Q, and U Stokes maps as dust templates, and cross-correlate them with the Planck and WMAP data at 12 frequencies from 23 to 353 GHz, over circular patches with 10° radius. The cross-correlation analysis is performed for both intensity and polarization data in a consistent manner. The conclusions are corrected for the chance correlation between the templates and the anisotropies of the cosmic microwave background. We use a mask that focuses our analysis on the diffuse interstellar medium at intermediate Galactic latitudes. We determine the spectral indices of dust emission in intensity and polarization between 100 and 353 GHz, for each sky patch. Both indices are found to be remarkably constant over the sky. The mean values, 1.59 ± 0.02 for polarization and 1.51 ± 0.01 for intensity, for a mean dust temperature of 19.6 K, are close, but significantly different (3.6σ). We determine the mean spectral energy distribution (SED) of the microwave emission, correlated with the 353 GHz dust templates, by averaging the results of the correlation over all sky patches. We find that the mean SED increases for decreasing frequencies at ν< 60 GHz for both intensity and polarization. The rise of the polarization SED towards low frequencies may be accounted for by a synchrotron component correlated with dust, with no need for any polarization of the anomalous microwave emission. We use a spectral model to separate the synchrotron and dust polarization and to characterize the spectral dependence of the dust polarization fraction. The polarization fraction (p) of the dust emission decreases by (21 ± 6)% from 353 to 70 GHz. We discuss this result within the context of existing dust models. The decrease in p could indicate differences in polarization efficiency among components of interstellar dust (e.g., carbon versus silicate grains). Our observational results provide inputs to quantify and optimize the separation between Galactic and cosmological polarization.