7 resultados para Spectral distribution

em BORIS: Bern Open Repository and Information System - Berna - Suiça


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We present a purely physical model to determine cosmogenic production rates for noble gases and radionuclides in micrometeorites (MMs) and interplanetary dust particles (IDPs) by solar cosmic-rays (SCR) and galactic cosmic-rays (GCR) fully considering recoil loss effects. Our model is based on various nuclear model codes to calculate recoil cross sections, recoil ranges, and finally the percentages of the cosmogenic nuclides that are lost as a function of grain size, chemical composition of the grain, and the spectral distribution of the projectiles. The main advantage of our new model compared with earlier approaches is that we consider the entire SCR particle spectrum up to 240 MeV and not only single energy points. Recoil losses for GCR-produced nuclides are assumed to be equal to recoil losses for SCR-produced nuclides. Combining the model predictions with Poynting-Robertson orbital lifetimes, we calculate cosmic-ray exposure ages for recently studied MMs, cosmic spherules, and IDPs. The ages for MMs and the cosmic-spherule are in the range <2.2–233 Ma, which corresponds, according to the Poynting-Robertson drag, to orbital distances in the range 4.0–34 AU. For two IDPs, we determine exposure ages of longer than 900 Ma, which corresponds to orbital distances larger than 150 AU. The orbital distance in the range 4–6 AU for one MM and the cosmic spherule indicate an origin either in the asteroid belt or release from comets coming either from the Kuiper Belt or the Oort Cloud. Three of the studied MMs have orbital distances in the range 23–34 AU, clearly indicating a cometary origin, either from short-period comets from the Kuiper Belt or from the Oort Cloud. The two IDPs have orbital distances of more than 150 AU, indicating an origin from Oort Cloud comets.

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Stable isotope ratios of nitrate preserved in deep ice cores are expected to provide unique and valuable information regarding paleoatmospheric processes. However, due to the post-depositional loss of nitrate in snow, this information may be erased or significantly modified by physical or photochemical processes before preservation in ice. We investigated the role of solar UV photolysis in the post-depositional modification of nitrate mass and stable isotoperatios at Dome C, Antarctica, during the austral summer of 2011/2012. Two 30 cm snow pits were filled with homogenized drifted snow from the vicinity of the base. One of these pits was covered with a plexiglass plate that transmits solar UV radiation, while the other was covered with a different plexiglass plate having a low UV transmittance. Samples were then collected from each pit at a 2–5 cm depth resolution and a 10-day frequency. At the end of the season, acomparable nitrate mass loss was observed in both pits for the top-level samples (0–7 cm) attributed to mixing with the surrounding snow. After excluding samples impacted by the mixing process, we derived an average apparent nitrogen isotopic fractionation (15" app/of role in driving the isotopic fractionation of nitrate in snow.We have estimated a purely photolytic nitrogen isotopic fractionation (15"photo) of -55.8 12.0 ‰ from the difference in the derived apparent isotopic ractionations of the two experimental fields, as both pits were exposed to similar physical processes except exposure to solar UV. This value is in close agreement with the 15" photo value of -47.9 6.8 ‰ derived in a laboratory experiment simulated for Dome C conditions (Berhanu et al., 2014). We have also observed an insensitivity of 15" with depth in the snowpack under the given experimental setup. This is due to the uniform attenuation of incoming solar UV by snow, as 15" is strongly dependent on the spectral distribution of the incoming light flux. Together with earlier work, the results presented here represent a strong body of evidence that solar UV photolysis is the most relevant post-depositional process modifying the stable isotope ratios of snow nitrate at low-accumulation sites, where many deep ice cores are drilled. Nevertheless, modeling the loss of nitrate in snow is still required before a robust interpretation of ice core records can be provided.

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A confocal imaging and image processing scheme is introduced to visualize and evaluate the spatial distribution of spectral information in tissue. The image data are recorded using a confocal laser-scanning microscope equipped with a detection unit that provides high spectral resolution. The processing scheme is based on spectral data, is less error-prone than intensity-based visualization and evaluation methods, and provides quantitative information on the composition of the sample. The method is tested and validated in the context of the development of dermal drug delivery systems, introducing a quantitative uptake indicator to compare the performances of different delivery systems is introduced. A drug penetration study was performed in vitro. The results show that the method is able to detect, visualize and measure spectral information in tissue. In the penetration study, uptake efficiencies of different experiment setups could be discriminated and quantitatively described. The developed uptake indicator is a step towards a quantitative assessment and, in a more general view apart from pharmaceutical research, provides valuable information on tissue composition. It can potentially be used for clinical in vitro and in vivo applications.

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We describe several simulation algorithms that yield random probability distributions with given values of risk measures. In case of vanilla risk measures, the algorithms involve combining and transforming random cumulative distribution functions or random Lorenz curves obtained by simulating rather general random probability distributions on the unit interval. A new algorithm based on the simulation of a weighted barycentres array is suggested to generate random probability distributions with a given value of the spectral risk measure.

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Water flow and solute transport through soils are strongly influenced by the spatial arrangement of soil materials with different hydraulic and chemical properties. Knowing the specific or statistical arrangement of these materials is considered as a key toward improved predictions of solute transport. Our aim was to obtain two-dimensional material maps from photographs of exposed profiles. We developed a segmentation and classification procedure and applied it to the images of a very heterogeneous sand tank, which was used for a series of flow and transport experiments. The segmentation was based on thresholds of soil color, estimated from local median gray values, and of soil texture, estimated from local coefficients of variation of gray values. Important steps were the correction of inhomogeneous illumination and reflection, and the incorporation of prior knowledge in filters used to extract the image features and to smooth the results morphologically. We could check and confirm the success of our mapping by comparing the estimated with the designed sand distribution in the tank. The resulting material map was used later as input to model flow and transport through the sand tank. Similar segmentation procedures may be applied to any high-density raster data, including photographs or spectral scans of field profiles.

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We solve two inverse spectral problems for star graphs of Stieltjes strings with Dirichlet and Neumann boundary conditions, respectively, at a selected vertex called root. The root is either the central vertex or, in the more challenging problem, a pendant vertex of the star graph. At all other pendant vertices Dirichlet conditions are imposed; at the central vertex, at which a mass may be placed, continuity and Kirchhoff conditions are assumed. We derive conditions on two sets of real numbers to be the spectra of the above Dirichlet and Neumann problems. Our solution for the inverse problems is constructive: we establish algorithms to recover the mass distribution on the star graph (i.e. the point masses and lengths of subintervals between them) from these two spectra and from the lengths of the separate strings. If the root is a pendant vertex, the two spectra uniquely determine the parameters on the main string (i.e. the string incident to the root) if the length of the main string is known. The mass distribution on the other edges need not be unique; the reason for this is the non-uniqueness caused by the non-strict interlacing of the given data in the case when the root is the central vertex. Finally, we relate of our results to tree-patterned matrix inverse problems.

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We present a novel approach for the reconstruction of spectra from Euclidean correlator data that makes close contact to modern Bayesian concepts. It is based upon an axiomatically justified dimensionless prior distribution, which in the case of constant prior function m(ω) only imprints smoothness on the reconstructed spectrum. In addition we are able to analytically integrate out the only relevant overall hyper-parameter α in the prior, removing the necessity for Gaussian approximations found e.g. in the Maximum Entropy Method. Using a quasi-Newton minimizer and high-precision arithmetic, we are then able to find the unique global extremum of P[ρ|D] in the full Nω » Nτ dimensional search space. The method actually yields gradually improving reconstruction results if the quality of the supplied input data increases, without introducing artificial peak structures, often encountered in the MEM. To support these statements we present mock data analyses for the case of zero width delta peaks and more realistic scenarios, based on the perturbative Euclidean Wilson Loop as well as the Wilson Line correlator in Coulomb gauge.