Thin-shell instability in collisionless plasma

Thin-shell instability is one process which can generate entangled structures in astrophysical plasma on collisional (fluid) scales. It is driven by a spatially varying imbalance between the ram pressure of the inflowing upstream plasma and the downstream's thermal pressure at a nonplanar shock. Here we show by means of a particle-in-cell simulation that an analog process can destabilize a thin shell formed by two interpenetrating, unmagnetized, and collisionless plasma clouds. The amplitude of the shell's spatial modulation grows and saturates after about ten inverse proton plasma frequencies, when the shell consists of connected piecewise linear patches.

Ocean Boundary Layer Dynamics and Air-Sea Interaction

Thesis (Ph.D.)--University of Washington, 2005-12

Practical signal-to-noise ratio quantification for sensitivity encoding: Application to coronary MR angiography.

Purpose: To develop and evaluate a practical method for the quantification of signal-to-noise ratio (SNR) on coronary MR angiograms (MRA) acquired with parallel imaging.Materials and Methods: To quantify the spatially varying noise due to parallel imaging reconstruction, a new method has been implemented incorporating image data acquisition followed by a fast noise scan during which radio-frequency pulses, cardiac triggering and navigator gating are disabled. The performance of this method was evaluated in a phantom study where SNR measurements were compared with those of a reference standard (multiple repetitions). Subsequently, SNR of myocardium and posterior skeletal muscle was determined on in vivo human coronary MRA.Results: In a phantom, the SNR measured using the proposed method deviated less than 10.1% from the reference method for small geometry factors (<= 2). In vivo, the noise scan for a 10 min coronary MRA acquisition was acquired in 30 s. Higher signal and lower SNR, due to spatially varying noise, were found in myocardium compared with posterior skeletal muscle.Conclusion: SNR quantification based on a fast noise scan is a validated and easy-to-use method when applied to three-dimensional coronary MRA obtained with parallel imaging as long as the geometry factor remains low.

Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches.

In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.

Content Based Image Retrieval Using Low Level Features of Automatically Extracted Regions of Interest

This paper proposes a region based image retrieval system using the local colour and texture features of image sub regions. The regions of interest (ROI) are roughly identified by segmenting the image into fixed partitions, finding the edge map and applying morphological dilation. The colour and texture features of the ROIs are computed from the histograms of the quantized HSV colour space and Gray Level co- occurrence matrix (GLCM) respectively. Each ROI of the query image is compared with same number of ROIs of the target image that are arranged in the descending order of white pixel density in the regions, using Euclidean distance measure for similarity computation. Preliminary experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.

Detection of flood extent in urban areas using high resolution TerraSAR-X data

Flooding is a major hazard in both rural and urban areas worldwide, but it is in urban areas that the impacts are most severe. An investigation of the ability of high resolution TerraSAR-X data to detect flooded regions in urban areas is described. An important application for this would be the calibration and validation of the flood extent predicted by an urban flood inundation model. To date, research on such models has been hampered by lack of suitable distributed validation data. The study uses a 3m resolution TerraSAR-X image of a 1-in-150 year flood near Tewkesbury, UK, in 2007, for which contemporaneous aerial photography exists for validation. The DLR SETES SAR simulator was used in conjunction with airborne LiDAR data to estimate regions of the TerraSAR-X image in which water would not be visible due to radar shadow or layover caused by buildings and taller vegetation, and these regions were masked out in the flood detection process. A semi-automatic algorithm for the detection of floodwater was developed, based on a hybrid approach. Flooding in rural areas adjacent to the urban areas was detected using an active contour model (snake) region-growing algorithm seeded using the un-flooded river channel network, which was applied to the TerraSAR-X image fused with the LiDAR DTM to ensure the smooth variation of heights along the reach. A simpler region-growing approach was used in the urban areas, which was initialized using knowledge of the flood waterline in the rural areas. Seed pixels having low backscatter were identified in the urban areas using supervised classification based on training areas for water taken from the rural flood, and non-water taken from the higher urban areas. Seed pixels were required to have heights less than a spatially-varying height threshold determined from nearby rural waterline heights. Seed pixels were clustered into urban flood regions based on their close proximity, rather than requiring that all pixels in the region should have low backscatter. This approach was taken because it appeared that urban water backscatter values were corrupted in some pixels, perhaps due to contributions from side-lobes of strong reflectors nearby. The TerraSAR-X urban flood extent was validated using the flood extent visible in the aerial photos. It turned out that 76% of the urban water pixels visible to TerraSAR-X were correctly detected, with an associated false positive rate of 25%. If all urban water pixels were considered, including those in shadow and layover regions, these figures fell to 58% and 19% respectively. These findings indicate that TerraSAR-X is capable of providing useful data for the calibration and validation of urban flood inundation models.

Variability of marine aerosol fine-mode fraction and estimates of anthropogenic aerosol component over cloud-free oceans from the Moderate Resolution Imaging Spectroradiometer (MODIS)

In this study, we examine seasonal and geographical variability of marine aerosol fine-mode fraction ( fm) and its impacts on deriving the anthropogenic component of aerosol optical depth (ta) and direct radiative forcing from multispectral satellite measurements. A proxy of fm, empirically derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5 data, shows large seasonal and geographical variations that are consistent with the Goddard Chemistry Aerosol Radiation Transport (GOCART) and Global Modeling Initiative (GMI) model simulations. The so-derived seasonally and spatially varying fm is then implemented into a method of estimating ta and direct radiative forcing from the MODIS measurements. It is found that the use of a constant value for fm as in previous studies would have overestimated ta by about 20% over global ocean, with the overestimation up to �45% in some regions and seasons. The 7-year (2001–2007) global ocean average ta is 0.035, with yearly average ranging from 0.031 to 0.039. Future improvement in measurements is needed to better separate anthropogenic aerosol from natural ones and to narrow down the wide range of aerosol direct radiative forcing.

An assessment of the impact of local processes on dust lifting in martian climate models

Simulation of the lifting of dust from the planetary surface is of substantially greater importance on Mars than on Earth, due to the fundamental role that atmospheric dust plays in the former’s climate, yet the dust emission parameterisations used to date in martian global climate models (MGCMs) lag, understandably, behind their terrestrial counterparts in terms of sophistication. Recent developments in estimating surface roughness length over all martian terrains and in modelling atmospheric circulations at regional to local scales (less than O(100 km)) presents an opportunity to formulate an improved wind stress lifting parameterisation. We have upgraded the conventional scheme by including the spatially varying roughness length in the lifting parameterisation in a fully consistent manner (thereby correcting a possible underestimation of the true threshold level for wind stress lifting), and used a modification to account for deviations from neutral stability in the surface layer. Following these improvements, it is found that wind speeds at typical MGCM resolution never reach the lifting threshold at most gridpoints: winds fall particularly short in the southern midlatitudes, where mean roughness is large. Sub-grid scale variability, manifested in both the near-surface wind field and the surface roughness, is then considered, and is found to be a crucial means of bridging the gap between model winds and thresholds. Both forms of small-scale variability contribute to the formation of dust emission ‘hotspots’: areas within the model gridbox with particularly favourable conditions for lifting, namely a smooth surface combined with strong near-surface gusts. Such small-scale emission could in fact be particularly influential on Mars, due both to the intense positive radiative feedbacks that can drive storm growth and a strong hysteresis effect on saltation. By modelling this variability, dust lifting is predicted at the locations at which dust storms are frequently observed, including the flushing storm sources of Chryse and Utopia, and southern midlatitude areas from which larger storms tend to initiate, such as Hellas and Solis Planum. The seasonal cycle of emission, which includes a double-peaked structure in northern autumn and winter, also appears realistic. Significant increases to lifting rates are produced for any sensible choices of parameters controlling the sub-grid distributions used, but results are sensitive to the smallest scale of variability considered, which high-resolution modelling suggests should be O(1 km) or less. Use of such models in future will permit the use of a diagnosed (rather than prescribed) variable gustiness intensity, which should further enhance dust lifting in the southern hemisphere in particular.

The spatial pattern of natural selection when selection depends upon experience

Apostatic (frequency‐ or density‐dependent) selection, aposematic signals, and mate choice behavior generally require that the mean prey or potential mate density m value be high enough (above a threshold T) to result in sufficient encounter rates for the searcher to learn or retain the association between conspicuous signals and prey unprofitability, to forage apostatically, or to choose among mates. This assumes that all searchers experience , which implicitly assumes an even dispersion of targets among searcher territories. Uneven dispersion generates new phenomena. If , then only territories with local density x values that are greater than T favor experience‐based behavior, leading to spatially variable frequency‐ or density‐dependent selection intensity. As aggregation increases, the increase in percentage of targets in favorable territories ( ) is greater than the increase in the percentage of territories that are favorable. The relationship is reversed when . In both cases, because as few as 10% of the territories can contain 80% of the targets, only a few territory holders may account for most of the selection on most of the target population; accidents of experience in only a few searchers can have unexpectedly large effects on the target population. This also provides an explanation for high searcher behavior variation (personalities) : individuals from favorable territories will behave differently in behavioral experiments than those from unfavorable territories, at least with respect to similar kinds of targets. These effects will generate spatial heterogeneity in natural and sexual selection in what are otherwise uniform environments.

Dielectrophoretic manipulation and separation of microparticles using curved microelectrodes

This paper presents the development and experimental analysis of a dielectrophoresis (DEP) system, which is used for the manipulation and separation of microparticles in liquid flow. The system is composed of arrays of microelectrodes integrated to a microchannel. Novel curved microelectrodes are symmetrically placed with respect to the centre of the microchannel with a minimum gap of 40 mm. Computational fluid dynamics method is utilised to characterise the DEP field and predict the dynamics of particles. The performance of the system is assessed with microspheres of 1, 5 and 12 mm diameters. When a high-frequency potential is applied to microelectrodes a spatially varying electric field is induced in the microchannel, which creates the DEP force. Negative-DEP behaviour is observed with particles being repelled from the microelectrodes. The particles of different dimensions experience different DEP forces and thus settle to separate equilibrium zones across the microchannel. Experiments demonstrate the capability of the system as a field flow fraction tool for sorting microparticles according to their dimensions and dielectric properties.

The spatial dynamics of native and introduced blowflies (Dipt., Calliphoridae)

The spatial dynamics of three blowfly species was investigated using a spatially extended model of density-dependent population growth and the results indicate an overall stabilizing effect. Introduction of diffusive dispersal induced a quantitative effect of damping variation in population size on the route to a one-fixed point equilibrium in the native species, Cochliomyia macellaria. On the other hand, diffusive dispersal caused qualitative shifts in the dynamics of two invading species, Chrysomya megacephala and Chrysomya putoria. In both species diffusive dispersal can produce a qualitative shift from a two-point limit cycle to a one fixed-point dynamics. Quantitatively, dispersal also has the effect of damping oscillations in population size in the invading species.

Evaluation epidermal p53 immunostaining by digital image analysis

Digital techniques have been developed and validated to assess semiquantitatively immunohistochemical nuclear staining. Currently visual classification is the standard for qualitative nuclear evaluation. Analysis of pixels that represents the immunohistochemical labeling can be more sensitive, reproducible and objective than visual grading. This study compared two semiquantitative techniques of digital image analysis with three techniques of visual analysis imaging to estimate the p53 nuclear immunostaining. Methods: Sixty-three sun-exposed forearm-skin biopsies were photographed and submitted to three visual analyses of images: the qualitative visual evaluation method (0 to 4 +), the percentage of labeled nuclei and HSCORE. Digital image analysis was performed using ImageJ 1.45p; the density of nuclei was scored per ephitelial area (DensNU) and the pixel density was established in marked suprabasal epithelium (DensPSB). Results: Statistical significance was found in: the agreement and correlation among the visual estimates of evaluators, correlation among the median visual score of the evaluators, the HSCORE and the percentage of marked nuclei with the DensNU and DensPSB estimates. DensNU was strongly correlated to the percentage of p53-marked nuclei in the epidermis, and DensPSB with the HSCORE. Conclusion: The parameters presented herein can be applied in routine analysis of immunohistochemical nuclear staining of epidermis. © 2012 John Wiley & Sons A/S.

Bayesian space-time data fusion for real-time forecasting and map uncertainty

Environmental computer models are deterministic models devoted to predict several environmental phenomena such as air pollution or meteorological events. Numerical model output is given in terms of averages over grid cells, usually at high spatial and temporal resolution. However, these outputs are often biased with unknown calibration and not equipped with any information about the associated uncertainty. Conversely, data collected at monitoring stations is more accurate since they essentially provide the true levels. Due the leading role played by numerical models, it now important to compare model output with observations. Statistical methods developed to combine numerical model output and station data are usually referred to as data fusion. In this work, we first combine ozone monitoring data with ozone predictions from the Eta-CMAQ air quality model in order to forecast real-time current 8-hour average ozone level defined as the average of the previous four hours, current hour, and predictions for the next three hours. We propose a Bayesian downscaler model based on first differences with a flexible coefficient structure and an efficient computational strategy to fit model parameters. Model validation for the eastern United States shows consequential improvement of our fully inferential approach compared with the current real-time forecasting system. Furthermore, we consider the introduction of temperature data from a weather forecast model into the downscaler, showing improved real-time ozone predictions. Finally, we introduce a hierarchical model to obtain spatially varying uncertainty associated with numerical model output. We show how we can learn about such uncertainty through suitable stochastic data fusion modeling using some external validation data. We illustrate our Bayesian model by providing the uncertainty map associated with a temperature output over the northeastern United States.

Semiparametric Latent Variable Regression Models for Spatio-temporal Modeling of Mobile Source Particles in the Greater Boston Area

Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic particles, with some recording only outdoor concentrations of black or elemental carbon, some recording indoor concentrations of black carbon, and others recording both indoor and outdoor concentrations of black carbon. A joint model for outdoor and indoor exposure that specifies a spatially varying latent variable provides greater spatial coverage in the area of interest. We propose a penalised spline formation of the model that relates to generalised kringing of the latent traffic pollution variable and leads to a natural Bayesian Markov Chain Monte Carlo algorithm for model fitting. We propose methods that allow us to control the degress of freedom of the smoother in a Bayesian framework. Finally, we present results from an analysis that applies the model to data from summer and winter separately

U/Th systematics of authigenic carbonates and d18O records from Hydrate Ridge, off the coast of Oregon, USA

Uranium (U) concentrations and activity ratios (d234U) of authigenic carbonates are sensitive recorders of different fluid compositions at submarine seeps of hydrocarbon-rich fluids ("cold seeps") at Hydrate Ridge, off the coast of Oregon, USA. The low U concentrations (mean: 1.3 ± 0.4 µg/g) and high 234U values (165-317 per mil) of gas hydrate carbonates reflect the influence of sedimentary pore water indicating that these carbonates were formed under reducing conditions below or at the seafloor. Their 230Th/234U ages span a time interval from 0.8 to 6.4 ka and cluster around 1.2 and 4.7 ka. In contrast, chemoherm carbonates precipitate from marine bottom water marked by relatively high U concentrations (mean: 5.2 ± 0.8 µg/g) and a mean d234U ratio of 166 ± 3 per mil. Their U isotopes reflect the d234U ratios of the bottom water being enriched in 234U relative to normal seawater. Simple mass balance calculations based on U concentrations and their corresponding d234U ratios reveal a contribution of about 11% of sedimentary pore water to the bottom water. From the U pore water flux and the reconstructed U pore water concentration a mean flow rate of about 147 ± 68 cm/a can be estimated. 230Th/234U ages of chemoherm carbonates range from 7.3 to 267.6 ka. 230Th/234U ages of two chemoherms (Alvin and SE-Knoll chemoherm) correspond to time intervals of low sealevel stands in marine isotope stages (MIS) 2, 4, 5, 6, 7 and 8. This observation indicates that fluid flow at cold seep sites sensitively reflects pressure changes of the hydraulic head in the sediments. The d18OPDB ratios of the chemoherm carbonates support the hypothesis of precipitation during glacial times. Deviations of the chemoherm d18O values from the marine d18O record can be interpreted as to reflect temporally and spatially varying bottom water and/or vent fluid temperatures during carbonate precipitation between 2.6 and 8.6°C.