When two fluid drops come close enough together to touch, surface tension quickly pulls the drops together into one larger drop. This is an example of a singular fluid flow, as the topology of the interface changes at the moment of contact. Similarly, when a pair of bubbles touch, the surface topology changes and a singular flow begins. Since the stress from surface tension depends on the surface curvature, these singularities are often characterised by divergent fluid velocities. Experimental observation or numerical simulation of these flows is therefore difficult due to the high velocities and small lengthscales. In this thesis, I will find multi-scale theoretical solutions for the singular flows during the initial stages of the coalescence of bubbles and drops, solving for the velocity field in the fluid and the rate of coalescence. Each solution has several lengthscales, and on each lengthscale, we must solve some form of the Navier--Stokes equations. I will employ a variety of analytical and numerical techniques to solve for the flow on each scale. These asymptotic solutions are valid at early times; future numerical simulations of the subsequent flow could be initialised with these solutions, rather than the actual singularity. In the course of solving for these singular flows, I will also describe the solution for the motion of a stretched fluid edge, the retraction of a narrow fluid wedge, the capillary flow around a parabola, and the effect of a time-dependent force on a fluid half-space. These fundamental flows have applications outside of coalescence, which I will outline throughout the thesis.

This thesis consists of three papers on matching and weighting methods for causal inference. The first paper conducts a Monte Carlo simulation study to evaluate the performance of multivariate matching methods that select a subset of treatment and control observations. The matching methods studied are the widely used nearest neighbor matching with propensity score calipers, and the more recently proposed methods, optimal matching of an optimally chosen subset and optimal cardinality matching. The main findings are: (i) covariate balance, as measured by differences in means, variance ratios, Kolmogorov-Smirnov distances, and cross-match test statistics, is better with cardinality matching since by construction it satisfies balance requirements; (ii) for given levels of covariate balance, the matched samples are larger with cardinality matching than with the other methods; (iii) in terms of covariate distances, optimal subset matching performs best; (iv) treatment effect estimates from cardinality matching have lower RMSEs, provided strong requirements for balance, specifically, fine balance, or strength-k balance, plus close mean balance. In standard practice, a matched sample is considered to be balanced if the absolute differences in means of the covariates across treatment groups are smaller than 0.1 standard deviations. However, the simulation results suggest that stronger forms of balance should be pursued in order to remove systematic biases due to observed covariates when a difference in means treatment effect estimator is used. In particular, if the true outcome model is additive then marginal distributions should be balanced, and if the true outcome model is additive with interactions then low-dimensional joints should be balanced. The second paper focuses on longitudinal studies, where marginal structural models (MSMs) are widely used to estimate the effect of time-dependent treatments in the presence of time-dependent confounders. Under a sequential ignorability assumption, MSMs yield unbiased treatment effect estimates by weighting each observation by the inverse of the probability of their observed treatment sequence given their history of observed covariates. However, these probabilities are typically estimated by fitting a propensity score model, and the resulting weights can fail to adjust for observed covariates due to model misspecification. Also, these weights tend to yield very unstable estimates if the predicted probabilities of treatment are very close to zero, which is often the case in practice. To address both of these problems, instead of modeling the probabilities of treatment, a design-based approach is taken and weights of minimum variance that adjust for the covariates across all possible treatment histories are directly found. For this, the role of weighting in longitudinal studies of treatment effects is analyzed, and a convex optimization problem that can be solved efficiently is defined. Unlike standard methods, this approach makes evident to the investigator the limitations imposed by the data when estimating causal effects without extrapolating. A simulation study shows that this approach outperforms standard methods, providing less biased and more precise estimates of time-varying treatment effects in a variety of settings. The proposed method is used on Chilean educational data to estimate the cumulative effect of attending a private subsidized school, as opposed to a public school, on students’ university admission tests scores. The third paper is centered on observational studies with multi-valued treatments. Generalizing methods for matching and stratifying to accommodate multi-valued treatments has proven to be a complex task. A natural way to address confounding in this case is by weighting the observations, typically by the inverse probability of treatment weights (IPTW). As in the MSMs case, these weights can be highly variable and produce unstable estimates due to extreme weights. In addition, model misspecification, small sample sizes, and truncation of extreme weights can cause the weights to fail to adjust appropriately for observed confounders. The conditions the weights need to satisfy in order to provide close to unbiased treatment effect estimates with a reduced variability are determined and the convex optimization problem that can be solved in polynomial time to obtain them is defined. A simulation study with different settings is conducted to compare the proposed weighting scheme to IPTW, including generalized propensity score estimation methods that also consider explicitly the covariate balance problem in the probability estimation process. The applicability of the methods to continuous treatments is also tested. The results show that directly targeting balance with the weights, instead of focusing on estimating treatment assignment probabilities, provides the best results in terms of bias and root mean square error of the treatment effect estimator. The effects of the intensity level of the 2010 Chilean earthquake on posttraumatic stress disorder are estimated using the proposed methodology.

In the present century, ecosystems across the globe will be subject to profound changes in climate. Forests are expected to be particularly sensitive to such change as the long life span of trees limits the potential for rapid adaptation. In order to preserve commercial viability and the essential ecosystem services provided by forests, there has been much interest in strategies for managing the adaptation of trees to their climatic environment. Climate Matching has emerged as one such strategy, whereby climate models are used to identify provenances – tree populations at a particular locality - with seed expected to be well adapted to the future conditions of a particular planting site. Debate continues about the feasibility and merit of this and other approaches, but it has yet to be demonstrated that the underlying assumptions of Climate Matching are valid for focal European tree species. Furthermore, a potentially major omission thus far has been consideration of how the Climate Matching strategy might influence associated organisms. Given the widely demonstrated bottom-up effects of foundation species genotype that have emerged from the field of community genetics, it is possible that planting seed of non-local provenance could effect forest organisms such as insect herbivores. In this thesis, I investigate the underlying assumptions of Climate Matching and its community level consequences using a model system of cynipid oak galls on Quercus petraea. Following a general introduction to Climate Matching and the study system, in Chapter 2 I use data from a provenance trial of Q. petraea in France to explore a central assumption of the Climate Matching strategy: that provenances of focal tree species show climate associated variation in adaptive phenotypic traits. In Chapter 3, I explore correlations between these phenotypic traits and the abundance, diversity, and community composition of an associated guild of specialist gall-inducing herbivores. Tree phenological traits in particular showed strong patterns of adaptation to climatic gradients, and influenced the abundance and community structure of galling species. However, as the response to non-local tree provenances was not strongly negative, it was considered unlikely that mixed planting of local and Climate Matched provenances would have sever impact on the gallwasp community. Having assessed the bottom-up effects of provenance phenotypic variation on the galling community, my ultimate aim is to extend analysis to include associated hymenopteran inquilines and parasitoids. However, interpretation of effects at this level is hindered by taxonomic uncertainty, with a growing appreciation that morpho-taxa may not represent independently evolving lineages (i.e. ‘true’ species). In Chapters 4 & 5 I therefore develop approaches for addressing taxonomic uncertainty with this ultimate aim in mind. In Chapter 4, I apply a DNA barcoding approach to parasitoid and inquiline specimens reared from the provenance trial, and compare taxa based on barcodes with those based on morphology to identify points of taxonomic uncertainty. I also investigate the extent to which networks based on morphological and molecular taxa support contrasting conclusions of network properties. In Chapter 5 I explore the potential for molecular based resolution of species level taxonomic error in a challenging group of parasitoids: the genus Cecidostiba. Beginning with a framework of single locus DNA barcoding, I use data from multiple nuclear loci to reveal the existence of cryptic species. Finally, in Chapter 6 I explore the practicalities of Climate Matching in light of my empirical results, and suggest fruitful avenues for further research.

Although fine granularity scalability (FGS) video coding based on matching pursuits and bit-plane coding have been proven to have better coding efficiency than discrete-cosine-transform-based FGS at low bit rates, it might not be the most efficient method in terms of rate-distortion optimization (RDO). We propose a rate-distortion optimization FGS video coding by dividing a frame into blocks to generate block-based embedded bit-streams and reorganize the bit-streams into several quality layers according to the rate-distortion slopes. The comparison between FGS matching pursuit video coding and RDO-FGS matching pursuit video coding is shown in the experimental results.

No description available.

Sequential mammograms contain important information, such as changes of the breast or developments of the masses, for diagnosis of disease. Comparison of sequential mammograms plays an important part for radiologists in identifying malignant masses. However, currently computer-aided detection (CAD) programs can not use such information eciently. The diculties lie in the registration of sequential mammograms. Most of current methods register sequential mammograms based on control points and image transformations. For these methods to work, extraction and correspondence of the control points is essential. This thesis presents a new approach in registering mammograms. The proposed method registers mammograms by associating mass-like objects in sequential mammograms directly. The mass-like objects appear in the images of normal breasts as well as images of breast with cancer. When the mass-like objects in sequential mammograms are accurately associated, measurements of changes in mass-like objects over time become possible. This is an important way to distinguish mass-like objects associated with cancer from cysts or other benign objects. The proposed method is based on graph matching. It uses the internal structure of the breast represented by the spatial relation between the mass-like objects to establish a correspondence between the sequential mammograms. In this method, the mammogram is firstly segmented into separate components using an adaptive pyramid (AP) segmentation algorithm. A series of filters, based on the features of components, is then applied to the components to remove the undesired ones. The remaining components, the mass-like objects, are represented by a complete graph. The spatial relations between the remaining mass-like objects are expressed by fuzzy spatial relation representation and are associated to the edges of the graph as weights. Association of the mass-like objects of two sequential mammograms is realized by finding a common subgraph of the corresponding two graphs using the backtrack algorithm. The segmentation methods developed in the course of this work were tested on a separate problem in computer-aided detection of breast cancer, namely the automatic extraction of the pectoral muscle. The graph matching method was tested independently of the segmentation method on artificially distorted mammograms and the full process, including the segmentation and the graph matching, was evaluated on 95 temporal mammogram pairs. The present implementation indicates only a small improvement in cancer detection rates but also presents opportunities for a substantial development of the basic method in the future.

Thesis (M.S.)--George Mason University, 2008. / Vita: p. 29. Thesis director: Jennifer Weller. Submitted in partial fulfillment of the requirements for the degree of Master of Science in Bioinformatics. Title from PDF t.p. (viewed July 7, 2008). Includes bibliographical references (p. 28). Also issued in print.

Thesis (Ph. D.)--Ohio State University, 2004. / Title from first page of PDF file. Document formatted into pages; contains x, 126 p. Includes bibliographical references (p. 121-126). Available online via OhioLINK's ETD Center

Proefschrift Universiteit Maastricht. / Met lit. opg. - Met samenvatting in het Engels.

There are numerous techniques used to measure strain. Most are only capable of taking surface measurements. The penetrating nature of X-rays has been used to measure deformation, and thus strain, but only with radiographic images. Radioscopic techniques are faster and do not require film processing, but produce less detailed results than digitised radiographic images. The research covered by this thesis tested radioscopic images and showed them to be suitable for strain measurement. The thesis includes details of the design and capabilities of the radioscopic equipment. Pin cushion distortion is a common feature of radioscopic images, and an automatic method of identifying, and correcting for the distortion was implemented.