Modeling dynamic controls on ice streams: a Bayesian statistical approach

Our main goal is to exemplify the study of ice-stream dynamics via Bayesian statistical analysis incorporating physical, though imperfectly known, models using data that are both incomplete and noisy. The physical-statistical models we propose account for these uncertainties in a coherent, hierarchical manner. The initial modeling assumption estimates basal shear stress as equal to driving stress, but subsequently includes a random corrector process to account for model error. The resulting stochastic equation is incorporated into a simple model for surface velocities. Use of Bayes\u27 theorem allows us to make inferences on all unknowns given basal elevation, surface elevation and surface velocity. The result is a posterior distribution of possible values that can be summarized in a number of ways. For example, the posterior mean of the stress field indicates average behavior at any location in the field, and the posterior standard deviations describe associated uncertainties. We analyze data from the \u27Northeast Greenland Ice Stream\u27 and illustrate bow scientific conclusions may be drawn from our Bayesian analysis

MNHT 2008-52096 SESSILE DROP EVAPORATION ON SURFACES OF VARIOUS WETTABILITY

ABSTRACT This work experimentally investigates the evaporation rates of water drops on surfaces of various wettability. By measuring the temporal evolutions of the drop radius and contact angle, we find the qualitative difference between the evaporation behavior on hydrophilic surfaces where the contact radius remains constant initially and that on the superhydrophobic surfaces where the contact angle remains constant. Also, the evaporation rate is observed to depend on the surface material although the currently available models assume that the rate is solely determined by the drop geometry. Although the theory to explain this dependence on the surface remains to be pursued by the future work, we give the empirical relations that can be used to predict the drop volume evolution for each surface

Synthesis and Control of the Shell Thickness of Polyaniline and Polypyrrole Half Hollow Spheres Using the Polystyrene Cores

Polyaniline (Pani) and polypyrrole (Ppy) half hollow spheres with different shell thicknesses were successfully synthesized by three steps process using polystyrene (PS) as the core. The PS core was synthesized by emulsion polymerization. Aniline and pyrrole monomers were polymerized on the surface of the PS core. The shells of Pani and Ppy were fabricated by adding different amounts of aniline and pyrrole monomers. PS cores were dissolved and removed from the core shell structure by solvent extraction. The thicknesses of the Pani and Ppy half hollow spheres were observed by FE-SEM and FE-TEM. The chemical structures of the Pani and Ppy half hollow spheres were characterized by FT-IR spectroscopy and UV-Vis spectroscopy. The shell thicknesses of the Pani half hollow spheres were 30.2, 38.0, 42.2, 48.2, and 52.4 nm, while the shell thicknesses of the Ppy half hollow spheres were 16.0, 22.0, 27.0, and 34.0 nm. The shell thicknesses of Pani and Ppy half hollow spheres linearly increased as the amount of the monomer increased. Therefore, the shell thickness of the Pani and Ppy half hollow spheres can be controlled in these ranges

Quantitative analysis of single bacterial chemotaxis using a linear concentration gradient microchannel

A microfluidic device to quantify bacterial chemotaxis has been proposed, which generates a linear concentration gradient of chemoattractant in the main channel only by convective and molecular diffusion, and which enables the bacteria to enter the main channel in a single file by hydrodynamic focusing technique. The trajectory of each bacterium in response to the concentration gradient of chemoattractant is photographed by a CCD camera and its velocity is acquired by a simple PTV (Particle Tracking Velocimetry) algorithm. An advantage of this assay is to measure the velocity of a single bacterium and to quantify the degree of chemotaxis by analyzing the frequency of velocities concurrently. Thus, the parameter characterizing the motility of wild-type Escherichia coli strain RP437 in response to various concentration gradients of L-aspartate is obtained in such a manner that the degree of bacterial chemotaxis is quantified on the basis of a newly proposed Migration Index

A Risk Assessment for Ozone Regulation Based on Statistical Rollback

In environmental studies, it is important to assess how regulatory standards for air pollutants affect public health. High ozone levels contribute to harmful air pollutants. The EPA regulates ozone levels by setting ozone standards to protect public health. It is thus crucial to assess how various regulatory ozone standards affect non-accidental mortality related to respiratory deaths during the ozone season. The original rollback approach provides an adjusted ozone process under a new regulation scenario in a deterministic fashion. Herein, we consider a statistical rollback approach to allow for uncertainty in the rollback procedure by adopting the quantile matching method so that it provides flexible rollback sets. Hierarchical Bayesian models are used to predict the potential effects of different ozone standards on human health. We apply the method to epidemiologic data

Objective Bayesian Entropy Inference for Two-Parameter Logistic Distribution Using Upper Record Values

In this paper, we provide an entropy inference method that is based on an objective Bayesian approach for upper record values having a two-parameter logistic distribution. We derive the entropy that is based on the i-th upper record value and the joint entropy that is based on the upper record values. Moreover, we examine their properties. For objective Bayesian analysis, we obtain objective priors, namely, the Jeffreys and reference priors, for the unknown parameters of the logistic distribution. The priors are based on upper record values. Then, we develop an entropy inference method that is based on these objective priors. In real data analysis, we assess the quality of the proposed models under the objective priors and compare them with the model under the informative prior

A Risk Assessment for Ozone Regulation Based on Statistical Rollback

In environmental studies, it is important to assess how regulatory standards for air pollutants affect public health. High ozone levels contribute to harmful air pollutants. The EPA regulates ozone levels by setting ozone standards to protect public health. It is thus crucial to assess how various regulatory ozone standards affect non-accidental mortality related to respiratory deaths during the ozone season. The original rollback approach provides an adjusted ozone process under a new regulation scenario in a deterministic fashion. Herein, we consider a statistical rollback approach to allow for uncertainty in the rollback procedure by adopting the quantile matching method so that it provides flexible rollback sets. Hierarchical Bayesian models are used to predict the potential effects of different ozone standards on human health. We apply the method to epidemiologic data

Bayesian Genetic Association Test when Secondary Phenotypes Are Available Only in the Case Group

In many case-control genetic association studies, a secondary phenotype that may have common genetic factors with disease status can be identified. When information on the secondary phenotype is available only for the case group due to cost and different data sources, a fitting linear regression model ignoring supplementary phenotype data may provide limited knowledge regarding genetic association. We set up a joint model and use a Bayesian framework to estimate and test the effect of genetic covariates on disease status considering the secondary phenotype as an instrumental variable. The application of our proposed procedure is demonstrated through the rheumatoid arthritis data provided by the 16th Genetic Analysis Workshop