Visualization of MRI Diffusion Data by a Multi-Kernel LIC Approach with Anisotropic Glyph Samples

2016 ◽  
pp. 157-177 ◽  
Author(s):  
Mark Höller ◽  
Uwe Klose ◽  
Samuel Gröschel ◽  
Kay-M. Otto ◽  
Hans-H. Ehricke
Keyword(s):  
Diffusion Data

Room-temperature grain boundary diffusion data measured from historical artifacts

2005 ◽  
Vol 96 (10) ◽  
pp. 1187-1192 ◽  
Author(s):  
Raymond J. Kremer ◽  
Mysore A. Dayananda ◽  
Alexander H. King
Keyword(s):  
Grain Boundary ◽  
Room Temperature ◽  
Boundary Diffusion ◽  
Grain Boundary Diffusion ◽  
Diffusion Data

Numerical estimation of volatility values from discretely observed diffusion data

2006 ◽  
Vol 9 (4) ◽  
pp. 1-36 ◽  
Author(s):  
Jakša Cvitanic ◽  
Boris Rozovskii ◽  
Ilya Zaliapin
Keyword(s):  
Numerical Estimation ◽  
Diffusion Data

QELSS diffusion data for moderately broad molar mass distribution polymer samples compared with data from classical gradient techniques

1989 ◽  
Vol 54 (7) ◽  
pp. 1821-1829
Author(s):  
Bedřich Porsch ◽  
Simon King ◽  
Lars-Olof Sundelöf
Keyword(s):  
Diffusion Coefficient ◽  
Mass Distribution ◽  
Molar Mass ◽  
Molar Mass Distribution ◽  
Diffusion Data ◽  
Mass Distributions ◽  
Classical Diffusion ◽  
Polydisperse Polymer

The differences between the QELSS and classical diffusion coefficient of a polydisperse polymer resulting from distinct definitions of experimentally accessible average values are calculated for two assumed specific forms of molar mass distributions. Predicted deviations are compared with the experiment using NBS 706 standard polystyrene. QELSS Dz of this sample relates within 2-4% to the classical diffusion coefficient, if the Schulz-Zimm molar mass distribution is assumed to be valid. In general, differences between the height-area and QELSS diffusion coefficient of about 20% may be found for Mw/Mn ~ 2, and this value may increase above 35%, if strongly tailing molar mass distribution pertains to the sample.

The Influence of Stress on Interstitial Diffusion - Carbon Diffusion Data in Austenite Revisited

2010 ◽  
Vol 297-301 ◽  
pp. 1408-1413 ◽  
Author(s):  
Thomas L. Christiansen ◽  
Marcel A.J. Somers
Keyword(s):  
High Temperatures ◽  
Carbon Diffusion ◽  
Interstitial Diffusion ◽  
Diffusion Data ◽  
Induced Stress ◽  
Induced Stresses ◽  
Expanded Austenite

The present paper addresses the influence of chemical induced stresses on diffusion in interstitial systems. This is exemplified by simulations of carbon diffusion in austenite at high temperatures and it is shown that old well established literature data is flawed by the occurrence of composition induced stress. For the technological relevant system of expanded austenite the diffusion can be dramatically affected by composition induced stress.

A direct method for the inversion of isotopic thermal diffusion data

Physica B+C ◽  
1981 ◽  
Vol 106 (1) ◽  
pp. 117-122
Author(s):  
E.Brian Smith ◽  
Andrew R. Tindell ◽  
Bryan H. Wells ◽  
JoséLuis Brun
Keyword(s):  
Thermal Diffusion ◽  
Direct Method ◽  
Diffusion Data ◽  
A Direct Method

Influence of Operational Conditions on Retardation Parameters Measured by Diffusion Experiment in Compacted Bentonite

2010 ◽  
Vol 1265 ◽  
Author(s):  
Ishii Yasuo ◽  
Yoshimi Seida ◽  
Yukio Tachi ◽  
Hideki Yoshikawa
Keyword(s):  
Mass Transfer ◽  
Ionic Strength ◽  
Test Solution ◽  
Test Method ◽  
Diffusion Experiment ◽  
Mass Transfer Resistance ◽  
Transfer Resistance ◽  
Operational Conditions ◽  
Diffusion Data ◽  
Compacted Bentonite

AbstractInfluence of operation factors in diffusion test of compacted bentonite (such as agitation of test solution in the reservoir, feed rate of the test solution and mass transfer resistance in the filter) on the diffusion data was examined by reservoir depletion (RD) test method using Cs+. The influence of these factors on the diffusion data was also analyzed based on the mathematical sorption-diffusion model which considered the feed of test solution and mass transfer resistance in the filter as well. The reservoir depletion data showed some remarkable influences of these operational conditions, especially in the system with low ionic strength. Change in mass transfer resistance at filter-compacted bentonite due to the operational conditions was found to be potential factor which disturb the diffusion data. The influence was reduced in the system with high ionic strength of solution.

Statistical Methods for Diffusion Data

2013 ◽  
Author(s):  
William Hoppitt ◽  
Kevin N. Laland
Keyword(s):  
Statistical Methods ◽  
Behavior Pattern ◽  
Social Environments ◽  
Spatial Spread ◽  
Behavioral Trait ◽  
Individual Level ◽  
Diffusion Data ◽  
Diffusion Analysis ◽  
Diffusion Curve ◽  
In The Wild

This chapter describes statistical methods for inferring and quantifying social transmission in groups of animals in the wild, or in “captive” groups of animals in naturalistic social environments. In particular, it considers techniques for analyzing time-structured data on the occurrence of a particular behavior pattern, or behavioral trait, in one or more groups. For the most part, the focus is on cases where a novel trait spreads through one or more groups. Following standard terminology in the field of social learning, the spread of a trait through a group is referred to as a diffusion, and the resulting data as diffusion data. The methods include diffusion curve analysis and network-based diffusion analysis. For the latter, inclusion of individual-level variables is taken into account, along with model selection and inference, modeling of multiple diffusions, choosing a social network, and “untransmitted” social effects. The chapter also examines the spatial spread of a behavioral trait.

Diffusion data

1972 ◽  
Vol 23 (7) ◽  
pp. 350-351
Author(s):  
John Meiser
Keyword(s):  
Diffusion Data
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