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Journal articles on the topic 'Diffusion geometry'

Author: Grafiati

Published: 11 March 2023

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1

Ambjørn, Jan, Konstantinos N. Anagnostopoulos, Lars Jensen, Takashi Ichihara, and Yoshiyuki Watabiki. "Quantum geometry and diffusion." Journal of High Energy Physics 1998, no. 11 (November 24, 1998): 022. http://dx.doi.org/10.1088/1126-6708/1998/11/022.

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2

Kaloshin, Vadim, and Mark Levi. "Geometry of Arnold Diffusion." SIAM Review 50, no. 4 (January 2008): 702–20. http://dx.doi.org/10.1137/070703235.

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3

Shaw, R. S., N. Packard, M. Schroter, and H. L. Swinney. "Geometry-induced asymmetric diffusion." Proceedings of the National Academy of Sciences 104, no. 23 (May 23, 2007): 9580–84. http://dx.doi.org/10.1073/pnas.0703280104.

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4

Hochgerner, Simon, and Tudor Ratiu. "Geometry of non-holonomic diffusion." Journal of the European Mathematical Society 17, no. 2 (2015): 273–319. http://dx.doi.org/10.4171/jems/504.

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5

De Lara, Michel. "On drift, diffusion and geometry." Journal of Geometry and Physics 56, no. 8 (August 2006): 1215–34. http://dx.doi.org/10.1016/j.geomphys.2005.06.012.

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6

SÖDERMAN, OLLE, and BENGT JÖNSSON. "Restricted Diffusion in Cylindrical Geometry." Journal of Magnetic Resonance, Series A 117, no. 1 (November 1995): 94–97. http://dx.doi.org/10.1006/jmra.1995.0014.

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7

Klaus, Colin James Stockdale, Krishnan Raghunathan, Emmanuele DiBenedetto, and Anne K. Kenworthy. "Analysis of diffusion in curved surfaces and its application to tubular membranes." Molecular Biology of the Cell 27, no. 24 (December 2016): 3937–46. http://dx.doi.org/10.1091/mbc.e16-06-0445.

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Diffusion of particles in curved surfaces is inherently complex compared with diffusion in a flat membrane, owing to the nonplanarity of the surface. The consequence of such nonplanar geometry on diffusion is poorly understood but is highly relevant in the case of cell membranes, which often adopt complex geometries. To address this question, we developed a new finite element approach to model diffusion on curved membrane surfaces based on solutions to Fick’s law of diffusion and used this to study the effects of geometry on the entry of surface-bound particles into tubules by diffusion. We show that variations in tubule radius and length can distinctly alter diffusion gradients in tubules over biologically relevant timescales. In addition, we show that tubular structures tend to retain concentration gradients for a longer time compared with a comparable flat surface. These findings indicate that sorting of particles along the surfaces of tubules can arise simply as a geometric consequence of the curvature without any specific contribution from the membrane environment. Our studies provide a framework for modeling diffusion in curved surfaces and suggest that biological regulation can emerge purely from membrane geometry.

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8

Gao, Tingran. "The diffusion geometry of fibre bundles: Horizontal diffusion maps." Applied and Computational Harmonic Analysis 50 (January 2021): 147–215. http://dx.doi.org/10.1016/j.acha.2019.08.001.

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9

Halle, Bertil, and Stefan Gustafsson. "Diffusion in a fluctuating random geometry." Physical Review E 55, no. 1 (January 1, 1997): 680–86. http://dx.doi.org/10.1103/physreve.55.680.

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10

Ledoux, Michel. "The geometry of Markov diffusion generators." Annales de la faculté des sciences de Toulouse Mathématiques 9, no. 2 (2000): 305–66. http://dx.doi.org/10.5802/afst.962.

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11

Vicsek, T., F. Family, and P. Meakin. "Multifractal Geometry of Diffusion-Limited Aggregates." Europhysics Letters (EPL) 12, no. 3 (June 1, 1990): 217–22. http://dx.doi.org/10.1209/0295-5075/12/3/005.

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12

Fischer, Paul F. "Anisotropic diffusion in a toroidal geometry." Journal of Physics: Conference Series 16 (January 1, 2005): 446–55. http://dx.doi.org/10.1088/1742-6596/16/1/060.

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13

Mitzithras, A., and J. H. Strange. "Diffusion of fluids in confined geometry." Magnetic Resonance Imaging 12, no. 2 (January 1994): 261–63. http://dx.doi.org/10.1016/0730-725x(94)91532-6.

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14

De Vecchi, Francesco C., Paola Morando, and Stefania Ugolini. "A note on symmetries of diffusions within a martingale problem approach." Stochastics and Dynamics 19, no. 02 (March 27, 2019): 1950011. http://dx.doi.org/10.1142/s0219493719500114.

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A geometric reformulation of the martingale problem associated with a set of diffusion processes is proposed. This formulation, based on second-order geometry and Itô integration on manifolds, allows us to give a natural and effective definition of Lie symmetries for diffusion processes.

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15

Allen, Rebecca, and Shuyu Sun. "Computing and Comparing Effective Properties for Flow and Transport in Computer-Generated Porous Media." Geofluids 2017 (2017): 1–24. http://dx.doi.org/10.1155/2017/4517259.

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We compute effective properties (i.e., permeability, hydraulic tortuosity, and diffusive tortuosity) of three different digital porous media samples, including in-line array of uniform shapes, staggered-array of squares, and randomly distributed squares. The permeability and hydraulic tortuosity are computed by solving a set of rescaled Stokes equations obtained by homogenization, and the diffusive tortuosity is computed by solving a homogenization problem given for the effective diffusion coefficient that is inversely related to diffusive tortuosity. We find that hydraulic and diffusive tortuosity can be quantitatively different by up to a factor of ten in the same pore geometry, which indicates that these tortuosity terms cannot be used interchangeably. We also find that when a pore geometry is characterized by an anisotropic permeability, the diffusive tortuosity (and correspondingly the effective diffusion coefficient) can also be anisotropic. This finding has important implications for reservoir-scale modeling of flow and transport, as it is more realistic to account for the anisotropy ofboththe permeability and the effective diffusion coefficient.

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16

Whitaker, R. "Geometry-Limited Diffusion in the Characterization of Geometric Patches in Images." Computer Vision and Image Understanding 57, no. 1 (January 1993): 111–20. http://dx.doi.org/10.1006/cviu.1993.1007.

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17

Whitaker, R. T. "Geometry-Limited Diffusion in the Characterization of Geometric Patches in Images." CVGIP: Image Understanding 57, no. 1 (January 1993): 111–20. http://dx.doi.org/10.1006/ciun.1993.1007.

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18

ZENG, QIUHUA, and HOUQIANG LI. "DIFFUSION EQUATION FOR DISORDERED FRACTAL MEDIA." Fractals 08, no. 01 (March 2000): 117–21. http://dx.doi.org/10.1142/s0218348x00000123.

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The movement of the fractal Brownian particle in isotropic and homogeneous two-dimensional assembling fractal spaces is studied by the standard diffusion equation on fractals, and we find that particle movement belongs to the anomalous diffusion. At the same time, by discussing the defectiveness of earlier proposed equations, a general form of analytic fractional diffusion equation is proposed for description of probability density of particles diffusing on fractal geometry at fractal time, and the solution connects with the ordinary solutions in the normal space time limit.

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19

van den Brink, Johan S., and Jos J. Koonen. "Inherent Geometry Correction for Diffusion EPI Using the Reference Echoes as Navigators." Concepts in Magnetic Resonance Part B 2019 (May 26, 2019): 1–8. http://dx.doi.org/10.1155/2019/4139726.

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Diffusion-weighted EPI has become an indispensable tool in body MRI. Geometric distortions due to field inhomogeneities are more prominent at large field–of–view and require correction for comparison with T2W TSE. Several known correction methods require acquisition of additional lengthy scans, which are difficult to apply in body imaging. We implement and evaluate a geometry correction method based on the already available non phase-encoded EPI reference data used for Nyquist ghost removal. The method is shown to provide accurate and robust global geometry correction in the absence of strong, local phase offsets. It does not require additional time for calibrations and is directly compatible with parallel imaging methods. The resulting images can serve as improved starting point for additional geometry correction methods relying on feature extraction and registration.

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20

Georgiev, Bogdan, and Mayukh Mukherjee. "Nodal geometry, heat diffusion and Brownian motion." Analysis & PDE 11, no. 1 (January 1, 2018): 133–48. http://dx.doi.org/10.2140/apde.2018.11.133.

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21

Ramsak, Matjaz, and Leopold Skerget. "Heat diffusion in fractal geometry cooling surface." Thermal Science 16, no. 4 (2012): 955–68. http://dx.doi.org/10.2298/tsci1204955r.

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22

Burić, Nikola. "Geometry and dynamics of quantum state diffusion." Journal of Physics A: Mathematical and Theoretical 40, no. 22 (May 14, 2007): 5937–48. http://dx.doi.org/10.1088/1751-8113/40/22/012.

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23

Blum, J. J., G. Lawler, M. Reed, and I. Shin. "Effect of cytoskeletal geometry on intracellular diffusion." Biophysical Journal 56, no. 5 (November 1989): 995–1005. http://dx.doi.org/10.1016/s0006-3495(89)82744-4.

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24

Andresen, Per Rønsholt, and Mads Nielsen. "Non-rigid registration by geometry-constrained diffusion☆." Medical Image Analysis 5, no. 2 (June 2001): 81–88. http://dx.doi.org/10.1016/s1361-8415(00)00036-0.

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25

Morrin, Gregory T., Daniel F. Kienle, James S. Weltz, Jeremiah C. Traeger, and Daniel K. Schwartz. "Polyelectrolyte Surface Diffusion in a Nanoslit Geometry." Macromolecules 53, no. 10 (May 13, 2020): 4110–20. http://dx.doi.org/10.1021/acs.macromol.9b02365.

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26

Erdélyi, Z., and G. Schmitz. "Reactive diffusion and stresses in spherical geometry." Acta Materialia 60, no. 4 (February 2012): 1807–17. http://dx.doi.org/10.1016/j.actamat.2011.12.006.

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27

Meakin, Paul. "Multiparticle diffusion-limited aggregation with strip geometry." Physica A: Statistical Mechanics and its Applications 153, no. 1 (November 1988): 1–19. http://dx.doi.org/10.1016/0378-4371(88)90098-2.

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28

Ercolani, N. M., R. Indik, A. C. Newell, and T. Passot. "The Geometry of the Phase Diffusion Equation." Journal of Nonlinear Science 10, no. 2 (February 1, 2000): 223–74. http://dx.doi.org/10.1007/s003329910010.

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29

Nuris, Ahmad Anwar. "Modeling of Ferrous Metal Diffusion in Liquid Lead Using Molecular Dynamics Simulation." Computational And Experimental Research In Materials And Renewable Energy 2, no. 1 (May 2, 2019): 45. http://dx.doi.org/10.19184/cerimre.v2i1.20561.

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Modeling of Iron metal diffusion in liquid lead using molecular dynamics simulation has been done. Molecular dynamics simulations are used to predict the value of physical quantities that we want to know based on the designed material model and on the input simulation data. In this research, effect of different geometry of material models was observed to know the diffusion coefficient. The material system was iron (Fe) in liquid lead (Pb). The material models is designed using Packmol software to get the initial configuration of atom's arrangement by inputting the material's characteristics such as mass, density, volume, number of atoms. This work examines the diffusion coefficient of iron in molten lead metal with the geometric shape of the simulation system in the form of iron in molten metal for various simulation models of boxes in a box, balls in a box and balls in balls. To design simulated geometric shapes we use the Packmol program. To calculate the diffusion coefficient we use the molecular dynamics simulation method. To find out which geometry is suitable, we compare the diffusion coefficient of the simulation results with existing references. The diffusion coefficient value of the spherical iron (Fe) system in the spherical liquid lead (Pb) has the best value compared to the other two forms with an accuracy rate of 99.94% because it is influenced by the even distribution of atoms in each part.

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30

Kusters, Remy, Stefan Paquay, and Cornelis Storm. "Confinement without boundaries: anisotropic diffusion on the surface of a cylinder." Soft Matter 11, no. 6 (2015): 1054–57. http://dx.doi.org/10.1039/c4sm02112f.

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In 2D systems, at sufficiently high surface coverage, diffusive motion is strongly affected by physical confinement. We explore this confinement by geometry on the diffusion of particles confined to the surface of a cylinder. We find that the magnitude and the directionality of lateral diffusion is strongly influenced by its radius and show that this effect is caused by screw-like packings on the surface of the cylinder.

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31

Sakaguchi, Shigeru. "Interaction between fast diffusion and geometry of domain." Kodai Mathematical Journal 37, no. 3 (October 2014): 680–701. http://dx.doi.org/10.2996/kmj/1414674616.

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32

Malik, John, Neil Reed, Chun-Li Wang, and Hau-tieng Wu. "Single-lead f-wave extraction using diffusion geometry." Physiological Measurement 38, no. 7 (June 22, 2017): 1310–34. http://dx.doi.org/10.1088/1361-6579/aa707c.

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33

D’Orazio, Franco, Sankar Bhattacharja, William P. Halperin, and Rosario Gerhardt. "Enhanced self-diffusion of water in restricted geometry." Physical Review Letters 63, no. 1 (July 3, 1989): 43–46. http://dx.doi.org/10.1103/physrevlett.63.43.

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34

Rezvani, S. J., N. Pinto, L. Boarino, F. Celegato, L. Favre, and I. Berbezier. "Diffusion induced effects on geometry of Ge nanowires." Nanoscale 6, no. 13 (2014): 7469–73. http://dx.doi.org/10.1039/c4nr01084a.

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35

Murphy, James M., and Mauro Maggioni. "Spectral–Spatial Diffusion Geometry for Hyperspectral Image Clustering." IEEE Geoscience and Remote Sensing Letters 17, no. 7 (July 2020): 1243–47. http://dx.doi.org/10.1109/lgrs.2019.2943001.

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36

Liu, G., M. Mackowiak, Y. Li, and J. Jonas. "Rotational diffusion of liquid toluene in confined geometry." Journal of Chemical Physics 94, no. 1 (January 1991): 239–42. http://dx.doi.org/10.1063/1.460381.

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37

Hironobu Ikeda, Shinichi Itoh, and Mark A. Adams. "Anomalous diffusion in percolating magnets with fractal geometry." Physica B: Condensed Matter 241-243 (December 1997): 585–87. http://dx.doi.org/10.1016/s0921-4526(97)00651-0.

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38

Savadjiev, Peter, Gordon L. Kindlmann, Sylvain Bouix, Martha E. Shenton, and Carl-Fredrik Westin. "Local white matter geometry from diffusion tensor gradients." NeuroImage 49, no. 4 (February 2010): 3175–86. http://dx.doi.org/10.1016/j.neuroimage.2009.10.073.

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39

Magnanini, Rolando, and Shigeru Sakaguchi. "Interaction between nonlinear diffusion and geometry of domain." Journal of Differential Equations 252, no. 1 (January 2012): 236–57. http://dx.doi.org/10.1016/j.jde.2011.08.017.

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40

Gentile, Francesco S., Ilaria De Santo, Gaetano D’Avino, Lucio Rossi, Giovanni Romeo, Francesco Greco, Paolo A. Netti, and Pier Luca Maffettone. "Hindered Brownian diffusion in a square-shaped geometry." Journal of Colloid and Interface Science 447 (June 2015): 25–32. http://dx.doi.org/10.1016/j.jcis.2015.01.055.

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41

Morrin, Greg. "Biopolyelectrolyte Surface Diffusion Within a Planar Slit Geometry." Biophysical Journal 118, no. 3 (February 2020): 615a. http://dx.doi.org/10.1016/j.bpj.2019.11.3321.

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42

Boltz, Horst-Holger, Alexei Sirbu, Nina Stelzer, Primal de Lanerolle, Stefanie Winkelmann, and Paolo Annibale. "The Impact of Membrane Protein Diffusion on GPCR Signaling." Cells 11, no. 10 (May 17, 2022): 1660. http://dx.doi.org/10.3390/cells11101660.

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Spatiotemporal signal shaping in G protein-coupled receptor (GPCR) signaling is now a well-established and accepted notion to explain how signaling specificity can be achieved by a superfamily sharing only a handful of downstream second messengers. Dozens of Gs-coupled GPCR signals ultimately converge on the production of cAMP, a ubiquitous second messenger. This idea is almost always framed in terms of local concentrations, the differences in which are maintained by means of spatial separation. However, given the dynamic nature of the reaction-diffusion processes at hand, the dynamics, in particular the local diffusional properties of the receptors and their cognate G proteins, are also important. By combining some first principle considerations, simulated data, and experimental data of the receptors diffusing on the membranes of living cells, we offer a short perspective on the modulatory role of local membrane diffusion in regulating GPCR-mediated cell signaling. Our analysis points to a diffusion-limited regime where the effective production rate of activated G protein scales linearly with the receptor–G protein complex’s relative diffusion rate and to an interesting role played by the membrane geometry in modulating the efficiency of coupling.

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43

Bouvier, J. M., and M. Gelus. "Diffusion of Heavy Oil in a Swelling Elastomer." Rubber Chemistry and Technology 59, no. 2 (May 1, 1986): 233–40. http://dx.doi.org/10.5254/1.3538196.

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Abstract The swelling of SBR by an aromatic oil has been experimentally studied at temperatures ranging from ambient to 200°C with thermally stable networks. A model based on Fick's law was developed, and the change of geometry of the elastomer sample was taken into account. The proposed approach is global or macroscopic, and a constant diffusion coefficient has been defined. The diffusion number, ND, defined by two characteristics of a solvent-polymer system, tf the swelling time and tD the diffusion time, represents an important result for engineering applications; it allows prediction of the behavior of amorphous elastomers in contact with a diffusing organic liquid.

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44

Kujawa, Sebastian, Jerzy Weres, and Wiesław Olek. "Computer-aided identification of the water diffusion coefficient for maize kernels dried in a thin layer." International Agrophysics 30, no. 3 (July 1, 2016): 323–29. http://dx.doi.org/10.1515/intag-2015-0099.

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Abstract Uncertainties in mathematical modelling of water transport in cereal grain kernels during drying and storage are mainly due to implementing unreliable values of the water diffusion coefficient and simplifying the geometry of kernels. In the present study an attempt was made to reduce the uncertainties by developing a method for computer-aided identification of the water diffusion coefficient and more accurate 3D geometry modelling for individual kernels using original inverse finite element algorithms. The approach was exemplified by identifying the water diffusion coefficient for maize kernels subjected to drying. On the basis of the developed method, values of the water diffusion coefficient were estimated, 3D geometry of a maize kernel was represented by isoparametric finite elements, and the moisture content inside maize kernels dried in a thin layer was predicted. Validation of the results against experimental data showed significantly lower error values than in the case of results obtained for the water diffusion coefficient values available in the literature.

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45

Carfora, Mauro, and Francesca Familiari. "Ricci curvature and quantum geometry." International Journal of Geometric Methods in Modern Physics 17, no. 04 (March 2020): 2050049. http://dx.doi.org/10.1142/s0219887820500498.

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We describe a few elementary aspects of the circle of ideas associated with a quantum field theory (QFT) approach to Riemannian Geometry, a theme related to how Riemannian structures are generated out of the spectrum of (random or quantum) fluctuations around a background fiducial geometry. In such a scenario, Ricci curvature with its subtle connections to diffusion, optimal transport, Wasserestein geometry and renormalization group, features prominently.

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46

Kapoor, Rajat, and S. T. Oyama. "Measurement of solid state diffusion coefficients by a temperature-programmed method." Journal of Materials Research 12, no. 2 (February 1997): 467–73. http://dx.doi.org/10.1557/jmr.1997.0068.

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This paper presents a method for determining diffusivities in solids where the diffusing species desorbs or reacts at the external surfaces, and where the diffusivity does not vary appreciably with concentration. The method involves measuring the flux of the diffusive species out of the solid under the influence of a temperature program. A general model is developed, based on nonisothermal Fickian diffusion, which is applicable to solid particles with slab or spherical geometry. The solution is presented both as an analytical expression and as correlation charts of experimentally observable quantities. These charts are contour diagrams of the temperatures of peak diffusion rate with ln(E/R) and ln(D0/h2) as the axes, where E and D0 are the activation energy and pre-exponential terms of the diffusivity expression D = D0 exp(−E/RT), where R is the gas constant, and h the size of the particles. This paper deals exclusively with the case of oxygen diffusion in the vanadium oxide system. In this case, vanadium oxide was reduced in a reactive ammonia stream at conditions in which the surface reaction was fast compared to the diffusive transport process. Using this method the diffusion parameters were found to be D0 = 1.9 × 10−5 cm2 s−1 and E = 101 kJ/mol. The method was checked by varying the crystallite size of the vanadium oxide sample in the range 2h = 0.14−0.29 μm.

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47

Koibuchi, Hiroshi, Masahiko Okumura, and Shuta Noro. "Finsler geometry modeling for anisotropic diffusion in Turing patterns." Journal of Physics: Conference Series 1730, no. 1 (January 1, 2021): 012035. http://dx.doi.org/10.1088/1742-6596/1730/1/012035.

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48

Mendonça, J. T. "Diffusion of magnetic field lines in a toroidal geometry." Physics of Fluids B: Plasma Physics 3, no. 1 (January 1991): 87–94. http://dx.doi.org/10.1063/1.859958.

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49

Djamah, T., S. Djennoune, and M. Bettayeb. "Diffusion processes identification in cylindrical geometry using fractional models." Physica Scripta T136 (October 2009): 014013. http://dx.doi.org/10.1088/0031-8949/2009/t136/014013.

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50

Der-Shan Luo, M. A. King, and S. Glick. "Local geometry variable conductance diffusion for post-reconstruction filtering." IEEE Transactions on Nuclear Science 41, no. 6 (December 1994): 2800–2806. http://dx.doi.org/10.1109/23.340650.

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