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Relevant bibliographies by topics / Diffusion Model

Academic literature on the topic 'Diffusion Model'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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Journal articles on the topic "Diffusion Model"

1

Jaichuang, Atit, and Wirawan Chinviriyasit. "Numerical Modelling of Influenza Model with Diffusion." International Journal of Applied Physics and Mathematics 4, no. 1 (2014): 15–21. http://dx.doi.org/10.7763/ijapm.2014.v4.247.

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Lin, C. C., C. L. Tsai, P. K. Wu, and H. J. Lee. "Advancing Diffusion Model for Diffusion in a Cube of Medium." Journal of Mechanics 28, no. 2 (May 8, 2012): 345–54. http://dx.doi.org/10.1017/jmech.2012.38.

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AbstractA solution based on an advancing model for the content of diffusion material in a cube of medium is derived. The cube is assumed to be surrounded by diffusion material, and the diffusion material penetrates through all six surfaces and diffuses toward the center of the cube. The model accounts for the interaction between the diffusions in the three principle coordinates of the Cartesian coordinate system. For the first time, an exact solution of the content of the diffusion material based on the advancing model is derived in a clean form for a three-dimensional case.

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Doremus, R. H. "Diffusion of water in crystalline and glassy oxides: Diffusion–reaction model." Journal of Materials Research 14, no. 9 (September 1999): 3754–58. http://dx.doi.org/10.1557/jmr.1999.0508.

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Diffusion of water in oxides is modeled as resulting from the solution and diffusion of molecular water in the oxide. This dissolved water can react and exchange with the oxide network to form immobile OH groups and different hydrogen and oxygen isotopes in the oxide. The model agrees with many experiments on water diffusion in oxides. The activation energy for diffusion of water in oxides correlates with the structural openness of the oxide, suggesting that molecular water is the diffusing species.

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Bengtsson, Lisa, Sander Tijm, Filip Váňa, and Gunilla Svensson. "Impact of Flow-Dependent Horizontal Diffusion on Resolved Convection in AROME." Journal of Applied Meteorology and Climatology 51, no. 1 (January 2012): 54–67. http://dx.doi.org/10.1175/jamc-d-11-032.1.

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AbstractHorizontal diffusion in numerical weather prediction models is, in general, applied to reduce numerical noise at the smallest atmospheric scales. In convection-permitting models, with horizontal grid spacing on the order of 1–3 km, horizontal diffusion can improve the model skill of physical parameters such as convective precipitation. For instance, studies using the convection-permitting Applications of Research to Operations at Mesoscale model (AROME) have shown an improvement in forecasts of large precipitation amounts when horizontal diffusion is applied to falling hydrometeors. The nonphysical nature of such a procedure is undesirable, however. Within the current AROME, horizontal diffusion is imposed using linear spectral horizontal diffusion on dynamical model fields. This spectral diffusion is complemented by nonlinear, flow-dependent, horizontal diffusion applied on turbulent kinetic energy, cloud water, cloud ice, rain, snow, and graupel. In this study, nonlinear flow-dependent diffusion is applied to the dynamical model fields rather than diffusing the already predicted falling hydrometeors. In particular, the characteristics of deep convection are investigated. Results indicate that, for the same amount of diffusive damping, the maximum convective updrafts remain strong for both the current and proposed methods of horizontal diffusion. Diffusing the falling hydrometeors is necessary to see a reduction in rain intensity, but a more physically justified solution can be obtained by increasing the amount of damping on the smallest atmospheric scales using the nonlinear, flow-dependent, diffusion scheme. In doing so, a reduction in vertical velocity was found, resulting in a reduction in maximum rain intensity.

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Dhar, Joydip, Mani Tyagi, and Poonam Sinha. "Three simultaneous innovations interrelationships: An adopter dynamics model." International Journal of Modeling, Simulation, and Scientific Computing 06, no. 03 (September 2015): 1550031. http://dx.doi.org/10.1142/s1793962315500312.

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In this paper, we develop a conceptual framework of a innovation diffusion dynamics model in which multiple parallel innovations are effecting each other during the diffusion process. A mathematical model is proposed to explore the interaction and diffusion of three innovations simultaneously available in market. The stability analysis is carried out for various types of diffusions on such system both analytically and numerically. It is observed that the association between innovations in product market could be complementary, substitute, independent or competitive. The co-existence and extinction of innovation depends on the level of diffusion between the innovations and it may or may not be sensitive to initial distribution of innovations.

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Wolschin, G. "Relativistic diffusion model." European Physical Journal A 5, no. 1 (May 1999): 85–90. http://dx.doi.org/10.1007/s100500050260.

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Zheng, Yingchun, and Yunfeng Yang. "Wealth optimization models on jump-diffusion model." Journal of Interdisciplinary Mathematics 21, no. 1 (January 2, 2018): 201–12. http://dx.doi.org/10.1080/09720502.2017.1406629.

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Nowok, J. W. "A model of diffusion/viscous mass transport in silicates during liquid-phase sintering." Journal of Materials Research 10, no. 2 (February 1995): 401–4. http://dx.doi.org/10.1557/jmr.1995.0401.

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The model of capillary transport of liquid metals driven by shear stress resulting from the displacement of menisci [J.W. Nowok, Scripta Metal]. Mater. 29, 931 (1993); Acta Metall. Mater. 42, 4025 (1994)] is applicable to liquid-phase sintering of silicate/aluminosilicate glasses. The movement of a liquid phase between adjacent particles is compared with that in capillaries. It appears that the transport property of intergranular melt may be expressed by the viscosity (η) and volume diffusion (D) parameters if mean displacement of menisci is compared with the mean diffusive jump lengths of atoms/molecules (L). This leads to the following relation: (γ/η)Lα = Dcap, where α and Dcap are a specific permeability and volume diffusion coefficient. The use of this model requires the assumption that the diffusing species are also the viscous flow units, and they can be either atoms or structural units. This assumption seems to be applicable for depolymerized silicate melts if the dominant mass transport is initiated by the diffusion of both nonbridging oxygen and silicon atoms.

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Xie, Wenhao, Gongqian Liang, Wei Wang, and Yanhong She. "A spatial SIS model with Holling II incidence rate." International Journal of Biomathematics 12, no. 08 (November 2019): 1950092. http://dx.doi.org/10.1142/s179352451950092x.

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A diffusive SIS epidemic model with Holling II incidence rate is studied in this paper. We introduce the basic reproduction number [Formula: see text] first. Then the existence of endemic equilibrium (EE) can be determined by the sizes of [Formula: see text] as well as the diffusion rates of susceptible and infected individuals. We also investigate the effect of diffusion rates on asymptotic profile of EE. Our results conclude that the infected population will die out if the diffusion rate of susceptible individuals is small and the total population [Formula: see text] is below a certain level; while the two populations persist eventually if at least one of the diffusion rates of the susceptible and infected individuals is large.

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TIAN, CHU-SHUN, SAI-KIT CHEUNG, and ZHAO-QING ZHANG. "CAN DIFFUSION MODEL LOCALIZATION IN OPEN MEDIA?" International Journal of Modern Physics: Conference Series 11 (January 2012): 96–101. http://dx.doi.org/10.1142/s201019451200596x.

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We employed a first-principles theory – the supersymmetric field theory – formulated for wave transport in very general open media to study static transport of waves in quasi-one-dimensional localized samples. We predicted analytically and confirmed numerically that in these systems, localized waves display an unconventional diffusive phenomenon. Different from the prevailing self-consistent local diffusion model, our theory is capable of capturing all disorder-induced resonant transmissions, which give rise to significant enhancement of local diffusion inside a localized sample. Our theory should be able to be generalized to two- and three-dimensional open media, and open a new direction in the study of Anderson localization in open media.

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Dissertations / Theses on the topic "Diffusion Model"

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Haag, Berthold R. "Model choice in structured nonparametric regression and diffusion models." [S.l. : s.n.], 2006. http://madoc.bib.uni-mannheim.de/madoc/volltexte/2006/1311.

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Luzardo, A. "The Rescorla-Wagner Drift-Diffusion model." Thesis, City, University of London, 2018. http://openaccess.city.ac.uk/19210/.

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Computational models of classical conditioning have made significant contributions to the theoretic understanding of associative learning, yet they still struggle when the temporal aspects of conditioning are taken into account. Interval timing models have contributed a rich variety of time representations and provided accurate predictions for the timing of responses, but they usually have little to say about associative learning. In this thesis we present a unified model of conditioning and timing that is based on the influential Rescorla-Wagner conditioning model and the more recently developed Timing Drift-Diffusion model. We test the model by simulating 11 experimental phenomena and show that it can provide an adequate account for 9, and a partial account for the other 2. We argue that the model can account for more phenomena in the chosen set than these other similar in scope models: CSCTD, MS-TD, Learning to Time and Modular Theory. A comparison and analysis of the mechanisms in these models is provided, with a focus on the types of time representation and associative learning rule used.

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Hrabe, Jan, Fanrong Xiao, Robert Colbourn, and Sabina Hrabetova. "A model of anomalous extracellular diffusion." Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-198254.

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Chen, Lu. "A Diffusion Model for Compositional Data." Kent State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=kent1478642808748389.

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Danda, Swetha. "Generalized diffusion model for image denoising." Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5481.

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Thesis (M.S.)--West Virginia University, 2007.
Title from document title page. Document formatted into pages; contains viii, 62 p. : ill. Includes abstract. Includes bibliographical references (p. 59-62).

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Beltran-Villegas, Daniel J., and Michael A. Bevan. "Smoluchowski model of colloidal crystallization dynamics." Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-184980.

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Beck, Christopher A. "Diffusion-collision model calculations of protein folding /." Thesis, Connect to Dissertations & Theses @ Tufts University, 2001.

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Thesis (Ph.D.)--Tufts University, 2001.
Adviser: David L. Weaver. Submitted to the Dept. of Physics. Includes bibliographical references (leaves 148-149). Access restricted to members of the Tufts University community. Also available via the World Wide Web;

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Samprovalaki, Konstantina. "Online visualisation of diffusion in model foods." Thesis, University of Birmingham, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.421736.

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Rudakova, Maya, and Andrey Filippov. "Diffusivity of water in a biological model membrane." Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-197030.

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Mukherjee, Sayak. "Applications of Field Theory to Reaction Diffusion Models and Driven Diffusive Systems." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/39293.

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In this thesis, we focus on the steady state properties of two systems which are genuinely out of equilibrium. The first project is an application of dynamic field theory to a specific non equilibrium critical phenomenon, while the second project involves both simulations and analytical calculations. The methods of field theory are used on both these projects. In the first part of this thesis, we investigate a generalization of the well-known field theory for directed percolation (DP). The DP theory is known to describe an evolving population, near extinction. We have coupled this evolving population to an environment with its own nontrivial spatio-temporal dynamics. Here, we consider the special case where the environment follows a simple relaxational (model A) dynamics. We find two marginal couplings with upper critical dimension of four, which couple the two theories in a nontrivial way. While the Wilson-Fisher fixed point remains completely unaffected, a mismatch of time scales destabilizes the usual DP fixed point. Some open questions and future work remain. In the second project, we focus on a simple particle transport model far from equilibrium, namely, the totally asymmetric simple exclusion process (TASEP). While its stationary properties are well studied, many of its dynamic features remain unexplored. Here, we focus on the power spectrum of the total particle occupancy in the system. This quantity exhibits unexpected oscillations in the low density phase. Using standard Monte Carlo simulations and analytic calculations, we probe the dependence of these oscillations on boundary effects, the system size, and the overall particle density. Our simulations are fitted to the predictions of a linearized theory for the fluctuation of the particle density. Two of the fit parameters, namely the diffusion constant and the noise strength, deviate from their naive bare values [6]. In particular, the former increases significantly with the system size. Since this behavior can only be caused by nonlinear effects, we calculate the lowest order corrections in perturbation theory. Several open questions and future work are discussed.
Ph. D.

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Books on the topic "Diffusion Model"

1

Rose, Richard. Global diffusion model of e-governance. Glasgow: University of Strathclyde, 2005.

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author, Durrett Richard 1951, Perkins, Edwin Arend, 1953- author, and Société mathématique de France, eds. Voter model perturbations and reaction diffusion equations. Paris: Societé mathématique de France, 2013.

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Loch, C. H. A punctuated-equilibrium model of technology diffusion. Fontainebleau: INSEAD, 1997.

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Loch, C. H. A punctuated-equilibrium model of technology diffusion. Fontainebleau: INSEAD, 1997.

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Loch, C. A punctuated-equilibrium model of technology diffusion. France: INSEAD, 1997.

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Technology & Development Program (U.S.), ed. VALDRIFT 1.0: A valley atmospheric dispersion model with deposition. Missoula, Mont: U.S. Dept. of Agriculture, Forest Service, Technology & Development Program, 1995.

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Technology & Development Program (U.S.), ed. VALDRIFT 1.0: A valley atmospheric dispersion model with deposition. Missoula, Mont: U.S. Dept. of Agriculture, Forest Service, Technology & Development Program, 1995.

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Roche, William K. The diffusion of the commitment model in Ireland. Blackrock (Co. Dublin, Ireland): UCD, Centre for Employment Relations and Organisational Performance, 1996.

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Toro, E. F. Viscous limiter functions for model convection-diffusion equations. Cranfield, Bedford, England: Cranfield Institute of Technology, College of Aeronautics, 1993.

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L, Physick W., and Commonwealth Scientific and Industrial Research Organization (Australia), eds. LADM: A Lagrangian Atmospheric Dispersion Model. [Melbourne]: CSIRO Australia, 1994.

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Book chapters on the topic "Diffusion Model"

1

Joyce, Philip. "Diffusion Lattice Model." In Practical Numerical C Programming, 185–96. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6128-6_12.

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Wang, Shiu-Huei. "Jump diffusion model." In Encyclopedia of Finance, 676–88. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/978-0-387-26336-6_69.

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Islam, Ahmad Ehteshamul, Nilesh Goel, Souvik Mahapatra, and Muhammad Ashraful Alam. "Reaction-Diffusion Model." In Fundamentals of Bias Temperature Instability in MOS Transistors, 181–207. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2508-9_5.

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Wang, Shin-Huei. "Jump Diffusion Model." In Encyclopedia of Finance, 1073–91. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91231-4_44.

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Wang, Shin-Huei. "Jump Diffusion Model." In Encyclopedia of Finance, 525–34. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-5360-4_44.

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Greene, Clint, Kate Revill, Cathrin Buetefisch, Ken Rose, and Scott Grafton. "Optimal Fiber Diffusion Model Restoration." In Computational Diffusion MRI, 35–47. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52893-5_4.

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Suciu, Nicolae. "Model, Scale, and Measurement." In Diffusion in Random Fields, 193–204. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15081-5_7.

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Joardder, Mohammad U. H., Washim Akram, and Azharul Karim. "Single-Phase Diffusion Model." In Heat and Mass Transfer Modelling During Drying, 105–19. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9780429461040-6.

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Nath, Vishwesh, Karthik Ramadass, Kurt G. Schilling, Colin B. Hansen, Rutger Fick, Sudhir K. Pathak, Adam W. Anderson, and Bennett A. Landman. "DW-MRI Microstructure Model of Models Captured Via Single-Shell Bottleneck Deep Learning." In Computational Diffusion MRI, 147–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73018-5_12.

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McLean, J. G., B. Krishnamachari, E. Chason, D. R. Peale, J. P. Sethna, and B. H. Cooper. "A Model and Simulation of the Decay of Isolated Nanoscale Surface Features." In Surface Diffusion, 377–88. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4899-0262-7_33.

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Conference papers on the topic "Diffusion Model"

1

Wang, Wenjie, Yiyan Xu, Fuli Feng, Xinyu Lin, Xiangnan He, and Tat-Seng Chua. "Diffusion Recommender Model." In SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3539618.3591663.

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Timothy J. Rennie, Jianming Dai, and Vijaya G. S. Raghavan. "Advection-Diffusion Model for Diffusion Channel Storage." In 2004, Ottawa, Canada August 1 - 4, 2004. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2004. http://dx.doi.org/10.13031/2013.16972.

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Deng, Zijun, Xiangteng He, Yuxin Peng, Xiongwei Zhu, and Lele Cheng. "MV-Diffusion: Motion-aware Video Diffusion Model." In MM '23: The 31st ACM International Conference on Multimedia. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3581783.3612405.

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Yin, Yuyang, Dejia Xu, Chuangchuang Tan, Ping Liu, Yao Zhao, and Yunchao Wei. "CLE Diffusion: Controllable Light Enhancement Diffusion Model." In MM '23: The 31st ACM International Conference on Multimedia. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3581783.3612145.

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Przymus, Marcin, and Piotr Szymański. "Map Diffusion - Text Promptable Map Generation Diffusion Model." In UrbanAI '23: 1st ACM SIGSPATIAL International Workshop on Advances in Urban-AI. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3615900.3628787.

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Yu, Zichuan. "Diffusion limited aggregation model." In International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), edited by Ke Chen, Nan Lin, Romeo Meštrović, Teresa A. Oliveira, Fengjie Cen, and Hong-Ming Yin. SPIE, 2022. http://dx.doi.org/10.1117/12.2628098.

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Videau, Mathurin, Nickolai Knizev, Alessandro Leite, Marc Schoenauer, and Olivier Teytaud. "Interactive Latent Diffusion Model." In GECCO '23: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3583131.3590471.

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Du, Yihong. "CHANGE OF ENVIRONMENT IN MODEL ECOSYSTEMS: EFFECT OF A PROTECTION ZONE IN DIFFUSIVE POPULATION MODELS." In The International Conference on Reaction-Diffusion System and Viscosity Solutions. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789812834744_0003.

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Riley, Jason D., Simon R. Arridge, Yiorgos Chrysanthou, Hamid Dehghani, Elizabeth M. C. Hillman, and Martin Schweiger. "Radiosity diffusion model in 3D." In European Conference on Biomedical Optics, edited by Stefan Andersson-Engels and Michael F. Kaschke. SPIE, 2001. http://dx.doi.org/10.1117/12.447415.

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Granik, Yuri. "PEB model with cross-diffusion." In Microlithography 2003, edited by Theodore H. Fedynyshyn. SPIE, 2003. http://dx.doi.org/10.1117/12.488801.

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Reports on the topic "Diffusion Model"

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Trowbridge, L. Isotopic selectivity of surface diffusion: An activated diffusion model. Office of Scientific and Technical Information (OSTI), November 1989. http://dx.doi.org/10.2172/5462238.

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Luo, Jingshu. The diffusion of NAIC model laws. Center for Insurance Policy and Research, June 2022. http://dx.doi.org/10.52227/25404.2022.

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In the state-based insurance regulatory system, the National Association of Insurance Commissioners (NAIC) develops model laws that create a framework for uniform standards. This uniformity among states is beneficial to insurers, especially those operating in multiple states. Since the first NAIC meeting in 1871, the NAIC has issued over 200 model laws. Though NAIC model laws have existed for decades, little attention has been paid to understanding how they spread from the NAIC to states and from states to states. This spread is a policy diffusion process, which refers to the external factors that impact states’ policy adoption. In this project, we study the general pattern in the diffusion process and key factors that affect states’ decisions to implement NAIC model laws. We aim to answer the following questions: Do the NAIC or early-adopting states exert greater influence on leading states to adopt model laws? Are there common factors that may explain state legislatures’ adoption of model laws?

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Weitsman, Y. A Continuum Diffusion Model for Viscoelastic Materials. Fort Belvoir, VA: Defense Technical Information Center, November 1988. http://dx.doi.org/10.21236/ada202588.

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Guin, J. A., and A. R. Tarrer. Configurational diffusion of asphaltenes in fresh and aged catalysts extrudates. [Mathematical configurational diffusion model]. Office of Scientific and Technical Information (OSTI), January 1992. http://dx.doi.org/10.2172/7030144.

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Keith, Michael P. Interleukin-2 Signal Transduction: A Diffusion-Kinetics Model. Fort Belvoir, VA: Defense Technical Information Center, May 1993. http://dx.doi.org/10.21236/ada270802.

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Tu, Weichao, Gregory S. Cunningham, Yue Chen, Michael G. Henderson, Steven K. Morley, Geoffrey D. Reeves, Bernard J. Blake, Daniel N. Baker, and Harlan Spence. Modeling Radiation Belt Electron Dynamics with the DREAM3D Diffusion Model. Office of Scientific and Technical Information (OSTI), February 2014. http://dx.doi.org/10.2172/1120721.

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Kimpland, Robert H., and Steven K. Klein. Neutron Diffusion Model for Prompt Burst Simulation in Fissile Solutions. Office of Scientific and Technical Information (OSTI), August 2013. http://dx.doi.org/10.2172/1091865.

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Simunovic, Srdjan, Stewart L. Voit, and Theodore M. Besmann. Oxygen Diffusion Model using the THERMOCHIMICA Module in Moose/Bison. Office of Scientific and Technical Information (OSTI), August 2014. http://dx.doi.org/10.2172/1157131.

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Ludwig, F. L. A Model of Airflow and Diffusion in Complex Terrain (MADICT). Fort Belvoir, VA: Defense Technical Information Center, November 1985. http://dx.doi.org/10.21236/ada163819.

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Kang, Sang-Wook. Species-diffusion phenomena in glass: A one-dimensional, transient model. Office of Scientific and Technical Information (OSTI), September 1988. http://dx.doi.org/10.2172/6815366.

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