Relevant bibliographies by topics / Stochastic processes Mathemetical models

Academic literature on the topic 'Stochastic processes Mathemetical models'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Stochastic processes Mathemetical models"

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Veretennikov, Alexander. "Stochastic Processes and Models." Bulletin of the London Mathematical Society 39, no. 1 (January 16, 2007): 167–69. http://dx.doi.org/10.1112/blms/bdl020.

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Fricks, John. "Stochastic Processes and Models." Journal of the American Statistical Association 102, no. 477 (March 2007): 381. http://dx.doi.org/10.1198/jasa.2007.s166.

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Duerr, Hans-Peter, and Klaus Dietz. "Stochastic models for aggregation processes." Mathematical Biosciences 165, no. 2 (June 2000): 135–45. http://dx.doi.org/10.1016/s0025-5564(00)00014-6.

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Anh, V. V., C. C. Heyde, and Q. Tieng. "Stochastic models for fractal processes." Journal of Statistical Planning and Inference 80, no. 1-2 (August 1999): 123–35. http://dx.doi.org/10.1016/s0378-3758(98)00246-8.

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Rota, Gian-Carlo. "Stochastic models for social processes." Advances in Mathematics 57, no. 1 (July 1985): 91. http://dx.doi.org/10.1016/0001-8708(85)90110-0.

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Belopolskaya, Ya I. "Stochastic Models of Chemotaxis Processes." Journal of Mathematical Sciences 251, no. 1 (October 12, 2020): 1–14. http://dx.doi.org/10.1007/s10958-020-05059-7.

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Ebeling, W. "Stochastic models of innovation processes." Annual Review in Automatic Programming 12 (January 1985): 54–57. http://dx.doi.org/10.1016/0066-4138(85)90328-3.

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Gottinger, Hans W. "Stochastic models of oil spill processes." International Journal of Environment and Pollution 15, no. 3 (2001): 266. http://dx.doi.org/10.1504/ijep.2001.005185.

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Dijkerman, R. W., and R. R. Mazumdar. "Wavelet representations of stochastic processes and multiresolution stochastic models." IEEE Transactions on Signal Processing 42, no. 7 (July 1994): 1640–52. http://dx.doi.org/10.1109/78.298272.

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Stoyanov, Jordan. "Stochastic Processes and Models by D. Stirzaker." Journal of the Royal Statistical Society: Series A (Statistics in Society) 169, no. 4 (October 2006): 1013–14. http://dx.doi.org/10.1111/j.1467-985x.2006.00446_16.x.

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Dissertations / Theses on the topic "Stochastic processes Mathemetical models"

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Le, Truc. "Stochastic volatility models." Monash University, School of Mathematical Sciences, 2005. http://arrow.monash.edu.au/hdl/1959.1/5181.

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Cole, D. J. "Stochastic branching processes in biology." Thesis, University of Kent, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270684.

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Rumsey, Deborah Jean. "Nonresponse models for social network stochastic processes /." The Ohio State University, 1993. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487846885778158.

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Rumsey, Deborah J. "Nonresponse models for social network stochastic processes /." Connect to resource, 1993. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1261508861.

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Chung, Kun-Jen. "Some topics in risk-sensitive stochastic dynamic models." Diss., Georgia Institute of Technology, 1985. http://hdl.handle.net/1853/28644.

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Gagliardini, Lucia. "Chargaff symmetric stochastic processes." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8699/.

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Scopo della modellizzazione delle stringhe di DNA è la formulazione di modelli matematici che generano sequenze di basi azotate compatibili con il genoma esistente. In questa tesi si prendono in esame quei modelli matematici che conservano un'importante proprietà, scoperta nel 1952 dal biochimico Erwin Chargaff, chiamata oggi "seconda regola di Chargaff". I modelli matematici che tengono conto delle simmetrie di Chargaff si dividono principalmente in due filoni: uno la ritiene un risultato dell'evoluzione sul genoma, mentre l'altro la ipotizza peculiare di un genoma primitivo e non intaccata dalle modifiche apportate dall'evoluzione. Questa tesi si propone di analizzare un modello del secondo tipo. In particolare ci siamo ispirati al modello definito da da Sobottka e Hart. Dopo un'analisi critica e lo studio del lavoro degli autori, abbiamo esteso il modello ad un più ampio insieme di casi. Abbiamo utilizzato processi stocastici come Bernoulli-scheme e catene di Markov per costruire una possibile generalizzazione della struttura proposta nell'articolo, analizzando le condizioni che implicano la validità della regola di Chargaff. I modelli esaminati sono costituiti da semplici processi stazionari o concatenazioni di processi stazionari. Nel primo capitolo vengono introdotte alcune nozioni di biologia. Nel secondo si fa una descrizione critica e prospettica del modello proposto da Sobottka e Hart, introducendo le definizioni formali per il caso generale presentato nel terzo capitolo, dove si sviluppa l'apparato teorico del modello generale.
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Li, Jianmin 1964. "Applications of Markovian Arrival Processes in Stochastic Models." Diss., The University of Arizona, 1996. http://hdl.handle.net/10150/565546.

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Andersson, Håkan. "Limit theorems for some stochastic epidemic models." Stockholm : Stockholm University, 1994. http://catalog.hathitrust.org/api/volumes/oclc/40258819.html.

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Wong, Ka-kuen, and 黃嘉權. "Stochastic models for customer relationship management." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30289968.

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Bataineh, Mohammad Saleh, University of Western Sydney, of Science Technology and Environment College, and of Science Food and Horticulture School. "Stochastic systems : models and polices [sic]." THESIS_CSTE_SFH_Bataineh_M.xml, 2001. http://handle.uws.edu.au:8081/1959.7/622.

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In a multi-server system, probability distributions and loss probabilities for customers arriving with different priority categories are studied. Customers arrive in independent Poisson streams and their service times are exponentially distributed, with different rates for different priorities. The non-queuing customers will be lost if the capacity is fully occupied. In these systems, particularly for higher priority customers, the reduction of the loss probabilities is essential to guarantee the quality of the service. Four different policies for high and low priorities were introduced utilizing the fixed capacity of the system, producing different loss probabilities. The same policies were introduced in the case of a low priority being placed in the queue when the system is fully occupied. An application to the Intensive Care and Coronary Care Unit in Campbelltown Public Hospital in Sydney was introduced. This application analyses the admission and discharge by using queuing theory to develop a model which predicts the proportion of patients from each category that would be prematurely transferred as a function of the size of the unit, number of categories, mean arrival rates, and length of stay.
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Books on the topic "Stochastic processes Mathemetical models"

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P, Heyman Daniel, and Sobel Matthew J, eds. Stochastic models. Amsterdam: North-Holland, 1990.

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P, Franken, and Lisek Bernd 1954-, eds. Stationary stochastic models. Chichester [England]: Wiley, 1990.

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Koroliuk, Vladimir S. Stochastic models of systems. Dordrecht: Kluwer Academic Publishers, 1999.

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Iosifescu, Marius. Introduction to stochastic models. London: ISTE, 2010.

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Introduction to stochastic models. 2nd ed. Mineola, NY: Dover Publications, 2006.

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Introduction to stochastic models. Menlo Park, Calif: Benjamin/Cummings Pub. Co., 1988.

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Alla, Sikorskii, ed. Stochastic models for fractional calculus. Berlin: De Gruyter, 2012.

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Operations Research Society of America. Communications in statistics: Stochastic models. New York, N.Y: Marcel Dekker, 1985.

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1931-, Jensen Uwe, ed. Stochastic models in reliability. New York: Springer, 1999.

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Bosq, Denis. A course in stochastic processes: Stochastic models and statistical inference. Dordrecht: Kluwer Academic Publishers, 1996.

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Book chapters on the topic "Stochastic processes Mathemetical models"

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Koller, Michael. "Stochastic Processes." In Stochastic Models in Life Insurance, 7–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28439-7_2.

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Nagylaki, Thomas. "Neutral models of geographical variation." In Stochastic Spatial Processes, 216–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/bfb0076251.

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Verma, Ajit Kumar, Srividya Ajit, and Manoj Kumar. "Stochastic Processes and Models." In Springer Series in Reliability Engineering, 37–58. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-318-3_3.

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Chalamandaris, George, and A. G. Malliaris. "Stochastic Processes and Models." In Financial Derivatives, 455–75. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118266403.ch31.

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Zhao, L. C., C. Radhakrishna Rao, and X. R. Chen. "A Note on the Consistency of M-Estimates in Linear Models." In Stochastic Processes, 359–67. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4615-7909-0_41.

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Korolyuk, Vladimir S., and Vladimir V. Korolyuk. "Markov renewal processes." In Stochastic Models of Systems, 21–41. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4625-8_2.

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Devolder, Pierre, Jacques Janssen, and Raimondo Manca. "Homogeneous and Non-Homogeneous Renewal Models." In Basic Stochastic Processes, 47–76. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119184584.ch2.

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Hassler, Uwe. "Interest Rate Models." In Stochastic Processes and Calculus, 285–302. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23428-1_13.

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Doukhan, Paul. "Linear Processes." In Stochastic Models for Time Series, 101–14. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76938-7_6.

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Doukhan, Paul. "Associated Processes." In Stochastic Models for Time Series, 167–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76938-7_8.

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Conference papers on the topic "Stochastic processes Mathemetical models"

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Baake, Ellen, and Robert Bialowons. "Ancestral processes with selection: Branching and Moran models." In Stochastic Models in Biological Sciences. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-2.

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Bulsara, A. R. "COOPERATIVE STOCHASTIC PROCESSES IN “PHYSIOLOGICAL” NEURON MODELS." In 101st WE-Heraeus-Seminar. WORLD SCIENTIFIC, 1993. http://dx.doi.org/10.1142/9789814503648_0002.

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Pavlov, Igor. "Some Processes and Models on Deformed Stochastic Bases." In 2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO). IEEE, 2016. http://dx.doi.org/10.1109/smrlo.2016.75.

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Malafeyev, Oleg A., Sergey A. Nemnyugin, and Georgy A. Ivaniukovich. "Stochastic models of social-economic dynamics." In 2015 International Conference "Stability and Control Processes" in Memory of V.I. Zubov (SCP). IEEE, 2015. http://dx.doi.org/10.1109/scp.2015.7342178.

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Claussen, Jens Christian. "Discrete stochastic processes, replicator and Fokker-Planck equations of coevolutionary dynamics in finite and infinite populations." In Stochastic Models in Biological Sciences. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-1.

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Ahuja, Sunil, and Slaven Peles. "Building energy models: Quantifying uncertainties due to stochastic processes." In 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6760644.

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Nie, Junhong, Hanlin Tang, and Jingxuan Wei. "Analysis on Convergence of Stochastic Processes in Cloud Computing Models." In 2018 14th International Conference on Computational Intelligence and Security (CIS). IEEE, 2018. http://dx.doi.org/10.1109/cis2018.2018.00024.

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Allison, Andrew G., and Derek Abbott. "Discrete games of chance as models for continuous stochastic transport processes." In SPIE's First International Symposium on Fluctuations and Noise, edited by Lutz Schimansky-Geier, Derek Abbott, Alexander Neiman, and Christian Van den Broeck. SPIE, 2003. http://dx.doi.org/10.1117/12.501497.

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Vasileva, Mariia, Fedor Shmarov, and Paolo Zuliani. "Probabilistic Reachability for Uncertain Stochastic Hybrid Systems via Gaussian Processes." In 2020 18th ACM-IEEE International Conference on Formal Methods and Models for System Design (MEMOCODE). IEEE, 2020. http://dx.doi.org/10.1109/memocode51338.2020.9315182.

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Steck, Roland, and Melissa L. Knothe Tate. "Application of Stochastic Network Models for the Study of Molecular Transport Processes in Bone." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59746.

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Osteocytes are the most abundant cells in bone. They are entombed in lacunae within the bone matrix, but are interconnected via their processes that run within the canaliculi with other osteocytes, as well as with the osteoblasts and bone lining cells on the bone surfaces, and thus from a cellular syncytium. However, the osteocytes are not immediately connected with the vasculature of bone, which means that the transport of nutrients and hormones to the cells and the removal of waste products from the cells, as well as transport of signaling molecules between the cells, has to occur either via the pericellular fluid spaces in the lacunocanalicular network, via the matrix micropores between the collagen fibers and the apatite crystals, or via intracellular transport mechanisms. Only recently our laboratory and other research groups have started to examine the transport pathways of different molecular size substances within bone systematically, using experimental tracer methods (e.g. [1, 7]). These experiments have unveiled the molecular sieving characteristics of bone: While small tracers with molecular weights of 300 Daltons (Da, e.g. glucose and small amino acids) are found in abundance throughout the bone matrix and the lacunocanalicular network, larger molecules (e.g. cytokines and serum derived proteins) are only transported through the pericellular spaces of the lacunocanalicular network. Furthermore, the transport of these substances through the lacunocanalicular network can be enhanced by mechanical loading of bone [1]. These findings highlight the importance of the lacunocanalicular network for the survival of the osteocytes and thereby tissue health. However, the state of the osteocyte syncytium is affected by age and bone diseases. It has been shown that the number of osteocytes in cortical bone decreases with age [6]. Furthermore, a histological study of cortical bone tissue samples from donors undergoing hip replacement surgery has shown that the morphology of the lacunocanalicular network is altered in diseased bone [2].
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Reports on the topic "Stochastic processes Mathemetical models"

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Ng, B. Survey of Bayesian Models for Modelling of Stochastic Temporal Processes. Office of Scientific and Technical Information (OSTI), October 2006. http://dx.doi.org/10.2172/900168.

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Yanev, Nikolay M., Vessela Stoimenova, and Dimitar V. Atanasov. Branching Stochastic Processes with Immigration as Models of Covid-19 Pandemic Development. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, November 2020. http://dx.doi.org/10.7546/crabs.2020.11.02.

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Myrskylä, Mikko, and Joshua R. Goldstein. Probabilistic forecasting using stochastic diffusion models, with applications to cohort processes of marriage and fertility. Rostock: Max Planck Institute for Demographic Research, February 2010. http://dx.doi.org/10.4054/mpidr-wp-2010-013.

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Соловйов, Володимир Миколайович, and D. N. Chabanenko. Financial crisis phenomena: analysis, simulation and prediction. Econophysic’s approach. Гумбольдт-Клуб Україна, November 2009. http://dx.doi.org/10.31812/0564/1138.

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With the beginning of the global financial crisis, which attracts the attention of the international community, the inability of existing methods to predict the events became obvious. Creation, testing, adaptation of the models to the concrete financial market segments for the purpose of monitoring, early prediction, prevention and notification of financial crises is gaining currency nowadays. Econophysics is an interdisciplinary research field, applying theories and methods originally developed by physicists in order to solve problems in economics, usually those including uncertainty or stochastic processes and nonlinear dynamics. Its application to the study of financial markets has also been termed statistical finance referring to its roots in statistical physics. The new paradigm of relativistic quantum econophysics is proposed.
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Jury, William A., and David Russo. Characterization of Field-Scale Solute Transport in Spatially Variable Unsaturated Field Soils. United States Department of Agriculture, January 1994. http://dx.doi.org/10.32747/1994.7568772.bard.

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This report describes activity conducted in several lines of research associated with field-scale water and solute processes. A major effort was put forth developing a stochastic continuum analysis for an important class of problems involving flow of reactive and non reactive chemicals under steady unsaturated flow. The field-scale velocity covariance tensor has been derived from local soil properties and their variability, producing a large-scale description of the medium that embodies all of the local variability in a statistical sense. Special cases of anisotropic medium properties not aligned along the flow direction of spatially variable solute sorption were analysed in detail, revealing a dependence of solute spreading on subtle features of the variability of the medium, such as cross-correlations between sorption and conductivity. A novel method was developed and tested for measuring hydraulic conductivity at the scale of observation through the interpretation of a solute transport outflow curve as a stochastic-convective process. This undertaking provided a host of new K(q) relationships for existing solute experiments and also laid the foundation for future work developing a self-consistent description of flow and transport under these conditions. Numerical codes were developed for calculating K(q) functions for a variety of solute pulse outflow shapes, including lognormal, Fickian, Mobile-Immobile water, and bimodal. Testing of this new approach against conventional methodology was mixed, and agreed most closely when the assumptions of the new method were met. We conclude that this procedure offers a valuable alternative to conventional methods of measuring K(q), particularly when the application of the method is at a scale (e.g. and agricultural field) that is large compared to the common scale at which conventional K(q) devices operate. The same problem was approached from a numerical perspective, by studying the feasibility of inverting a solute outflow signal to yield the hydraulic parameters of the medium that housed the experiment. We found that the inverse problem was solvable under certain conditions, depending on the amount of noise in the signal and the degree of heterogeneity in the medium. A realistic three dimensional model of transient water and solute movement in a heterogeneous medium that contains plant roots was developed and tested. The approach taken was to generate a single realization of this complex flow event, and examine the results to see whether features were present that might be overlooked in less sophisticated model efforts. One such feature revealed is transverse dispersion, which is a critically important component in the development of macrodispersion in the longitudinal direction. The lateral mixing that was observed greatly exceeded that predicted from simpler approaches, suggesting that at least part of the important physics of the mixing process is embedded in the complexity of three dimensional flow. Another important finding was the observation that variability can produce a pseudo-kinetic behavior for solute adsorption, even when the local models used are equilibrium.
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