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Thematische Bibliographien / Stochastic processes Mathematical models

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Auswahl der wissenschaftlichen Literatur zum Thema „Stochastic processes Mathematical models“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 29. Januar 2023

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Zeitschriftenartikel zum Thema "Stochastic processes Mathematical models"

1

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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Jaeger, Herbert. "Observable Operator Models for Discrete Stochastic Time Series." Neural Computation 12, no. 6 (June 1, 2000): 1371–98. http://dx.doi.org/10.1162/089976600300015411.

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A widely used class of models for stochastic systems is hidden Markov models. Systems that can be modeled by hidden Markov models are a proper subclass of linearly dependent processes, a class of stochastic systems known from mathematical investigations carried out over the past four decades. This article provides a novel, simple characterization of linearly dependent processes, called observable operator models. The mathematical properties of observable operator models lead to a constructive learning algorithm for the identification of linearly dependent processes. The core of the algorithm h

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Nikolova, Iveta. "On stochastic models in biology and medicine." Asian-European Journal of Mathematics 13, no. 08 (May 21, 2020): 2050168. http://dx.doi.org/10.1142/s1793557120501685.

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Stochastic models along with deterministic models are successfully used for mathematical description of biological processes. They apply knowledge from probability theory and mathematical statistics to analyze specific characteristics of living systems. The paper is devoted to some stochastic models of various phenomena in biology and medicine. Basic concepts and definitions used in classical probability models are considered and illustrated by several examples with solutions. The stochastic kinetic modeling approach is described. A new kinetic model of autoimmune disease is presented. It is a

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Butov, Alexander A., Maxim A. Volkov, Viktor N. Golovanov, Anatoly A. Kovalenko, Boris M. Kostishko, and Leonid M. Samoilov. "Mathematical Modeling of Main Classes of Stochastic Productive Systems." Engineering Technologies and Systems 29, no. 4 (December 31, 2019): 496–509. http://dx.doi.org/10.15507/2658-4123.029.201904.496-509.

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Introduction. The article deals with mathematical models of two main classes of processes in stochastic productive systems. For a multistage system, conditions of belonging to a “just-in-time” class or a class with infinite support of the time distribution function for productive operations are determined. Materials and Methods. Descriptions and investigations of models are carried out by trajectory (martingale) methods. For “just-in-time” systems and multistage stochastic productive systems, terms and methods of random walks in a random environment and birth and death processes are used. The

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Larina, Ludmila, Dmitryi Ruslyakov, Olga Tikhonova, and Boris Kalmykov. "Research of processes of the heatmass transfer in the porous environments having stochastic characteristics on the basis of methods of applied synergetic." E3S Web of Conferences 273 (2021): 01023. http://dx.doi.org/10.1051/e3sconf/202127301023.

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On the basis of a synergetic approach, mathematical models of the stochastic similarity of the functioning of heat and mass transfer processes in porous media (grain materials) have been developed. In these models, the indicators of the stochastic characteristics of these media are combined with the parameters of the processes of hygrothermal treatment under vacuum conditions: residual pressure - P, temperature - T, time-τ, with a density of couple - ρ. The resulting models can be used to control hygrothermal processes in the processing of natural tanning and grain materials that have a stocha

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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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Holubec, Viktor, Artem Ryabov, Sarah A. M. Loos, and Klaus Kroy. "Equilibrium stochastic delay processes." New Journal of Physics 24, no. 2 (February 1, 2022): 023021. http://dx.doi.org/10.1088/1367-2630/ac4b91.

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Abstract Stochastic processes with temporal delay play an important role in science and engineering whenever finite speeds of signal transmission and processing occur. However, an exact mathematical analysis of their dynamics and thermodynamics is available for linear models only. We introduce a class of stochastic delay processes with nonlinear time-local forces and linear time-delayed forces that obey fluctuation theorems and converge to a Boltzmann equilibrium at long times. From the point of view of control theory, such ‘equilibrium stochastic delay processes’ are stable and energetically

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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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Butusov, O. B., O. P. Nikiforova, and N. I. Redikultseva. "Mathematical methods for the analysis of migration processes on the basis of demographic data." Izvestiya MGTU MAMI 9, no. 1-4 (July 10, 2015): 21–25. http://dx.doi.org/10.17816/2074-0530-67102.

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The problem of mathematical analysis of demographic data was investigated for data, where information on migration flows is taken into account implicitly. Regression techniques, neural networks and stochastic analysis were used for the mathematical analysis of demographic processes. Two age groups were considered: young (0 - 39 years) and elderly (40 - 70 years). While development of stochastic models the theory of Markov chains and transition matrix were used. The parameterization and model identification were conducted according to Rosstat data.

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Dissertationen zum Thema "Stochastic processes Mathematical models"

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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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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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Shepherd, Tricia D. "Models for chemical processes : activated dynamics across stochastic potentials." Diss., Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/27062.

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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

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Leung, Ho-yin, and 梁浩賢. "Stochastic models for optimal control problems with applications." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42841781.

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Zhang, Dongxiao. "Conditional stochastic analysis of solute transport in heterogeneous geologic media." Diss., The University of Arizona, 1993. http://hdl.handle.net/10150/186553.

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This dissertation develops an analytical-numerical approach to deterministically predict the space-time evolution of concentrations in heterogeneous geologic media conditioned on measurements of hydraulic conductivities (transmissivities) and/or hydraulic heads. Based on the new conditional Eulerian-Lagrangian transport theory by Neuman, we solve the conditional transport problem analytically at early time, and express it in pseudo-Fickian form at late time. The stochastically derived deterministic pseudo-Fickian mean concentration equation involves a conditional, space-time dependent dispersi

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Thompson, Mery H. "Optimum experimental designs for models with a skewed error distribution with an application to stochastic frontier models /." Connect to e-thesis, 2008. http://theses.gla.ac.uk/236/.

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Thesis (Ph.D.) - University of Glasgow, 2008.<br>Ph.D. thesis submitted to the Faculty of Information and Mathematical Sciences, Department of Statistics, 2008. Includes bibliographical references. Print version also available.

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Uyar, Emrah. "Routing in stochastic environments." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26554.

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Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2009.<br>Committee Co-Chair: Erera, Alan L.; Committee Co-Chair: Savelsbergh, Martin W. P.; Committee Member: Ergun, Ozlem; Committee Member: Ferguson, Mark; Committee Member: Kleywegt, Anton J.. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Gong, Bo. "Numerical methods for backward stochastic differential equations with applications to stochastic optimal control." HKBU Institutional Repository, 2017. https://repository.hkbu.edu.hk/etd_oa/462.

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The concept of backward stochastic differential equation (BSDE) was initially brought up by Bismut when studying the stochastic optimal control problem. And it has been applied to describe various problems particularly to those in finance. After the fundamental work by Pardoux and Peng who proved the well-posedness of the nonlinear BSDE, the BSDE has been investigated intensively for both theoretical and practical purposes. In this thesis, we are concerned with a class of numerical methods for solving BSDEs, especially the one proposed by Zhao et al.. For this method, the convergence theory of

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Hashad, Atalla I. "Analysis of non-Gaussian processes using the Wiener model of discrete nonlinear systems." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA297343.

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Dissertation (Ph. D. in Electrical Engineering) Naval Postgraduate School, December 1994.<br>"December 1994." Dissertation supervisor(s): Charles W. Therrien. Includes bibliographical references. Also available online.

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Bücher zum Thema "Stochastic processes Mathematical models"

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

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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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Janssen, Jacques. Mathematical fianance: Deterministic and stochastic models. Hoboken, N.J: ISTE/John Wiley, 2008.

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

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Koroli͡uk, V. S. Stochastic models of systems. Dordrecht: Kluwer Academic Publishers, 1999.

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Tomasz, Rolski, ed. Stochastic processes for insurance and finance. Chicester: J. Wiley, 1999.

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Janssen, Jacques. Mathematical fianance: Deterministic and stochastic models. Hoboken, N.J: ISTE/John Wiley, 2008.

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N, Limnios, and Oprișan Gheorghe, eds. Introduction to stochastic models. London: ISTE, 2010.

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A, Molchanov S., and Woyczyński W. A. 1943-, eds. Stochastic models in geosystems. New York: Springer, 1997.

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Buchteile zum Thema "Stochastic processes Mathematical models"

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Bender, Christian, Tommi Sottinen, and Esko Valkeila. "Fractional Processes as Models in Stochastic Finance." In Advanced Mathematical Methods for Finance, 75–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18412-3_3.

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Silvestrov, Dmitrii S. "Nonlinearly Perturbed Stochastic Processes and Systems." In Mathematical and Statistical Models and Methods in Reliability, 19–37. Boston, MA: Birkhäuser Boston, 2010. http://dx.doi.org/10.1007/978-0-8176-4971-5_2.

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Chen, Taolue, Klaus Dräger, and Stefan Kiefer. "Model Checking Stochastic Branching Processes." In Mathematical Foundations of Computer Science 2012, 271–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32589-2_26.

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Xue, Chuan, and Gregory Jameson. "Recent Mathematical Models of Axonal Transport." In Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology, 265–85. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62627-7_12.

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Grindel, Ria, Wieger Hinderks, and Andreas Wagner. "Application of Continuous Stochastic Processes in Energy Market Models." In Mathematical Modeling, Simulation and Optimization for Power Engineering and Management, 25–50. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62732-4_2.

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Ogorodnikov, Vasily, Nina Kargapolova, and Olga Sereseva. "Numerical Stochastic Models of Meteorological Processes and Fields." In Springer Proceedings in Mathematics & Statistics, 409–17. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-2104-1_40.

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Swishchuk, Anatoly. "Stochastic Stability and Optimal Control of Semi-Markov Risk Processes in Insurance Mathematics." In Semi-Markov Models and Applications, 313–23. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4613-3288-6_19.

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Balakrishnan, V. "Stochastic Processes." In Mathematical Physics, 461–93. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39680-0_21.

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Palacios, Antonio. "Stochastic Models." In Mathematical Engineering, 431–85. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04729-9_9.

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Serovajsky, Simon. "Stochastic models." In Mathematical Modelling, 339–60. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003035602-18.

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Konferenzberichte zum Thema "Stochastic processes Mathematical models"

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Granita and Arifah Bahar. "Stochastic differential equation model to Prendiville processes." In THE 22ND NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM22): Strengthening Research and Collaboration of Mathematical Sciences in Malaysia. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4932498.

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Maslovskaya, A. G., S. K. Sarukhanian, and Ch Kuttler. "A Stochastic Algorithm for Reaction-Diffusion Model of Communication Processes in Evolving Bacteria." In Mathematical Biology and Bioinformatics. Pushchino: IMPB RAS - Branch of KIAM RAS, 2022. http://dx.doi.org/10.17537/icmbb22.49.

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Musabekova, Leila. "Mathematical model of diffusion-limited aggregation based on a three-dimensional stochastic lattice." In INTERNATIONAL SCIENTIFIC-TECHNICAL SYMPOSIUM (ISTS) «IMPROVING ENERGY AND RESOURCE-EFFICIENT AND ENVIRONMENTAL SAFETY OF PROCESSES AND DEVICES IN CHEMICAL AND RELATED INDUSTRIES». The Kosygin State University of Russia, 2021. http://dx.doi.org/10.37816/eeste-2021-1-145-148.

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This paper is devoted to the problem of developing the newly efficient method for modeling the aggregation processes in polydisperse systems, without limitation of considering only binary collisions. The submitted method is an extension of the method previously developed by the authors for the case of a three-dimensional stochastic lattice. Such an extension increases the practical significance and reliability of the simulation results.

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Ghiocel, Dan M. "Advanced Stochastic Techniques for Jet Engine Component Life Prediction." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2650.

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Abstract The paper addresses significant aspects of stochastic modeling for jet engine component life prediction. Probabilistic life prediction for gas turbine engine components represents a very difficult engineering problem involving stochastic modeling of multiple, complex random phenomena. A key aspect for developing a probabilistic life prediction tool is to incorporate, and to be open to modeling advances related to dynamic complex random phenomena, including space-time random variabilities of mission environment and material parameters, aero-elastic interactions, friction at contact int

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Nakamura, Masato, Hanwei Zhang, Karsten Millrath, and Nickolas J. Themelis. "Modeling of Waste-to-Energy Combustion With Continuous Variation of the Solid Waste Fuel." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-55342.

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A mathematical model of a mass-burn, waste-to-energy combustion chamber has been developed that includes stochastic representation of the variability of the fuel (municipal solid waste, MSW). The drying, pyrolysis, gasification and combustion processes on the moving grate are governed by several factors such as proximate and ultimate analysis, particle size, moisture, heating value, and bulk density, all of which change continuously. This extreme variability has not been considered in past mathematical models of WTE combustion that used mean values of the MSW properties. The Monte Carlo stocha

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Baghdasaryan, Lusine, Wei Chen, Thaweepat Buranathiti, and Jian Cao. "Model Validation via Uncertainty Propagation Using Response Surface Models." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/dac-34140.

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Model validation has become a primary means to evaluate accuracy and reliability of computational simulations in engineering design. Mathematical models enable engineers to establish what the most likely response of a system is. However, despite the enormous power of computational models, uncertainty is inevitable in all model-based engineering design problems, due to the variation in the physical system itself, or lack of knowledge, and the use of assumptions by model builders. Therefore, realistic mathematical models should contemplate uncertainties. Due to the uncertainties, the assessment

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Hu, Zhen, Sankaran Mahadevan, and Xiaoping Du. "Uncertainty Quantification in Time-Dependent Reliability Analysis." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47925.

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One of the essential steps in time-dependent reliability analysis is the characterization of stochastic load processes and system random variables based on experimental or historical data. Limited data results in uncertainty in the modeling of random variables and stochastic loadings. The uncertainty in random variable and stochastic load models later causes uncertainty in the results of reliability analysis. An uncertainty quantification framework is developed in this paper for time-dependent reliability analysis. The effects of two kinds of uncertainty sources, namely data uncertainty and mo

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Sambaturu, Prathyush, Marco Minutoli, Mahantesh Halappanavar, Ananth Kalyanaraman, and Anil Vullikanti. "Scalable and Memory-Efficient Algorithms for Controlling Networked Epidemic Processes Using Multiplicative Weights Update Method." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/717.

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We study the problem of designing scalable algorithms to find effective intervention strategies for controlling stochastic epidemic processes on networks. This is a common problem arising in agent based models for epidemic spread. Previous approaches to this problem focus on either heuristics with no guarantees or approximation algorithms that scale only to networks corresponding to county-sized populations, typically, with less than a million nodes. In particular, the mathematical-programming based approaches need to solve the Linear Program (LP) relaxation of the problem using an LP solver,

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Dion, Jean-Luc, Fatma Abid, Gaël Chevallier, Hugo Festjens, Nicolas Peyret, Franck Renaud, Moustafa Seifeddine, and Cyrille Stephan. "Compact Model Synthesis for Partially Observed Operational Systems." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12111.

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This work proposes a Compact Model Synthesis (CMS) for Partially Observed Operational Systems (POOS) without using the complete knowledge of models. Series of “grey boxes” fed with partial observations are built in order to synthesize target variables with compact models. The recursive process for real time computation is based on Kalman Filters (KF). This stochastic approach allows to converge in line toward deterministic models with estimated uncertainties and without intrusion on the complete model process. Mathematical context is described first and illustrated secondly with two examples.

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Wang, Zequn, Yan Fu, Ren-Jye Yang, Saeed Barbat, and Wei Chen. "Model Validation of Dynamic Engineering Models Under Uncertainty." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59437.

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Validating dynamic engineering models is critically important in practical applications by assessing the agreement between simulation results and experimental observations. Though significant progresses have been made, the existing metrics lack the capability of managing uncertainty in both simulations and experiments, which may stem from computer model instability, imperfection in material fabrication and manufacturing process, and variations in experimental conditions. In addition, it is challenging to validate a dynamic model aggregately over both the time domain and a model input space wit

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Berichte der Organisationen zum Thema "Stochastic processes Mathematical models"

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Perdigão, Rui A. P. Earth System Dynamic Intelligence - ESDI. Meteoceanics, April 2021. http://dx.doi.org/10.46337/esdi.210414.

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Earth System Dynamic Intelligence (ESDI) entails developing and making innovative use of emerging concepts and pathways in mathematical geophysics, Earth System Dynamics, and information technologies to sense, monitor, harness, analyze, model and fundamentally unveil dynamic understanding across the natural, social and technical geosciences, including the associated manifold multiscale multidomain processes, interactions and complexity, along with the associated predictability and uncertainty dynamics. The ESDI Flagship initiative ignites the development, discussion and cross-fertilization of

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Siebke, Christian, Maximilian Bäumler, Madlen Ringhand, Marcus Mai, Felix Elrod, and Günther Prokop. Report on integration of the stochastic traffic simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.246.

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As part of the AutoDrive project, the OpenPASS framework is used to develop a cognitive-stochastic traffic flow simulation for urban intersection scenarios described in deliverable D1.14. This framework was adapted and further developed. The deliverable D5.13 deals with the construction of the stochastic traffic simulation. At this point of the process, the theoretical design aspects of D4.20 are implemented. D5.13 explains the operating principles of the different modules. This includes the foundations, boundary conditions, and mathematical theory of the traffic simulation.

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Chen, Xiaojun, Hailin Sun, and Roger J. Wets. Regularized Mathematical Programs with Stochastic Equilibrium Constraints: Estimating Structural Demand Models. Fort Belvoir, VA: Defense Technical Information Center, July 2013. http://dx.doi.org/10.21236/ada609521.

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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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Lovianova, Iryna V., Dmytro Ye Bobyliev, and Aleksandr D. Uchitel. Cloud calculations within the optional course Optimization Problems for 10th-11th graders. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3267.

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The article deals with the problem of introducing cloud calculations into 10th-11th graders’ training to solve optimization problems in the context of the STEM-education concept. After analyzing existing programmes of optional courses on optimization problems, the programme of the optional course Optimization Problems has been developed and substantiated implying solution of problems by the cloud environment CoCalc. It is a routine calculating operation and not a mathematical model that is accentuated in the programme. It allows considering more problems which are close to reality without adap

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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 stoch

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Lieth, J. Heiner, Michael Raviv, and David W. Burger. Effects of root zone temperature, oxygen concentration, and moisture content on actual vs. potential growth of greenhouse crops. United States Department of Agriculture, January 2006. http://dx.doi.org/10.32747/2006.7586547.bard.

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Soilless crop production in protected cultivation requires optimization of many environmental and plant variables. Variables of the root zone (rhizosphere) have always been difficult to characterize but have been studied extensively. In soilless production the opportunity exists to optimize these variables in relation to crop production. The project objectives were to model the relationship between biomass production and the rhizosphere variables: temperature, dissolved oxygen concentration and water availability by characterizing potential growth and how this translates to actual growth. As p

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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 alig

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