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1

Building and Fire Research Laboratory (U.S.) and Factory Mutual Research Corporation, eds. Prediction of fire dynamics. Gaithersburg, MD: The Institute, 1997.

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2

Hein, Putter, ed. Dynamic prediction in clinical survival analysis. Boca Raton: CRC Press, 2012.

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3

Lalanne, M. Rotordynamics prediction in engineering. Chichester: Wiley, 1990.

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4

A, Ladd J., Yuhas A. J, and United States. National Aeronautics and Space Administration., eds. Dynamic inlet distortion prediction with a combined computational fluid dynamics and distortion synthesis approach. [Washington, DC: National Aeronautics and Space Administration, 1996.

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5

Hiermaier, Stefan, ed. Predictive Modeling of Dynamic Processes. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0727-1.

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6

Lughofer, Edwin, and Moamar Sayed-Mouchaweh, eds. Predictive Maintenance in Dynamic Systems. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2.

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7

Sreeramesh, Kalluri, Bonacuse Peter J. 1960-, and Symposium on Multiaxial Fatigue and Deformation: Testing and Prediction (1999 : Seattle, Wash.), eds. Multiaxial fatigue and deformation: Testing and prediction. W. Conshohocken, PA: ASTM, 2000.

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8

Sebastian, James W. Parametric prediction of the transverse dynamic stability of ships. Monterey, Calif: Naval Postgraduate School, 1998.

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9

Phillips, Norman A. Dispersion processes in large-scale weather prediction. Geneva, Switzerland: Secretariat of the World Meteorological Organization, 1990.

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10

Phillips, Norman A. Dispersion processes in large scale weather prediction. [Geneva]: World Meteorological Organization, 1990.

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11

Núñez, Alfredo A., Doris A. Sáez, and Cristián E. Cortés. Hybrid Predictive Control for Dynamic Transport Problems. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4351-2.

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12

Núñez, Alfredo A. Hybrid Predictive Control for Dynamic Transport Problems. London: Springer London, 2013.

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13

Church, C., D. Burgess, C. Doswell, and R. Davies‐Jones, eds. The Tornado: Its Structure, Dynamics, Prediction, and Hazards. Washington, D. C.: American Geophysical Union, 1993. http://dx.doi.org/10.1029/gm079.

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14

Keilis-Borok, Vladimir I., and Alexandre A. Soloviev, eds. Nonlinear Dynamics of the Lithosphere and Earthquake Prediction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05298-3.

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15

United States. National Aeronautics and Space Administration., ed. A model for prediction of STOVL ejector dynamics. [Washington, DC]: National Aeronautics and Space Administration, 1989.

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16

United States. National Aeronautics and Space Administration., ed. A model for prediction of STOVL ejector dynamics. [Washington, DC]: National Aeronautics and Space Administration, 1989.

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17

Keilis-Borok, Vladimir I. Nonlinear Dynamics of the Lithosphere and Earthquake Prediction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003.

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18

R, Church Christopher, ed. The Tornado: Its structure, dynamics, prediction, and hazards. Washington, DC: American Geophysical Union, 1993.

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19

Chan, Yew Wing. Characterisation and prediction of the dynamic response of viscoelastic elements. Manchester: University of Manchester, 1995.

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20

B, Oswald Fred, and United States. National Aeronautics and Space Administration., eds. Dynamic forces in spur gears--measurement, prediction, and code validation. [Washington, DC]: National Aeronautics and Space Administration, 1996.

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21

United States. National Aeronautics and Space Administration., ed. Unsteady jet flow computation towards noise prediction. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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22

Taylor, William W. Chaotic evolution and nonlinear prediction in signal separation applications. Santa Monica, CA: Rand, 1992.

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23

Manolopoulos, Yannis, and Thanasis Vergoulis, eds. Predicting the Dynamics of Research Impact. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86668-6.

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24

M, Trusler J. P., and Tsolakis Thomas F, eds. Thermophysical properties of fluids: An introduction to their prediction. London: Imperial College Press, 1996.

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25

Sangiorgio, Matteo, Fabio Dercole, and Giorgio Guariso. Deep Learning in Multi-step Prediction of Chaotic Dynamics. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-94482-7.

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26

Griffa, Annalisa, A. D. Jr Kirwan, Arthur J. Mariano, Tamay Ozgokmen, and H. Thomas Rossby, eds. Lagrangian Analysis and Prediction of Coastal and Ocean Dynamics. Cambridge: Cambridge University Press, 2007. http://dx.doi.org/10.1017/cbo9780511535901.

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27

Roffel, Brian. Process dynamics and control: Modeling for control and prediction. Chichester, UK: John Wiley & Sons, 2005.

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28

Annalisa, Griffa, ed. Lagrangian analysis and prediction of coastal and ocean dynamics. Cambridge: Cambridge University Press, 2007.

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29

H, Betlem B., ed. Process dynamics and control: Modeling for control and prediction. Chichester, England: John Wiley & Sons, 2006.

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30

Mao, Guobin. Development of an accident risk prediction approach for dynamic route guidance. Ottawa: National Library of Canada, 2003.

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31

Tao, Zhang. Prediction of dynamic response of Nomex honeycomb sandwich plates subjected to object impact. Harbin, Heilongjiang Province, China: Harbin Institute of Technology, School of Aeronautics, 1989.

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32

Glahn, U. Von. Rectangular nozzle plume velocity modeling for use in jet noise prediction. [Washington, D.C.]: National Aeronautics and Space Administration, 1989.

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33

Jian-Shun, Shuen, Faeth G. M, and United States. National Aeronautics and Space Administration., eds. Particle-laden weakly swirling free jets: Measurements and predictions. [Washington, DC]: National Aeronautics and Space Administration, 1988.

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34

United States. National Aeronautics and Space Administration., ed. Particle-laden weakly swirling free jets: Measurements and predictions. [Washington, DC]: National Aeronautics and Space Administration, 1988.

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35

Jian-Shun, Shuen, Faeth G. M, and United States. National Aeronautics and Space Administration., eds. Particle-laden weakly swirling free jets: Measurements and predictions. [Washington, DC]: National Aeronautics and Space Administration, 1988.

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36

United States. National Aeronautics and Space Administration., ed. Particle-laden weakly swirling free jets: Measurements and predictions. [Washington, DC]: National Aeronautics and Space Administration, 1988.

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37

Mason, Lee S. A Solar Dynamic power option for Space Solar Power. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 1999.

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38

Jürgen, Millat, Dymond J. H, Castro, C. A. Nieto de., and International Union of Pure and Applied Chemistry., eds. Transport properties of fluids: Their correlation, prediction and estimation. Cambridge: IUPAC, 1996.

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39

Chen, Kai Sheng. Dynamic plastic response and failure prediction of clamped pipelines under lateral impact. Portsmouth: University of Portsmouth, Dept. of Mechanical and Manufacturing Engineering, 1997.

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40

Farhan, Ali. Bus arrival time prediction for dynamic operations control and passenger information systems. Ottawa: National Library of Canada, 2002.

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41

Kalitzin, Stiliyan, and Fernando Lopes da Silva. EEG-Based Anticipation and Control of Seizures. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0023.

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Abstract:
Early seizure-prediction paradigms were based on detecting electroencephalographic (EEG) features, but recent approaches are based on dynamic systems theory. Methods that attempted to detect predictive features during the preictal period proved difficult to validate in practice. Brain systems can display bistability (both normal and epileptic states can coexist), and the transitions between states may be initiated by external or internal dynamic factors. In the former case prediction is impossible, but in the latter case prediction is conceivable, leading to the hypothesis that as seizure onset approaches, the excitability of the underlying neuronal networks tends to increase. This assumption is being explored using not only the ongoing EEG but also active probes, applying appropriate stimuli to brain areas to estimate the excitability of the neuronal populations. Experimental results support this assumption, suggesting that it may be possible to develop paradigms to estimate the risk of an impending transition to an epileptic state.
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42

Houwelingen, Hans van, and Hein Putter. Dynamic Prediction in Clinical Survival Analysis. Taylor & Francis Group, 2011.

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43

Houwelingen, Hans van, and Hein Putter. Dynamic Prediction in Clinical Survival Analysis. Taylor & Francis Group, 2011.

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44

Hans, Van. Dynamic Prediction in Clinical Survival Analysis. Taylor & Francis Group, 2013.

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45

Lalanne, Michel, and Guy Ferraris. Rotordynamics Prediction in Engineering. Wiley & Sons, Incorporated, John, 2000.

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46

Prediction of Turbulent Flows. Cambridge University Press, 2005.

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47

Taylor, Stephen J. Asset Price Dynamics, Volatility, and Prediction. Princeton University Press, 2011.

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48

Asset price dynamics, volatility, and prediction. Princeton, N.J: Princeton University Press, 2005.

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49

Taylor, Stephen J. Asset Price Dynamics, Volatility, and Prediction. Princeton University Press, 2007.

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50

Taylor, Stephen J. Asset Price Dynamics, Volatility, and Prediction. Princeton University Press, 2011.

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