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Books on the topic 'Earthquake interactions and probability'

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Author: Grafiati
Published: 10 March 2023

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1

1939-, Williams M. E., Nishenko S. P, Ebel J. E, Geological Survey (U.S.), United States. Federal Emergency Management Agency., and New England States Earthquake Consortium., eds. Proceedings of the working group meeting on northeast United States earthquake probability, April 28-29, 1993, Massachusetts Institute of Technology, Cambridge, Massachusetts. [Denver, CO]: U.S. Geological Survey, 1995.

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2

Manolis, G. D. Stochastic structural dynamics in earthquake engineering. Southampton: WITPress, 2001.

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3

1939-, Williams M. E., Nishenko S. P, Ebel J. E, Geological Survey (U.S.), United States. Federal Emergency Management Agency., and New England States Earthquake Consortium., eds. Proceedings of the working group meeting on northeast United States earthquake probability, April 28-29, 1993, Massachusetts Institute of Technology, Cambridge, Massachusetts. [Denver, CO]: U.S. Geological Survey, 1995.

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4

Shamsher, Prakash, American Society of Civil Engineers. Committee on Geotechnical Earthquake Engineering., and ASCE National Convention (1997 : Minneapolis, Minn.), eds. Seismic analysis and design for soil-pile-structure interactions: Proceedings of a session sponsored by the Committee on Geotechnical Earthquake Engineering of the Geo-Institute of the American Society of Civil Engineers in conjunction with the ASCE National Convention in Minneapolis, Minnesota, October 5-8, 1997. Reston, VA: The Society, 1997.

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5

L, Crosson William, and United States. National Aeronautics and Space Administration., eds. Regional-scale hydrology with a new land surface processes model. Washington, DC: National Aeronautics and Space Administration, 1995.

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6

L, Crosson William, and United States. National Aeronautics and Space Administration., eds. Regional-scale hydrology with a new land surface processes model. Washington, DC: National Aeronautics and Space Administration, 1995.

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7

L, Crosson William, and United States. National Aeronautics and Space Administration., eds. Regional-scale hydrology with a new land surface processes model. Washington, DC: National Aeronautics and Space Administration, 1995.

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8

United States. National Aeronautics and Space Administration., ed. A remote-sensing based technique to account for sub-grid scale variability of land surface properties. Washington, DC: National Aeronautics and Space Administration, 1995.

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9

Koliopoulos, P. K., and G. D. Manolis. Stochastic Structural Dynamics in Earthquake Engineering (Advances in Earthquake Engineering, Vol. 8). Computational Mechanics, Inc., 2001.

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10

Cooke, Roger M., Daan Nieboer, and Jolanta Misiewicz. Fat-Tailed Distributions: Data, Diagnostics and Dependence. Wiley & Sons, Incorporated, John, 2014.

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11

Cooke, Roger M., Daan Nieboer, and Jolanta Misiewicz. Fat-Tailed Distributions: Data, Diagnostics and Dependence. Wiley-Interscience, 2014.

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12

Cooke, Roger M., Daan Nieboer, and Jolanta Misiewicz. Fat-Tailed Distributions: Data, Diagnostics and Dependence. Wiley & Sons, Incorporated, John, 2014.

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13

Cooke, Roger M., Daan Nieboer, and Jolanta Misiewicz. Fat-Tailed Distributions. Wiley & Sons, Incorporated, John, 2014.

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14

Cooke, Roger M., Daan Nieboer, and Jolanta Misiewicz. Fat-Tailed Distributions: Data, Diagnostics and Dependence, Volume 1. Wiley & Sons, Incorporated, John, 2014.

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15

Heavytailed Distributions In Disaster Analysis. Springer, 2010.

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16

Pisarenko, V., and M. Rodkin. Heavy-Tailed Distributions in Disaster Analysis. Springer Netherlands, 2012.

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17

Pisarenko, V., and M. Rodkin. Heavy-Tailed Distributions in Disaster Analysis. Springer London, Limited, 2010.

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18

Minn.) ASCE National Convention (1997 : Minneapolis. Seismic Analysis and Design for Soil-Pile-Structure Interactions: Proceedings of a Session Sponsored by the Committee on Geotechnical Earthquake Engineering ... of Civil (Geotechnical Special Publication). American Society of Civil Engineers, 1997.

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19

Modeling Interactions Between Vector-Borne Diseases and Environment Using GIS. Taylor & Francis Group, 2015.

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20

A remote-sensing based technique to account for sub-grid scale variability of land surface properties. Washington, DC: National Aeronautics and Space Administration, 1995.

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21

A remote-sensing based technique to account for sub-grid scale variability of land surface properties. Washington, DC: National Aeronautics and Space Administration, 1995.

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22

A remote-sensing based technique to account for sub-grid scale variability of land surface properties. Washington, DC: National Aeronautics and Space Administration, 1995.

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23

Regional-scale hydrology with a new land surface processes model. Washington, DC: National Aeronautics and Space Administration, 1995.

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24

Coaffee, Jon. Futureproof. Yale University Press, 2019. http://dx.doi.org/10.12987/yale/9780300228670.001.0001.

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Catastrophic events such as 9/11, Hurricane Katrina, and the Tohoku ‘Triple Disaster’ of earthquake, tsunami, and nuclear meltdown that hit the eastern seaboard of Japan in 2012 are seen as surprises that have a low probability of occurring but have a debilitating impact when they do. In this eye-opening journey through modern and ancient risk management practices, the author explains why we need to find a new way to navigate the deeply uncertain world that we live in. Examining how governments have responded to terrorist threats, climate change, and natural hazards, the book shows how and why these measures have proven inadequate and what should be done to make us more resilient. While conventional approaches have focused on planning and preparing for disruptions and enhanced our ability to ‘bounce back’, our focus should be on anticipating future challenges and enhancing our capacity to adapt to new threats.
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25

Saha, Prasenjit, and Paul A. Taylor. Nuclear Fusion in Stars. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198816461.003.0006.

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This chapter enumerates some of the many nuclear reactions in stars. It focuses on a general principle: nuclear fusion requires overcoming the Coulomb barrier between nuclei, which is possible through the relatively infrequent process of quantum tunnelling. The tunnelling probability depends on the atomic num-bers and mass numbers of the nuclei involved, and also on their relative speed. These translate into steep and interesting temperature dependencies for nuclear reactions. Analytic approximations yield a rate that is almost vanishingly rare, yet within the incredibly large number of potential interactions within stars quantum tunnelling can still provide the underlying ignition of nuclear reactions.
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26

Gelfand, Alan, and Sujit K. Sahu. Models for demography of plant populations. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.17.

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This article discusses the use of Bayesian analysis and methods to analyse the demography of plant populations, and more specifically to estimate the demographic rates of trees and how they respond to environmental variation. It examines data from individual (tree) measurements over an eighteen-year period, including diameter, crown area, maturation status, and survival, and from seed traps, which provide indirect information on fecundity. The multiple data sets are synthesized with a process model where each individual is represented by a multivariate state-space submodel for both continuous (fecundity potential, growth rate, mortality risk, maturation probability) and discrete states (maturation status). The results from plant population demography analysis demonstrate the utility of hierarchical modelling as a mechanism for the synthesis of complex information and interactions.
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27

Butz, Martin V., and Esther F. Kutter. Top-Down Predictions Determine Perceptions. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.003.0009.

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While bottom-up visual processing is important, the brain integrates this information with top-down, generative expectations from very early on in the visual processing hierarchy. Indeed, our brain should not be viewed as a classification system, but rather as a generative system, which perceives something by integrating sensory evidence with the available, learned, predictive knowledge about that thing. The involved generative models continuously produce expectations over time, across space, and from abstracted encodings to more concrete encodings. Bayesian information processing is the key to understand how information integration must work computationally – at least in approximation – also in the brain. Bayesian networks in the form of graphical models allow the modularization of information and the factorization of interactions, which can strongly improve the efficiency of generative models. The resulting generative models essentially produce state estimations in the form of probability densities, which are very well-suited to integrate multiple sources of information, including top-down and bottom-up ones. A hierarchical neural visual processing architecture illustrates this point even further. Finally, some well-known visual illusions are shown and the perceptions are explained by means of generative, information integrating, perceptual processes, which in all cases combine top-down prior knowledge and expectations about objects and environments with the available, bottom-up visual information.
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