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Książki na temat „Mechanistic model”

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Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 27 stycznia 2024

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1

Baird, Mark E. Towards a verified mechanistic model of plankton population dynamics. [s.l.]: typescript, 1999.

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2

Michelsen, Finn Are. A dynamic mechanistic model and model-based analysis of a continuous Kamyr digester. Trondheim: Universitetet i Trondheim, 1995.

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3

Krol, A. A. A mechanistic model for the leaching of retorted rundle oil shale. S.l: s.n, 1985.

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4

Great Britain. Health and Safety Executive., red. Risk assessment of inhaled particles using a physiologically based mechanistic model. Sudbury: HSE Books, 2003.

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5

M, Tayal, Atomic Energy of Canada Limited. CANDU. i IAEA Technical Committee, red. INTEGRITY: A semi-mechanistic model for stress corrosion cracking of fuel. Mississauga, Ont: AECL CANDU, 1994.

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Johannes Adrianus Sebastianus Joseph Razenberg. Epoxidation of alkenes catalysed by a mono-oxygenase model system: Mechanistic aspects. S.l: s.n.], 1986.

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7

Perkins, Steven W. Mechanistic-empirical modeling and design model development of geosynthetic reinforced flexible pavements: Final report. Bozeman, Mont: Western Transportation Institute, Dept. of Civil Engineering, Montana State University, 2001.

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8

Perkins, Steven W. Mechanistic-empirical modeling and design model development of geosynthetic reinforced flexible pavements: Appendix C--DARWin output. Bozeman, Mont: Western Transportation Institute, Dept. of Civil Engineering, Montana State University, 2001.

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9

J, Heames T., U.S. Nuclear Regulatory Commission. Office of Nuclear Regulatory Research. Division of Systems Research. i Sandia National Laboratories, red. VICTORIA: A mechanistic model of radionuclide behavior in the reactor coolant system under severe accident conditions. Washington, DC: Division of Systems Research, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1992.

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10

Sorin, Andrei. Software and Mind: The Mechanistic Myth and Its Consequences. Toronto, Canada: Andsor Books, 2013.

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11

U.S. Nuclear Regulatory Commission. Office of Nuclear Regulatory Research. Division of Systems Technology. i Sandia National Laboratories, red. VICTORIA 2.0: A mechanistic model for radionuclide behavior in a nuclear reactor coolant system under severe accident conditions. Washington, DC: Division of Systems Technology, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1998.

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12

U.S. Nuclear Regulatory Commission. Office of Nuclear Regulatory Research. Division of Systems Technology. i Sandia National Laboratories, red. VICTORIA 2.0: A mechanistic model for radionuclide behavior in a nuclear reactor coolant system under severe accident conditions. Washington, DC: Division of Systems Technology, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1998.

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13

Penelope, Morgan, Running S. W i Intermountain Research Station (Ogden, Utah), red. FIRE-BGC--a mechanistic ecological process model for simulating fire succession on coniferous forest landscapes of the northern Rocky Mountains. Ogden, UT (324 25th St., Ogden 84401): U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1996.

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14

Penelope, Morgan, Running S. W i Intermountain Research Station (Ogden, Utah), red. FIRE-BGC--a mechanistic ecological process model for simulating fire succession on coniferous forest landscapes of the northern Rocky Mountains. Ogden, UT (324 25th St., Ogden 84401): U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1996.

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15

Keane, Robert E. FIRE-BGC, a mechanistic ecological process model for simulating fire succession on coniferous forest landscapes of the northern Rocky Mountains. Ogden, UT: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1996.

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16

Keane, Robert E. FIRE-BGC, a mechanistic ecological process model for simulating fire succession on coniferous forest landscapes of the northern Rocky Mountains. Ogden, UT: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1996.

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17

Penelope, Morgan, Running S. W i Intermountain Research Station (Ogden, Utah), red. FIRE-BGC--a mechanistic ecological process model for simulating fire succession on coniferous forest landscapes of the northern Rocky Mountains. Ogden, UT (324 25th St., Ogden 84401): U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1996.

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18

Henson, Ian E. Modelling dry matter production, partitioning, and yield of oil palm: OPRODSIM : a mechanistic simulation model for teaching and research : technical manual and user's guide. Kuala Lumpur, Malaysia: Malaysian Palm Oil Board, Ministry of Plantation Industries and Commodities, Malaysia, 2009.

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19

Ohno, Atsuyoshi, i Satoshi Ushida. Mechanistic Models of Asymmetric Reductions. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-3123-8.

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20

Ohno, Atsuyoshi, i Satoshi Ushida. Mechanistic Models of Asymmetric Reductions. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-48868-9.

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21

S, Ushida, red. Mechanistic models of asymmetric reductions. Berlin: Springer-Verlag, 1986.

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22

Yeh, Chung-Heng. Mechanistic Models of Neural Computation in the Fruit Fly Brain. [New York, N.Y.?]: [publisher not identified], 2019.

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23

Korshin, Gregory V. Modeling DBP formation kinetics: Mechanistic and spectroscopic approaches. Denver, CO: AWWA Research Foundation, 2004.

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24

C, Fisher, Gous R i Morris T. R, red. Mechanistic modelling in pig and poultry production. Wallingford, UK: CABI, 2006.

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25

Barber, Stanley A. Soil nutrient bioavailability: A mechanistic approach. Wyd. 2. New York: Wiley, 1995.

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26

Lytton, Robert L., Xue Luo, Meng Ling, Yu Chen, Sheng Hu i Fan Gu. A Mechanisticâ€"Empirical Model for Topâ€"Down Cracking of Asphalt Pavements Layers. Washington, D.C.: Transportation Research Board, 2018. http://dx.doi.org/10.17226/25304.

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27

Nicholson, R. V. Acidification of sulphide tailings: The use of mechanistic models to ask the correct questions. S.l: s.n, 1989.

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28

Bigl, Susan R. Modeling of Mn/ROAD test sections with the CRREL mechanistic pavement design procedure. Hanover, NH: U.S. Army Corps of Engineers, Cold Regions Research & Engineering Laboratory, 1996.

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29

National Aeronautics and Space Administration (NASA) Staff. Mechanistic Model for Catalytic Recombination During Aerobraking Maneuvers. Independently Published, 2018.

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30

Mechanistic model for catalytic recombination during aerobraking maneuvers: Final report. [Washington, DC: National Aeronautics and Space Administration, 1989.

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31

Teitenberg, Tony. Tool wear analysis through an analytic mechanistic model of milling processes. 1991.

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32

FIRE-BGC -- A Mechanistic Ecological Process Model for Simulating Fire Succession. Diane Pub., 1996.

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33

Galvanin, Federico. Optimal Experimental Design for Chemical Engineers: Mechanistic Model-Based Design with Case Studies. Royal Society of Chemistry, The, 2022.

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34

Galvanin, Federico. Optimal Experimental Design for Chemical Engineers: Mechanistic Model-Based Design with Case Studies. Royal Society of Chemistry, The, 2022.

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35

Galvanin, Federico. Optimal Experimental Design for Chemical Engineers: Mechanistic Model-Based Design with Case Studies. Royal Society of Chemistry, The, 2022.

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36

Murphy, Dominic. The Medical Model and the Philosophy of Science. Redaktorzy K. W. M. Fulford, Martin Davies, Richard G. T. Gipps, George Graham, John Z. Sadler, Giovanni Stanghellini i Tim Thornton. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199579563.013.0057.

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This chapter sketches an account of psychiatric explanation with roots in contemporary philosophy of science and suggests that it is a natural fit with what it will call the strong interpretation of the medical model in psychiatry. The chapter starts by distinguishing between strong and minimal ways to understand the medical model before it moves on to talk about explanation. The basic idea of the chapter is that the logic of the medical model, together with recent developments in the sciences of the brain, suggests that psychiatry should be seen as a kind of cognitive neuroscience. The second part of the chapter discusses some issues in applying mechanistic explanatory models to mental disorders. Recent philosophical work on explanation in the cognitive neurosciences has seen it asmechanisticexplanation. A mechanistic explanation shows how components of a system interact to give rise to the phenomenon to be explained.
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37

Federico, Lisa Marie. Mechanistic link between intestinal insulin signaling and lipoprotein metabolism in a model of insulin resistance. 2004.

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38

VICTORIA: A mechanistic model of radionuclide behavior in the reactor coolant system under severe accident conditions. Supt. of Docs., U.S. G.P.O. [distributor], 1990.

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39

VICTORIA 2.0: A mechanistic model for radionuclide behavior in a nuclear reactor coolant system under severe accident conditions. Washington, DC: Division of Systems Technology, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1998.

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40

Woodward, James. Explanation in Neurobiology. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199685509.003.0004.

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This chapter employs an interventionist framework to elucidate some issues having to do with explanation in neurobiology. I argue that this framework can be used to distinguish theories and models that are explanatory from those that are merely descriptive. This framework can also be used to characterize a notion of a mechanistic explanation, according to which mechanistic explanations are those that meet interventionist criteria for successful explanation and certain additional constraints as well. However, from an interventionist perspective, although mechanistic theories have a number of virtues, it is a mistake to think that such models are the only legitimate kind of explanation in neuroscience and psychology. In particular, some (but not all) dynamical systems models in neuroscience are explanatory as are many models, such as the Hodgkin-Huxley model, that abstract away from mechanistically relevant low-level detail.
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41

FIRE-BGC--a mechanistic ecological process model for simulating fire succession on coniferous forest landscapes of the northern Rocky Mountains. Ogden, UT (324 25th St., Ogden 84401): U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1996.

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42

Al-Sahan, Muzahem Abdulraham. On the development of the flow regimes and the formulation of a mechanistic non-equilibrium model for critical two-phase flow. 1988.

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43

Bawden, Peter G. Developing an adaptive controller for endmilling using feature based abstraction of a part program and cutting parameters derived from a mechanistic model. 1989.

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44

Weiskopf, Daniel A. The Explanatory Autonomy of Cognitive Models. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199685509.003.0003.

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Psychology and neuroscience offer distinctive ways of modeling the mind/brain. However, cognitive and neural models often have significantly different structures, raising challenging questions about how they should be integrated to provide a complete picture of how the mind/brain system is organized. According to a certain mechanistic perspective, cognitive models should be viewed as being sketchy, incomplete versions of the fuller and more adequate models produced by neuroscience. Psychology is essentially an approximation to the mechanistic explanations given in neuroscience. Cognitive models are inherently inadequate, pending their gaps being filled in by a completed neuroscientific model. I argue that cognitive models are autonomous: they are sufficient in themselves to give adequate explanations of psychological and behavioral phenomena. In particular, they are not mere sketches, or approximations to underlying neuroscientific explanations. I offer a criterion for how psychological entities and processes may be real despite not mapping onto entities in neural mechanisms.
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45

Wentland, Steven H. Mechanistic Models in Chemistry. Kendall/Hunt Publishing Company, 1990.

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46

McCracken, Lance M., i Whitney Scott. Motivation from the Perspective of Contextual Cognitive Behavioral Approaches and the Psychological Flexibility Model. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190627898.003.0014.

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In everyday uses, the term motivation may imply a kind of mechanistic, “inside” the person, type of process. Contextual approaches, on the other hand, adopt an evolutionary perspective on motivation that emphasizes the selection of behavior patterns through the joint actions of historical consequences and verbal or cognitive processes, themselves considered the product of the same contextual processes of selection by consequences. The contextual focus on building, maintaining, and elaborating behavior patterns from directly manipulable contextual features enables a focus on variables that are able to serve the purpose of prediction and influence over behavior. Current studies of these processes apply the psychological flexibility model, including its processes of values-based and committed action. Laboratory studies of these processes demonstrate their potential importance in healthy functioning in relation to chronic pain. Treatment studies, including studies of Acceptance and Commitment Therapy (ACT), also demonstrate that enhancing these motivation-related processes has clinical utility.
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47

Ohno, Atsuyoshi, i Satoshi Ushida. Mechanistic Models of Asymmetric Reductions. Springer, 2013.

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48

Jaenicke, L., Atsuyoshi Ohno, Satoshi Ushida, V. Massey i E. Baulieu. Mechanistic Models of Asymmetric Reductions. Springer, 2012.

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49

Oakley, Jeremy E., i Helen E. Clough. Sensitivity analysis in microbial risk assessment: Vero-cytotoxigenic E. coli O157 in farm-pasteurized milk. Redaktorzy Anthony O'Hagan i Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.4.

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This article discusses the use of Bayesian methods for performing uncertainty analysis in complex computer models, focusing on a mechanistic model that has been applied in a risk assessment of contamination of farm-pasteurized milk with the bacterium Vero-cytotoxigenic E. coli (VTEC) O157. The VTEC model has uncertain input parameters, which makes outputs from the model used to inform the risk assessment also uncertain. The question that arises is how to reduce output uncertainty in the most efficient manner possible. The article first provides an overview of microbial risk assessment before analysing the frequency and consequences of food-borne outbreaks associated with VTEC O157. It then introduces the risk assessment model, along with model input distributions. Finally, it presents the results of a variance-based sensitivity analysis that was conducted to identify the most important uncertain model inputs.
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50

Narancic, Sanja. Mechanistic studies of organometallic complexes: Part I : Development of a new model for ligand binding energy determinations : Computational study of the conversion of rhenium diolates to metallaoxetanes and carbenes. 2007.

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