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Książki na temat „Life Prediction Model”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 5 lutego 2022

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1

Strangman, T. E. Thermal barrier coating life-prediction model development: Second annual report. Phoenix, Ariz: Garrett Turbine Engine Co., 1986.

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2

Strangman, T. E. Thermal barrier coating life prediction model development: First annual report. Phoenix, AZ: Garrett Turbine Engine Co., 1985.

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3

Kitamura, Takayuki. Creep life prediction based on stochastic model of microstructurally short crack growth. [Washington, DC]: National Aeronautics and Space Administration, 1988.

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4

Kitamura, Takayuki. Creep life prediction based on stochastic model of microstructurally short crack growth. [Washington, DC]: National Aeronautics and Space Administration, 1988.

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5

Radhakrishnan, V. Application of an energy-based life prediction model to bithermal and thermomechanical fatigue. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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6

Radhakrishnan, V. Application of an energy-based life prediction model to bithermal and thermomechanical fatigue. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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7

Brenner, Martin J. On-line robust modal stability prediction using wavelet processing. Edwards, Calif: National Aeronautics and Space Administration, Dryden Flight Research Center, 1998.

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8

Waskan, Jonathan A. Models and cognition: Prediction and explanation in everyday life and in science. Cambridge, Mass: MIT Press, 2006.

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9

Lifetime prediction and constitutive modelling for creep fatigue interaction. Berlin: Borntraeger, 1996.

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10

Jones, Steven P. Neural network models of simple mechanical systems illustrating the feasibility of accelerated life testing. [Washington, DC]: National Aeronautics and Space Administration, 1996.

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11

Hofman, Tadeusz. The prediction of thermodynamic properties of systems formed by chain-like molecules using a cell-hole group contribution model. Warszawa: Oficyna Wydawnicza Politechniki Warszawskiej, 2003.

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12

Hurd, Michael D. The predictive validity of subjective probabilities of survival. Cambridge, MA: National Bureau of Economic Research, 1997.

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13

Ray, Asok. Damage-mitigating control of a reusable rocket engine for high performance and extended life. Cleveland, Ohio: Lewis Research Center, 1995.

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14

The new Weibull handbook: Reliability & statistical analysis for predicting life, safety, risk, support costs, failures, and forecasting warranty claims, substantiation and accelerated testing, using Weibull, Log normal, crow-AMSAA, probit, and Kaplan-Meier models. Wyd. 5. North Palm Beach, Fla: R.B. Abernethy, 2006.

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15

M, Nissley D., Sheffler Keith D i United States. National Aeronautics and Space Administration., red. Thermal barrier coating life prediction model development. [Washington, DC]: National Aeronautics and Space Administration, 1991.

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16

Center, Lewis Research, red. Thermal barrier coating life prediction model: Second annual report. [Cleveland, Ohio]: Lewis Research Center, 1986.

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17

H, Pilsner B., i Lewis Research Center, red. Thermal barrier coating life prediction model: First annual report. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1985.

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18

Thermal barrier coating life prediction model: Second annual report. [Cleveland, Ohio]: Lewis Research Center, 1986.

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19

V, Hillery R., i United States. National Aeronautics and Space Administration., red. Thermal barrier coating life prediction model development: Final report. [Washington, DC]: National Aeronautics and Space Administration, 1989.

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20

United States. National Aeronautics and Space Administration, red. Thermal barrier coating life prediction model development: Second annual report. [East Hartford, CT]: United Technologies Corporation, Pratt & Whitney Group, Engineering Division., 1986.

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21

Allen, D. H. Life Prediction Model for Laminated Composite Structural Components/N90 22614/ll. Natl Technical Information, 1990.

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22

D, Sheffler K., Ortiz Milton i Lewis Research Center, red. Thermal barrier coating life prediction model development: Phase 1, final report. Cleveland, Ohio: NASA Lewis Research Center, 1989.

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23

H, Allen David, Texas A & M University. Mechanics and Materials Center. i Langley Research Center, red. A life prediction model for laminated composite structural components: Final technical report. College Station, Texas: Mechanics and Materials Center, Texas A&M University, 1990.

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24

Sreeramesh, Kalluri, Halford Gary R i United States. National Aeronautics and Space Administration., red. Application of an energy-based life prediction model to bithermal and thermomechanical fatigue. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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25

Sreeramesh, Kalluri, Halford Gary R i United States. National Aeronautics and Space Administration., red. Application of an energy-based life prediction model to bithermal and thermomechanical fatigue. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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26

R, Halford Gary, i United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., red. Application of thermal life prediction model to high-temperature aerospace alloys B1900+Hf and Haynes 188. [Washington, DC]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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27

J, Verilli Michael, Halford G. R i United States. National Aeronautics and Space Administration., red. A creep cavity growth model for creep-fatigue life prediction of a unidirectional W/Cu composite. [Washington, DC]: National Aeronautics and Space Administration, 1992.

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28

Thompson, Summer L., i Stephanie C. Dulawa. Pharmacological and Behavioral Rodent Models of OCD. Redaktor Christopher Pittenger. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228163.003.0035.

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Obsessive-compulsive disorder (OCD) is a severe psychiatric disorder characterized by obsessions and/or compulsions. Only half of patients respond to first-line pharmacological treatments, and symptom relief is typically partial, even in responders. Gaining a better understanding of OCD etiology could lead to better treatments, and potentially to prevention. Animal models are a useful tool for studying neurobiological mechanisms underlying psychiatric phenotypes. Effective use of animal models requires identification of reliable, quantifiable features of the disorder of interest that can be measured across species. The modeled phenotypes then require assessment for predictive validity: the accuracy with which a model makes accurate predictions about the human condition. Once a model has been shown to make accurate predictions, using it to study neurobiological mechanisms is justified. The validity of spontaneous and induced rodent models of OCD is reviewed in this chapter.
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29

Birch, Jonathan. The Rule under Attack. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198733058.003.0003.

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HRG has been criticized for being an ‘empty statement’ or tautology, for failing to yield predictions, and for failing to yield causal explanations of change. There is some justification for these charges, yet they do not undermine the value of HRG as an organizing framework. In response to the ‘tautology’ complaint, we should admit that HRG is tautology-like, in that it avoids detailed dynamical assumptions. But this is an advantage in an organizing framework, because it ensures its compatibility with a wide range of more detailed models. In response to the ‘prediction’ complaint, we should concede that HRG is not very useful for prediction, but the role of an organizing framework is not predictive. In response to the ‘causal explanation’ complaint, this chapter argues that HRG, by organizing our thinking about ultimate causes, generates understanding of those causes. It also compares favourably to other possible organizing frameworks.
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30

Rick, Lind, i Dryden Flight Research Facility, red. On-line robust modal stability prediction using wavelet processing. Edwards, Calif: National Aeronautics and Space Administration, Dryden Flight Research Center, 1998.

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31

Rick, Lind, i Dryden Flight Research Facility, red. On-line robust modal stability prediction using wavelet processing. Edwards, Calif: National Aeronautics and Space Administration, Dryden Flight Research Center, 1998.

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32

Rick, Lind, i Dryden Flight Research Facility, red. On-line robust modal stability prediction using wavelet processing. Edwards, Calif: National Aeronautics and Space Administration, Dryden Flight Research Center, 1998.

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33

Rick, Lind, i Dryden Flight Research Facility, red. On-line robust modal stability prediction using wavelet processing. Edwards, Calif: National Aeronautics and Space Administration, Dryden Flight Research Center, 1998.

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34

P, Phillips E., Swain M. H i Langley Research Center, red. Fatigue-life prediction methodology using small-crack theory. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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35

P, Phillips E., Swain M. H i Langley Research Center, red. Fatigue-life prediction methodology using small-crack theory. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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36

P, Phillips E., Swain M. H i Langley Research Center, red. Fatigue-life prediction methodology using small-crack theory. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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37

P, Phillips E., Swain M. H i Langley Research Center, red. Fatigue-life prediction methodology using small-crack theory. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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38

Tibaldi, Stefano, i Franco Molteni. Atmospheric Blocking in Observation and Models. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.611.

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The atmospheric circulation in the mid-latitudes of both hemispheres is usually dominated by westerly winds and by planetary-scale and shorter-scale synoptic waves, moving mostly from west to east. A remarkable and frequent exception to this “usual” behavior is atmospheric blocking. Blocking occurs when the usual zonal flow is hindered by the establishment of a large-amplitude, quasi-stationary, high-pressure meridional circulation structure which “blocks” the flow of the westerlies and the progression of the atmospheric waves and disturbances embedded in them. Such blocking structures can have lifetimes varying from a few days to several weeks in the most extreme cases. Their presence can strongly affect the weather of large portions of the mid-latitudes, leading to the establishment of anomalous meteorological conditions. These can take the form of strong precipitation episodes or persistent anticyclonic regimes, leading in turn to floods, extreme cold spells, heat waves, or short-lived droughts. Even air quality can be strongly influenced by the establishment of atmospheric blocking, with episodes of high concentrations of low-level ozone in summer and of particulate matter and other air pollutants in winter, particularly in highly populated urban areas.Atmospheric blocking has the tendency to occur more often in winter and in certain longitudinal quadrants, notably the Euro-Atlantic and the Pacific sectors of the Northern Hemisphere. In the Southern Hemisphere, blocking episodes are generally less frequent, and the longitudinal localization is less pronounced than in the Northern Hemisphere.Blocking has aroused the interest of atmospheric scientists since the middle of the last century, with the pioneering observational works of Berggren, Bolin, Rossby, and Rex, and has become the subject of innumerable observational and theoretical studies. The purpose of such studies was originally to find a commonly accepted structural and phenomenological definition of atmospheric blocking. The investigations went on to study blocking climatology in terms of the geographical distribution of its frequency of occurrence and the associated seasonal and inter-annual variability. Well into the second half of the 20th century, a large number of theoretical dynamic works on blocking formation and maintenance started appearing in the literature. Such theoretical studies explored a wide range of possible dynamic mechanisms, including large-amplitude planetary-scale wave dynamics, including Rossby wave breaking, multiple equilibria circulation regimes, large-scale forcing of anticyclones by synoptic-scale eddies, finite-amplitude non-linear instability theory, and influence of sea surface temperature anomalies, to name but a few. However, to date no unique theoretical model of atmospheric blocking has been formulated that can account for all of its observational characteristics.When numerical, global short- and medium-range weather predictions started being produced operationally, and with the establishment, in the late 1970s and early 1980s, of the European Centre for Medium-Range Weather Forecasts, it quickly became of relevance to assess the capability of numerical models to predict blocking with the correct space-time characteristics (e.g., location, time of onset, life span, and decay). Early studies showed that models had difficulties in correctly representing blocking as well as in connection with their large systematic (mean) errors.Despite enormous improvements in the ability of numerical models to represent atmospheric dynamics, blocking remains a challenge for global weather prediction and climate simulation models. Such modeling deficiencies have negative consequences not only for our ability to represent the observed climate but also for the possibility of producing high-quality seasonal-to-decadal predictions. For such predictions, representing the correct space-time statistics of blocking occurrence is, especially for certain geographical areas, extremely important.
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39

United States. National Aeronautics and Space Administration., red. Acoustic fatigue life prediction for nonlinear structures with multiple resonant modes: Final report. [Washington, DC: National Aeronautics and Space Administration, 1992.

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40

United States. National Aeronautics and Space Administration., red. [Life prediction and constitutive models for engine hot section anisotropic materials program]: [interim report]. [Washington, DC: National Aeronautics and Space Administration, 1992.

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41

Sang-Shik, Kim, i Langley Research Center, red. Environment enhanced fatigue crack propagation in metals: Inputs to fracture mechanics life prediction models. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1993.

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42

G, Meyer T., Walker K. P i United States. National Aeronautics and Space Administration., red. Life prediction and constitutive models for engine hot section anisotropic materials program: Final report. [Washington, DC: National Aeronautics and Space Administration, 1992.

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43

Life prediction and constitutive models for engine hot section anisotropic materials program: Annual status report. [East Hartford, CT: United Technologies Corporation, Pratt & Whitney, Engineering Division, 1986.

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44

Oaksford, Mike, i Nick Chater. Causal Models and Conditional Reasoning. Redaktor Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.5.

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There are deep intuitions that the meaning of conditional statements relate to probabilistic law-like dependencies. In this chapter it is argued that these intuitions can be captured by representing conditionals in causal Bayes nets (CBNs) and that this conjecture is theoretically productive. This proposal is borne out in a variety of results. First, causal considerations can provide a unified account of abstract and causal conditional reasoning. Second, a recent model (Fernbach & Erb, 2013) can be extended to the explicit causal conditional reasoning paradigm (Byrne, 1989), making some novel predictions on the way. Third, when embedded in the broader cognitive system involved in reasoning, causal model theory can provide a novel explanation for apparent violations of the Markov condition in causal conditional reasoning (Ali et al, 2011). Alternative explanations are also considered (see, Rehder, 2014a) with respect to this evidence. While further work is required, the chapter concludes that the conjecture that conditional reasoning is underpinned by representations and processes similar to CBNs is indeed a productive line of research.
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45

Chiu, Alan Wing Lun. Online classification and prediction of spontaneous seizure-like events in in-vitro epilepsy models. 2006.

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46

A, Swanson G., i United States. National Aeronautics and Space Administration., red. Life prediction and constitutive models for engine hot section anisotropic materials program: Second annual status report. [Washington, DC: National Aeronautics and Space Administration, 1987.

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47

Kleespies, Phillip M., i Christopher G. AhnAllen. Evaluating and Managing Suicide Risk in Veterans. Redaktor Phillip M. Kleespies. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199352722.013.14.

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This chapter examines the findings on which populations of military veterans are known to be at risk of suicide. The impact of military culture on veterans as well as the impact of deployment, combat trauma, and sexual trauma are discussed, as well as the difficulties of readjusting to civilian life, particularly when the veteran has served in a combat zone. The chapter reviews some of the barriers that veterans must deal with when in need of mental health care. The limits of suicide prediction are discussed and a model for assessing suicide risk using risk factors within high risk diagnoses, including risk in combat-related posttraumatic stress disorder, is presented. Finally, suggestions for managing suicide risk in veterans are discussed. Since veterans are more likely to own firearms and commit suicide with a firearm than nonveterans, an emphasis is placed on employing means restriction counseling for veterans at risk.
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48

Fitzpatrick, Mark W. Analytical method for the prediction of reliability and maintainability based life-cycle labor costs. 1996.

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49

1945-, McMeekin T. A., red. Predictive microbiology: Theory and application. Taunton, Somerset, England: Research Studies Press Ltd., 1993.

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50

Reliability, Life Testing and the Prediction of Service Lives: For Engineers and Scientists (Springer Series in Statistics). Springer, 2007.

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