Books on the topic 'SIZE PREDICTION'

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1

Conway, Thomas Michael. A computer programme for the prediction of hydrocyclone performance, parameters, and product-size distributions. Randburg, South Africa: Mintek (Ore-Dressing Division), printed and published by the Council for Mineral technology, 1985.

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2

K, De Groh Kim, and NASA Glenn Research Center, eds. The dependence of atomic oxygen undercutting of protected polyimide Kapton® H upon defect size. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.

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3

Snyder, Aaron. The dependence of atomic oxygen undercutting of protected polyimide Kapton® H upon defect size. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.

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4

J, Vaz Roy, and Klabunde Thomas, eds. Antitargets: Prediction and prevention of drug side effects. Weinheim: Wiley-VCH, 2008.

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5

J, Vaz Roy, and Klabunde Thomas, eds. Antitargets: Prediction and prevention of drug side effects. Weinheim: Wiley-VCH, 2008.

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6

Canada. Natural Resources Canada. Canadian Forest Service. Great Lakes Forestry Centre. Predicting canopy closure for habitat modeling. Ottawa: Natural Resources Canada., 1995.

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7

Bernreuter, D. L. Development of site specific response spectra. Washington, DC: Division of Engineering Safety, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1987.

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8

Bernreuter, D. L. Development of site specific response spectra. Washington, DC: Division of Engineering Safety, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1987.

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9

Verhagen, Philip. Case studies in archaeological predictive modelling [sic. [Leiden]: Leiden University Press, 2007.

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10

Verhagen, Philip. Case studies in archaeological predictive modelling [sic. [Leiden]: Leiden University Press, 2007.

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11

Inc, Dendron Resource Surveys, and Great Lakes Forest Research Centre., eds. Predicting canopy closure for habitat modeling. Sault Ste. Marie, Ont: Great Lakes Forestry Centre, 1995.

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12

Ltd, Dendron Resource Surveys, Great Lakes Forestry Centre, Canada-Ontario Subsidiary Agreement on Northern Ontario Development., and Northern Forestry Program (Canada), eds. Predicting canopy closure for habitat modeling. Sault Ste. Marie, Ont: Great Lakes Forestry Centre, 1995.

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13

Dolph, K. Leroy. Prediction of periodic basal area increment for young-growth mixed conifers in the Sierra Nevada. Berkeley, Calif: U.S. Dept. of Agriculture, Forest Service, Pacific Southwest Forest and Range Experiment Station, 1988.

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14

Leathwick, J. R. Predictive models of archaeological site distributions in New Zealand. Wellington, N.Z: Department of Conservation, 2000.

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15

C, Newman J., and United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., eds. Analysis and prediction of multiple-site damage (MSD) fatigue crack growth. [Washington, DC]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1992.

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16

Kirk, M. T. Approximate techniques for predicting size effects on cleavage fracture toughness (Jc). Washington, DC: Division of Engineering, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1993.

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17

Rothermel, Richard C. Predicting behavior and size of crown fires in the northern Rocky Mountains. [Ogden, Utah]: U.S. Dept. of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, 1991.

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18

McDonald, Greg N. Earthquake site conditions in the Wasatch Front urban corridor, Utah. Salt Lake City, Utah: Utah Geological Survey, 2008.

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19

Steele, Brian M. Predicting site index and height for selected tree species of northern Idaho. Ogden, UT: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1986.

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20

Steele, Brian M. Predicting site index and height for selected tree species of northern Idaho. Ogden, Utah: U.S.D.A. Forest Service, Intermountain Research Station, 1986.

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21

Sawyer, Richard. Determining minimum sample sizes for estimating prediction equations for college freshman grade average. Iowa City, Iowa: American College Testing Program, 1987.

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22

Pilgram, Tom. Predicting archaeological sites from environmental variables: A mathematical model for the Sierra Nevada foothills, California. Oxford, England: B.A.R., 1987.

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23

Matsumoto, Hiroko. Predicting Surgical Site Infection in Pediatric Patients Undergoing Spinal Deformity Surgery. [New York, N.Y.?]: [publisher not identified], 2020.

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24

Richards, Thomas. A predictive model of Aboriginal archaeological site distribution in the Otway Range. [Melbourne]: Aboriginal Affairs Victoria, 1998.

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25

Coastal and Hydraulics Laboratory (U.S. Army Engineer Waterways Experiment Station). A Predictive model for sediment transport at the Portland disposal site, Maine. [Concord, MA: The Corps, 1998.

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26

Ertsen, Anna Catharina Doesjka. Ecohydrological response modelling: Predicting plant species response to changes in site conditions. Utrecht: Koninklijk Nederlands Aardrijkskundig Genootschap, Faculteit Ruimtelijke Wetenschappen, Universiteit Utrecht, 1998.

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27

R, Miles P., BBN Laboratories, and United States. Minerals Management Service. Alaska OCS Region., eds. Prediction of drilling site-specific interaction of industrial acoustic stimuli and endangered whales, Beaufort Sea (1985). Cambridge, MA: BBN Laboratories, 1986.

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28

Un modelo predictivo arqueológico: El caso de la minería del cobre durante el siglo XVI, en la región de Tierra Caliente, Michoacán. Zamora, Michoacán: El Colegio de Michoacán, 2011.

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29

G, Skogerboe John, U.S. Army Engineer Waterways Experiment Station., United States. Army. Corps of Engineers., and United States. Environmental Protection Agency., eds. Prediction of surface runoff water quality from Black Rock Harbor dredged material placed in an upland disposal site. Vicksburg, Miss: Dept. of the Army, Waterways Experiment Station, Corps of Engineers, 1987.

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30

Roy, Kunal. Quantitative structure-activity relationships in drug design, predictive toxicology, and risk assessment. Hershey PA: Medical Information Science Reference, an imprint of IGI Global, 2015.

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31

Cotaras, Frederick D. Acoustic propagation loss predictions for a site on the Bermuda rise at low and very low frequencies. Dartmouth, N.S: National Defence, Research and Development Branch, 1992.

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32

Lau, Boulderson B. An evaluation of oocyte size in multiple regressions predicting gonad weight from body weight: A test using hawaiian ehu, Etelis carbunculus. [La Jolla, Calif.]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, [Southwest Fisheries Science Center, 1994.

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33

Endres, J. C. T. Scale-up and system influences on hydrodynamic and mass transfer model parameters (especially drop-side mass coefficients) for prediction of extraction column performance. Manchester: UMIST, 1993.

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34

U.S. Nuclear Regulatory Commission. Office of Nuclear Regulatory Research. Division of Engineering Technology. and Argonne National Laboratory, eds. Bases for predicting the earliest penetrations due to SCC for alloy 600 on the secondary side of PWR steam generators. Washington, DC: Division of Engineering Technology, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 2001.

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35

Hann, David W. Equations for predicting height-to-crown-base, 5-year diameter-growth rate, 5-year height-growth rate, 5-year mortality rate, and maximum size-density trajectory for Douglas-fir and western hemlock in the coastal region of the Pacific Northwest. Corvallis, Or: Oregon State University, College of Forestry, Forest Research Laboratory, 2003.

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36

Łapuszek, Marta. Intensywność i prognoza zmian położenia den koryt rzecznych w lewobrzeżnych dopływach górnej Wisły: The establish and prediction of the river channel evolution of the left-side tributaries of the Upper Vistula River = Intensité et la prévision de l'évolution du lit de la riviére des tributaires du côté gauche de la Haute Vistule. Kraków: Politechnika Krakowska im. Tadeusza Kościuszki, 2013.

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37

Westoff, Charles F., and R. G. Potter. Third Child: A Study in the Prediction of Fertility. Princeton University Press, 2016.

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38

Westoff, Charles F., and R. G. Potter. Third Child: A Study in the Prediction of Fertility. Princeton University Press, 2015.

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39

Westoff, Charles F., and R. G. Potter. Third Child: A Study in the Prediction of Fertility. Princeton University Press, 2015.

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40

Sneider, Christopher Anthony. Prediction of maximum density for coarse grain soils containing gravel size particles. 1991.

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41

Dearie, Sigmund. Notebook: Game of Throne Prediction Dracarys Wide Ruled , Journal for Writing, Size 6 X 9 , 110 Pages. Independently Published, 2020.

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42

Adoption of automation innovations in college libraries: A study of the effects of size and financial factors affecting prediction of adoption of eight technological innovations. Ann Arbor, Mich: University Microfilms International, 1988.

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43

Pal, Ranadip. Predictive Modeling of Drug Sensitivity. Elsevier Science & Technology Books, 2016.

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44

Pal, Ranadip. Predictive Modeling of Drug Sensitivity. Elsevier Science & Technology Books, 2016.

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45

Folkers, Gerd, Raimund Mannhold, Thomas Klabunde, Hugo Kubinyi, and Roy J. Vaz. Antitargets: Prediction and Prevention of Drug Side Effects. Wiley & Sons, Incorporated, John, 2008.

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46

Folkers, Gerd, Raimund Mannhold, Thomas Klabunde, Hugo Kubinyi, and Roy J. Vaz. Antitargets: Prediction and Prevention of Drug Side Effects. Wiley & Sons, Limited, John, 2008.

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47

Clarke, Andrew. The Metabolic Theory of Ecology. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199551668.003.0012.

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The model of West, Brown & Enquist (WBE) is built on the assumption that the metabolic rate of cells is determined by the architecture of the vascular network that supplies them with oxygen and nutrients. For a fractal-like network, and assuming that evolution has minimised cardiovascular costs, the WBE model predicts that s=metabolism should scale with mass with an exponent, b, of 0.75 at infinite size, and ~ 0.8 at realistic larger sizes. Scaling exponents ~ 0.75 for standard or resting metabolic rate are observed widely, but far from universally, including in some invertebrates with cardiovascular systems very different from that assumed in the WBE model. Data for field metabolic rate in vertebrates typically exhibit b ~ 0.8, which matches the WBE prediction. Addition of a simple Boltzmann factor to capture the effects of body temperature on metabolic rate yields the central equation of the Metabolic Theory of Ecology (MTE). The MTE has become an important strand in ecology, and the WBE model is the most widely accepted physical explanation for the scaling of metabolic rate with body mass. Capturing the effect of temperature through a Boltzmann factor is a useful statistical description but too simple to qualify as a complete physical theory of thermal ecology.
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48

Kwon, Rachel J. Size as a Predictor of Malignancy of Adrenal Cortical Carcinoma. Edited by Randall Owen. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199384075.003.0042.

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This chapter provides a summary of a landmark study in endocrine surgery. Does size correlate with malignancy in adrenal cortical carcinoma? Starting with that question, it describes the basics of the study, including year study began, year study was published, study location, who was studied, who was excluded, how many patients, study design, study intervention, follow-up, endpoints, results, and criticism and limitations. The chapter briefly reviews other relevant studies and information, gives a summary and discusses implications, and concludes with a relevant clinical case involving postoperative treatment of a patient with adrenocortical carcinoma who has a large adrenal incidentaloma.
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49

(Editor), Roy J. Vaz, Thomas Klabunde (Editor), Raimund Mannhold (Series Editor), Hugo Kubinyi (Series Editor), and Gerd Folkers (Series Editor), eds. Antitargets: Prediction and Prevention of Drug Side Effects (Methods and Principles in Medicinal Chemistry). Wiley-VCH, 2008.

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50

Approximate techniques for predicting size effects on cleavage fracture toughness (Jc). Washington, DC: Division of Engineering, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1993.

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