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Libri sul tema "Estimations de gradient"

Autore: Grafiati

Pubblicato: 15 febbraio 2025

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1

Glasserman, Paul. Gradient estimation via perturbation analysis. Boston: Kluwer Academic, 1991.

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2

Fu, Michael. Conditional Monte Carlo: Gradient Estimation and Optimization Applications. Boston, MA: Springer US, 1997.

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3

Fu, Michael. Conditional Monte Carlo: Gradient estimation and optimization applications. Boston: Kluwer Academic Publishers, 1997.

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4

Skifstad, Kurt D. High-Speed Range Estimation Based on Intensity Gradient Analysis. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3112-7.

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5

Skifstad, Kurt D. High-speed range estimation based on intensity gradient analysis. New York: Springer-Verlag, 1991.

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6

Skifstad, Kurt D. High-speed range estimation based on intensity gradient analysis. New York: Springer-Verlag, 1991.

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7

Cuddapah, Prabhakara, e Goddard Space Flight Center, a cura di. Global warming estimation from microwave sounding unit. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1998.

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8

Atherton, Gary L. Grading and paving construction: A practical approach to management and estimating. Concord, Calif: Sandcastles, 1986.

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9

Stevens, James A. Managing the stands of the future based on the lessons of the past: Estimating western timber species product recovery by using historical data. [Portland, Or.]: U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 2000.

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10

Gradient estimation via perturbation analysis. Boston: Kluwer Academic Publishers, 1991.

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11

Error Norm Estimation in the Conjugate Gradient Algorithm. Society for Industrial and Applied Mathematics, 2024.

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12

Conditional Monte Carlo: Gradient Estimation and Optimization Applications. Springer, 2011.

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13

High-Speed Range Estimation Based on Intensity Gradient Analysis. Springer, 2012.

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14

Skifstad, Kurt D. High-Speed Range Estimation Based on Intensity Gradient Analysis. Springer, 2011.

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15

Skifstad, Kurt D. High-Speed Range Estimation Based on Intensity Gradient Analysis. Springer, 2012.

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16

High-speed range estimation based on intensity gradient analysis. New York: Springer-Verlag, 1991.

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17

Joynt, Gavin M., e Gordon Y. S. Choi. Blood gas analysis in the critically ill. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0072.

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Abstract (sommario):

Arterial blood gases allow the assessment of patient oxygenation, ventilation, and acid-base status. Blood gas machines directly measure pH, and the partial pressures of carbon dioxide (PaCO2) and oxygen (PaO2) dissolved in arterial blood. Oxygenation is assessed by measuring PaO2 and arterial blood oxygen saturation (SaO2) in the context of the inspired oxygen and haemoglobin concentration, and the oxyhaemoglobin dissociation curve. Causes of arterial hypoxaemia may often be elucidated by determining the alveolar–arterial oxygen gradient. Ventilation is assessed by measuring the PaCO2 in the context of systemic acid-base balance. A rise in PaCO2 indicates alveolar hypoventilation, while a decrease indicates alveolar hyperventilation. Given the requirement to maintain a normal pH, functioning homeostatic mechanisms result in metabolic acidosis, triggering a compensatory hyperventilation, while metabolic alkalosis triggers a compensatory reduction in ventilation. Similarly, when primary alveolar hypoventilation generates a respiratory acidosis, it results in a compensatory increase in serum bicarbonate that is achieved in part by kidney bicarbonate retention. In the same way, respiratory alkalosis induces kidney bicarbonate loss. Acid-base assessment requires the integration of clinical findings and a systematic interpretation of arterial blood gas parameters. In clinical use, traditional acid-base interpretation rules based on the bicarbonate buffer system or standard base excess estimations and the interpretation of the anion gap, are substantially equivalent to the physicochemical method of Stewart, and are generally easier to use at the bedside. The Stewart method may have advantages in accurately explaining certain physiological and pathological acid base problems.

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18

An introduction to cut-off grade estimation. Littleton, CO: Society for Mining, Metallurgy, and Exploration, 2008.

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19

Rendu, Jean-Michel. An Introduction to Cut-Off Grade Estimation, Second Edition. Society for Mining, Metallurgy, and Exploration, 2014.

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20

Kallmeyer, Andrea, José Luis Zamorano, G. Locorotondo, Madalina Garbi, José Juan Gómez de Diego e Miguel Ángel García Fernández. Non-invasive haemodynamic assessment. Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780199599639.003.0005.

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Abstract (sommario):

The diagnostic power of two-dimensional (2D) echocardiography resides not only in its capability of providing anatomical information and of studying myocardial contractile function, but also in the possibility of performing a non-invasive haemodynamic assessment. Such non-invasive haemodynamic assessment is the subject of this chapter.2D echocardiography, colour flow imaging, and Doppler modality make this haemodynamic assessment possible, by studying the following parameters: ◆ Blood flow velocities. ◆ Transvalvular pressure gradients. ◆ Valvular areas. ◆ Stroke volume, regurgitant volume, and regurgitant fraction. ◆ Cardiac function.The application of these concepts in clinical practice will be explained through this chapter. They can be summarized in the following points: ◆ The study of valvular insufficiencies. ◆ The study of the valvular stenosis. ◆ The study of intracardiac shunts. ◆ The study of myocardial systolic and diastolic function. ◆ The estimation of intracardiac pressures.Finally, non-invasive haemodynamic study represents an alternative to invasive procedures in some clinical circumstances and it is very important in the diagnostic and therapeutic decision making. Therefore, it is necessary for the cardiologist to understand how this echocardiographic study is performed, as well as its advantages and limitations.

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