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Uncertainties associated with CPT data acquisition
Published in Guido Gottardi, Laura Tonni, Cone Penetration Testing 2022, 2022
Uncertainties associated with the measurement system can be reduced by the introduction of a detailed calibration and verification procedures and prescribed methodology for uncertainty quantification. This also enables the development of a performance-based cone penetrometer class scheme. The approach allows for easy comparison of cone penetrometers, regardless of manufacturer or supplier, and assists the appropriate selection of cone penetrometer for specific applications.
Beta and Alpha Particle Autoradiography
Published in Michael Ljungberg, Handbook of Nuclear Medicine and Molecular Imaging for Physicists, 2022
Anders Örbom, Brian W. Miller, Tom Bäck
A key benefit of single-particle digital autoradiography with alphas is the ability to quantify the activity directly. Beta emitters have a continuous energy spectrum ranging from low to high energies. Accordingly, most detectors employ an energy threshold for detection above noise levels. Also, there will be an energy dependent efficiency associated with the scintillator. These factors and beta spectra differences among isotopes require the use of isotope-specific calibration standards for quantification. Alpha particles, however, have discrete energies and a short range (e.g., 20–80 μm) in the scintillator detector. This makes them easy to detect at nearly 100 percent efficiency in single-particle detectors, without the need for relative activity calibration standards. Miller showed with the iQID approximately 100 per cent detection efficiency (within the uncertainty of certified electroplated alpha sources) for alphas [47]. Imaging and activity quantification is illustrated in Figure 30.14 with 211At (7.2h half-life) and estimating the activity using spatio-temporal information for each event.
Play
Published in Krystina Castella, Designing for Kids, 2018
STEM–STEAM–STEAM-D: Through play blocks, puzzles, sand, balls, crayons and paper, children begin to understand logical scientific thinking, such as the concept of cause and effect. They practice experimentation, observation, measurement, quantification, classification, counting, ordering and part–whole relations.
Quantitative and covariational reasoning opportunities provided by calculus textbooks: the case of the derivative
Published in International Journal of Mathematical Education in Science and Technology, 2022
A quantity is a measurable attribute of an object (Thompson, 1994b). Examples of objects in this study include a ball that is dropped from the upper observation deck of a tower, a person, and a house. Examples of quantities in the present study include the speed of a ball when it hits the ground, the height of a person, and the size of a house. Thompson (1993) distinguished between quantities and numerical values-the former have units of measurement and the latter do not. Thompson (1993) remarked that ‘quantities, when measured, have numerical values, but we need not measure them or know their measures to reason about them’ (pp. 165–166). For example, one may reason about how revenue from airline ticket sales for an airline compares (bigger or smaller) in two fiscal years, without having to know the actual revenues generated in each fiscal year. Quantification is the process of assigning numerical values to quantities (Thompson, 1994b). Quantification might, for example, entail evaluating a given total revenue function at a specific sales level such as units in order to determine the total revenue generated by a company during a particular trading period. We examined opportunities to interpret quantities (derivatives), engage in quantification, and to reason about units of measure for quantities, respectively, provided in expository sections, examples, and exercises from the two textbooks analysed in this study.
Students’ quantitative reasoning about an absolute extrema optimization problem in a profit maximization context
Published in International Journal of Mathematical Education in Science and Technology, 2019
Thompson [22] distinguished between a quantity and a numerical value: A quantity has a unit of measurement but a numerical value does not. Thompson [22,pp.165–166] pointed out that ‘quantities, when measured, have numerical values, but we need not measure them or know their measures to reason about them.’ For example, a student may reason about how a manufacturer’s cost at one level of output compares (bigger or smaller) with the manufacturer’s cost at another level of output, without having to know the actual numerical values of the cost at each of the output levels. Examples of quantities in this study include total cost, total revenue, and total profit. In the remainder of the paper, we refer to these quantities as cost, revenue, and profit. Quantification is the process of assigning numerical values to quantities [23]. A quantitative operation is the process of forming a new quantity from other quantities [23]. ‘Comparing two quantities with the intent to find the excess of one against the other’ is a specific example of a quantitative operation formed by comparing two quantities additively [22,p.166].