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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Poisson Probability Distribution01:09

Poisson Probability Distribution

A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by

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Related Experiment Video

Updated: Jun 16, 2026

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry
12:14

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry

Published on: August 12, 2013

Leaf optical system modeled as a stochastic process.

C J Tucker, M W Garratt

    Applied Optics
    |February 20, 2010
    PubMed
    Summary
    This summary is machine-generated.

    A new stochastic model accurately predicts how dicot leaves absorb, reflect, and transmit light across wavelengths. This leaf radiation model uses physical and physiological properties for precise light interaction estimations.

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    Last Updated: Jun 16, 2026

    The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry
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    Published on: August 12, 2013

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    Area of Science:

    • Plant Biology
    • Biophysics
    • Optics

    Background:

    • Understanding light interactions with plant leaves is crucial for photosynthesis and remote sensing.
    • Existing models may not fully capture the complex optical properties of leaves.

    Purpose of the Study:

    • To develop a stochastic model for predicting leaf radiation interactions.
    • To simulate absorbed, reflected, and transmitted radiation as a function of wavelength for dicot leaves.

    Main Methods:

    • Developed a stochastic leaf radiation model based on physical and physiological leaf properties.
    • Represented the leaf optical system as a Markov process with wavelength-specific transition matrices.
    • Calculated optical probabilities using leaf thickness, structure, pigment composition, and water content.

    Main Results:

    • The model accurately predicts absorbed, reflected, and transmitted radiation for normal incidence light.
    • Predictions cover the spectral interval of 0.40–2.50 micrometers.
    • Simulation results closely match measured values for dicot leaf optical properties.

    Conclusions:

    • The developed stochastic Markov process model provides accurate estimations of dicot leaf radiative transfer.
    • This model enhances our ability to understand and predict light interactions with vegetation.