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Related Concept Videos

Variance01:15

Variance

The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.The standard deviation measures the spread in the same units as the data.
Uniform Distribution01:19

Uniform Distribution

The uniform distribution is a continuous probability distribution of events with an equal probability of occurrence. This distribution is rectangular.Two essential properties of this distribution are The area under the rectangular shape equals 1. There is a correspondence between the probability of an event and the area under the curve.Further, the mean and standard deviation of the uniform distribution can be calculated when the lower and upper cut-offs, denoted as a and b,...
Mean Absolute Deviation01:13

Mean Absolute Deviation

The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
Standard Deviation01:10

Standard Deviation

The most commonly used measure of variation is the standard deviation. It is a numerical value measuring how far data values are from their mean. The standard deviation value is small when the data are concentrated close to the mean, exhibiting slight variation or spread. The standard deviation value is never negative, it is either positive or zero. The standard deviation is larger when the data values are more spread out from the mean, which means the data values are exhibiting more...
Two-Compartment Open Model: Extravascular Administration01:12

Two-Compartment Open Model: Extravascular Administration

The two-compartment model for extravascular administration represents a drug's absorption and distribution process. It features a central compartment, where the drug is first absorbed, and a peripheral compartment, which illustrates the drug's distribution throughout the body. The rate of change in drug concentration in the central compartment is calculated by three exponents: absorption, distribution, and elimination.
The absorption exponent (ka) indicates the speed at which the drug is...
Range Rule of Thumb to Interpret Standard Deviation01:13

Range Rule of Thumb to Interpret Standard Deviation

The range rule of thumb in statistics helps us calculate a dataset's minimum and maximum values with known standard deviation. This rule is based on the concept that 95% of all values in a dataset lie within two standard deviations from the mean.
For instance, the range rule of thumb can be used to find the tallest and the shortest student in a class, given the mean student height and standard deviation. If the mean student height is 1.6 m and the standard deviation, s is 0.05 m, the height of...

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Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells
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Large deviation function for the current in the open asymmetric simple exclusion process.

Jan de Gier1, Fabian H L Essler

  • 1Department of Mathematics and Statistics, The University of Melbourne, Victoria, Australia.

Physical Review Letters
|July 30, 2011
PubMed
Summary
This summary is machine-generated.

We studied particle flow in a one-dimensional system with boundary interactions. An exact formula for current statistics in the infinite system limit was conjectured, revealing insights into system dynamics.

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Area of Science:

  • Statistical Mechanics
  • Non-equilibrium Physics
  • Complex Systems

Background:

  • The asymmetric exclusion process is a fundamental model in statistical mechanics.
  • Understanding non-equilibrium systems with boundary conditions is crucial for various fields.
  • The model exhibits rich phase behavior, including four distinct stationary states.

Purpose of the Study:

  • To analyze the current statistics in the low and high density phases of the one-dimensional asymmetric exclusion process.
  • To investigate particle injection and extraction dynamics at system boundaries.
  • To derive an exact expression for the current large deviation function in the infinite system size limit.

Main Methods:

  • Analysis of current statistics at the first site of the system.
  • Focus on the low and high density phases.
  • Asymptotic analysis in the limit of infinite system size.

Main Results:

  • Detailed analysis of current fluctuations in specific phases.
  • Conjectured an exact expression for the current large deviation function.
  • Provided insights into the stationary state properties of the model.

Conclusions:

  • The study provides a significant step towards understanding the exact statistical properties of the asymmetric exclusion process.
  • The conjectured large deviation function offers a powerful tool for future research.
  • The findings contribute to the broader field of non-equilibrium statistical mechanics.