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Graphs of Functions01:30

Graphs of Functions

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Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
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Graphs of Equations in Two Variables01:30

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An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
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Radical Chain-Growth Polymerization: Chain Branching01:17

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The skeletal structure of polymers synthesized via radical polymerization is always branched. For example, the polymerization of ethylene by radical polymerization results in a low-density grade of polyethylene with a heavily branched skeletal structure. Here, the radical site abstracts hydrogen from the growing chain, and the radical site shifts from the end (a primary carbon center) to anywhere within the growing chain (a secondary carbon center). Consequently, the part of the chain from the...
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Exponential Equations for Modeling Growth02:33

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Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
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Entropy Change in Reversible Processes01:10

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In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
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Related Experiment Video

Updated: Jan 4, 2026

Quantifying Branching Density in Rat Mammary Gland Whole-mounts Using the Sholl Analysis Method
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Quantifying Branching Density in Rat Mammary Gland Whole-mounts Using the Sholl Analysis Method

Published on: July 12, 2017

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Volume explored by a branching random walk on general graphs.

Ignacio Bordeu1,2,3,4, Saoirse Amarteifio5,6, Rosalba Garcia-Millan5,6

  • 1Department of Mathematics, Imperial College London, London, SW7 2AZ, UK. ib443@cam.ac.uk.

Scientific Reports
|November 1, 2019
PubMed
Summary

We analyzed the branching random walk (BRW) to understand how epidemics spread in various networks. Our findings reveal how network structure influences spreading dynamics and provide new ways to characterize real-world systems.

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Area of Science:

  • Mathematical modeling
  • Network science
  • Epidemiology

Background:

  • Branching processes model diverse phenomena, but lack spatial context.
  • Branching random walks (BRW) incorporate spatial aspects for phenomena like epidemics.
  • Previous BRW research had limited exact results, especially in higher dimensions.

Purpose of the Study:

  • To analytically and numerically investigate the volume explored by BRW in critical regimes.
  • To characterize spreading dynamics in various network environments.
  • To explore the relationship between network dimensionality and epidemic propagation.

Main Methods:

  • Developed analytical results for BRW scaling in general environments.
  • Employed numerical simulations to support analytical findings.
  • Applied BRW to analyze spectral properties of real-world networks.

Main Results:

  • Derived exact results for BRW volume exploration in critical regimes.
  • Demonstrated how graph dimensionality directly impacts viral process propagation rates.
  • Identified discrepancies in spreading behavior due to incomplete network information.

Conclusions:

  • The study provides a framework for understanding spatial epidemic spread using BRW.
  • Results offer insights into the behavior of viral processes on diverse network structures.
  • The findings yield valuable observables for characterizing real-world lattices, tissues, and networks.