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Related Concept Videos

Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Sampling Methods: Overview01:06

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of sampling...
Streamlines, Streaklines, and Pathlines01:18

Streamlines, Streaklines, and Pathlines

A streamline represents the trajectory that is always tangent to the fluid's velocity vector at any given point. The velocity of a fluid particle is always directed along the streamline, ensuring the particle continuously follows the streamline's path. Streamlines are particularly useful for visualizing the overall direction of flow in a fluid system, and they provide an instantaneous representation of the flow's velocity field. In steady flow, where conditions do not change over time,...
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
Planar Rigid-Body Motion01:22

Planar Rigid-Body Motion

Understanding the movement of a rigid body in planar motion involves recognizing that every particle within this body is traversing a path that maintains a consistent distance from a specific plane. This concept is fundamental in the study of physics and mechanical engineering, and it allows us to comprehend better how objects move in space.
Planar motion is typically divided into three distinct categories. The first is rectilinear translation, demonstrated by a subway train that moves along...
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...

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Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

Steered transition path sampling.

Nicholas Guttenberg1, Aaron R Dinner, Jonathan Weare

  • 1James Franck Institute, The University of Chicago, Chicago, Illinois 60637, USA.

The Journal of Chemical Physics
|July 12, 2012
PubMed
Summary
This summary is machine-generated.

We developed a novel path sampling method to efficiently calculate statistical properties of stochastic dynamics. This approach enhances the sampling of dynamic events, improving accuracy in complex systems.

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Area of Science:

  • Computational physics
  • Statistical mechanics
  • Stochastic processes

Background:

  • Accurate statistical properties of stochastic dynamics are crucial for understanding complex systems.
  • Traditional methods like shooting struggle with barrier crossing and absorbing states.

Purpose of the Study:

  • To introduce a novel path sampling method for arbitrary stochastic dynamics.
  • To enable efficient calculation of statistical properties, especially currents of dynamic events.
  • To address limitations of existing methods in barrier crossing and systems with absorbing states.

Main Methods:

  • Decomposing trajectories in time.
  • Estimating probabilities of satisfying progress constraints.
  • Modifying dynamics based on these probabilities.
  • Reweighting to compute averages.

Main Results:

  • The method effectively calculates transition probabilities in barrier crossing problems.
  • It accurately determines survival probabilities in strongly diffusive systems with absorbing states.
  • Demonstrated suitability for sampling currents of dynamic events.

Conclusions:

  • The proposed path sampling method offers a robust alternative for analyzing stochastic dynamics.
  • It provides significant advantages for systems with challenging event sampling requirements.
  • The method's flexibility makes it applicable to a wide range of dynamic processes.