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Related Concept Videos

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...
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...
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...
Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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...
Sampling Distribution01:12

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...

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Related Experiment Video

Updated: Jul 5, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

Improved transition path sampling methods for simulation of rare events.

Manan Chopra1, Rohit Malshe, Allam S Reddy

  • 1Department of Chemical Engineering, University of Wisconsin, Madison, Wisconsin 53706-1691, USA.

The Journal of Chemical Physics
|April 17, 2008
PubMed
Summary
This summary is machine-generated.

New algorithms significantly enhance transition path sampling (TPS) efficiency for complex systems. These methods accelerate the study of molecular transitions on rugged energy landscapes, reducing computational demands by over 100-fold.

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Standardized Method for Measuring Collection Efficiency from Wipe-sampling of Trace Explosives
07:22

Standardized Method for Measuring Collection Efficiency from Wipe-sampling of Trace Explosives

Published on: April 10, 2017

Related Experiment Videos

Last Updated: Jul 5, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

Standardized Method for Measuring Collection Efficiency from Wipe-sampling of Trace Explosives
07:22

Standardized Method for Measuring Collection Efficiency from Wipe-sampling of Trace Explosives

Published on: April 10, 2017

Area of Science:

  • Computational Chemistry and Physics
  • Statistical Mechanics
  • Biophysics

Background:

  • Complex systems often feature rugged free energy landscapes with deep minima separated by barriers.
  • Understanding transitions between these minima is crucial in physics, chemistry, and biology.
  • Traditional transition path sampling (TPS) methods are computationally intensive.

Purpose of the Study:

  • To develop more efficient algorithms for transition path sampling (TPS).
  • To improve the accuracy and speed of simulating transitions between free energy minima.
  • To address the computational limitations of existing TPS approaches.

Main Methods:

  • Introduction of biased shooting moves for more efficient sampling of reactive trajectories.
  • Incorporation of local transition path simulations within the transition state ensemble.
  • Application to a representative two-dimensional rough energy surface model.

Main Results:

  • Achieved significant improvements in the efficiency of TPS simulations.
  • Demonstrated gains in efficiency exceeding two orders of magnitude compared to traditional TPS.
  • Enhanced accuracy in characterizing the transition state ensemble.

Conclusions:

  • The developed algorithms substantially reduce the computational cost of TPS.
  • These advancements enable more effective numerical investigation of complex system transitions.
  • The improved efficiency opens new possibilities for studying challenging problems in computational science.