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Related Concept Videos

Entropy02:39

Entropy

32.5K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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Entropy01:18

Entropy

3.1K
The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
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Sample Handling01:02

Sample Handling

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Transportation of samples from the collection point to the laboratory, as well as storage and preservation techniques, are crucial for maintaining sample integrity and ensuring accurate and reliable test results.
Samples should be transported carefully from collection points to the laboratory. They should be properly sealed and clearly labeled to prevent cross-contamination. To preserve the sample integrity, optimal temperature conditions during transport are essential. This could involve using...
284
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

2.9K
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.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
2.9K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

3.5K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
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Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

22.3K
Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
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Experimental Study of the Relationship Between Particle Size and Methane Sorption Capacity in Shale
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Transporting experimental results with entropy balancing.

Kevin P Josey1, Seth A Berkowitz2, Debashis Ghosh1

  • 1Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.

Statistics in Medicine
|May 21, 2021
PubMed
Summary
This summary is machine-generated.

Entropy balancing effectively transports experimental treatment effects to target populations. This robust method ensures consistent average treatment effect estimates, even with model misspecification, using only target population data.

Keywords:
calibrationcausal inferenceeffect modificationgeneralizability

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Services Research

Background:

  • Transporting treatment effects across populations is crucial for generalizability.
  • Existing methods face challenges with model misspecification and data requirements.

Purpose of the Study:

  • To introduce and evaluate entropy balancing for transporting experimental treatment effects.
  • To assess the robustness and efficiency of entropy balancing compared to alternative methods.

Main Methods:

  • Entropy balancing for treatment effect transport.
  • Doubly robust estimation framework.
  • Comparison with alternative transport methods using simulation and real-world data.

Main Results:

  • Entropy balancing provides consistent estimates of target population average treatment effects.
  • The method is robust to misspecification of the outcome model or trial participation probability.
  • Entropy balancing demonstrated efficiency and robustness in finite-sample performance comparisons.

Conclusions:

  • Entropy balancing is a reliable and efficient technique for transporting treatment effects.
  • It offers a robust solution for generalizability in comparative effectiveness research.
  • The method was successfully applied to transport findings from a diabetes blood pressure trial to a broader US adult population.