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相关概念视频

Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

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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.
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.
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

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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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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
84
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

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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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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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相关实验视频

Updated: Jul 25, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

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算法的变化来实现信念函数的最大.

Joaquín Abellán1, Alejandro Pérez-Lara1, Serafín Moral-García1

  • 1Department of Computer Science and Artificial Intelligence, University of Granada, 18014 Granada, Spain.

Entropy (Basel, Switzerland)
|June 28, 2023
PubMed
概括

证据理论 (TE) 使用最大 (ME) 来量化信息,但它的计算是复杂的. 本研究介绍了一种修改后的算法,它减少了计算步骤,提高了TE中ME在不完整信息场景中的适用性.

科学领域:

  • 信息理论 信息理论
  • 决策科学 决策科学 决策科学
  • 数学基础数学基础的基础

背景情况:

  • 经典的概率理论 (PT) 与不准确或不完整的信息作斗争.
  • 证据理论 (TE) 为不精确的概率提供了一个框架,其中最大的 (ME) 量化了证据.
  • ME的计算复杂性限制了其在TE中的实际应用.

研究的目的:

  • 解决与证据理论 (TE) 中计算最大 (ME) 相关的计算挑战.
  • 为ME计算提出一个修改后的算法,以提高效率.
  • 增强ME在TE的实用适用性,用于涉及不确定的数据的现实问题.

主要方法:

  • 在证据理论 (TE) 中开发了计算最大 (ME) 的现有算法的变化.
  • 修改后的算法专注于在每个计算步骤中减少可能的功率集的大小.
  • 在所需的计算步骤数量方面分析了新算法的效率.

主要成果:

  • 修改后的算法大大减少了计算最大 (ME) 所需的步骤数量.
  • 通过代地减少功率集大小,可以降低计算成本.
  • 拟议的变化为 TE 内的 ME 计算提供了更有效的方法.
关键词:
信念的功能是信仰的功能.的最大值 的最大值不确定性衡量不确定性的措施.

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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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结论:

  • 修改后的算法提供了一种更易于计算的方法,用于确定证据理论 (TE) 中的最大 (ME).
  • 这一改进预计将增加ME在TE信息量化中的采用和实用性.
  • 这项研究有助于在具有不准确或不完整数据的领域更广泛地应用 TE.