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相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure 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.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
444
Random Sampling Method01:09

Random Sampling Method

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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...
11.0K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

47
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...
47
Sampling Plans01:23

Sampling Plans

170
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...
170
Sampling Methods: Overview01:06

Sampling Methods: Overview

292
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...
292

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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在多维雨采样中的参数优化方法.

Yuki Mitsuta1,2, Toshio Asada1,2

  • 1Department of Chemistry, Osaka Metropolitan University, 3-3-138, Sugimoto, Sumiyoshi-ku, Osaka 558-8585, Japan.

Journal of chemical theory and computation
|August 5, 2024
PubMed
概括

本研究介绍了一种优化的采样 (US) 方法,用于计算复杂的自由能源景观 (FEL). 这种新方法有效地控制了采样,使得高维度的FEL计算以前无法通过传统的美国技术实现.

科学领域:

  • 计算化学计算化学
  • 生物物理学的生物物理.
  • 统计力学 统计力学

背景情况:

  • 雨采样 (美国) 是计算自由能源景观 (FEL) 的关键技术.
  • 由于难以控制采样位置,多维FEL计算具有挑战性.
  • 现有的方法往往难以应对高维系统的复杂性.

研究的目的:

  • 开发一个优化的雨采样 (US) 方法,以加强对自由能源景观 (FEL) 计算中的采样位置的控制.
  • 为了使多维和高维FEL的有效计算.
  • 在FEL搜索中引入用于确定目标点的自动化方法.

主要方法:

  • 提出了一种通过引入目标点和最小化分布差异来优化美国参数的新方法.
  • 采用变化增强的采样来指导在目标点周围的采样.
  • 使用偏差潜力与非对角线条用于高效的多维FEL计算.
  • 开发了一种基于分布重叠的自动目标点选择算法.

主要成果:

  • 成功演示了该方法控制美国窗口数量的能力.
  • 计算了高维的FEL,包括一个16维的阿拉宁十酸的案例,这些与传统的US.无法处理.
  • 通过计算通过脂质双层和氨酸二形状变化的水透率来验证方法.

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结论:

  • 提议的优化美国方法显著提高了计算多维自由能源景观的控制和效率.
  • 这种方法克服了传统US的局限性,使得高维度FEL计算成为可能.
  • 开发的算法促进了自动化的FEL勘探和分析.