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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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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

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Step growth polymerization involves bi or multifunctional monomers. Bifunctional monomers react to form linear step growth polymers, whereas multifunctional monomers react to form non-linear or branched polymers.
As the step-growth polymerization involves step-wise condensation of monomers, the molecular weight also builds up eventually. Consequently, high molecular weight polymers are obtained at the late stages of the polymerization, where 99% of monomers have been consumed.
The extent of the...
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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...
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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...
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Sampling Plans01:23

Sampling Plans

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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...
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一个高效的增长模式算法 (GrowPAL) 用于集群结构预测.

Carlos López-Castro1, Filiberto Ortiz-Chi2, Gabriel Merino1

  • 1Departamento de Física Aplicada, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mérida 97310, Yucatán, México.

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概括
此摘要是机器生成的。

新的增长模式算法 (GrowPAL) 在大型原子集群中有效地找到最低能量结构. 这种计算方法可以降低在各种集群系统中识别全球最小值的优化成本.

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科学领域:

  • 计算化学的计算化学
  • 材料科学 材料科学 材料科学
  • 纳米技术纳米技术

背景情况:

  • 在大型原子集群中识别最低能量异构体在计算上具有挑战性.
  • 现有的方法往往需要广泛的优化,增加计算成本.
  • 了解集群生长途径对于设计新材料至关重要.

研究的目的:

  • 引入一种新的算法,即增长模式算法 (GrowPAL),用于在原子集群中有效识别全球最小值.
  • 评估GrowPAL在各种集群系统上的有效性,包括伦纳德-斯,萨顿-陈和集群.
  • 分析算法的性能并确定潜在的增长途径.

主要方法:

  • GrowPAL通过间位式 (I型) 添加机制将原子添加到较小的集群中来产生初始种子.
  • 该算法在Lennard-Jones (LJ) 集群中测试了多达80个原子,包括挑战LJ38和LJ69等最小值.
  • 用一个解构方案来分析GrowPAL的优势和局限性,并确定用于研究增长的"前"结构.

主要成果:

  • 在LJ集群中,GrowPAL成功地确定了具有挑战性的全球最小值,其优化比现有方法要少.
  • 对萨顿-星团 (5-80个原子) 的应用揭示了三种新的最低能量形式.
  • GrowPAL准确地确定了所有报道的集群 (8-15个原子) 的最小值.

结论:

  • GrowPAL提供了一种实用且高效的解决方案,用于识别层次原子系统中的全球最小值.
  • 该算法显著降低了与集群结构预测相关的计算成本.
  • GrowPAL促进了复杂的集群景观的探索和发现新的稳定结构.