Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Fast Fourier Transform01:10

Fast Fourier Transform

1.0K
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
1.0K
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

790
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
790
Parallel Processing01:20

Parallel Processing

819
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
819
Rapidly Varying Flow01:24

Rapidly Varying Flow

561
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
561
Average and Instantaneous Velocity Vectors01:12

Average and Instantaneous Velocity Vectors

8.9K
To calculate other physical quantities in kinematics, the time variable must be introduced. The time variable not only allows us to state where an object is (its position) during its motion, but also how fast it’s moving. The speed at which an object is moving is given by the rate at which the position changes with time. For each position, a particular time is assigned. If the details of the motion at each instant are not important, the rate is usually expressed as the average velocity v.
8.9K
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.6K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.6K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

GitHub and Google Colab for Sharing Data and Code.

Journal of insurance medicine (New York, N.Y.)·2026
Same author

Regular Expressions: Mixed Effects Models.

Journal of insurance medicine (New York, N.Y.)·2017
Same author

T-Tests: The BMI Study Continues.

Journal of insurance medicine (New York, N.Y.)·2017
Same author

Mortality and Disease Prevalence among the Oldest Old.

Journal of insurance medicine (New York, N.Y.)·2016
Same author

Converting Lab Report Files into Usable Data.

Journal of insurance medicine (New York, N.Y.)·2016
Same author

Regular Expressions: The Build Study Vignette.

Journal of insurance medicine (New York, N.Y.)·2016

相关实验视频

Updated: Feb 28, 2026

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM
07:19

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

Published on: June 28, 2017

10.8K

快速的实际/预期数据处理

David Wesley

    Journal of insurance medicine (New York, N.Y.)
    |February 25, 2026
    PubMed
    概括

    处理大型死亡数据集用于公司或注册表分析可能很慢. 本研究介绍了一种基于Python的方法,使用Polars库来显著加快数据处理时间.

    科学领域:

    • 数据科学数据科学数据科学
    • 计算统计学 计算统计学
    • 生物信息学是一种生物信息学.

    背景情况:

    • 死亡率分析通常涉及来自公司或注册源的大型数据集.
    • 延长数据处理时间阻碍了交互式分析和及时洞察力.
    • 有效的数据处理对于可复制和可扩展的研究至关重要.

    研究的目的:

    • 介绍一种方法来加快死亡率分析中大量数据集的处理.
    • 为了展示Python中Polars数据框架库在性能优化方面的实用性.
    • 为了克服交互式数据分析中的处理时间瓶.

    主要方法:

    • 使用Polars数据框架库,这是一个高性能数据处理工具.
    • 实现了用于数据处理工作流程的Python编程.
    • 应用于典型死亡率分析数据集的方法.

    主要成果:

    • 与传统方法相比,实现了数据处理时间的显著加快.
    • 证明了Polars在处理大规模数据集的有效性.
    • 实现了更具互动性和高效的分析过程.

    结论:

    关键词:
    死亡率 死亡率 死亡率极地 极地 是一个极地.在这里,Python是Python.实际到预期的实际情况.方法论 方法论 方法论旋转表的旋转表.

    更多相关视频

    Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
    09:43

    Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

    Published on: November 22, 2019

    6.8K
    An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
    11:03

    An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

    Published on: December 4, 2017

    9.1K

    相关实验视频

    Last Updated: Feb 28, 2026

    Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM
    07:19

    Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

    Published on: June 28, 2017

    10.8K
    Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
    09:43

    Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

    Published on: November 22, 2019

    6.8K
    An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
    11:03

    An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

    Published on: December 4, 2017

    9.1K
    • 在Python中的Polars库为加速死亡率数据分析提供了显著的优势.
    • 这种方法解决了处理大数据集的关键挑战.
    • 研究人员可以从更快的处理中获益,以便进行更动态的数据探索.