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相关概念视频

Quantifying X-Ray Fluorescence Data Using MAPS14:58

Quantifying X-Ray Fluorescence Data Using MAPS

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Here, we demonstrate the use of the X-ray fluorescence fitting software, MAPS, created by Argonne National Laboratory for the quantification of fluorescence microscopy data. The quantified data that results is useful for understanding the elemental distribution and stoichiometric ratios within a sample of interest.
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Using modern plastic extrusion and printing technologies, it is now possible to quickly and inexpensively produce physical models of X-ray CT data taken in a laboratory. The three -dimensional printing of tomographic data is a powerful visualization, research, and educational tool that may now be accessed by the preclinical imaging...
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A methodology for obtaining visual and quantitative root structure information from X-ray computed tomography data acquired in-soil is...
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How Data are Classified: Numerical Data00:59

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
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How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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Fixed Target Serial Data Collection at Diamond Light Source06:19

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We present a comprehensive guide to fixed target sample preparation, data collection, and data processing for serial synchrotron crystallography at Diamond beamline I24.
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相关实验视频

Updated: Jan 20, 2026

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从不完整的数据采样中重建X射线数据.

Kárel García Medina1,2, Ernesto Estevez Rams2, Reinhard B Neder1

  • 1Lehrstuhl für Kristallographie und Strukturphysik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany.

Journal of applied crystallography
|January 19, 2026
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的方法来重建在衍射模式中缺失的数据,这对于使用多个探测器的实验至关重要. 修改后的Papoulis-Gerchberg算法有效地填补了空白,提高了衍射分析中的数据完整性.

关键词:
帕普利斯格尔赫贝格的情况在X射线中,X射线的衍射效果是不同的.采样采样 采样采样

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Last Updated: Jan 20, 2026

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科学领域:

  • 衍射物理 衍射物理
  • 图像重建 图像的重建
  • 信号处理 信号处理

背景情况:

  • 由于实验设置,衍射模式可能会缺少数据,例如探测器之间的间隙.
  • 不完整的衍射数据限制了结构分析的准确性和完整性.

研究的目的:

  • 提出和验证一种用于在衍射模式中重建缺失信号信息的新方法.
  • 适应Papoulis-Gerchberg算法用于衍射数据的重建.

主要方法:

  • 为衍射模式量身定制的修改版帕普利斯-格尔赫伯格算法的开发.
  • 重建算法的数学公式. 重建算法的数学公式.
  • 使用模拟和实验衍射数据测试算法.

主要成果:

  • 拟议的算法成功地在衍射模式中重建了缺失的信号.
  • 在各种模拟和实验案例中展示了强度和性能.
  • 有效地处理衍射模式的特征,而不会失去一般性.

结论:

  • 修改后的Papoulis-Gerchberg算法提供了一个可靠的解决方案,用于重建缺失的衍射数据.
  • 这种方法增强了具有不完全角覆盖的衍射实验的实用性.
  • 经过验证的程序提高了基于衍射的分析的完整性.