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

Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

67
Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
67
Contaminants and Errors01:16

Contaminants and Errors

82
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
82
Interpreting R Charts01:22

Interpreting R Charts

49
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
49
Random Error01:04

Random Error

798
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
798
The X̄ Chart00:58

The X̄ Chart

98
The  x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality...
98
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.2K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.2K

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

Novel Mutations in the MC2R Gene in a Patient With Familial Glucocorticoid Deficiency (FGD): A Case Report and Functional Study.

Molecular genetics & genomic medicine·2026
Same author

Stereopsis impairment and its association with fovea-disc angle in congenital superior oblique palsy patients with compensatory head posture: a cross-sectional study.

Frontiers in medicine·2026
Same author

Missing Not at Random: A Potential Hidden Loneliness Profile in Caregiver-Recipient Dyads With Cognitive Impairment.

Geriatrics & gerontology international·2026
Same author

Eutectic-Based Polymer Electrolyte With High Ionic Conductivity by Regulating Solvation for Solid-State Lithium Metal Batteries.

Angewandte Chemie (International ed. in English)·2026
Same author

High-performance X-ray imaging enabled by <i>in situ</i> recrystallized antimony(III)-based halide glass-ceramics.

Chemical communications (Cambridge, England)·2026
Same author

Study on the Process and Mechanism of Preparing Lanthanum Carbonate from Rare Earth Chloride Solution.

Materials (Basel, Switzerland)·2026

相关实验视频

Updated: May 24, 2025

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

8.2K

通过结合统计过程控制和错误曲线模型,选择局部容忍限的工作流.

Xin Yi1,2, Yanbo Song2, Hanyin Zhang2

  • 1The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.

Medical physics
|February 28, 2025
PubMed
概括

这项研究开发了一种方法,用于在放射治疗的患者特异性质量保证 (QA) 中设定局部耐受性极限. 这种新方法有助于医学物理学家评估错误敏感性,并改善临床实践.

关键词:
错误曲线的错误曲线错误灵敏度是错误的灵敏度.当地的耐受性限制.患者特定的QA统计过程控制统计过程控制

更多相关视频

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation
13:04

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation

Published on: January 18, 2022

3.8K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

648

相关实验视频

Last Updated: May 24, 2025

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

8.2K
Experimental and Data Analysis Workflow for Soft Matter Nanoindentation
13:04

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation

Published on: January 18, 2022

3.8K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

648

科学领域:

  • 医学物理 医学物理
  • 辐射瘤学 辐射瘤学
  • 质量保证 质量保证 质量保证

背景情况:

  • 患者特异性质量保证 (QA) 是复杂的,需要局部耐受性限制.
  • 万能限值可能不适合所有临床情况.
  • 医学物理学家需要方法来确定和验证局部界限.

研究的目的:

  • 制定一个全面的方法来确定适当的局部耐受性极限在患者特定的QA.
  • 提供一个定量方法来评估这些极限的错误敏感性.

主要方法:

  • 在RapidArc图纸中模拟的多叶合仪 (MLC) 位置错误.
  • 从没有错误的计划中提取了六个质量保证指标 (GP10,GP50,μGI50,PTV95,PTV5,PTVmean).
  • 使用统计过程控制建立了局部容忍限值.
  • 开发错误曲线模型来评估质量保证指标对MLC错误的敏感性.
  • 使用二进制分类性能指标验证的容忍限度.

主要成果:

  • 对于个别质量保证指标的理论检测极限在1.93毫米 (PTV95) 到3.52毫米 (μGI50) 之间.
  • 对PTV95的局部容忍限值检测到系统的MLC误差>0.6毫米,检测率为76.19%.
  • 结合GP10和PTV95的公差值,实现了超过0.6毫米的误差检测率的80.16%.

结论:

  • 拟议的工作流程整合了对患者特异性QA的局部耐受性极限的建立和验证.
  • 提供了一个实用的工具来设定临床相关的极限.
  • 为医学物理学家提供了一种定量方法来评估错误灵敏度.