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

Run Charts01:12

Run Charts

59
Run charts serve as an essential instrument for visualizing the performance of various processes over time, enabling the identification of trends and patterns crucial for quality improvement. These charts map out a series of data points chronologically, offering insights into the stability and efficiency of a process. A run chart's creation involves plotting data points on a graph, with the time intervals on the horizontal axis and the specific measurements on the vertical axis. For...
59
Interpreting Run Charts01:25

Interpreting Run Charts

100
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
100
High-Level and Low-Level Awareness01:19

High-Level and Low-Level Awareness

267
Controlled processes in human consciousness represent high-alert mental states where individuals deliberately focus their attention on achieving specific goals. Controlled processes can be seen in situations like mastering new technology, where a person might become so absorbed that they ignore surrounding distractions. Such processes involve selective attention, requiring one to concentrate on particular elements of experience while disregarding others. These are governed by executive...
267

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

A fusion deep Q-learning and particle swarm optimization algorithm for adaptive resource allocation in cloud computing circumstances.

Scientific reports·2026
Same author

A Hybrid Approach to Universal Intrusion Detection Systems for Automotive Security.

Sensors (Basel, Switzerland)·2026
Same author

Online exam cheating detection and blockchain trusted deposit based on YOLOv12.

Scientific reports·2025
Same author

HoRNS-CNN model: an energy-efficient fully homomorphic residue number system convolutional neural network model for privacy-preserving classification of dyslexia neural-biomarkers.

Brain informatics·2025
Same author

Restoring private autism dataset from sanitized database using an optimized key produced from enhanced combined PSO-GWO framework.

Scientific reports·2024
Same author

Post-quantum healthcare: A roadmap for cybersecurity resilience in medical data.

Heliyon·2024
Same journal

Novel Parent Survey Measures Sensory Behaviors Incorporating Sensory Modality and Stimulus Intensity.

Heliyon·2026
Same journal

Expression of concern: "SQSTM1/p62 promotes the progression of gastric cancer through epithelial-mesenchymal transition" [Heliyon 10 (2024) e24409].

Heliyon·2026
Same journal

Expression of concern: "TL1A promotes metastasis and EMT process of colorectal cancer" [Heliyon 10 (2024) e24392].

Heliyon·2026
Same journal

Expression of concern: "Factors affecting timing of surgery following neoadjuvant chemoradiation for esophageal cancer" [Heliyon 9 (2023) e23212].

Heliyon·2026
Same journal

Expression of concern: "On stratified single-valued soft topogenous structures" [Heliyon 10 (2024) e27926].

Heliyon·2026
Same journal

Expression of concern: "Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN" [Heliyon 10 (2024) e27198].

Heliyon·2026
查看所有相关文章

相关实验视频

Updated: Jul 1, 2025

Fabrication and Use of MicroEnvironment microArrays MEArrays
11:57

Fabrication and Use of MicroEnvironment microArrays MEArrays

Published on: October 11, 2012

10.0K

LPMSAEF:基于轻量级过程挖掘的软件架构评估框架,用于安全性和性能分析.

Mahdi Sahlabadi1, Ravie Chandren Muniyandi2, Zarina Shukur2

  • 1Department of Information Security Engineering, Soonchunhyang University, Chungnam, Asan-si, 31538, South Korea.

Heliyon
|March 8, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用过程挖掘和彼得里网的轻量级软件架构评估框架. 它有效地检测复杂系统中的安全性和性能问题,并减少了工作量,帮助软件架构师.

关键词:
轻量级的早期和晚期评估.培养物网复杂而异质的架构 复杂而异质的架构过程采矿的过程采矿.软件架构 软件架构 软件架构

更多相关视频

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
11:29

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

9.1K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

8.7K

相关实验视频

Last Updated: Jul 1, 2025

Fabrication and Use of MicroEnvironment microArrays MEArrays
11:57

Fabrication and Use of MicroEnvironment microArrays MEArrays

Published on: October 11, 2012

10.0K
Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
11:29

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

9.1K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

8.7K

科学领域:

  • 软件工程 软件工程 软件工程
  • 计算机科学 计算机科学
  • 信息系统信息系统信息系统

背景情况:

  • 传统的软件架构文档方法 (例如,统一建模语言图) 面临着局限性.
  • 仅从代码文物中提取软件架构通常会导致以代码为导向的,而不是以架构为导向的图形.
  • 软件模型与实际代码实现之间存在差距,阻碍了有效的架构评估.

研究的目的:

  • 提出一个轻量级的软件架构评估框架,以解决从业人员的担忧.
  • 通过分析软件架构的安全性和性能行为来弥合模型-代码差距.
  • 为检测复杂和异质软件架构中的问题提供可行和有效的解决方案.

主要方法:

  • 使用过程挖掘和培养网来分析安全性和性能.
  • 在六个案例研究中实施框架,以验证其有效性.
  • 将不同的过程挖掘算法进行比较,以达成对架构描述的共识,并使用可视化.

主要成果:

  • 该框架成功地检测了复杂软件架构中的安全性和性能问题.
  • 它证明了可行性和有效性,与传统方法相比,需要更少的时间和精力.
  • 案例研究证实了该框架能够处理异质架构的能力.

结论:

  • 拟议的框架为软件架构评估提供了一种实际的方法.
  • 分析以前的系统版本,并使用可追溯性辅助工具来检测偏差和不一致.
  • 该框架支持软件架构师改进系统设计和维护.