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

Improving Translational Accuracy02:07

Improving Translational Accuracy

14.1K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.1K
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

5.3K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
5.3K
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

941
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
941
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

1.1K
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
1.1K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

522
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
522
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

380
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
380

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

AI-driven adaptive adversaries and the erosion of cryptographic trust in public key systems.

Journal of analytical science and technology·2026
Same author

What Country, University, or Research Institute, Performed the Best on Covid-19 During the First Wave of the Pandemic?: Bibliometric analysis of scientific literature - analysing a 'snapshot in time' of the first wave of COVID-19.

Annals of data science·2024
Same author

Super-forecasting the 'technological singularity' risks from artificial intelligence.

Evolving systems·2023
Same author

Disease X vaccine production and supply chains: risk assessing healthcare systems operating with artificial intelligence and industry 4.0.

Health and technology·2023
Same author

Advancing the cybersecurity of the healthcare system with self-optimising and self-adaptative artificial intelligence (part 2).

Health and technology·2022
Same author

New and emerging forms of data and technologies: literature and bibliometric review.

Multimedia tools and applications·2022
Same journal

Non-local modeling of enhancer-promoter interactions, a correspondence on "LOCO-EPI: Leave-one-chromosome-out (LOCO) as a benchmarking paradigm for deep learning based prediction of enhancer-promoter interactions".

Applied intelligence (Dordrecht, Netherlands)·2026
Same journal

AI-driven 5G IoT e-nose for whiskey classification.

Applied intelligence (Dordrecht, Netherlands)·2025
Same journal

DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis.

Applied intelligence (Dordrecht, Netherlands)·2025
Same journal

ROCIP: robust continuous inertial position tracking for complex actions emerging from the interaction of human actors and environment.

Applied intelligence (Dordrecht, Netherlands)·2025
Same journal

RETRACTED ARTICLE: Deep learning system to screen coronavirus disease 2019 pneumonia.

Applied intelligence (Dordrecht, Netherlands)·2024
Same journal

Temporally extended goal recognition in fully observable non-deterministic domain models: Temporally extended goal recognition in FOND planning.

Applied intelligence (Dordrecht, Netherlands)·2024
查看所有相关文章

相关实验视频

Updated: Jun 18, 2026

Preparation and Testing of Impedance-based Fluidic Biochips with RTgill-W1 Cells for Rapid Evaluation of Drinking Water Samples for Toxicity
11:19

Preparation and Testing of Impedance-based Fluidic Biochips with RTgill-W1 Cells for Rapid Evaluation of Drinking Water Samples for Toxicity

Published on: March 7, 2016

合作透测试套件用于新兴的生成性AI算法.

Petar Radanliev1,2,3

  • 1Department of Computer Science, University of Oxford, Wolfson Building, Parks Rd, OX1 3QG Oxford, England.

Applied intelligence (Dordrecht, Netherlands)
|October 20, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一套新的透测试套件,以保护生成AI免受量子威胁. 它有效地识别和修复漏洞,增强整体系统安全性.

关键词:
先进的持久威胁 先进的持久威胁区块链增强的日志记录动态应用程序安全测试生成型人工智能安全性安全性量子电阻密码学 量子电阻密码学

更多相关视频

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces
12:04

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

相关实验视频

Last Updated: Jun 18, 2026

Preparation and Testing of Impedance-based Fluidic Biochips with RTgill-W1 Cells for Rapid Evaluation of Drinking Water Samples for Toxicity
11:19

Preparation and Testing of Impedance-based Fluidic Biochips with RTgill-W1 Cells for Rapid Evaluation of Drinking Water Samples for Toxicity

Published on: March 7, 2016

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces
12:04

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

科学领域:

  • 网络安全 网络安全
  • 量子计算是一种量子计算.
  • 人工智能的人工智能

背景情况:

  • 生成型人工智能系统面临着重大网络威胁和量子计算挑战.
  • 现有的安全措施不足以抵御先进的对抗性和量子辅助攻击.

研究的目的:

  • 提出和评估一套新的透测试套件,以保护生成AI免受量子安全问题的威胁.
  • 整合先进的安全测试方法和量子抗性协议.

主要方法:

  • 使用OWASP ZAP,Burp Suite,SonarQube和Fortify.com进行动态和静态应用程序安全测试 (DAST和SAST) 的集成.
  • 通过互动应用安全测试 (IAST) 与对比评估进行实时监控.
  • 使用Hyperledger Fabric进行区块链增强的日志记录,以防改记录.
  • 实施量子抗性加密协议 (基于格子的,RLWE).
  • 用AI驱动的红色团队模拟用于对抗和量子辅助攻击模拟.

主要成果:

  • 识别和修复300多个漏洞,在两周内减少70%的严重问题.
  • 通过区块链记录的漏洞的解决效率为90%.
  • 证明了量子抗性协议对模拟量子攻击的弹性.
  • 成功安全的API加密和数据传输.

结论:

  • 开发的透测试套件建立了一个新协议,用于保护生成性AI系统.
  • 综合方法有效地解决了传统和量子相关的安全漏洞.
  • 这些发现强调了在人工智能和量子计算时代主动和先进安全措施的重要性.