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

Turnover Number and Catalytic Efficiency01:19

Turnover Number and Catalytic Efficiency

10.2K
The turnover number of an enzyme is the maximum number of substrate molecules it can transform per unit time. Turnover numbers for most enzymes range from 1 to 1000 molecules per second. Catalase has the known highest turnover number, capable of converting up to 2.8×106 molecules of hydrogen peroxide into water and oxygen per second. Lysozyme has the lowest known turnover number of half a molecule per second.
Chymotrypsin is a pancreatic enzyme that breaks down proteins during digestion....
10.2K
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

171
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
171
Mass Analyzers: Overview01:13

Mass Analyzers: Overview

741
The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
741
Production Efficiency01:01

Production Efficiency

16.9K
Net production efficiency (NPE) is the efficiency at which organisms assimilate energy into biomass for the next trophic level. Due to low metabolic rates and less energy spent on thermoregulatory processes, the NPE of ectotherms (cold-blooded animals) is 10 times higher than endotherms (warm-blooded animals).
16.9K
Optimizing Chromatographic Separations01:15

Optimizing Chromatographic Separations

440
Optimizing chromatographic separations is crucial for obtaining clean separations in a minimum amount of time. Optimization is required for several factors, including kinetic effects related to band broadening, plate height, capacity factor, and separation factor.
Band broadening refers to spreading solute bands as they travel through the column. This broadening can impact resolution. Plate height (H) represents the length required for one theoretical plate. A lower plate height corresponds to...
440
Quality Assurance01:19

Quality Assurance

161
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
161

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

Retraction Note: Plant disease recognition using residual convolutional enlightened Swin transformer networks.

Scientific reports·2026
Same author

Improved COOT optimization: An approach to multilevel thresholding in image segmentation.

Scientific reports·2025
Same author

IAROA: An Enhanced Attraction-Repulsion Optimisation Algorithm Fusing Multiple Strategies for Mechanical Optimisation Design.

Biomimetics (Basel, Switzerland)·2025
Same author

A Novel Artificial Eagle-Inspired Optimization Algorithm for Trade Hub Location and Allocation Method.

Biomimetics (Basel, Switzerland)·2025
Same author

IPO: An Improved Parrot Optimizer for Global Optimization and Multilayer Perceptron Classification Problems.

Biomimetics (Basel, Switzerland)·2025
Same author

Success History Adaptive Competitive Swarm Optimizer with Linear Population Reduction: Performance benchmarking and application in eye disease detection.

Computers in biology and medicine·2025
Same journal

Computational Intelligence in Stochastic Reconstruction of Porous Microstructures for Image-Based Poro/Micro-Mechanical Modeling.

Archives of computational methods in engineering : state of the art reviews·2026
Same journal

A review of recent advances in data-driven computer vision methods for structural damage evaluation: algorithms, applications, challenges, and future opportunities.

Archives of computational methods in engineering : state of the art reviews·2025
Same journal

A Scoping Review on Simulation-Based Design Optimization in Marine Engineering: Trends, Best Practices, and Gaps.

Archives of computational methods in engineering : state of the art reviews·2024
Same journal

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives.

Archives of computational methods in engineering : state of the art reviews·2023
Same journal

Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides.

Archives of computational methods in engineering : state of the art reviews·2023
Same journal

A Comprehensive Analysis of Deep Learning-Based Approaches for Prediction and Prognosis of Infectious Diseases.

Archives of computational methods in engineering : state of the art reviews·2023
查看所有相关文章

相关实验视频

Updated: Jul 25, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K

一个关于Aquila优化器的全面调查

Buddhadev Sasmal1, Abdelazim G Hussien2,3,4, Arunita Das1

  • 1Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal India.

Archives of computational methods in engineering : state of the art reviews
|June 26, 2023
PubMed
概括
此摘要是机器生成的。

阿奎拉优化器 (AO) 是一种以自然为灵感的算法,在复杂的优化任务中表现出强的性能. 本调查详细介绍了AO的改进,并证实了其与其他算法相比的竞争结果.

更多相关视频

Optimization, Test and Diagnostics of Miniaturized Hall Thrusters
12:22

Optimization, Test and Diagnostics of Miniaturized Hall Thrusters

Published on: February 16, 2019

9.0K
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.6K

相关实验视频

Last Updated: Jul 25, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
Optimization, Test and Diagnostics of Miniaturized Hall Thrusters
12:22

Optimization, Test and Diagnostics of Miniaturized Hall Thrusters

Published on: February 16, 2019

9.0K
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.6K

科学领域:

  • 计算智能是一种计算智能.
  • 优化算法 优化算法
  • 大自然启发的计算

背景情况:

  • 阿奎拉优化器 (AO) 是一个基于人口的自然灵感优化算法 (NIOA),于2021年开发.
  • AO模仿了鱼鸟捕食猎物的行为.
  • 它因其在解决复杂和非线性优化问题的有效性而迅速获得认可.

研究的目的:

  • 为了展示Aquila优化器的最新调查.
  • 记录和分析增强的AO变体及其多样化的应用.
  • 严格评估 AO 与竞争对手 NIOA 的业绩.

主要方法:

  • 对现有的关于AO变体和应用的文献进行了全面的审查.
  • 使用标准数学基准函数对AO与同行NIOA进行比较分析.
  • 对算法性能指标的经验评估.

主要成果:

  • AO在处理复杂和非线性优化挑战方面表现出显著的有效性.
  • 增强的AO变体在各种应用领域显示出更好的性能.
  • 对比研究证实了AO相对于其他最先进的NIOA的竞争结果.

结论:

  • 阿奎拉优化器是优化领域的一个强大而有竞争力的算法.
  • 对AO变异和应用的进一步研究是有必要的.
  • AO是解决复杂计算问题的宝贵工具.