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

BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

408
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
408
Neural Circuits01:25

Neural Circuits

1.3K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.3K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

351
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
351
Survival Tree01:19

Survival Tree

89
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
89
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

111
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
111
Current Growth And Decay In RL Circuits01:30

Current Growth And Decay In RL Circuits

3.9K
The current growth and decay in RL circuits can be understood by considering a series RL circuit consisting of a resistor, an inductor, a constant source of emf, and two switches. When the first switch is closed, the circuit is equivalent to a single-loop circuit consisting of a resistor and an inductor connected to a source of emf. In this case, the source of emf produces a current in the circuit. If there were no self-inductance in the circuit, the current would rise immediately to a steady...
3.9K

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

The many faces of extrapulmonary tuberculosis: A case series.

Journal of family medicine and primary care·2026
Same author

Implications of regional variations in climate change vulnerability and mitigation behaviour for social-climate dynamics.

Nature communications·2026
Same author

When simple is enough: Binary models capture social complexity in coupled human-environment systems.

Mathematical biosciences·2026
Same author

Prejudice and objectivity, not social influence, determine long-term outcomes in coupled opinion-environment dynamics in polarized populations.

Journal of theoretical biology·2026
Same author

Phase resetting in human stem cell derived cardiomyocytes explains complex cardiac arrhythmias.

PLoS computational biology·2026
Same author

Global functional shifts in trees driven by alien naturalization and native extinction.

Nature plants·2026
Same journal

Demonstration of a quantum C-NOT gate in a time-multiplexed fully reconfigurable photonic processor.

Nature communications·2026
Same journal

Nonlinear quantum light source with van der Waals ferroelectric NbOX<sub>2</sub> (X = Br, I).

Nature communications·2026
Same journal

Antagonistic histone H2A variants and autonomous heterochromatin formation shape epigenomic patterns in Arabidopsis.

Nature communications·2026
Same journal

The long tail of nitrate pollution in groundwater challenges governance of global water quality.

Nature communications·2026
Same journal

Select microbial metabolites promote tau aggregation in a murine tauopathy model.

Nature communications·2026
Same journal

Warming climate has lengthened global intense tropical cyclone seasons.

Nature communications·2026
查看所有相关文章

相关实验视频

Updated: Jul 14, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K

通过深度学习预测离散时间分叉.

Thomas M Bury1, Daniel Dylewsky2, Chris T Bauch2

  • 1Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, Canada. thomas.bury@mcgill.ca.

Nature communications
|October 10, 2023
PubMed
概括
此摘要是机器生成的。

深度学习模型现在可以通过识别离散时间分叉来检测系统中的关键过渡. 与传统方法相比,这种方法提供了更好的早期预警信号,增强了系统监控.

更多相关视频

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.1K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K

相关实验视频

Last Updated: Jul 14, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.1K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K

科学领域:

  • 复杂系统科学 复杂系统科学
  • 机器学习 机器学习
  • 动态系统理论 动态系统理论

背景情况:

  • 自然和人造系统可以经历突然的关键过渡.
  • 对这些转变的早期预警信号对于预测和缓解至关重要.
  • 目前的深度学习模型主要关注连续时间的分叉,忽视离散时间的动态.

研究的目的:

  • 训练深度学习分类器用于离散时间分叉的早期预警信号.
  • 评估分类器在各种模拟和实验数据上的表现.
  • 将深度学习方法与已建立的早期预警信号进行比较.

主要方法:

  • 开发了一个深度学习分类器,用于训练5个局部离散时间分叉的模拟数据.
  • 使用来自生理学,经济学和生态学的离散时间模型测试了分类器.
  • 验证的性能实验数据从小心集成表现出周期翻倍的分叉.

主要成果:

  • 深度学习分类器表现出比常见的早期预警信号更高的灵敏度和特异性.
  • 性能在各种噪声强度和接近分叉的速度上都很强大.
  • 能够准确地预测特定的分叉,包括周期翻倍,尼马克-萨克和折叠分叉.

结论:

  • 深度学习有效地预测离散时间分叉,为早期预警提供了强大的工具.
  • 这种方法在准确性和稳定性方面超过了传统方法.
  • 深度学习具有显著的潜力,可以彻底改变对关键转型系统的监控.