Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

505
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
505
Crossed Aldol Reaction Using Strong Bases: Directed Aldol Reaction00:56

Crossed Aldol Reaction Using Strong Bases: Directed Aldol Reaction

2.8K
The reaction between two different carbonyl compounds comprising α hydrogen in the presence of a strong base like lithium diisopropylamide (LDA) to form a crossed aldol product is known as a directed aldol reaction. The directed aldol reaction is depicted in Figure 1.
2.8K
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Network Function of a Circuit01:25

Network Function of a Circuit

941
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
941
Ziegler–Natta Chain-Growth Polymerization: Overview01:17

Ziegler–Natta Chain-Growth Polymerization: Overview

4.1K
Ziegler–Natta polymerization is another form of addition or chain‐growth polymerization used for synthesizing linear polymers over branched polymers. The catalyst used for polymerization is the Ziegler–Natta catalyst, named after Karl Ziegler and Giulio Natta, who developed it in 1953. This catalyst is an organometallic complex of titanium tetrachloride and triethyl aluminum, with the active form of the catalyst being an alkyl titanium compound. Using the Ziegler–Natta...
4.1K
Anionic Chain-Growth Polymerization: Mechanism01:04

Anionic Chain-Growth Polymerization: Mechanism

2.6K
The mechanism for anionic chain-growth polymerization involves initiation, propagation, and termination steps. In the initiation step, a nucleophilic anion, such as butyl lithium, initiates the polymerization process by attacking the π bond of the vinylic monomer. As a result, a carbanion, stabilized by the electron‐withdrawing group, is generated. The resulting carbanion acts as a Michael donor in the propagation step and attacks the second vinylic monomer, which acts as a Michael...
2.6K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Expressed and private opinion dynamics in signed networks based on path-dependence framework.

Chaos (Woodbury, N.Y.)·2026
Same author

Optimal inter-layer connections for maximizing synchronizability in two-layer chain network.

Chaos (Woodbury, N.Y.)·2025
Same author

Human prophylaxis-driven cooperative spreading between information and epidemics in duplex networks.

Chaos (Woodbury, N.Y.)·2025
Same author

Selection and optimization of drive nodes in drive-response networks.

Chaos (Woodbury, N.Y.)·2025
Same author

Stepwise reconstruction of higher-order networks from dynamics.

Chaos (Woodbury, N.Y.)·2024
Same author

Dynamical vaccination behavior with risk perception and vaccination rewards.

Chaos (Woodbury, N.Y.)·2024

関連する実験動画

Updated: Feb 22, 2026

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

3.4K

2層チェーンネットワークの同期能力: 誘導対非誘導

Ziyang Wang1, Juan Wei2, Ziting Tang1

  • 1School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei 430072, China.

Chaos (Woodbury, N.Y.)
|February 20, 2026
PubMed
まとめ

この研究は,インターレイヤエッジが2層チェーンネットワークの同期を最適化する方法を示しています. 戦略的な配置とエッジの数は,ネットワークの同期能力を大幅に高めます.

さらに関連する動画

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K

関連する実験動画

Last Updated: Feb 22, 2026

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

3.4K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K

科学分野:

  • ネットワーク科学 ネットワーク科学
  • 複雑なシステム 複雑なシステム
  • システム生物学 システム生物学

背景:

  • 多くの複雑なシステムにおいて,同期は極めて重要です.
  • 多層ネットワークは,単層ネットワークよりも機能が強化されています.
  • 層間のカップリングを理解することは,ネットワークの行動を制御する鍵です.

研究 の 目的:

  • 同様のチェーン構造を持つ二層ネットワークの同期能力を調査する.
  • 同期を最大限にするために,最適な配置と層間のエッジの数を決定します.
  • 層間のコップリング強度とエッジの方向性が同期に与える影響を分析する.

主な方法:

  • 2層の非誘導および誘導連鎖ネットワークの体系的な分析.
  • 最適な層間縁の構成 (位置,数,方向) を特定する.
  • カップリング強さの関数として同期能力の数学分析.

主要な成果:

  • 2つの指向されたインターレイヤエッジの最適な配置は,非指向のチェーンにおける同期を最大化します.
  • 同期能力は,層間のコップリング強度とエッジ構成に敏感です.
  • インターレイヤエッジの数とタイプ (方向付け/非方向付け) は,方向付けチェーンにおける同期に影響を及ぼします.

結論:

  • 層間接続の戦略的構造設計は,多層鎖ネットワークの同期を最適化するために不可欠です.
  • 発見は,同期ネットワーク行動のエンジニアリングのための理論的指針を提供します.
  • この研究は,ネットワークダイナミクスにおける層間のエッジ特性の重要性を強調しています.