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Time-Series Graph00:54

Time-Series Graph

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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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...
466
Graphs of Functions01:30

Graphs of Functions

210
Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
210
Exponential Equations for Modeling Growth02:33

Exponential Equations for Modeling Growth

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Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
180
State Space Representation01:27

State Space Representation

496
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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Graphs of Equations in Two Variables01:30

Graphs of Equations in Two Variables

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An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
155

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Updated: Jan 8, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.5K

Emerging activity temporal hypergraph: A model for generating realistic time-varying hypergraphs

Marco Mancastroppa1, Giulia Cencetti1, Alain Barrat1

  • 1CPT, CNRS, Université de Toulon, Aix Marseille Univ, Turing Center for Living Systems, 13009 Marseille, France.

Physical review. E
|December 23, 2025
PubMed
まとめ
この要約は機械生成です。

A new model, the Emerging Activity Temporal Hypergraph (EATH), generates synthetic temporal hypergraphs that mimic real-world group interactions. This allows for better understanding of complex systems and dynamical processes, even with limited data.

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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

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関連する実験動画

Last Updated: Jan 8, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

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科学分野:

  • Complex Systems Science; Network Science; Data Science

背景:

  • Time-varying group interactions are fundamental to complex systems.; Temporal hypergraphs capture higher-order, time-dependent interactions.; Empirical datasets are often incomplete, necessitating surrogate models.

研究 の 目的:

  • Introduce a novel temporal hypergraph model (EATH) for generating synthetic datasets.; Enable the study of dynamical processes on complex interaction networks.; Facilitate understanding of systems with limited or incomplete interaction data.

主な方法:

  • Developed the Emerging Activity Temporal Hypergraph (EATH) model.; EATH uses node activity dynamics and memory mechanisms to generate interactions.; Validated EATH against empirical face-to-face interaction datasets.

主要な成果:

  • EATH successfully generated surrogate temporal hypergraphs mirroring empirical data properties.; Simulations of higher-order contagion dynamics showed comparable outcomes on real and synthetic data.; Demonstrated EATH's flexibility in creating tunable and hybrid hypergraphs.

結論:

  • The EATH model provides a powerful tool for creating realistic synthetic temporal hypergraphs.; Synthetic data aids in studying complex system dynamics where data collection is challenging.; Opens new avenues for understanding emergent behaviors in group interactions.