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Related Concept Videos

Induced Electric Fields: Applications01:27

Induced Electric Fields: Applications

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An important distinction exists between the electric field induced by a changing magnetic field and the electrostatic field produced by a fixed charge distribution. Specifically, the induced electric field is nonconservative because it does not work in moving a charge over a closed path. In contrast, the electrostatic field is conservative and does no net work over a closed path. Hence, electric potential can be associated with the electrostatic field but not the induced field. The following...
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Ampere-Maxwell's Law: Problem-Solving01:17

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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
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Divergence and Curl of Electric Field01:25

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The divergence of a vector is a measure of how much the vector spreads out (diverges) from a point. For example, an electric field vector diverges from the positive charge and converges at the negative charge. The divergence of an electric field is derived using Gauss's law and is equal to the charge density divided by the permittivity of space. Mathematically, it is expressed as
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Induced Electric Fields01:23

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The fact that emfs are induced in circuits implies that work is being done on the conduction electrons in the wires. What can possibly be the source of this work? We know that it’s neither a battery nor a magnetic field, as a battery does not have to be present in a circuit where current is induced, and magnetic fields never do any work on moving charges. The source of the work is in fact an electric field that is induced in the wires. For example, if a stationary conductor is placed in a...
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Ampere's Law: Problem-Solving01:31

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Ampere's law states that for any closed looped path, the line integral of the magnetic field along the path equals the vacuum permeability times the current enclosed in the loop. If the fingers of the right hand curl along the direction of the integration path, the current in the direction of the thumb is considered positive. The current opposite to the thumb direction is considered negative.
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Electrical Current01:10

Electrical Current

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Electrical current is defined as the rate at which charge flows. When there is a large current present, such as that used to run a refrigerator, a large amount of charge moves through the wire in a small amount of time. If the current is small, such as that used to operate a handheld calculator, a small amount of charge moves through the circuit over a long period of time. The SI unit for current is the ampere (A), named for the French physicist André-Marie Ampère (1775–1836).
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Next-generation graph computing with electric current-based and quantum-inspired approaches.

Yoon Ho Jang1, Janguk Han1, Soo Hyung Lee1

  • 1Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, Republic of Korea.

Nature Communications
|August 28, 2025
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Summary
This summary is machine-generated.

Electric current-based and quantum-inspired graph computing offer hardware solutions for complex data. Further research in materials, devices, and architectures is needed for advanced real-world applications.

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Area of Science:

  • Computer Science
  • Materials Science
  • Physics

Background:

  • Conventional graph computing struggles with large-scale, complex graph data.
  • Innovative hardware-based solutions are needed to address these limitations.

Purpose of the Study:

  • To introduce electric current-based graph computing using memristive crossbar arrays.
  • To discuss quantum-inspired graph computing for complex optimization problems.
  • To highlight the potential of these emerging computing paradigms.

Main Methods:

  • Exploration of crossbar array-based electric current-based graph computing for Euclidean and non-Euclidean data.
  • Review of quantum-inspired approaches utilizing probabilistic bits and oscillatory neural networks.

Main Results:

  • Electric current-based computing demonstrates flexibility in representing complex graphs for diverse applications.
  • Quantum-inspired computing offers methods for solving intricate optimization challenges.

Conclusions:

  • Both electric current-based and quantum-inspired graph computing are in early developmental stages.
  • Advancements in materials, devices, and architectures are crucial for realizing their full potential.
  • These technologies promise to enable more complex and diverse real-world applications.