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Related Concept Videos

Line Loss01:10

Line Loss

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The different configurations of source-load connections include wye (star) and delta connections. The relationship between line and phase voltages and currents varies depending on the configuration. When the source is supplying power, it is transmitted through the wires to the load, and during this transmission, some power is absorbed by the wires, leading to line loss.
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The work done by an external force on a particle changes its kinetic energy. However, internal forces must also be considered for a system of interacting particles. The potential energy formulation helps formulate the effect of internal forces. The net work done by an external force can be written in terms of the total change of mechanical energy, which includes both kinetic and potential energies.
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Reducing Line Loss01:18

Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Network Covalent Solids02:18

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Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
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Preventing Neurodegenerative Memory Loss in Hopfield Neuronal Networks Using Cerebral Organoids or External

M Morrison1,2, P D Maia1, J N Kutz1

  • 1Department of Applied Mathematics, University of Washington, Seattle, WA, USA.

Computational and Mathematical Methods in Medicine
|January 10, 2018
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Summary
This summary is machine-generated.

Brain-machine interfaces (BMIs) show promise for restoring memory functions lost due to neurodegenerative diseases or traumatic brain injury (TBI). This study models BMIs using Hopfield networks, demonstrating their potential for memory recovery.

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Area of Science:

  • Computational neuroscience
  • Biomedical engineering

Background:

  • Brain-machine interfaces (BMIs) offer potential for restoring motor and cognitive functions.
  • Neurodegenerative diseases and traumatic brain injury (TBI) can cause significant memory loss.

Purpose of the Study:

  • To investigate the viability of BMIs for mitigating memory loss.
  • To model and evaluate BMI effectiveness in aiding memory retrieval.

Main Methods:

  • Utilized a computational study involving Hopfield networks, an autoassociative memory model.
  • Connected an auxiliary neural network (modeling the BMI) to a Hopfield network representing memory.

Main Results:

  • Dense connectivity between auxiliary and Hopfield networks enhanced memory retrieval robustness.
  • Computations estimated damage levels and parameter ranges for memory recovery.

Conclusions:

  • BMIs show potential for aiding memory retrieval in cases of neurodegenerative damage or TBI.
  • This study provides a foundation for developing novel therapeutic strategies for memory restoration.