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Related Concept Videos

Stress Concentrations01:13

Stress Concentrations

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The concept of stress concentration is crucial for understanding how materials respond under bending stresses, particularly when there are irregularities or discontinuities in the material's geometry. Normally, stress in a symmetric member subjected to pure bending is assumed to be uniformly distributed across the entire cross-section. However, this assumption does not hold when there are variations in the cross-sectional geometry or the presence of notches and holes.
The stress...
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Stress: General Loading Conditions01:15

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To grasp the intricacy of real-world conditions where multiple loads are applied simultaneously to a structure, one might visualize a section passing through a specific point within a body, aligned parallel to the xy plane. This section is subjected to various forces, including original loads, normal forces, and shearing forces.
The shearing force, possessing potential directionality within the plane of the section, is simplified into two component forces running parallel to the x and y axes....
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As a system undergoes a change, its internal energy can change, and energy can be transferred from the system to the surroundings, or from the surroundings to the system. 
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Updated: Aug 4, 2025

Reducing State Anxiety Using Working Memory Maintenance
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Mental workload level assessment based on compounded hysteresis effect.

Shabnam Samima1, Monalisa Sarma1

  • 1Subir Chowdhury School of Quality and Reliability, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal India.

Cognitive Neurodynamics
|April 3, 2023
PubMed
Summary
This summary is machine-generated.

This study demonstrates that electroencephalography (EEG) rhythms can effectively monitor cognitive workload changes. An artificial neural network (ANN) model achieved 98.66% accuracy in classifying workload levels using EEG data.

Keywords:
ElectroencephalographyHysteresis effectLoad graphMental workloadPower spectral density

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Area of Science:

  • Neuroergonomics
  • Cognitive Science
  • Biomedical Engineering

Background:

  • Cognitive workload estimation is crucial in neuroergonomics for task distribution and operator support.
  • Brain signals, particularly electroencephalography (EEG), offer a promising avenue for understanding cognitive states.
  • Continuous monitoring of cognitive workload is essential for real-time human-system interaction.

Purpose of the Study:

  • To explore the feasibility of using EEG rhythms for continuous cognitive workload monitoring.
  • To develop a method for interpreting cumulative changes in EEG rhythms using hysteresis.
  • To classify cognitive workload levels using an artificial neural network (ANN).

Main Methods:

  • Utilized electroencephalography (EEG) to capture brain signals.
  • Applied the hysteresis effect to interpret cumulative changes in EEG rhythms over time.
  • Developed and implemented an artificial neural network (ANN) architecture for classification.

Main Results:

  • The proposed model demonstrated high accuracy in classifying cognitive workload.
  • Achieved a classification accuracy of 98.66% for predicting workload data class labels.
  • Successfully demonstrated the feasibility of EEG-based continuous cognitive workload monitoring.

Conclusions:

  • EEG rhythms are a viable tool for continuous cognitive workload estimation.
  • The hysteresis-based interpretation combined with ANN provides an effective approach for workload monitoring.
  • This research contributes to advancements in neuroergonomics and human-computer interaction.