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Related Concept Videos

Distributed Loads01:19

Distributed Loads

958
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
958
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.1K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.1K
Resultant of a General Distributed Loading01:13

Resultant of a General Distributed Loading

1.0K
While designing structures exposed to non-uniform loads, it is crucial to consider the resultant force and its location. This resultant force is a single vector representing the net force applied due to the distributed load.
Examples such as load distribution due to wind and load distribution on a bridge illustrate how this concept is used to analyze and design safe, reliable structures under variable loading conditions. Most structures, such as residential buildings, bridges, and towers, are...
1.0K
Cable Subjected to a Distributed Load01:24

Cable Subjected to a Distributed Load

1.1K
The analysis of suspension bridges is a complex and critical process that involves multiple factors, including the shape and tension of the main cables. The main cables of suspension bridges are subjected to distributed loads, which result in changes in tensile forces and deformation of the cable. These loads must be carefully considered to ensure that the bridge is safe and capable of supporting the weight of different loads.
1.1K
Elastic Curve from the Load Distribution01:16

Elastic Curve from the Load Distribution

520
The structural behavior of beams under distributed loads is critical for engineering analysis, which focuses on predicting how beams bend and react under such conditions. Different types of beams (e.g., cantilever, supported, or overhanging) behave differently under distributed load conditions.
For all beams, the analysis of the beam's reaction to distributed loads begins by understanding the relationship between a beam's load and the resulting shear forces and bending moments. Initially, this...
520
Relation Between the Distributed Load and Shear01:23

Relation Between the Distributed Load and Shear

1.1K
Understanding the relationship between the distributed load and shear force in structural analysis is crucial for analyzing beams subjected to various loading conditions. Consider the case of a beam experiencing a distributed load, two concentrated loads, and a couple moment.
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Related Experiment Video

Updated: Jan 25, 2026

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
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The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

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Load effects on spatial working memory performance are linked to distributed alpha and beta oscillations.

Amy L Proskovec1,2,3, Alex I Wiesman2,3, Elizabeth Heinrichs-Graham2,3

  • 1Department of Psychology, University of Nebraska, Omaha, Nebraska.

Human Brain Mapping
|May 12, 2019
PubMed
Summary
This summary is machine-generated.

Brain activity in specific regions, like the prefrontal cortex, during spatial working memory (SWM) tasks can predict performance. Alpha and beta oscillations during SWM tasks are linked to how well individuals maintain accuracy under increasing cognitive load.

Keywords:
magnetoencephalography (MEG)oscillatory activityposterior parietal cortex (PPC)prefrontal cortex (PFC)superior temporal cortex

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Area of Science:

  • Cognitive Neuroscience
  • Neuroimaging
  • Human Brain Research

Background:

  • Spatial working memory (SWM) performance typically declines as cognitive load increases.
  • The neural mechanisms underlying these performance changes, particularly oscillatory activity, are not well understood.
  • Previous research has not comprehensively linked brain activity patterns to behavioral outcomes across different SWM loads.

Purpose of the Study:

  • To investigate how oscillatory brain activity changes with increasing SWM load.
  • To examine the relationship between these load-related neural changes and behavioral performance.
  • To identify specific brain regions and frequency bands associated with SWM load effects.

Main Methods:

  • Magnetoencephalography (MEG) was used to record brain activity in 22 healthy adults.
  • Participants performed SWM tasks at two different load levels (two- and four-load).
  • Time-frequency analysis and beamforming were applied to identify and localize significant oscillatory responses, followed by whole-brain correlation analyses.

Main Results:

  • Decreased left inferior frontal alpha activity during encoding and maintenance correlated with better accuracy under higher SWM load.
  • Similar neurobehavioral correlations were found for right superior temporal alpha and right superior parietal beta activity during maintenance.
  • This study uniquely employed a voxel-wise whole-brain approach to link oscillatory activity and SWM performance.

Conclusions:

  • Specific patterns of oscillatory activity, particularly in the alpha and beta bands, are crucial for maintaining cognitive performance under increased SWM load.
  • The findings highlight the role of prefrontal, temporal, and parietal regions in adapting neural function to cognitive demands.
  • This research provides novel insights into the neural basis of working memory capacity and its relationship to behavioral efficiency.