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Stress analysis under multiple loading conditions is intricate, necessitating a comprehensive grasp of normal and shearing stresses. Consider a small cube at point O, subjected to stress on all six faces, visible or not. Normal stress components σx, σy, σz act perpendicularly to the x, y, and z axes. Shearing stress components τxy and τxz are exerted on faces perpendicular to these axes.
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Related Experiment Video

Updated: Jan 29, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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KnetMaps: a BioJS component to visualize biological knowledge networks.

Ajit Singh1, Christopher J Rawlings1, Keywan Hassani-Pak1

  • 1Computational and Analytical Sciences, Rothamsted Research, Harpenden, AL5 2JQ, UK.

F1000Research
|February 14, 2019
PubMed
Summary
This summary is machine-generated.

KnetMaps is a BioJS component that interactively visualizes biological knowledge networks. This tool helps researchers navigate complex data, linking genotypes to phenotypes for deeper biological insights.

Keywords:
biojsknowledge discoveryknowledge graphknowledge networknetwork visualisation

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Visualization

Background:

  • Biological research generates vast, interconnected datasets.
  • Visualizing complex biological networks is challenging.
  • Existing tools may not effectively integrate diverse data types.

Purpose of the Study:

  • To introduce KnetMaps, a BioJS component for interactive biological knowledge network visualization.
  • To provide a unified view of complementary, connected, and content-rich biological data.
  • To facilitate pathway traversal from genotype to phenotype.

Main Methods:

  • KnetMaps utilizes JavaScript libraries for visualization.
  • Data is loaded and processed in JSON format.
  • Interactive touch gestures are supported for user interaction.

Main Results:

  • KnetMaps offers an interactive visualization of biological knowledge networks.
  • It enables users to traverse pathways linking biological entities.
  • Effective visualization techniques prevent information overload.

Conclusions:

  • KnetMaps is a valuable tool for researchers needing to visualize complex biological networks.
  • It supports progressive knowledge building by allowing users to explore interconnected data.
  • The component enhances the understanding of genotype-phenotype relationships.