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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Open access image repositories: high-quality data to enable machine learning research.

F Prior1, J Almeida2, P Kathiravelu3

  • 1Department of Biomedical Informatics, University of Arkansas for Medical Sciences, 4301 W. Markham St, Little Rock, AR 72205, USA.

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This summary is machine-generated.

Large, open-access data repositories are crucial for advancing machine learning in biomedical research. Effective curation ensures data quality, enabling reproducible research and improved cancer diagnosis and treatment.

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Area of Science:

  • Biomedical Informatics
  • Machine Learning
  • Medical Imaging

Background:

  • Open-access repositories are vital for machine learning in biomedical research.
  • Data quality is essential for reproducibility and reliable AI model training.
  • The Cancer Imaging Archive (TCIA) is a key resource for cancer imaging data.

Purpose of the Study:

  • To review the requirements of machine learning applications for biomedical data repositories.
  • To discuss how to ensure data quality in these repositories.
  • To highlight the role of curation in advancing cancer research.

Main Methods:

  • Review of machine learning needs for data repositories.
  • Analysis of data quality factors (protocol variance, misrepresentation).
  • Experience from constructing and utilizing The Cancer Imaging Archive.

Main Results:

  • High-quality, large datasets are critical for advanced machine learning.
  • Curation is the primary method for ensuring data quality.
  • TCIA serves as a model for open science in cancer imaging.

Conclusions:

  • Meeting machine learning demands requires robust data repositories.
  • Prioritizing data quality through curation is paramount.
  • Open science initiatives like TCIA accelerate cancer research and treatment.