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Related Concept Videos

Machines01:19

Machines

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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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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Machines: Problem Solving I01:22

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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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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Human-machine interaction in computational cancer pathology.

A Syrnioti1, A Polónia2,3, J Pinto2

  • 1Department of Pathology, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.

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Summary
This summary is machine-generated.

Augmented pathologists using artificial intelligence (AI) show superior accuracy. Addressing trust, bias, and workforce adaptation is essential for AI integration in pathology.

Keywords:
artificial intelligencecancer diagnosiscomputational pathologysynergy

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Area of Science:

  • Pathology
  • Medical Artificial Intelligence
  • Human-Computer Interaction

Background:

  • The integration of artificial intelligence (AI) into pathology workflows is rapidly advancing.
  • Augmented pathologists, combining human expertise with AI, are increasingly recognized for enhanced diagnostic accuracy.
  • Concerns regarding trust, bias, and workforce implications necessitate careful consideration.

Purpose of the Study:

  • To evaluate the performance of augmented pathologists compared to AI alone and pathologists alone.
  • To identify and address challenges associated with human-machine interaction in daily AI-assisted pathology work.
  • To propose strategies for the sustainable integration of AI in pathology, including workforce education and validation guidelines.

Main Methods:

  • Comparative performance analysis of augmented pathologists, AI-only systems, and human pathologists.
  • Qualitative assessment of human-machine interactions, focusing on trust and bias.
  • Review of current practices and literature regarding AI in pathology workforce training and validation.

Main Results:

  • Augmented pathologists demonstrate superior accuracy over AI alone and pathologists alone.
  • Human-machine interactions present challenges related to trust and potential biases.
  • Deskilling of the pathology workforce and the need for updated educational curricula are significant concerns.

Conclusions:

  • The synergy between pathologists and AI offers the highest diagnostic accuracy.
  • Proactive measures are required to manage trust, mitigate bias, and prevent workforce deskilling.
  • Establishing clear guidelines for AI tool verification and validation is critical for maintaining high-quality cancer pathology.