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Machines01:19

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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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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Visible Machine Learning for Biomedicine.

Michael K Yu1, Jianzhu Ma2, Jasmin Fisher3

  • 1Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA; Cancer Cell Map Initiative, University of California San Diego, La Jolla, CA, USA; UCSD Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA, USA.

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Summary
This summary is machine-generated.

Artificial intelligence can improve therapies by analyzing patient data. Visible machine learning approaches, guided by experimental biology, address challenges like data variety and lack of insight in biomedical predictions.

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Area of Science:

  • Biomedical research
  • Artificial intelligence
  • Machine learning

Background:

  • Translating patient data into effective therapies is a key goal for artificial intelligence (AI).
  • Machine learning (ML) models in biomedicine struggle with highly diverse data and understanding the biological basis of their predictions.
  • Lack of mechanistic insight hinders the clinical application of AI in medicine.

Purpose of the Study:

  • To advocate for "visible" artificial intelligence approaches in biomedicine.
  • To propose integrating experimental biology principles into machine learning model design.
  • To enhance the interpretability and reliability of AI-driven therapeutic strategies.

Main Methods:

  • Developing machine learning frameworks that incorporate biological knowledge.
  • Designing AI models with structures informed by experimental biology.
  • Focusing on "visible" approaches for greater transparency in predictions.

Main Results:

  • Proposed visible AI methods offer a path to overcome data heterogeneity challenges.
  • Integrating experimental biology enhances mechanistic understanding of AI predictions.
  • Visible approaches improve the translation of patient data to therapies.

Conclusions:

  • Visible AI, guided by experimental biology, is crucial for advancing biomedical applications.
  • This approach addresses key limitations of current machine learning in medicine.
  • It facilitates more reliable and interpretable AI-driven therapeutic development.