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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.
A free-body diagram of the...
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Machines: Problem Solving II01:30

Machines: Problem Solving II

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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

Machines: Problem Solving I

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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.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
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Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Associative Learning01:27

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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.
Classical conditioning, also known...
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Purposive Learning01:22

Purposive 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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Machine Learning and Imaging Informatics in Oncology.

Huan-Hsin Tseng1, Lise Wei1, Sunan Cui1

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This summary is machine-generated.

Machine learning (ML) and imaging informatics are transforming cancer care, from diagnosis to treatment decisions. This review explores ML applications in oncology, current challenges, and future potential in precision medicine.

Keywords:
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Area of Science:

  • Medical Informatics
  • Oncology
  • Machine Learning

Background:

  • Personalized and precision medicine are increasingly important in healthcare.
  • Informatics technologies, including machine learning (ML) and quantitative imaging, play a growing role in medicine, especially oncology.
  • These technologies offer potential to revolutionize cancer management, from diagnosis to treatment support.

Purpose of the Study:

  • To provide an overview of machine learning (ML) methodologies and imaging informatics techniques.
  • To review recent applications of ML in modern oncology.
  • To identify current challenges and future potentials of ML in cancer care.

Main Methods:

  • Literature review of ML applications in oncology.
  • Analysis of imaging informatics techniques in cancer management.
  • Discussion of challenges and future directions.

Main Results:

  • ML and quantitative imaging are increasingly utilized in oncology for computer-aided diagnosis and treatment decision support.
  • Diverse applications of ML in oncology have been reported in the literature.
  • Current challenges and future potentials are being identified.

Conclusions:

  • Machine learning and imaging informatics hold significant promise for advancing precision medicine in oncology.
  • Further research and development are needed to overcome current challenges and fully realize the potential of these technologies in cancer care.