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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

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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 Helplessness01:14

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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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Machine Learning in Acute Ischemic Stroke Neuroimaging.

Haris Kamal1, Victor Lopez1, Sunil A Sheth1

  • 1Department of Neurology, University of Texas at Houston Health Science Center, Houston, TX, United States.

Frontiers in Neurology
|November 24, 2018
PubMed
Summary
This summary is machine-generated.

Machine learning aids neurological disease diagnosis and treatment. This review highlights machine learning applications in neuroimaging for acute ischemic stroke, improving patient outcomes.

Keywords:
machine learning (artificial intelligence)neuroimagingneurosciencesstrokestroke diagnosisstroke managementsupport vector machina (SVM)

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Machine learning (ML) algorithms are increasingly vital for diagnosing and predicting outcomes in neurological diseases.
  • Neuroimaging plays a critical role in the evaluation and treatment of acute ischemic stroke (AIS).

Purpose of the Study:

  • To review recent advancements in machine learning applications within neuroimaging.
  • To focus on the specific use of ML in acute ischemic stroke.

Main Methods:

  • Literature review of machine learning techniques applied to neuroimaging data.
  • Analysis of ML algorithm performance in AIS diagnosis, treatment, and outcome prediction.

Main Results:

  • ML demonstrates significant potential in enhancing decision-making processes for AIS.
  • Emerging ML tools offer improved accuracy in identifying stroke-related patterns in neuroimaging.

Conclusions:

  • Machine learning is a transformative tool in neuroimaging for acute ischemic stroke.
  • Continued research in ML neuroimaging will further advance AIS patient care and outcomes.