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Related Concept Videos

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.
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.
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

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Sequence Networks of Rotating Machines01:24

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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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Simplified Synchronous Machine Model01:30

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The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
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Related Experiment Video

Updated: Jan 21, 2026

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
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Machine learning and glioma imaging biomarkers.

T C Booth1, M Williams2, A Luis3

  • 1School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London SE1 7EH, UK; Department of Neuroradiology, King's College Hospital NHS Foundation Trust, London SE5 9RS, UK.

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Machine learning (ML) shows promise in analyzing neuro-oncology imaging biomarkers for diagnosis and prognosis. However, current evidence is limited, and ML models need further validation against traditional methods for clinical use.

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Area of Science:

  • Neuro-oncology
  • Medical Imaging
  • Machine Learning

Background:

  • Magnetic resonance imaging (MRI) is crucial in neuro-oncology, providing anatomical and physiological details.
  • Machine learning (ML) algorithms can identify complex image features for accurate classification.
  • ML aids in determining tumor characteristics, grade, and prognosis from initial MRI scans.

Purpose of the Study:

  • To review the application of machine learning (ML) in neuro-oncology imaging biomarkers.
  • To explore ML's role in diagnosis, prognosis, and treatment response monitoring for brain tumors.
  • To assess ML's utility in differentiating treatment effects from tumor progression.

Main Methods:

  • Systematic literature search of PubMed and MEDLINE databases up to September 2018.
  • Focus on studies applying ML to high-grade glioma biomarkers for prediction and monitoring.
  • Analysis of research utilizing MRI features for ML-based classification and prediction.

Main Results:

  • ML is frequently used with MRI features for accurate classification and identification of image biomarkers.
  • Significant research applies ML to predict molecular profiles, tumor grade, and prognosis from initial MRIs.
  • ML is actively studied for distinguishing treatment response from post-treatment effects in glioma imaging.

Conclusions:

  • Current evidence for ML in neuro-oncology biomarkers is largely retrospective and from single centers.
  • ML models have not yet demonstrated a clear advantage over traditional statistical methods in neuro-oncology.
  • Development requires large, well-annotated datasets, necessitating multidisciplinary, multi-center collaborations.