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Related Concept Videos

Machines01:19

Machines

559
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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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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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.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
488
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

753
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.
In this model, each generator is connected to a...
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A Cell Segmentation/Tracking Tool Based on Machine Learning.

Heather S Deter1, Marta Dies2, Courtney C Cameron1

  • 1Biology and Microbiology Department, South Dakota State University, Brookings, SD, USA.

Methods in Molecular Biology (Clifton, N.J.)
|August 22, 2019
PubMed
Summary
This summary is machine-generated.

This study details a method for segmenting and tracking cells in microscopy images using machine learning and Python scripts. This enables quantifiable single-cell data acquisition from time-lapse experiments.

Keywords:
Bacterial growthCell lineage analysisCell segmentationCell trackingComputational image analysisFluorescence microscopyMachine learningSingle-cell quantification

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Area of Science:

  • Cell biology
  • Bioimage analysis
  • Computational biology

Background:

  • Accurate cell segmentation and tracking are crucial for quantitative analysis of time-lapse microscopy data.
  • Existing methods may lack accessibility or require specialized expertise.

Purpose of the Study:

  • To present a comprehensive protocol for acquiring high-quality time-lapse microscopy movies.
  • To introduce a robust and accessible method for cell segmentation and tracking using machine learning and open-source tools.

Main Methods:

  • Utilized Fiji's Trainable Weka Segmentation for cell identification.
  • Developed custom, open-source Python scripts for cell tracking.
  • Established a protocol for generating optimal time-lapse microscopy image datasets.

Main Results:

  • Successfully implemented a machine learning-based approach for accurate cell segmentation.
  • Demonstrated reliable cell tracking over time using custom Python scripts.
  • Provided accessible datasets for hands-on user experience.

Conclusions:

  • The presented protocol and computational methods enable robust, quantifiable single-cell data extraction from time-lapse microscopy.
  • This approach enhances the accessibility of advanced bioimage analysis techniques for researchers.
  • The open-source nature promotes reproducibility and further development in the field.