Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

The Mitotic Spindle02:27

The Mitotic Spindle

8.0K
The mitotic spindle—or spindle apparatus—is a eukaryotic, cytoskeletal structure made up of long protein fibers called microtubules. Formed during cell division, the spindle separates sister chromatids and moves them to opposite ends of a parental cell, where the now individual chromosomes are distributed to two daughter cell nuclei.
The bipolar configuration of the mitotic spindle facilitates chromosomal segregation, preparing the cell for division. One mechanism that ensures...
8.0K
Spindle Assembly02:50

Spindle Assembly

4.3K
Spindle assembly occurs through three, often coexisting, pathways – the centrosome-mediated pathway, the chromatin-mediated pathway, and the microtubule-mediated pathway – collectively contributing to form a robust spindle apparatus.
In most cells, centrosomes are the primary microtubule nucleation centers. In the centrosome-mediated pathway, the G2-prophase transition triggers centrosome maturation and increased microtubule nucleation. Progressive nucleation results in a...
4.3K
The Spindle Assembly Checkpoint02:19

The Spindle Assembly Checkpoint

3.8K
The spindle assembly checkpoint is a molecular surveillance mechanism ensuring the fidelity of chromosome segregation during anaphase. The checkpoint monitors the completion of all the prerequisite steps before chromosome segregation to determine whether the segregation process should proceed or be delayed.
Many proteins function together to control the spindle assembly checkpoint. Mutations affecting these proteins may allow cells to proceed into anaphase prematurely, resulting in the...
3.8K
Machines01:19

Machines

573
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...
573
Thermal expansion and Thermal stress: Problem Solving01:27

Thermal expansion and Thermal stress: Problem Solving

2.1K
San Francisco's Golden Gate Bridge is exposed to temperatures ranging from -15 °C to 40 °C. At its coldest, the main span of the bridge is 1275 m long. Assuming that the bridge is made entirely of steel, what is the change in its length between these temperatures?
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in temperature (ΔT) is 55...
2.1K
Machines: Problem Solving II01:30

Machines: Problem Solving II

661
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.
661

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Automatic wild bird repellent system that is based on deep-learning-based wild bird detection and integrated with a laser rotation mechanism.

Scientific reports·2024
Same author

Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise.

Diagnostics (Basel, Switzerland)·2023
Same author

Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials.

Diagnostics (Basel, Switzerland)·2023
Same author

Leveraging Computational Intelligence Techniques for Diagnosing Degenerative Nerve Diseases: A Comprehensive Review, Open Challenges, and Future Research Directions.

Diagnostics (Basel, Switzerland)·2023
Same author

AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions.

Healthcare (Basel, Switzerland)·2023
Same author

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks.

Sensors (Basel, Switzerland)·2022
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jan 28, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.0K

Thermal-Feature System Identification for a Machine Tool Spindle.

Yuh-Chung Hu1, Ping-Jung Chen2, Pei-Zen Chang3

  • 1Department of Mechanical and Electro-Mechanical Engineering, National ILan University, Yilan 26047, Taiwan. ychu@niu.edu.tw.

Sensors (Basel, Switzerland)
|March 13, 2019
PubMed
Summary
This summary is machine-generated.

This study developed a method to model spindle thermal features, enabling accurate internal temperature calculation from surface measurements. This non-invasive approach improves spindle maintenance and performance prediction.

Keywords:
machine learningmachine tool spindlesystem identificationtemperature sensorthermal feature model

More Related Videos

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

18.5K
Identification of Novel Regulators of Plant Transpiration by Large-Scale Thermal Imaging Screening in Helianthus Annuus
07:08

Identification of Novel Regulators of Plant Transpiration by Large-Scale Thermal Imaging Screening in Helianthus Annuus

Published on: January 30, 2020

6.4K

Related Experiment Videos

Last Updated: Jan 28, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.0K
A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

18.5K
Identification of Novel Regulators of Plant Transpiration by Large-Scale Thermal Imaging Screening in Helianthus Annuus
07:08

Identification of Novel Regulators of Plant Transpiration by Large-Scale Thermal Imaging Screening in Helianthus Annuus

Published on: January 30, 2020

6.4K

Area of Science:

  • Mechanical Engineering
  • Thermal Management
  • System Identification

Background:

  • Internal spindle temperature is crucial for maintenance but difficult to measure directly.
  • Direct temperature sensing is impractical due to space constraints and potential damage to mechanical stiffness.

Purpose of the Study:

  • To develop a methodology for identifying the thermal-feature model of an externally driven spindle.
  • To enable accurate prediction and calculation of internal spindle temperature using non-invasive methods.

Main Methods:

  • Developed a temperature sensing and wireless transmission module (TSWTM) for data acquisition.
  • Utilized system identification (SID) software integrating MATLAB's 'n4sid' and k-fold cross-validation.
  • Modeled the spindle's thermal-feature using a linearly time-invariant state-space model.

Main Results:

  • Achieved a thermal-feature model with validation accuracy exceeding 98.5%.
  • The model accurately predicts spindle temperature at various speeds.
  • Successfully calculates internal temperature from directly measurable surface temperature.

Conclusions:

  • The developed methodology effectively models spindle thermal-features.
  • Accurate non-invasive internal temperature monitoring of spindles is feasible.
  • This approach enhances spindle maintenance and operational efficiency.