Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Method of Sections

Method of Sections

Consider a truss structure, as shown in the figure.

Method of Sections: Problem Solving II

Method of Sections: Problem Solving II

Consider an arbitrary truss structure composed of diagonal, vertical, and horizontal members fixed to the wall. To calculate the force acting on members CB, GB, and GH, method of sections can be used. The loads and lengths of the horizontal and vertical members are known parameters, as shown in the figure.

Method of Sections: Problem Solving I

Method of Sections: Problem Solving I

Consider a symmetrical roof truss structure, composed of vertical, diagonal, and horizontal members. The length of each horizontal member is 4 m. The lengths of the vertical members FB and HD are 4 m, while the length of member GC is 6 m. The loads acting at joints F, G, and H are 2 kN, while those at joints A and E are 1 kN.

Cross-Sectional Research

Cross-Sectional Research

In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...

Avoidance Learning and Learned Helplessness

Avoidance Learning and Learned Helplessness

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...

Associative Learning

Associative Learning

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...

You might also read

Related Articles

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

Sort by

Same author

Superspreading Shear-Flow-Induced Layered MXene Films with Enhanced Conductivity, Strength, and EMI Shielding Performance.

ACS applied materials & interfaces·2026

Same author

Timing of immune checkpoint inhibitors infusion and prognosis in recurrent/metastatic nasopharyngeal carcinoma: a single-center, retrospective study.

Cancer immunology, immunotherapy : CII·2026

Same author

<i>Webervirus</i> phages exploit capsule- and O-antigen-targeting receptor-binding proteins to access diverse <i>Klebsiella pneumoniae</i> hosts.

mBio·2026

Same author

Racial trends in outcomes and access to treatment for mitral regurgitation: a South London population cohort study.

Open heart·2026

Same author

Mechanisms underlying pyogenic bacterial infections of the skin.

Archives of microbiology·2026

Same author

Neoadjuvant Chemotherapy for Male Breast Cancer: A Retrospective Cohort Study.

Cancer medicine·2026

Same journal

Denoising algorithm of Φ-OTDR systems based on adaptive fractional wavelet transform denoising.

Optics express·2026

Same journal

Millisecond photon-to-photon latency and high-speed volumetric projection system for optogenetics.

Optics express·2026

Same journal

Polarization-encoded coaxial structured light for high-precision 3D surface profilometry.

Optics express·2026

Same journal

Discrete freeform optical design based on collaborative optimization of point cloud and local normals.

Optics express·2026

Same journal

Ultrafast ghost imaging with 25 GHz speckle switching and wavelength-division multiplexing.

Optics express·2026

Same journal

Atomic vapor cells fabricated by femtosecond laser welding of standard-optical-quality glass.

Optics express·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 2, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Deep learning optical-sectioning method.

Xiaoyu Zhang, Yifan Chen, Kefu Ning

|November 25, 2018

Summary

This summary is machine-generated.

We developed a deep learning method for optical sectioning of wide-field images. This technique achieves high-quality imaging with reduced noise and increased depth, enabling faster, cost-effective, high-throughput applications.

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Related Experiment Videos

Last Updated: Feb 2, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Area of Science:

Biomedical Optics
Machine Learning
Image Processing

Background:

Traditional optical sectioning techniques often involve complex setups or lengthy computations.
Improving imaging quality in microscopy is crucial for detailed biological and material science studies.

Purpose of the Study:

To introduce a novel deep learning-based method for efficient optical sectioning of wide-field images.
To demonstrate a cost-effective and rapid alternative to conventional optical sectioning approaches.

Main Methods:

Utilized a deep learning model trained on a single pair of contrast images for optical sectioning.
Developed a reconstruction algorithm for generating optically sectioned images from wide-field inputs.

Main Results:

Achieved background removal and resolution comparable to traditional methods.
Demonstrated lower noise levels and enhanced imaging depth compared to existing techniques.
Optimized reconstruction speed up to 14 Hz, facilitating high-throughput imaging.

Conclusions:

The proposed deep learning method offers a significant advancement in optical sectioning.
This approach provides a convenient, cost-effective solution for high-throughput imaging applications.
Enables development of next-generation optical sectioning techniques with improved performance.