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

Distributed Loads: Problem Solving

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...

Network Covalent Solids

Network Covalent Solids

Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...

Types of Genetic Transfer Between Organisms

Types of Genetic Transfer Between Organisms

The Maximum Power Transfer Theorem

The Maximum Power Transfer Theorem

Consider a linear AC Thevenin equivalent circuit connected to a load impedance.
The load connected draws the current, and the circuit delivers the power to the load. The alternating current flowing through the load is determined using the rectangular form of voltages, currents, network impedance, and load impedance. The average power delivered to the load is obtained from the product of the square of current and load resistance.

Sampling Theorem

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.

Distributed Loads

Distributed Loads

Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...

You might also read

Related Articles

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

Sort by

Same author

Robot-assisted resection of abdominal lymphatic malformations in children: a novel minimally invasive strategy for complex lesions.

Journal of robotic surgery·2026

Same author

A novel method for measuring the energy spectrum of an inverse Compton scattering source based on nuclear resonance fluorescence.

The Review of scientific instruments·2026

Same author

A negative survival pressure selection system enables GPCR antagonist screening.

Cell discovery·2026

Same author

Precocious Puberty as a Significant Risk Factor for Scoliosis: A Retrospective Cohort Study Demonstrating Synergistic Effects of GnRHa Therapy and Protective Role of Vitamin D.

Spine·2026

Same author

Transcriptional and functional dissection of cytochrome P450 and ABC transporters underlying tobacco tolerance in Lasioderma serricorne.

Journal of insect physiology·2026

Same author

Robot-assisted choledochal cysts resection in children across all ages: a larger cohort single-center experience with 318 Cases.

Journal of robotic surgery·2026

Search research articles

Related Experiment Video

Updated: Jun 9, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

MatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharing.

Ran Wang^1,2,3, Cheng Xu^4,5,6, Shuhao Zhang³

¹School of Computer and Communication Engineering, University of Science and Technology Beijing, 100083, Beijing, China.

Nature Communications

|October 29, 2024

Summary

This summary is machine-generated.

MatSwarm, a novel framework integrating federated learning and blockchain, enhances collaborative material research. It improves model accuracy and data security, overcoming challenges in Industry 4.0 material development.

More Related Videos

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Published on: December 1, 2023

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: Jun 9, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Published on: December 1, 2023

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Materials Science
Computer Science
Data Science

Background:

Industry 4.0 drives demand for novel materials, requiring inter-institutional collaboration.
Data silos and non-i.i.d. data in multi-institutional settings hinder collaborative model accuracy.
Protecting sensitive data is a major challenge in collaborative material research.

Purpose of the Study:

To introduce the MatSwarm framework for secure and accurate collaborative material data analysis.
To address challenges of data heterogeneity, non-i.i.d. data, and data confidentiality in material research.
To enhance model generalization and accuracy in federated learning environments for material science.

Main Methods:

Developed MatSwarm framework combining swarm learning, federated learning, and blockchain technology.
Implemented a swarm transfer learning method with a regularization term for parameter alignment.
Utilized Trusted Execution Environments (TEE) with Intel SGX for enhanced data security and confidentiality.

Main Results:

MatSwarm successfully aggregated over 14 million material data entries from thirty+ institutions.
The framework demonstrated superior accuracy and generalization compared to independently trained models.
Ensured data confidentiality throughout the model training and aggregation processes.

Conclusions:

MatSwarm effectively overcomes data silos and security challenges in collaborative material research.
The framework significantly improves accuracy and generalization for material data analysis.
MatSwarm facilitates secure and efficient multi-institutional collaboration for accelerated material discovery.