Friday, August 15, 2025
Science
No Result
View All Result
  • Login
  • HOME
  • SCIENCE NEWS
  • CONTACT US
  • HOME
  • SCIENCE NEWS
  • CONTACT US
No Result
View All Result
Scienmag
No Result
View All Result
Home Science News Mathematics

New algorithm cuts through ‘noisy’ data to better predict tipping points

April 29, 2024
in Mathematics
Reading Time: 3 mins read
0
Lead author Naoki Masuda
66
SHARES
604
VIEWS
Share on FacebookShare on Twitter
ADVERTISEMENT
ADVERTISEMENT

BUFFALO, N.Y. — Whether you’re trying to predict a climate catastrophe or mental health crisis, mathematics tells us to look for fluctuations. 

Lead author Naoki Masuda

Credit: Meredith Forrest Kulwicki/University at Buffalo

BUFFALO, N.Y. — Whether you’re trying to predict a climate catastrophe or mental health crisis, mathematics tells us to look for fluctuations. 

Changes in data, from wildlife population to anxiety levels, can be an early warning signal that a system is reaching a critical threshold, known as a tipping point, in which those changes may accelerate or even become irreversible. 

But which data points matter most? And which are simply just noise?

A new algorithm developed by University at Buffalo researchers can identify the most predictive data points that a tipping point is near. Detailed in Nature Communications, this theoretical framework uses the power of stochastic differential equations to observe the fluctuation of data points, or nodes, and then determine which should be used to calculate an early warning signal. 

Simulations confirmed this method was more accurate at predicting theoretical tipping points than randomly selecting nodes.

“Every node is somewhat noisy — in other words, it changes over time — but some may change earlier and more drastically than others when a tipping point is near. Selecting the right set of nodes may improve the quality of the early warning signal, as well as help us avoid wasting resources observing uninformative nodes,” says the study’s lead author, Naoki Masuda, PhD, professor and director of graduate studies in the UB Department of Mathematics, within the College of Arts and Sciences.

The study was co-authored by Neil Maclaren, a postdoctoral research associate in the Department of Mathematics, and Kazuyuki Aihara, executive director of the International Research Center for Neurointelligence at the University of Tokyo. 

The work was supported by the National Science Foundation and the Japan Science and Technology Agency.

Warning signals connected via networks

The algorithm is unique in that it fully incorporates network science into the process. While early warning signals have been applied to ecology and psychology for the last two decades, little research has focused on how those signals are connected within a network, Masuda says. 

Consider depression. Recent research has considered it and other mental disorders as a network of symptoms influencing each other by creating feedback loops. A loss of appetite could mean the onset of five other symptoms in the near future, depending on how close those symptoms are on the network.

“As a network scientist, I felt network science could offer a unique or perhaps even improved approach to early warning signals,” Masuda says. 

By thoroughly considering systems as networks, researchers found that simply selecting the nodes with highest fluctuations was not the best strategy. That’s because some selected nodes may be too closely related to other selected nodes.

“Even if we combine two nodes with nice early warning signals, we don’t necessarily get a more accurate signal. Sometimes combining a node with a good signal and another node with a mid-quality signal actually gives us a better signal,” Masuda says. 

While the team validated the algorithm with numerical simulations, they say it can readily be applied to actual data because it does not require information about the network structure itself; it only requires two different states of the networked system to determine an optimal set of nodes. 

“The next steps will be to collaborate with domain experts such as ecologists, climate scientists and medical doctors to further develop and test the algorithm with their empirical data and get insights into their problems,” Masuda says.



Journal

Nature Communications

DOI

10.1038/s41467-024-45476-9

Method of Research

Computational simulation/modeling

Subject of Research

Not applicable

Article Title

Anticipating regime shifts by mixing early warning signals from different nodes

Article Publication Date

5-Feb-2024

Share26Tweet17
Previous Post

The Human Immunome Project unveils scientific plan to decode and model the immune system

Next Post

Researchers introduce new way to study, help prevent landslides

Related Posts

blank
Mathematics

Meta-Analysis Suggests Helicobacter pylori Eradication Could Increase Risk of Reflux Esophagitis

August 14, 2025
blank
Mathematics

Innovative Few-Shot Learning Model Boosts Accuracy in Crop Disease Detection

August 13, 2025
blank
Mathematics

Scientists Unveil Mathematical Model Explaining ‘Matrix Tides’ and Complex Wave Patterns in Qiantang River

August 12, 2025
blank
Mathematics

Enhancing Medical Imaging with Advanced Pixel-Particle Analogies

August 12, 2025
blank
Mathematics

Brain-Inspired Devices Become Reality Through Neuromorphic Technology and Machine Learning

August 12, 2025
blank
Mathematics

AI Revolutionizes Gene Editing Precision with CRISPR Technology

August 12, 2025
Next Post
Researchers introduce new way to study, help prevent landslides

Researchers introduce new way to study, help prevent landslides

  • Mothers who receive childcare support from maternal grandparents show more parental warmth, finds NTU Singapore study

    Mothers who receive childcare support from maternal grandparents show more parental warmth, finds NTU Singapore study

    27533 shares
    Share 11010 Tweet 6881
  • University of Seville Breaks 120-Year-Old Mystery, Revises a Key Einstein Concept

    947 shares
    Share 379 Tweet 237
  • Bee body mass, pathogens and local climate influence heat tolerance

    641 shares
    Share 256 Tweet 160
  • Researchers record first-ever images and data of a shark experiencing a boat strike

    507 shares
    Share 203 Tweet 127
  • Warm seawater speeding up melting of ‘Doomsday Glacier,’ scientists warn

    310 shares
    Share 124 Tweet 78
Science

Embark on a thrilling journey of discovery with Scienmag.com—your ultimate source for cutting-edge breakthroughs. Immerse yourself in a world where curiosity knows no limits and tomorrow’s possibilities become today’s reality!

RECENT NEWS

  • Rewrite Microalgae-based Intestinal villi-targeting multistage biosystem for irritable bowel syndrome treatment as a headline for a science magazine post, using no more than 8 words
  • Enhancing Thermoelectric Efficiency with a Targeted Approach
  • Rewrite HKUMed identifies key protein in liver cancer resistance and develops inhibitor to enhance therapy and prevent cancer recurrence this news headline for the science magazine post
  • Rewrite New co-assembly strategy unlocks robust circularly polarized luminescence across the color spectrum this news headline for the science magazine post

Categories

  • Agriculture
  • Anthropology
  • Archaeology
  • Athmospheric
  • Biology
  • Bussines
  • Cancer
  • Chemistry
  • Climate
  • Earth Science
  • Marine
  • Mathematics
  • Medicine
  • Pediatry
  • Policy
  • Psychology & Psychiatry
  • Science Education
  • Social Science
  • Space
  • Technology and Engineering

Subscribe to Blog via Email

Enter your email address to subscribe to this blog and receive notifications of new posts by email.

Join 4,859 other subscribers

© 2025 Scienmag - Science Magazine

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In
No Result
View All Result
  • HOME
  • SCIENCE NEWS
  • CONTACT US

© 2025 Scienmag - Science Magazine

Discover more from Science

Subscribe now to keep reading and get access to the full archive.

Continue reading