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1,000 days since October 7 - Bringing survivors' voices to the world through AI-powered living archive - Hebrew University and Edut 710

The Hebrew University of Jerusalem and Edut 710 have partnered to create a groundbreaking AI-powered living archive, featuring nearly 2,000 survivor, witness, and first responder testimonies. The archive will enable users to search, translate, and explore the accounts through natural language, preserving authenticity and integrity.

Turning down the heat

A University of Houston professor has found that tree-like thin films release heat at least three times better than traditional methods, enabling more efficient cooling in AI data centers. The discovery demonstrates the power of physics-aware AI design for validating high-impact cooling solutions.

SourceUniversity of Houston·JournalInternational Journal of Heat and Mass Transfer·DateFeb 12, 2026

The future of data storage is double-helical, research indicates

A team of researchers has developed a DNA-based data storage platform with an expanded molecular alphabet, enabling the storage of vast amounts of digital information. The new system uses nanopores to distinguish between natural and chemically modified nucleotides, increasing storage density and sustainability.

SourceBeckman Institute for Advanced Science and Technology·JournalNano Letters·TypeExperimental study·DateMar 3, 2022

Open-source collaborative platform to collect content from over 350 institutions' archives

A collaborative collection development platform called Cobweb is proposed to support comprehensive web archiving by coordinating independent activities of the web archiving community. The platform will leverage existing tools and sources of archival information to retrieve holdings data from over 3,500 collections across 350 institutions.

SourcePensoft Publishers·JournalResearch Ideas and Outcomes·DateApr 12, 2016

Computers get with the beat

Researchers developed a simple system to automate music genre categorization by focusing on just pitch, tempo, and amplitude variation patterns. The approach uses random sample consensus (RANSAC) as a classifier and demonstrated accuracy in seven major musical genres.

SourceInderscience Publishers·JournalInternational Journal of Computational Intelligence Studies·DateJun 29, 2015