Tootfinder

The DBL Survey II: towards a mass-period distribution of double white dwarf binaries
James Munday, Ingrid Pelisoli, Pier-Emmanuel Tremblay, David Jones, Gijs Nelemans, Mukremin Kilic, Tim Cunningham, Silvia Toonen, Alejandro Santos-Garc\'ia, Harry Dawson, Viktoria Pinter, Benjamin Godson, Llanos Martinez, Jaya Chand, Ross Dobson, Kiran Jhass, Shravya Shenoy

The DBL Survey II: towards a mass-period distribution of double white dwarf binaries
Double white dwarf binaries are an important remnant of binary evolution as they are possible type Ia supernova progenitors and strong sources of gravitational waves in the low-frequency regime. The double-lined double white dwarf (DBL) survey searches for compact double white dwarfs where both stars are spectrally disentangleable. Candidates are identified by being overluminous compared to the cooling sequence of a typical mass, single white dwarf. In this second DBL survey instalment, we pres…

Software developer Heval Hazal Kurt discusses the pros and cons of relational databases, in comparison with their document-based counterparts, in this July 2025 article. Polyglot systems combining both paradigms are discussed as an ideal solution to differing data access needs within a same project.
"When to Choose NoSQL Over SQL"

A Comprehensive MARTINI Coarse-Grained Framework for Phyllosilicate Clay/Polymer Nanocomposites: From Atomistic Validation to Mesoscale Insights
Ankit Patidar, Gaurav Goel
https://arxiv.org/abs/2507.06159

A Comprehensive MARTINI Coarse-Grained Framework for Phyllosilicate Clay/Polymer Nanocomposites: From Atomistic Validation to Mesoscale Insights
Phyllosilicate clays have diverse applications in packaging industries and are found highly suitable for formulation, including thermoplastic starch (TPS), polyethylene (PE), or their combination. We developed CG MARTINI-3 parameters of the pyrophyllite using Lifshitz theory and experimental surface tension data. These initial bead assignments of pyrophyllite and periodic tetramethylammonium-montmorillonite (TMA-MMT) sheet were fine-tuned using optimal reproduction of structural, thermodynamic,…

In Ursula K. Le Guin's "A Man of the People" (part of "Four Ways to Forgiveness") there's a scene where the Hainish protagonist begins studying history. It's excellent in many respects, but what stood out the most to me was the softly incomprehensible idea of a people with multiple millions of years of recorded history. As one's mind starts to try to trace out the implications of that, it dawns on you that you can't actually comprehend the concept. Like, you read the sentence & understood all the words, and at first you were able to assemble them into what seemed like a conceptual understanding, but as you started to try to fill out that understating, it began to slip away, until you realized you didn't in fact have the mental capacity to build a full understanding and would have you paper things over with a shallow placeholder instead.
I absolutely love that feeling, as one of the ways in which reading science fiction can stretch the brain, and I connected it to a similar moment in Tsutomu Nihei's BLAME, where the android protagonists need to ride an elevator through the civilization/galaxy-spanning megastructure, and turn themselves off for *millions of years* to wait out the ride.
I'm not sure why exactly these scenes feel more beautifully incomprehensible than your run-of-the-mill "then they traveled at lightspeed for a millennia, leaving all their family behind" scene, other than perhaps the authors approach them without trying to use much metaphor to make them more comprehensible (or they use metaphor to emphasize their incomprehensibility).
Do you have a favorite mind=expanded scene of this nature?
#AmReading

Teaching at Scale: Leveraging AI to Evaluate and Elevate Engineering Education
Jean-Francois Chamberland, Martin C. Carlisle, Arul Jayaraman, Krishna R. Narayanan, Sunay Palsole, Karan Watson
https://arxiv.org/abs/2508.02731

Teaching at Scale: Leveraging AI to Evaluate and Elevate Engineering Education
Evaluating teaching effectiveness at scale remains a persistent challenge for large universities, particularly within engineering programs that enroll tens of thousands of students. Traditional methods, such as manual review of student evaluations, are often impractical, leading to overlooked insights and inconsistent data use. This article presents a scalable, AI-supported framework for synthesizing qualitative student feedback using large language models. The system employs hierarchical summa…