Physics Channel

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Welcome to the Newsmast Physics Channel. A curated feed of posts from the Fediverse, handmade by @newsmast@newmast.social, and broadcasting to Bluesky (if you've opted-in via @bsky.brid.gy)!

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Newsmast Foundation Websitehttps://www.newsmastfoundation.org/
https://www.europesays.com/uk/234525/ A.P. govt. and partners issue Amaravati Quantum Declaration #Physics #Science #UK #UnitedKingdom
Discovery in quantum materials could make electronics 1,000 times faster

Researchers at Northeastern University have discovered how to change the electronic state of matter on demand, a breakthrough that could make electronics 1,000 times faster and more efficient.

Phys.org
https://www.europesays.com/uk/234008/ Astrophysicist explains how black hole mergers and quasars help detect gravitational wave networks #Materials #Nanotech #Physics #PhysicsNews #Science #ScienceNews #Technology #TechnologyNews #UK #UnitedKingdom

This shape is the 3d associahedron.

To get it, take a hexagon and triangulate it by drawing red lines between its corners - lines that don't cross each other. There are 14 ways to do this, and these are the vertices of the associahedron. You get an edge of the associahedron from two triangulations that have two red lines in common. You get a face from all the triangulations that have one red line in common.

If you replace the hexagon by a polygon with more sides, you get a higher-dimensional associahedron. The associahedra have many magical properties, and here's one of the most astounding.

Take a formal power series like this:

𝐢(π‘₯) = π‘₯ + 𝑐₁π‘₯Β² + 𝑐₂π‘₯Β³ + β‹―

If you take its inverse under composition, meaning the power series 𝐷 with

𝐢(𝐷(π‘₯)) = π‘₯

you get another formal power series of the same type:

𝐷(π‘₯) = π‘₯ + 𝑑₁π‘₯Β² +𝑑₂π‘₯Β³ + β‹―

How are the numbers 𝑑ₙ related to the numbers 𝑐ₙ? Do some calculations:

𝑑₁ = βˆ’π‘β‚
𝑑₂ = βˆ’π‘β‚‚ + 2𝑐₁²
𝑑₃ = βˆ’π‘β‚ƒ + 5𝑐₂𝑐₁ βˆ’ 5𝑐₁³
𝑑₄ =βˆ’π‘β‚„ + 6𝑐₃𝑐₁ + 3𝑐₂² βˆ’ 21𝑐₂𝑐₁² + 14𝑐₁⁴

What are these coefficients? They're controlled by the associahedra! I'll show how it works for 𝑑₄.

Call the n-dimensional associahedron 𝑐ₙ₋₁, so that 𝑐₁ is a point, 𝑐₂ is an interval, 𝑐₃ is a pentagon, and so on. From the picture notice that the 3d associahedron 𝑐₄ has

β€’ 1 face shaped like 𝑐₄ (the whole thing)

β€’ 6 faces shaped like 𝑐₃ Γ— 𝑐₁ (pentagons) and 3 faces shaped like 𝑐₂ Γ— 𝑐₂ (squares)

β€’ 21 faces shaped like 𝑐₂ Γ— 𝑐₁× 𝑐₁ (the edges)

β€’ 14 faces shaped like 𝑐₁ Γ— 𝑐₁ Γ— 𝑐₁ Γ— 𝑐₁ (the vertices)

All this information is packed into here:

𝑑₄ = βˆ’π‘β‚„ + 6𝑐₃𝑐₁ + 3𝑐₂² βˆ’ 21𝑐₂𝑐₁² + 14𝑐₁⁴

Look at it!

We get the other 𝑑ₙ from the associahedra of other dimensions, in the same way!

(1/2)

πŸ“° "Orthotropic Viscoelastic Creep in Cellular Scaffolds"
https://arxiv.org/abs/2507.01071 #Cond-Mat.Mtrl-Sci #Physics.Bio-Ph #Mechanical #Q-Bio.Cb #Cell
Orthotropic Viscoelastic Creep in Cellular Scaffolds

Recent measurements of Norway spruce have revealed stress-state-dependent normalized creep behavior, highlighting a gap in our fundamental understanding. This study examines whether the anisotropic response originates from the micro-structural, cellular nature of composite cell walls with varying tracheid types. Cell wall creep parameters are identified via surrogate-based inverse parameter identification, applied to hierarchical micro-mechanical and FEM models of increasing topological complexity up to the growth ring scale. Despite microstructural disorder, simulated creep curves converge toward a universal set of proportionality factors. The results indicate that directional creep behavior cannot be attributed solely to tissue-scale topology, and that realistic predictions require the inclusion of non-linear material responses at stress concentration sites.

arXiv.org
πŸ“° "Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a cell's division history"
https://arxiv.org/abs/2506.09374 #Physics.Data-An #CellDivision #Q-Bio.Qm #Stat.Ml #Stat.Ap #Cell
Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a cell's division history

Inferring protein production kinetics for dividing cells is complicated protein inheritance from the mother cell. For instance, fluorescence measurements -- commonly used to assess gene activation -- may reflect not only newly produced proteins but also those inherited through successive cell divisions. In such cases, observed protein levels in any given cell are shaped by its division history. As a case study, we examine activation of the glc3 gene in yeast involved in glycogen synthesis and expressed under nutrient-limiting conditions. We monitor this activity using snapshot fluorescence measurements via flow cytometry, where GFP expression reflects glc3 promoter activity. A naΓ―ve analysis of flow cytometry data ignoring cell division suggests many cells are active with low expression. Explicitly accounting for the (non-Markovian) effects of cell division and protein inheritance makes it impossible to write down a tractable likelihood -- a key ingredient in physics-inspired inference, defining the probability of observing data given a model. The dependence on a cell's division history breaks the assumptions of standard (Markovian) master equations, rendering traditional likelihood-based approaches inapplicable. Instead, we adapt conditional normalizing flows (a class of neural network models designed to learn probability distributions) to approximate otherwise intractable likelihoods from simulated data. In doing so, we find that glc3 is mostly inactive under stress, showing that while cells occasionally activate the gene, expression is brief and transient.

arXiv.org
Γ—
https://www.europesays.com/uk/225055/ Shortly before his death, Stephen Hawking reportedly solved one of the universe’s greatest mysteries #Physics #Science #UK #UnitedKingdom