Cool biophysical modeling of the endoplasmic reticulum!
Active liquid network [ https://www.czbiohub.org/life-science/a-simple-model-for-an-…structure/](https://www.czbiohub.org/life-science/a-simple-model-for-an-…structure/)
Scientists use math and physics to address the mystery of just how the endoplasmic reticulum, an organelle essential to life at the cellular level, continually re-arranges itself.
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Up until last week, physicists believed that matter is made up of only two types of particles: those whose spin has full-integer values (bosons) and those whose spin comes has half-integer values (fermions). But in a new paper, a group of researchers turned the world of physics upside down by mathematically proving that a third type of particles – the “paraparticles” are possible.
Paper: https://www.nature.com/articles/s4158…
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The challenge for researchers is to develop the often complicated series of equations that are needed to describe these phenomena and ensure that they can be solved to recover information on the location of the objects over time. Often the systems of equations needed to describe such phenomena are based on partial differential equations: the series of equations that describe the location and time-evolution of a system are known as a distributed parameter system.
Mathematical models can help us not just understand historical behaviour but predict where the smoke particles will spread next.
Professor Francisco Jurado at the Tecnológico Nacional de México has been working on approaches to solve the problem of distributed parameter systems to describe diffusion–convection systems. He has recently developed an approach using a combination of approaches, including the Sturm-Liouville differential operator and the regulator problem, to develop a model for diffusion–convection behaviour that is sufficiently stable and free of external disturbances. Importantly, this approach allows us to yield meaningful information for real systems.
Most of us assume reality is made up of physical matter. In line with this, scientists have built ever larger machines to identify the ultimate particles. Instead of getting closer to the most elementary bits in the universe, the particle zoo has got ever more complex and seemingly less like material stuff at all. Is there a danger that the very idea of an ultimate foundation to reality is a profound mistake? Some have proposed that instead of material, the ultimate foundation is to be found in consciousness, information, or even mathematics. But such proposals are no closer to identifying ultimate elements than particle physicists. Should we give up the attempt to uncover an ultimate foundation to the universe? Is our inability to find an ultimate foundation a limitation of language, or of our capacity to make sense of the world, or is it to do with the nature of reality itself?
How Symmetry Shapes the Universe: A Peek into Persistent Symmetry Breaking.
Imagine a world where certain symmetries—like the balance between left and right or up and down—are spontaneously disrupted, but this disruption persists regardless of temperature. Scientists are exploring this fascinating behavior in a special type of mathematical framework known as biconical vector models. These models examine how symmetries behave under specific conditions, especially in a universe with two spatial dimensions and one time dimension (2+1 dimensions).
This study takes a closer look at these models and reveals exciting new insights about symmetry breaking in a way that respects established physical principles. Here’s what the researchers discovered:
1. Symmetry Breaking Basics: The study confirms that symmetry can break persistently when these models are designed to include both continuous and discrete symmetry features (described by the mathematical groups O(N)×Z₂). This breaking shifts from one type of symmetry (O(N)×Z₂) to another (O(N)) as temperature rises, but only under certain conditions.
2. Precision at Zero Temperature: By using advanced computational methods, the team accurately described how these models behave when the temperature is absolute zero. Their findings are valid for a wide range of systems, provided the number of components, N, is 2 or greater.
Nemourlon armor of reasonable weight resists penetration by most fragments and any bullet that is not both reasonably heavy and fairly high-velocity.’ — Jerry Pournelle, 1976.
Goldene — A Two-Dimensional Sheet Of Gold One Atom Thick ‘Hasan always pitched a Gauzy — a one-molecule-layer tent, opaque, feather-light, and very tough.’ — Roger Zelazny, 1966.
GNoME AI From DeepMind Invents Millions Of New Materials ‘…the legendary creativity of our finest human authors pales against the mathematical indefatigability of GNoME.’
Our reality is a 3 + 1 pseudo-Riemannian spacetime manifold whose intrinsic curvature manifests itself as gravity, right? Well no, because descriptions are not reality, and math is not physics. Indeed, when taken at its most literal, face-value, what the \.
Even so, many wonder: If the universe is at bottom deterministic (via stable laws of physics), how do these quantum-like phenomena arise, and could they show up in something as large and complex as the human brain?
Quantum-Prime Computing is a new theoretical framework offering a surprising twist: it posits that prime numbers — often celebrated as the “building blocks” of integers — can give rise to “quantum-like” behavior in a purely mathematical or classical environment. The kicker? This might not only shift how we view computation but also hint at new ways to understand the brain and the nature of consciousness.
Below, we explore why prime numbers are so special, how they can host quantum-like states, and what that might mean for free will, consciousness, and the future of computational science.
Theoretical physicists predict the existence of exotic “paraparticles” that defy classification and could have quantum computing applications.
By Davide Castelvecchi & Nature magazine
Theoretical physicists have proposed the existence of a new type of particle that doesn’t fit into the conventional classifications of fermions and bosons. Their ‘paraparticle’, described in Nature on January 8, is not the first to be suggested, but the detailed mathematical model characterizing it could lead to experiments in which it is created using a quantum computer. The research also suggests that undiscovered elementary paraparticles might exist in nature.
Reservoir computing (RC) is a powerful machine learning module designed to handle tasks involving time-based or sequential data, such as tracking patterns over time or analyzing sequences. It is widely used in areas such as finance, robotics, speech recognition, weather forecasting, natural language processing, and predicting complex nonlinear dynamical systems. What sets RC apart is its efficiency―it delivers powerful results with much lower training costs compared to other methods.
RC uses a fixed, randomly connected network layer, known as the reservoir, to turn input data into a more complex representation. A readout layer then analyzes this representation to find patterns and connections in the data. Unlike traditional neural networks, which require extensive training across multiple network layers, RC only trains the readout layer, typically through a simple linear regression process. This drastically reduces the amount of computation needed, making RC fast and computationally efficient.
Inspired by how the brain works, RC uses a fixed network structure but learns the outputs in an adaptable way. It is especially good at predicting complex systems and can even be used on physical devices (called physical RC) for energy-efficient, high-performance computing. Nevertheless, can it be optimized further?
