Toggle light / dark theme

Philosophy Of Physics (@PhilosophyOfPhy) on X

The continuity equation was not the work of a single physicist. Its development grew from early hydraulic studies and the work of Daniel and Johann Bernoulli. In the eighteenth century, Jean le Rond d’Alembert produced the first partial-differential expression of mass conservation in fluid motion, and Leonhard Euler soon placed it in the general mathematical framework that became the foundation of modern fluid mechanics. It should therefore not be attributed solely to Giovanni Battista Venturi, whose later work concerned flow through constricted tubes. Its general form is ∂ρ/∂t + ∇·(ρv) = 0 where ρ is fluid density and v is the velocity field. The equation says that mass cannot simply appear or disappear: any change in the amount of fluid inside a region must be explained by fluid entering or leaving it. For steady flow through a pipe, this becomes ρ₁A₁v₁ = ρ₂A₂v₂ If the fluid is effectively incompressible, its density remains constant, giving the familiar form: A₁v₁ = A₂v₂ The meaning is simple. The same volume of fluid must pass through every section of the pipe each second. When the pipe becomes narrower, the fluid must move faster; when it becomes wider, the fluid slows down. This equation is fundamental to the study of pipes, nozzles, rivers, aircraft flow, circulation systems and computational fluid dynamics. More broadly, continuity equations appear throughout physics wherever something locally conserved, such as mass or electric charge, moves through space. The equation is not merely about fluids; it is the mathematical language of the principle that what flows into a region must either flow out or remain inside.

Tiny memristor chip cuts brain modeling time to under 10 milliseconds

A research team has developed the world’s first chip that can match the speed at which the human brain functions. The study, titled “A sub–10-millisecond neural dynamical system based on phase-change memristors,” was published in Science and was led by Professor Yang Yuchao of Peking University, together with researchers from the Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences.

Neural dynamical systems combine neural networks with mathematical equations that describe how complex systems change over time. They are useful for physical modeling, medical imaging and three-dimensional brain reconstruction. However, these systems require repeated calculations, error checks and adjustments to the size of each calculation step. In conventional computers, data must also move frequently between memory and the processor, increasing processing time and energy use.

Fast and accurate brain modeling is important for technologies that must respond in real time, including brain–computer interfaces, surgical navigation and medical imaging. Existing hardware often requires too much time and power for these demanding calculations. By performing key operations directly in memory, the new chip reduces data movement and brings high-quality brain modeling closer to real-time use.

From molecules to networks, siibra integrates brain data into a unified atlas

In the current issue of the journal Nature Methods, siibra is introduced as a software suite that integrates data from different multimodal sources into a comprehensive atlas of the human brain and makes the data easily accessible—for interactive exploration and automated, reproducible data analyses, simulations and AI applications. siibra is developed by an international team of scientists led by the Institute of Neuroscience and Medicine (INM-1) at Forschungszentrum Jülich.

To better understand the human brain, information from various levels must be integrated, from molecules and cells to their organization and entire networks. A central challenge is that these data are often scattered across sources and organized differently. They originate from methods such as microscopy, MRI and connectivity analysis; exist in formats ranging from images to tables; and rely on different spatial reference systems and conceptual taxonomies.

“Using siibra, we are now able to access and analyze brain data in a structured way from micro-to macrolevels—for more precise neuroscience studies, bio-inspired AI and clinical applications such as deep brain stimulation,” says Dr. Timo Dickscheid, working group leader for “Big Data Analytics.”

George Dvorsky: Specialization is for Insects

Fourteen years ago, I sat down with George Dvorsky for almost 1 hour and 40 minutes. We argued about everything: transhumanism and the paleo diet, animal uplift and non-human person rights, mass extinction, SETI, and whether alien intelligence would be friendly or hostile.

One quote from that conversation has stayed with me ever since. Robert A. Heinlein:

“A human being should be able to change a diaper, plan an invasion, butcher a hog, conn a ship, design a building, write a sonnet, balance accounts, build a wall, set a bone, comfort the dying, take orders, give orders, cooperate, act alone, solve equations, analyze a new problem, pitch manure, program a computer, cook a tasty meal, fight efficiently, die gallantly. Specialization is for insects.”

In 2012, that sounded like a provocation. In 2026, in the age of AI, it sounds like a survival strategy. When machines out-specialize us at everything, what is left for humans? Perhaps precisely this: to be generalists. To be whole.

Was George right then? Is Heinlein right now?

Watch the full interview and judge for yourself: [ https://snglrty.co/4pU2ZtQ](https://snglrty.co/4pU2ZtQ)

New machine-learning equation accurately assess LDL cholesterol risk

The Martin-Hopkins equation to assess low-density lipoprotein (LDL) cholesterol levels in blood samples has been used by laboratories in the U.S. and other countries to guide efforts to lower cardiovascular disease risk. Now, a simplified machine-learning version of this equation has been shown in a study of millions of U.S. adult and child blood samples to match the accuracy of the original—making it broadly accessible. The findings and code were published in JAMA Cardiology.

“We’ve optimized the calculation of LDL cholesterol and made this equation accessible and easier for all labs to implement,” says Seth Martin, M.D., M.H.S., the senior study author and director of the Advanced Lipid Disorders Program and Digital Health Lab at the Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease.

“Our goal is to enable clinicians and patients to make better decisions about starting treatments that prevent heart attacks and strokes and save lives.”

Braided, exotic particles could build reliable, universal quantum computers

A truly useful quantum computer must be able to run any algorithm, with the same versatility an ordinary laptop offers. Physicists have now shown a new way to give a quantum computer exactly that flexibility, harnessing the capabilities of exotic quantum particles called non-Abelian anyons.

A team of scientists from the University of Chicago Pritzker School of Molecular Engineering (UChicago PME), Harvard, Stony Brook University and Quantinuum built and tested a complete toolkit of operations using non-Abelian anyons, proving for the first time the broad utility of this approach.

“We demonstrated a so-called universal gate set—meaning that if you store information in these emergent versions of quarks, and you move them around, you can do any quantum computation you might want to do,” said Ruben Verresen, assistant professor of molecular engineering at UChicago PME and a co-author of the new study published in Nature.

DNA origami turns secret messages into nano–Morse code that acts as multiplayer molecular encryption

Mathematics has always been at the core of securing information. From online banking to government communications, modern society relies on cryptography, in which complex mathematical algorithms transform readable information into an unreadable form to keep it secure. But as computing power grows and quantum technology advances, these mathematical safeguards are increasingly vulnerable to being broken. That’s where biology stepped in.

Choosing DNA as their information protector, researchers from China developed a multilayer encryption device that takes advantage of the double-helix molecule’s programmable nature to create an origami structure that can store information with high security.

This new system used tiny, custom-built rectangular structures made of DNA, in which researchers stored the message as dots and dashes, creating a nanoscale version of Morse code. To hide the message further, they turned the flat DNA origami surfaces into tubes, physically blocking the patterns from being read or imaged. With the help of a matching unlocking key, the recipient can trigger a reaction that unrolls the DNA back to its flat form, allowing them to read and verify the message.

Testing the limits of what’s possible (and what isn’t) with AI

When can we trust the results we get from AI, and when is learning impossible? Researchers have shown that there are some problems that even the most powerful AI cannot reliably solve, no matter how much data it is given.

The researchers, from the University of Cambridge and the University of California, Santa Barbara, designed “adversarial” mathematical systems to fool any AI algorithm. Like ethical hackers stress-testing a network’s security, these adversarial systems were designed to map out exactly where and why AI prediction breaks down.

Many real-world systems—like those in the oceans, the human brain or robotics—are too complex to describe neatly with equations, so researchers often learn how they behave by using machine learning. But these AI methods don’t always work well, returning unreliable results or poor predictions.

Frontiers: The relentless advancement of artificial intelligence (AI) across sectors such as healthcare

The automotive industry, and social media necessitates the development of more efficient hardware solutions that can implement diverse learning algorithms. This lead article explores the evolution of AI learning algorithms and their computational demands, using autonomous drone navigation as a case study to highlight the limitations of traditional hardware. Traditional hardware, based on the von Neumann architecture, suffers from limited computational efficiency due to the separation of compute units and memory, also known as the “memory wall” problem. To overcome this barrier, this article discusses novel approaches to AI hardware design, focusing on compute-in-memory (CIM) techniques and stochastic hardware.

Entanglement Goes Steady

Two independent groups have demonstrated ways to entangle quantum bits without the need for precisely timed control pulses.

Quantum entanglement describes a link, or correlation, between the states of two or more quantum particles. For example, given a pair of entangled qubits—particles that can be in either a ground state or an excited state—measuring the state of one qubit can inform us about the state of the other. Entanglement is puzzling because it has no analogue in the classical world, where our physical intuition can be relied upon. In particular, entanglement appears to violate the principle of locality: The qubits’ states remain correlated even if we move them far apart before measuring them. But entanglement is more than a curiosity: It is also critical to quantum computing, where it serves as a resource for performing quantum algorithms and remote operations between distant qubits.

/* */