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An atomic nucleus consists of protons and neutrons (collectively referred to as nucleons) bound together through the strong nuclear force. Models for the nuclear binding energy were introduced in a couple of previous posts on this blog.

Fitting a set of data points in the $xy$ plane to an ellipse is a suprisingly common problem in image recognition and analysis. In principle, the problem is one that is open to a linear least squares solution, since the general equation of any conic section can be written
$$
F(x, y) = ax^2 + bxy + cy^2 + dx + ey + f = 0,
$$
which is linear in its parameters $a$, $b$, $c$, $d$, $e$ and $f$. The polynomial $F(x,y)$ is called the *algebraic distance* of any point $(x, y)$ from the conic (and is zero if $(x, y)$ happens to lie *on* the conic).

Inspired by this recent Numberphile video, here is a demonstration of chaos in a simple dynamical system: two balls, with near-identical starting conditions, bounce around elastically off a circular wall. After a short time, the balls' trajectories diverge completely.

In his 1986 book, *Surely You're Joking, Mr. Feynman!* physicist Richard Feynman writes:

Prompted by this tweet and campaign for icebergs to be depicted in their most stable equilibrium orientation, here is a Python script modelling the dynamics of a two-dimensional iceberg which starts in an arbitrary orientation and position and relaxes under gravitational and buoyant forces to its most stable configuration. A cork floats "on its side": with its longest axis parallel to the water's surface (it doesn't bob around with its longest axis vertical), and an iceberg does the same.