One of the classic mistakes you can make as a teacher is to spot what you fondly think is a small gap in the curriculum, and then commit to filling it. The not-so-small gap is in our teaching of data analysis. Analysing data is, as I just said to our second years, a key part of doing science. As I also said to them, it is poor practice to use formulas or software such as Excel without knowing what they are doing. Both of these statements are true. The problem is that data analysis is a huge subject, and it is underpinned by lots of maths whose details I don’t know myself and will not be teaching to the students. So, by committing to do one extra lecture to try and improve matters here, I bit off a bit more than I could chew.
Of course, you should never serve champagne at room temperature, champagne.fr recommends 8 to 10 C. But an interesting paper by Liger-Belair and coworkers reckons that you can actually form (tiny) ice crystals if you pop a bottle of luke warm champagne. Surprisingly, the warmer the champagne the colder the expanding gas of the champagne pop gets.
One of the (many) things that makes middle aged academics like me grumpy, is students giving answers like speed = 4.738281 m/s, by just copying the number from the screen of their calculator/mobile-phone-app, without any thought at all. But I am actually paid to do something more constructive that be grumpy about this sort of poor practice, so this year I have done a little set of notes on accuracy for my final year class. They take the students through a couple of examples.
Forty years ago Stephen Jay Gould and Richard Lewontin introduced what they called spandrels, to the field of evolutionary biology. My impression is that this idea has been controversial in evolutionary biology ever since. Spandrels in the original sense of the word are illustrated above. The word spandrel comes from architecture, and basically it refers to the parts of the arches above which have the blue discs with a relief person in them. They are the spaces between the arch and the roof. The point that inspired Gould and Lewontin, is that arches are directly functional parts of archtecture, they hold up the roof. But by their very nature arches leave gaps, that is unavoidable but not directly functional. These gaps can be filled in by spandrels, which themselves are not directly functional — the ceiling will not collapse if they are removed.
I am revising a numerical physics course for the forthcoming semester, in particular the bits about data analysis. So I have been reading a couple of books to both learn from them, and to see if they could be useful to the students. One compact but good summary is The Data Loom by Stephen Few. It is quite introductory and short, so I am thinking that it could be good to recommend to the students. It covers a lot of ground and I like the author’s practical, sceptical tone. It is also has some excellent examples.
Above are droplets of balsamic vinegar (mainly water, but tastier of course) in olive oil. Water and oil phase separate, hence the droplets. The droplets above are maybe a few millimetres across, and they won’t move unless you stir the oil. This blog post is about much smaller droplets, too small to be seen with the eye, so the picture above of much larger droplets, will have to do. Smaller droplets can move. And two scientists working in Darmstadt in Germany, Hajian and Hardt, have seen small droplets move, which is not so surprising. But what is surprising is that they then dissolved.
The movie shows a system that starts to separate into two liquids (yellow and purple), just as oil and water do, but is then kept as a dynamic system of droplets that split, evaporate and form again, by a chemical reaction. This chemical reaction converts yellow molecules to purple, and then back to purple again, and this cycle drives the droplet breakup seen in the last two-thirds of the movie. This simulation is of a very bad model of liquid droplets in living cells, there is a movie here of real droplets in real living cells, from the work of Cliff Brangwynne and co-workers.
Life on Earth, including ourselves, relies totally on photosynthesis. Photosynthesis pulls carbon from carbon dioxide in the air to make the molecules of which plants are made of. Then we eat these plants, and, if we are not vegan, the products of animals that eat these plants. Photosynthesis, like everything else in biology, is the product of evolution. Very simply speaking there are two schools of thought on evolution. The first is that it is an incredible process that has produced marvels such as a soaring eagle with eyesight keen enough to see a rabbit a kilometre away. The second is that it is a blind process that gradually cobbles together just-about-working solutions to the problem of living and reproducing.
I am starting to sort out my Python teaching for the coming semester; the course contains some introductory data analysis. As part of this, I have just read a relatively old (2001) but I think influential article that compares and contrasts two schools of data analysis. Roughly speaking these are:
- School A): fit a simple-as-possible model function to the data, for example a straight-line or exponential fit, to try and understand what is going on.
- School B): use a machine learning algorithm such as a neural net, or a support vector machine, to obtain the best possible predictions.
The author is Leo Breiman, a statistician, who was encouraging his fellow statisticians to give School B a try. He thought many statisticians were sticking too rigidly to School A, and this inspired him to write this article, which argues for School B.
Over the summer I am teaching myself a bit of the Lattice Boltzmann simulation method, and rewriting my second-year computing teaching, to be in Python and use Jupyter notebooks. As always with my coding, I am getting problems with numbers that should be positive (such as a density) being zero or negative, which fatal consequences for that run. In parallel, I am doing a bit of trawling the web to see, and learn from, what other people do, when they teach computational physics using Python and Jupyter notebooks.