science – Dan Q

FlipFlop Solitaire’s Deck-Generation Secret

For the last few years, my go-to mobile game¹ has been FlipFlop Solitaire.

The game

The premise is simple enough:

5-column solitaire game with 1-5 suits.
23 cards dealt out into those columns; only the topmost ones face-up.
2 “reserve” cards retained at the bottom.
Stacks can be formed atop any suit based on value-adjacency (in either order, even mixing the ordering within a stack)
Individual cards can always be moved, but stacks can only be moved if they share a value-adjacency chain and are all the same suit.
Aim is to get each suit stacked in order at the top.

One of the things that stands out to me is that the game comes with over five thousand pre-shuffled decks to play, all of which guarantee that they are “winnable”.

Playing through these is very satisfying because it means that if you get stuck, you know that it’s because of a choice that you made², and not (just) because you get unlucky with the deal.

Every deck is “winnable”?

When I first heard that every one of FlipFlop‘s pregenerated decks were winnable, I misinterpreted it as claiming that every conceivable shuffle for a game of FlipFlop was winnable. But that’s clearly not the case, and it doesn’t take significant effort to come up with a deal that’s clearly not-winnable. It only takes a single example to disprove a statement!

Paper-based sketch showing an 'unwinnable' deck of 'five suit' FlopFlop, formed by stacking the cards in a strict reverse order to make it as hard as possible. — If you think you’ve found a solution to this deal – for example, by (necessarily) dealing out all of the cards, then putting both reserve kings out and stacking everything else on top of them in order to dig down to the useful cards, bear in mind that (a) the maximum stack depth of 20 means you can get to a 6, or a 5, but not both, and (b) you can’t then move any of those stacks in aggregate because – although it’s not clear in my monochrome sketch – the suits cycle in a pattern to disrupt such efforts.

That it’s possible for a fairly-shuffled deck of cards to lead to an “unwinnable” game of FlipFlop Solitaire means the author must have necessarily had some mechanism to differentiate between “winnable” (which are probably the majority) and “unwinnable” ones. And therein lies an interesting problem.

If the only way to conclusively prove that a particular deal is “winnable” is to win it, then the developer must have had an algorithm that they were using to test that a given deal was “winnable”: that is – a brute-force solver.

So I had a go at making one³. The code is pretty hacky (don’t judge me) and, well… it takes a long, long time.

Partially that’s because the underlying state engine I used, BfsBruteForce, is a breadth-first optimising algorithm. It aims to find the absolute fewest-moves solution, which isn’t necessarily the fastest one to find because it means that it has to try all of the “probably stupid” moves it finds⁴ with the same priority as the the “probably smart” moves⁵.

I played about with enhancing it with some heuristics, and scoring different play states, and then running that in a pruned depth-first way, but it didn’t really help much. It came to an answer eventually, sure… but it took a long, long time, and it’s pretty easy to see why: the number of permutations for a shuffled deck of cards is greater than the number of atoms on Earth!

If you pull off a genuinely random shuffle, then – statistically-speaking – you’ve probably managed to put that deck into an order that no deck of cards has never been in before!⁶

And sure: the rules of the game reduce the number of possibilities quite considerably… but there’s still a lot of them.

So how are “guaranteed winnable” decks generated?

I think I’ve worked out the answer to this question: it came to me in a dream!

Show this puzzle to any smarter-than-average child and they’ll quickly realise that the fastest way to get the solution is not to start from each programmer and trace their path… but to start from the laptop and work backwards!

The trick to generating “guaranteed winnable” decks for FlipFlop Solitaire (and, probably, any similar game) is to work backwards.

Instead of starting with a random deck and checking if it’s solvable by performing every permutation of valid moves… start with a “solved” deck (with all the cards stacked up neatly) and perform a randomly-selected series of valid reverse-moves! E.g.:

The first move is obvious: take one of the kings off the “finished” piles and put it into a column.
For the next move, you’ll either take a different king and do the same thing, or take the queen that was exposed from under the first king and place it either in an empty column or atop the first king (optionally, but probably not, flipping the king face down).
With each subsequent move, you determine what the valid next-reverse-moves are, choose one at random (possibly with some kind of weighting), and move on!

In computational complexity theory, you just transformed an NP-Hard problem⁷ into a P problem.

Once you eliminate repeat states and weight the randomiser to gently favour moving “towards” a solution that leaves the cards set-up and ready to begin the game, you’ve created a problem that may take an indeterminate amount of time… but it’ll be finite and its complexity will scale linearly. And that’s a big improvement.

I started implementing a puzzle-creator that works in this manner, but the task wasn’t as interesting as the near-impossible brute-force solver so I gave up, got distracted, and wrote some even more-pointless code instead.

If you go ahead and make an open source FlipFlop deck generator, let me know: I’d be interested to play with it!

Footnotes

¹ I don’t get much time to play videogames, nowadays, but I sometimes find that I’ve got time for a round or two of a simple “droppable” puzzle game while I’m waiting for a child to come out of school or similar. FlipFlop Solitaire is one of only three games I have installed on my phone for this purpose, the other two – both much less frequently-played – being Battle of Polytopia and the buggy-but-enjoyable digital version of Twilight Struggle.

² Okay, it feels slightly frustrating when you make a series of choices that are perfectly logical and the most-rational decision under the circumstances. But the game has an “undo” button, so it’s not that bad.

³ Mostly I was interested in whether such a thing was possible, but folks who’re familiar with how much I enjoy “cheating” at puzzles (don’t get me started on Jigidi jigsaws…) by writing software to do them for me will also realise that this is Just What I Am Like.

⁴ An example of a “probably stupid” move would be splitting a same-suit stack in order to sit it atop a card of a different suit, when this doesn’t immediately expose any new moves. Sometimes – just sometimes – this is an optimal strategy, but normally it’s a pretty bad idea.

⁵ Moving a card that can go into the completed stacks at the top is usually a good idea… although just sometimes, and especially in complex mid-game multi-suit scenarios, it can be beneficial to keep a card in play so that you can use it as an anchor for something else, thereby unblocking more flexible play down the line.

⁶ Fun fact: shuffling a deck of cards is a sufficient source of entropy that you can use it to generate cryptographic keystreams, as Bruce Schneier demonstrated in 1999.

⁷ I’ve not thought deeply about it, but determining if a given deck of cards will result in a winnable game probably lies somewhere between the travelling salesman and the halting problem, in terms of complexity, right? And probably not something a right-thinking person would ask their desktop computer to do for fun!

The machines are fine. I’m worried about us.

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…

Unlike Alice, who spent the year reading papers with a pencil in hand, scribbling notes in the margins, getting confused, re-reading, looking things up, and slowly assembling a working understanding of her corner of the field, Bob has been using an AI agent. When his supervisor sent him a paper to read, Bob asked the agent to summarize it. When he needed to understand a new statistical method, he asked the agent to explain it. When his Python code broke, the agent debugged it. When the agent’s fix introduced a new bug, it debugged that too. When it came time to write the paper, the agent wrote it. Bob’s weekly updates to his supervisor were indistinguishable from Alice’s. The questions were similar. The progress was similar. The trajectory, from the outside, was identical.

Here’s where it gets interesting. If you are an administrator, a funding body, a hiring committee, or a metrics-obsessed department head, Alice and Bob had the same year. One paper each. One set of minor revisions each. One solid contribution to the literature each. By every quantitative measure that the modern academy uses to assess the worth of a scientist, they are interchangeable. We have built an entire evaluation system around counting things that can be counted, and it turns out that what actually matters is the one thing that can’t be.

…

The strange thing is that we already know this. We have always known this. Every physics textbook ever written comes with exercises at the end of each chapter, and every physics professor who has ever stood in front of a lecture hall has said the same thing: you cannot learn physics by watching someone else do it. You have to pick up the pencil. You have to attempt the problem. You have to get it wrong, sit with the wrongness, and figure out where your reasoning broke. Reading the solution manual and nodding along feels like understanding. It is not understanding. Every student who has tried to coast through a problem set by reading the solutions and then bombed the exam knows this in their bones. We have centuries of accumulated pedagogical wisdom telling us that the attempt, including the failed attempt, is where the learning lives. And yet, somehow, when it comes to AI agents, we’ve collectively decided that maybe this time it’s different. That maybe nodding at Claude’s output is a substitute for doing the calculation yourself. It isn’t. We knew that before LLMs existed. We seem to have forgotten it the moment they became convenient.

Centuries of pedagogy, defeated by a chat window.

…

Minas Karamanis, in The machines are fine. I’m worried about us.

This piece by Minas Karamanis is excellent throughout, and if you’ve got the time to read it then you should. He’s a physics postdoc, and this post comes from his experience in his own field, but I feel that the concerns he raises are more-widely valid, too.

In my field – of software engineering – I have similar concerns.

Let’s accept for a moment that an LLM significantly improves the useful output of a senior software engineer (which is very-definitely disputed, especially for the “10x” level of claims we often hear, but let’s just take it as-read for now). I’ve experimented with LLM-supported development for years, in various capacities, and it certainly sometimes feels like they do (although it sometimes also feels like they have the opposite effect!). But if it’s true, then yes: an experienced senior software engineer could conceivably increase their work performance by shepherding a flock of agents through a variety of development tasks, “supervising” them and checking their work, getting them back on-course when they make mistakes, approving or rejecting their output, and stepping in to manually fix things where the machines fail.

In this role, the engineer acts more like an engineering team lead, bringing their broad domain experience to maximise the output of those they manage. Except who they manage is… AI.

Again, let’s just accept all of the above for the sake of argument. If that’s all true… how do we make new senior developers?

Junior developers can use LLMs too. And those LLMs will make mistakes that the junior developer won’t catch, because the kinds of mistakes LLMs make are often hard to spot and require significant experience to identify. But if they’re encouraged to use LLMs rather than making mistakes by hand and learning from them – to keep up, for example, or to meet corporate policies – then these juniors will never gain the essential experience they’ll one day need. They’ll be disenfranchised of the opportunity to grow and learn.

It’s yet to be proven that more-sophisticated models will “solve” this problem, but my understanding is that issues like hallucination are fundamentally unsolvable: you might get fewer hallucinations in a better model, but that just means that those hallucinations that slip through will be better-concealed and even harder to identify in code review or happy-path testing.

Maybe – maybe – the trajectory of GPTs is infinite, and they’ll keep getting “smarter” to the point at which this doesn’t matter: programming genuinely will become a natural language exercise, and nobody will need to write or understand code at all. In this possible reality, the LLMs will eventually develop entire new programming languages to best support their work, and humans will simply express ideas and provide feedback on the outputs. But I’m very sceptical of that prediction: it’s my belief that the mechanisms by which LLMs work has a fundamental ceiling – a capped level of sophistication that can be approached but never exceeded. And sure, maybe some other, different approach to AI might not have this limitation, but if so then we haven’t invented it yet.

Which suggests that we will always need experienced engineers to shepherd our AIs. Which brings us back to the fundamental question: if everybody uses AI to code, how do we make new senior developers?

I have other concerns about AI too, of course, some of which I’ve written about. But this one’s top-of-mind today, thanks to Minas’ excellent article. Go read it to learn more about how physics research faces a similar threat… and, perhaps, consider how your own field might need to face this particular challenge.

Reading more rolled papyri

In kind-of local news, I see that the folks at Diamond Light Source (which I got to visit last year) have been helping the Bodleian (who I used to work for) to X-ray one of their Herculaneum scrolls (which I’ve written about before). That’s really cool.

Science Weekend

This weekend was full of science.

Research

String of large electromagnets used for steering an electron beam. — If you wanna bend a stream of electrons travelling at nearly the speed of light, you’re gonna need a lot of big magnets.

This started on Saturday with a trip to the Harwell Campus, whose first open day in eight years provided a rare opportunity for us to get up close with cutting edge science (plus some very kid-friendly and accessible displays) as well as visit the synchrotron at Diamond Light Source.

Dan with a child in front of beamlines coming out of the Diamond Light Synchrotron ring. — It’s hard to convey the scale of the thing; turns out you need a big ol’ ring if you want to spin electrons fast enough to generate a meaningful amount of magnetobremsstrahlung radiation.

The whole thing’s highly-recommended if you’re able to get to one of their open days in the future, give it a look. I was particularly pleased to see how enthused about science it made the kids, and what clever questions they asked.

For example: the 7-year-old spent a long time cracking a variety of ciphers in the computing tent (and even spotted a flaw in one of the challenge questions that the exhibitors then had to hand-correct on all their handouts!); the 10-year-old enjoyed quizzing a researcher who’d been using x-ray crystallography of proteins.

Medicine

And then on Sunday I finally got a long-overdue visit to my nearest spirometry specialist for a suite of experiments to try to work out what exactly is wrong with my lungs, which continue to be a minor medical mystery.

Dan holds a piece of medical apparatus to his mouth. — “Once you’ve got your breath back, let’s fill you with drugs and do those experiments again…”

It was… surprisingly knackering. Though perhaps that’s mostly because once I was full of drugs I felt briefly superpowered and went running around the grounds of the wonderfully-named Brill Hill Windmill with the dog until was panting in pretty much the way that I might normally have been, absent an unusually-high dose of medication.

For amusement purposes alone, I’d be more-likely to recommend the first day’s science activities than the second, but I can’t deny that it’s cool to collect a load of data about your own body and how it works in a monitorable, replicable way. And maybe, just maybe, start to get to the bottom of why my breathing’s getting so much worse these last few years!

Water Science #2

Back in 2019, the kids – so much younger back then! – and I helped undertake some crowdsourced citizen science for the Thames WaterBlitz. This year, we’re helping out again.

We’ve moved house since then, but we’re still within the Thames basin and can provide value by taking part in this weekend’s sampling activity. The data that gets collected on nitrate and phosphate levels in local water sources – among other observations – gets fed into an open dataset for the benefit of scientists and laypeople.

It’d have been tempting to be exceptionally lazy and measure the intermittent water course that runs through our garden! It’s an old, partially-culverted drainage ditch¹, but it’s already reached the “dry” part of its year and taking a sample wouldn’t be possible right now.

Two children wearing wellies stand in a ditch, breaking ice into chunks. — The ditch in our garden is empty 75% and full of water 25% of the time. Oh, and full of ice for a few days each winter, to the delight of children who love smashing things. (It’s also full of fallen wood and leaf detritus most of the year and JTA spends a surprising amount of time dredging it so that it drains properly into its next section.)

But more-importantly: the focus of this season’s study is the River Evenlode, and we’re not in its drainage basin! So we packed up a picnic and took an outing to the North Leigh Roman Villa, which I first visited last year when I was supposed to be on the Isle of Man with Ruth.

Dan kneels on a striped picnic mat with a 7-year-old and a 10-year old child alongside some sandwiches, iced fingers, Pepperami, fruit, and pretzels. — “Kids, we’re going outside…” / “Awww! Noooo!” / “…for a picnic and some science!” / “Yayyy!”

Our lunch consumed, we set off for the riverbank, and discovered that the field between us and the river was more than a little waterlogged. One of the two children had been savvy enough to put her wellies on when we suggested, but the other (who claims his wellies have holes in, or don’t fit, or some other moderately-implausible excuse for not wearing them) was in trainers and Ruth and I needed to do a careful balancing act, holding his hands, to get him across some of the tougher and boggier bits.

A 7-year-old boy in a grey camo coat balances on a blank over a large muddy puddle: he's about to attempt to cross a log to a gate into the next field (which also looks pretty wet). Ruth, who doesn't much like featuring in photos, has been digitally-removed from this one (she was standing at the far side ready to catch the balancing child!). — Trainers might not have been the optimal choice of footwear for this particular adventure.

Eventually we reached the river, near where the Cotswold Line crosses it for the fifth time on its way out of Oxford. There, almost-underneath the viaduct, we sent the wellie-wearing eldest child into the river to draw us out a sample of water for testing.

Looking into our bucket, we were pleased to discover that it was, relatively-speaking, teeming with life: small insects and a little fish-like thing wriggled around in our water sample². This, along with the moorhen we disturbed³ as we tramped into the reeds, suggested that the river is at least in some level of good-health at this point in its course.

A 10-year old girl wearing sunglasses and purple wellies holds her skirt up out of the water as she wades up the muddy bank of a river carrying a tub of water. — I’m sure our eldest would have volunteered to be the one to traipse through the mud and into the river even if she *hadn’t* been the only one of that was wearing wellies.

We were interested to observe that while the phosphate levels in the river were very high, the nitrate levels are much lower than they were recorded near this spot in a previous year. Previous years’ studies of the Evenlode have mostly taken place later in the year – around July – so we wondered if phosphate-containing agricultural runoff is a bigger problem later in the Spring. Hopefully our data will help researchers answer exactly that kind of question.

Children stand around at a riverside stile while a colour-changing chemical in a vial does its thing. — The chemical experiments take up to 5 minutes each to develop before you can read their colours, so the kids had plenty of time to write-up their visual observations while they waited.

Regardless of the value of the data we collected, it was a delightful excuse for a walk, a picnic, and to learn a little about the health of a local river. On the way back to the car, I showed the kids how to identify wild garlic, which is fully in bloom in the woods nearby, and they spent the rest of the journey back chomping down on wild garlic leaves.

A 7-year-old wearing his coat inside-out and as a cape runs excitedly into a forest path overgrown with wild garlic. — Seriously, that’s a *lot* of wild garlic.

The car now smells of wild garlic. So I guess we get a smelly souvenir from this trip, too⁴!

Footnotes

¹ Our garden ditch, long with a network of similar channels around our village, feeds into Limb Brook. After a meandering journey around the farms to the East this eventually merges with Chill Brook to become Wharf Stream. Wharf Stream passes through a delightful nature reserve before feeding into the Thames near Swinford Toll Bridge.

² Needless to say, we were careful not to include these little animals in our chemical experiments but let them wait in the bucket for a few minutes and then be returned to their homes.

³ We didn’t catch the moorhen in a bucket, though, just to be clear.

⁴ Not counting the smelly souvenir that was our muddy boots after splodging our way through a waterlogged field, twice

Bumblebees surprise scientists with ‘sophisticated’ social learning

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…

First, bees had to push a blue lever that was blocking a red lever… too complex for a bee to solve on its own. So scientists trained some bees by offering separate rewards for the first and second steps.

These trained bees were then paired with bees who had never seen the puzzle, and the reward for the first step was removed.

Some of the untrained bees were able to learn both steps of the puzzle by watching the trained bees, without ever receiving a reward for the first step.

…

Ian Aikman (BBC News)

This news story is great for two reasons.

Firstly, it’s a really interesting experimental result. Just when you think humankind’s learned everything they ever will about the humble bumblebee (humblebee?), there’s something more to discover.

That a bee can be trained to solve a complex puzzle by teaching it to solve each step independently and then later combining the steps isn’t surprising. But that these trained bees can pass on their knowledge to their peers (bee-ers?); who can then, one assumes, pass it on to yet other bees. Social learning.

Which, logically, means that a bee that learns to solve the two-lever puzzle second-hand would have a chance of solving an even more-complex three-lever puzzle; assuming such a thing is within the limits of the species’ problem-solving competence (I don’t know for sure whether they can do this, but I’m a firm bee-lever).

But the second reason I love this story is that it’s a great metaphor in itself for scientific progress. The two-lever problem is, to an untrained bee, unsolvable. But if it gets a low-effort boost (a free-bee, as it were) by learning from those that came before it, it can make a new discovery.

(I suppose the secret third reason the news story had me buzzing was that I appreciated the opportunities for puns that it presented. But you already knew that I larva pun, right?)

Announcers and Automation

Duration 4:24

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Nowadays if you’re on a railway station and hear an announcement, it’s usually a computer stitching together samples¹. But back in the day, there used to be a human with a Tannoy microphone sitting in the back office, telling you about the platform alternations and destinations.

I had a friend who did it as a summer job, once. For years afterwards, he had a party trick that I always quite enjoyed: you’d say the name of a terminus station on a direct line from Preston, e.g. Edinburgh Waverley, and he’d respond in his announcer-voice: “calling at Lancaster, Oxenholme the Lake District, Penrith, Carlisle, Lockerbie, Haymarket, and Edinburgh Waverley”, listing all of the stops on that route. It was a quirky, beautiful, and unusual talent. Amazingly, when he came to re-apply for his job the next summer he didn’t get it, which I always thought was a shame because he clearly deserved it: he could do the job blindfold!

There was a strange transitional period during which we had machines to do these announcements, but they weren’t that bright. Years later I found myself on Haymarket station waiting for the next train after mine had been cancelled, when a robot voice came on to announce a platform alteration: the train to Glasgow would now be departing from platform 2, rather than platform 1. A crowd of people stood up and shuffled their way over the footbridge to the opposite side of the tracks. A minute or so later, a human announcer apologised for the inconvenience but explained that the train would be leaving from platform 1, and to disregard the previous announcement. Between then and the train’s arrival the computer tried twice more to send everybody to the wrong platform, leading to a back-and-forth argument between the machine and the human somewhat reminiscient of the white zone/red zone scene from Airplane! It was funny perhaps only because I wasn’t among the people whose train was in superposition.

Clearly even by then we’d reached the point where the machine was well-established and it was easier to openly argue with it than to dig out the manual and work out how to turn it off. Nowadays it’s probably even moreso, but hopefully they’re less error-prone.

When people talk about how technological unemployment, they focus on the big changes, like how a tipping point with self-driving vehicles might one day revolutionise the haulage industry… along with the social upheaval that comes along with forcing a career change on millions of drivers.

But in the real world, automation and technological change comes in salami slices. Horses and carts were seen alongside the automobile for decades. And you still find stations with human announcers. Even the most radically-disruptive developments don’t revolutionise the world overnight. Change is inevitable, but with preparation, we can be ready for it.

Footnotes

¹ Like ScotRail’s set, voiced by Alison McKay, which computers can even remix for you over a low-fi hiphop beat if you like.

Using every car parking space in a supermarket car park

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For the last six years I’ve kept a spreadsheet listing every parking spot I’ve used at the local supermarket in a bid to park in them all. This week I completed my Magnum Opus! A thread.

I live in Bromley and almost always shop at the same Sainsbury’s in the centre of town, here’s a satellite view of their car park. It’s a great car park because you can always get a space and it is laid out really well. Comfortably in my top 5 Bromley car parks.

After quite a few years of going each week I started thinking about how many of the different spots I’d parked in and how long it would take to park in them all. My life is one long roller coaster.

…

Gareth Wild

A glorious story from a man with the kind of dedication that would have gotten him far in CNPS back in the day (I wonder if Claire ever got past 13 points…).

This is the kind of thing that I occasionally consider adding to the list of mundane shit I track about my life. But then I start thinking about the tracking infrastructure and I end up adding far more future-proofing than I intend: I start thinking about tracking how often my hayfever causes me problems so I can correlate it to the time and the location data I already record to work out which tree species’ pollen affects me the most. Or tracking a variety of mood metrics so I can see if, as I’ve long suspected, the number of unread emails in my inboxen negatively correlates to my general happiness.

Measure all the things!

Gratitude

In these challenging times, and especially because my work and social circles have me communicate regularly with people in many different countries and with many different backgrounds, I’m especially grateful for the following:

My partner, her husband, and I each have jobs that we can do remotely and so we’re not out-of-work during the crisis.
Our employers are understanding of our need to reduce and adjust our hours to fit around our new lifestyle now that schools and nurseries are (broadly) closed.
Our kids are healthy and not at significant risk of serious illness.
We’ve got the means, time, and experience to provide an adequate homeschooling environment for them in the immediate term.
(Even though we’d hoped to have moved house by now and haven’t, perhaps at least in part because of COVID-19,) we have a place to live that mostly meets our needs.
We have easy access to a number of supermarkets with different demographics, and even where we’ve been impacted by them we’ve always been able to work-around the where panic-buying-induced shortages have reasonably quickly.
We’re well-off enough that we were able to buy or order everything we’d need to prepare for lockdown without financial risk.
Having three adults gives us more hands on deck than most people get for childcare, self-care, etc. (we’re “parenting on easy mode”).
We live in a country in which the government (eventually) imposed the requisite amount of lockdown necessary to limit the spread of the virus.
We’ve “only” got the catastrophes of COVID-19 and Brexit to deal with, which is a bearable amount of crisis, unlike my colleague in Zagreb for example.

Bowl of ice, glasses of water, salt and sugar supplies. — Today’s homeschool science experiment was about what factors make ice melt faster. Because of course that’s the kind of thing I’d do with the kids when we’re stuck at home.

Whenever you find the current crisis getting you down, stop and think about the things that aren’t-so-bad or are even good. Stopping and expressing your gratitude for them in whatever form works for you is good for your happiness and mental health.

Water Science

This afternoon, the kids and I helped with some citizen science as part of the Thames WaterBlitz, a collaborative effort to sample water quality of the rivers, canals, and ponds of the Thames Valley to produce valuable data for the researchers of today and tomorrow.

My two little science assistants didn’t need any encouragement to get out of the house and into the sunshine and were eager to go. I didn’t even have to pull out my trump card of pointing out that there were fruiting brambles along the length of the canal. As I observed in a vlog last year, it’s usually pretty easy to motivate the tykes with a little foraging.

The EarthWatch Institute had provided all the chemicals and instructions we needed by post, as well as a mobile app with which to record our results (or paper forms, if we preferred). Right after lunch, we watched their instructional video and set out to the sampling site. We’d scouted out a handful of sites including some on the River Cherwell as it snakes through Kidlington but for this our first water-watch expedition we figured we’d err on the safe side and aim to target only a single site: we chose this one both because it’s close to home and because a previous year’s citizen scientist was here, too, improving the comparability of the results year-on-year.

Plus, this expedition provided the opportunity to continue to foster the 5 year-old’s growing interest in science, which I’ve long tried to encourage (we launched bottle rockets the other month!).

Our results are now online, and we’re already looking forward to seeing the overall results pattern (as well as taking part in next year’s WaterBlitz!).

Note #14651

On this day 50 years ago launched the first mission to take people to the moon. As part of #GlobalRocketLaunch day the 5-year-old and I fired off stomp rockets and learned about the science and engineering of Apollo 11.

The man who made Einstein world-famous

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It is hard to imagine a time when Albert Einstein’s name was not recognised around the world.

But even after he finished his theory of relativity in 1915, he was nearly unknown outside Germany – until British astronomer Arthur Stanley Eddington became involved.

Einstein’s ideas were trapped by the blockades of the Great War, and even more by the vicious nationalism that made “enemy” science unwelcome in the UK.

But Einstein, a socialist, and Eddington, a Quaker, both believed that science should transcend the divisions of the war.

It was their partnership that allowed relativity to leap the trenches and make Einstein one of the most famous people on the globe.

…

Matthew Stanley (BBC News)

I hadn’t heard this story before, and it’s well-worth a read.

The Side Effects of Vaccines – How High is the Risk?

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Kurzgesagt – In a Nutshell

Kurzgesagt tells it like it is. The thing I love the most about this video is the clickbaity title that’s clearly geared to try to get the attention of antivaxxers and the “truth teaser” opening which plays a little like many antivaxxer videos and other media… and then the fact that it goes and drops a science bomb on all the fools who tout their superstitious bollocks. Awesome.

Mercury is the closest planet to all seven other planets

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Just showing the simulation which we used to validate the Whirly Dirly Corollary. Kind of a fun fact about our Solar System and orbiting bodies in general. Check out our Physics Today article!

Tomment Section (YouTube)

Alka-Seltzer added to spherical water drop in microgravity

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YouTube (youtube.com)

Plasma Ben (YouTube)

This is just…wooooah. Proper “dude, my hands are huge” grade moment, for me, when I watched the bubbles form in the droplet of water. I had an idea about what would happen, and I was partially right, but by the time we were onto the third run-through of this experiment I realised that I’d been seeing more in it every single time.

Microgravity is weird, y’all.