SciBlogs

Why you need a physicist Marcus Wilson Sep 01

I was at a conference on ‘brain research’ last week in sunny Queenstown. There were some great talks, but I was particularly taken by one on the final morning by Jason Kerr, a kiwi now at Max Planck Institute in Germany. He was talking about the vision of rats, and described a very interesting series of experiments on working out just what a rat is seeing and how it uses this information. The experiments involved tracking what each eye was doing in different situations. It turns out that rats use their eyes in a very different way to humans. Rather than both eyes moving together (in phase) as a human usually does (left-eye moves left, right-eye also moves left) they can move either in phase or out of phase (left eye moves left, right-eye moves right) depending on the situation. While they have a decent amount of their field of view that is usually covered by both eyes, they don’t appear to use stereo-vision, as such. Each eye might be looking at something different. But, maybe most interestingly, they have excellent coverage of the area above and behind their heads. 

Jason showed a neat video of what happens when a rat in a large enclosure is presented with moving images in its field of view. Mess around with what a rat sees on the horizontal plane, and the rat doesn’t bat an eyelid. But change anything above it, and the rat dives for cover. Why? It’s one of those conclusions that are obvious in hindsight: What is the largest predator of rats? Birds of prey. Looking out for a threat from above seems to be the major role of the rat’s eyes. 

Anyway, so what has this got to do physics? Jason came up with a wonderful quote in his talk, relating to the power of a multi-disciplinary team for tackling a tricky problem.   If I’d had a twitter account, I’d have tweeted it there and then. Instead, I hurriedly scribbled it down. Speaking to an audience primarily of biologists, he said:

Always hang out with physicists – they’ve already solved everything for you – they just don’t know what to do with it yet.

So there! 

 

 

Engineering, lego and line followers Marcus Wilson Aug 19

In the last few weeks I’ve been working with some second-year software engineering students on a design project. Their particular task is to build (with Lego – but the high-tech variety) a robot that can follow a white line on a bench. It’s fun to watch them play with different ideas and concepts – there’s the occasional disaster when the robot roars off at high speed in an unexpected direction and falls off the bench top. 

To produce something that approximately works isn’t that difficult. We can use a couple of lights and detectors, sitting either side of the white line. If the robot is going straight, neither gets much reflection. But if one records a high amount of reflected light (and so is on top of the line) we need to turn the robot  - if it’s the other that records a high amount, we ned to turn the other way. Indeed, many, many years ago I made something very similar using analogue electronics (a few LEDs, photocells, transistors etc and a couple of motors to turn the wheels). It approximately worked, but there were a lot of conditions that would fool it – give it shadows and sharp corners to deal with and it was lousy. 

The lego robots that the students have can be programmed – and as such there is a huge array of different options for their control. The exercise is just as much in the development of the software as the hardware. Indeed, since these students are software engineering students, that is the bit they are most familiar with. 

One thing we’re trying to get them to think about are different concepts. It’s easy to think of one solution and just go with that. But is that the best solution? In engineering we can’t afford just to develop the first idea that comes into our heads. We don’t really have much idea about what is ‘best’ until we think through other possibilities and assess them against relevant criteria.  Too often we can be constrained by traditional thinking – “it has to be done that way” – without really considering novel options.  Two light sensors might work. But would three (or even four) be better? How are they best placed?  What about sensors that aren’t rigidly mounted but can move (actively search for the line)? The possibilities are almost endless. 

But the hardware is only half the problem. How should the robot best respond to the input signals? Simply turning one way or the other is easy to implement, but can lead to excessive oscillation. There are smarter control systems available (e.g. Proportional Integral-Differential control), but at a cost of increased complexity. Is it worth pursuing them?

These are questions that the students need to think about with their project. We can get them to do that (rather than just thinking up one solution that might work and considering nothing else) by setting the assessment tasks appropriately. So they are not just judged on how well their robots can follow the white line, but what concepts they thought about, and whether they selected one appropriately using reasonable specifications and design criteria (i.e. how well they followed the established process for engineering design). In fact, following the design process well should ensure that the end result actually does do a good job of following the line accurately, repeatedly and quickly . 

There are still several weeks until the end of semester, when these line-following robots need to be perfected. They’ll be tested at the Engineering Design Show where we’ll find out how to build a good line-follower.

 

Being a Nobel Laureate doesn’t mean you can give a lecture Marcus Wilson Aug 06

I’m at the International Union of Pure and Applied Biophysics Congress in Brisbane this week. Besides being a nice escape from the winter, I’m learning a lot – mostly molecular biology. The ‘physics’ content in some of the talks and posters is rather hard to spot – the ‘bio’ is rather more evident. I wonder, though, if a biologist would complain that it’s the ‘bio’ bit that is missing and the physics dominates. It’s easy to take what we know forgranted and forget how tricky it can be for someone not in that field.

An example came in the first talk, on Sunday, by Nobel Laureate Brian Kobilka. His title was ‘Structural insights into G protein coupled receptor signalling’ though that was pretty irrelevant, since I failed to gain any insight into anything. He started by putting up a rather complicated diagram and saying “I don’t need to show this to such an audience as you’ll already be familiar with this…” and then went on from there. Well, I was not familiar with it, and was completely lost right from the start. That’s not how to do a plenary talk at a conference (or in any other forum). My thought is “how often do I do this with my students?”

Then, in one of the parallel sessions on Monday, a speaker started: “I don’t need to cover this introductory material since you had such an excellent introduction by Brian Kobilka on Sunday…” That was like rubbing salt into the wound. Yes, I do need an introduction. What are GPCRs and why is their signalling so important?” As much as I hate to say it – hooray for Wikipedia – it gives me more learning than the word’s expert in the field does. (Is this why students turn to Wikipedia so often?)

Contrast this with Carl Wieman, who I heard talk at the New Zealand Institute of Physics conference several years ago. Carl is a Nobel Laureate who can give a lecture.

 

Weather and statistics Marcus Wilson Jul 30

I overheard the following conversation at the best coffee outlet on campus yesterday:

“Well, winter’s nearly over. We’re past the shortest day so it’s getting warmer. And we’ve had eleven frosts so far this year, and the record for Hamilton is twelve, so there can only be one more to come.” – Anonymous

Where do I begin?

Well, first let’s point out that the shortest day does not equal the coldest day. Not by a long shot. In fact, I believe that statistically speaking the coldest week of the year for New Zealand is the last week of July (i.e. now). Why the difference? While it’s true that it’s the sun that provides the heat input to the earth, and that’s at a minimum on the shortest day, there’s a lot of thermal inertia on the earth, and particularly on the sea. And there is a lot of sea surrounding New Zealand. Temperatures are slow to change. While the sun remains low in the sky, the sea temperatures are slowly cooling, and that is going to influence the temperature in Hamilton. Conversely, the sea temperature in December is still pretty nippy. It’s late summer before the sea temperature hits its maximum. Seasonal temperature variation is more about the cumulative heat put in over an extended period of time, as opposed to the heat input from the sun on a particular day.

And then the second point. I’ve always found it amusing that Hamiltonians count frosts, and think that  minus 4 Celsius (as it has been a couple of mornings recently) is cold. It is only cold because in New Zealand it is (near enough) compulsory to live in poorly heated, uninsulated, single-glazed detached houses. Europeans find this concept laughable, and, I think, Canadians probably sink their heads in their hands in despair.  Anyway, let’s leave that aside. So if there have only ever been twelve frosts in a single winter in Hamilton (I doubt this, but don’t have statistics on this at hand), and we’ve had eleven so far, then does that mean there is only at most one more to come?

Um, no. Probability doesn’t work like that.  Our weather systems don’t have a memory (not in that sense anyway), and they certainly aren’t intelligent enough record the number of frosts a particular place has a year and act accordingly in the weeks ahead. I’d say we would be in for a few more frosts yet. That’s simply based on the metservice statistics. Go to http://www.metservice.com/towns-cities/hamilton and look at the historical data tab. You’ll see that the mean minimum temperature for August is -2 C, and for September it’s 0 C, suggesting there can easily be some more negative temperatures coming for 2014. Enjoy. 

I remember several years ago playing a board game with a few friends. We’d had a long run of throws of the dice without seeing a ‘six’. One of my friends asked me what the probability was that the next through would be a ‘six’.  ”One sixth” I answered – “same as for any other throw.”  This sparked an intense discussion on whether that was right or not. It is. The dice does not have a memory. It doesn’t remember what side it has landed up on in the past. Each throw is equally likely to show 1, 2, 3, 4, 5, or 6.  The probability of a ‘six’ is one sixth. What was perhaps most interesting is that a friend of mine who was doing a maths degree at the time refused to back me up. 

So is winter nearly over? While it’s true that today feels rather spring-like, and the days are now noticeably longer than they were a month ago, winter still has plenty of teeth left.  

 

 

 

 

 

 

Saddle-points and today’s weather Marcus Wilson Jul 18

I’ve been following the weather with interest this week. First of all, I was very glad when the wind and rain disappeared late last weekend. We were at a wedding in Whakatane on Saturday afternoon/evening, and boy, did it rain. With the wedding in a garden in something that was a bit more substantial than a marquee (think marquee with hard walls and floor), with a portaloo outside,  and a four minute walk up a long, dark, mud and puddle infested driveway in a storm separating you from the car, it was certainly a memorable wedding reception. 

Now, with beautiful clear skies, light winds, and frosty mornings, you’d be forgiven for thinking there’s a big fat high pressure system sitting over us.  But there isn’t.  For the last few days, we (by which I mean at this end of the country) have been in or around a saddle-point, in terms of pressure. There have been lows to the north and south, highs to the east and west, and somewhere in the middle over us. I note today that things have rotated a bit, so the lows now lie east and west, with a high to the north and another approximately south. Here’s a picture I’ve stolen from the metservice website this morning (www.metservice.com, 18 July 2014, 11am); it’s the forecaset for noon today. Note how NZ is sandwiched between two lows, but isn’t really covered by either. 

Capture.JPG

 
You can see the strength of the wind on this plot by the feathers on the arrow symbols. The more feathers, the stronger the winds. (The arrows point in the direction of the wind). Note how the wind blows clockwise around the low pressures (and anticlockwise, less strongly, around the highs). Have a look just around Cape Reinga (for non-NZ dwellers, and I know there’s a few of you out there, that’s the northern-most tip of the North Island.) There’s a point where the wind (anthropromorphising) doesn’t know what to do. It’s in what’s mathematically termed a saddle point. It’s a point where locally there is no gradient in pressure, but is neither a high or a low. Winds are light.  In two dimensions (this is what we have on the earth’s surface) with a single variable such as pressure, there are those three possibilities where the gradient of pressure is zero – a the maximum of a high, the minimum of a low, or a saddle. 
 
In terms of terrain, a mountain pass is a saddle point. It’s where one goes from valley to valley (low to low), between two mountains. On top of the pass, you are at a point where the gradient is zero. But it’s neither a peak or a trough. It’s called a ‘saddle’, because the shape looks rather like a saddle for a horse – which is low on both flanks, but high at the front and back. A marble placed on top of a saddle should, if it were placed exactly at the equilibrium point with no vibrations, stay there. 
 
Saddle-points crop up in all kinds of dynamical systems (e.g. brain dynamics) where there’s more than one variable involved.  Such a point is termed an unstable equilibrium – any deviation from the equilibrium point will cause the system to move away from it. However, the change may not be terribly rapid. When there are lots of variables involved, such local equilibria may have very complicated dynamics associated with them indeed – the range of possibilities gets very large and dynamics can become very rich indeed. 

Check those approximations Marcus Wilson Jul 15

A common technique in physics is ‘modelling’. This is about constructing a description of a physical phenomenon in terms of physical principles. Often these can be encapsulated with mathematical equations. For example, it’s common to model the suspension system of a car as two masses connected by springs to a much larger mass. Here, the large mass represents the car body, one of the small masses represents the wheel, and the other the tyre. The two springs represent the ‘spring’ in the suspension system (which on a car is usually a curly spring – though it can take other forms on trucks or motorbikes), and the tyre (which has springyness itself). We can then add in some damping effect (the shock absorber). What we’ve done is to reduce the actual system into a stylized system that maintains the essential characteristics of the original but is simpler and more suitable for making mathematical calculations. 

That’s great. We can now work on the much simpler stylized system, and make predictions on how it behaves. Transferring those predictions to the real situation, that helps us to design suspension systems for real situations. 

There are however, some drawbacks. We have to be sure that our stylized system really does capture the essential features of the actual system. Otherwise we can get predictions completely wrong. On the other hand, we don’t want to make our model too complicated, otherwise there is no advantage in using the model. “A model should be as simple as possible, but not simpler” as Einstein might have said

There’s another trap for modellers, which is going outside the realm of applicability for the model. What do I mean by that? Well, some simplifications work really well, but only in certain regimes. For example, Newton’s laws are a great simplification on relativistic mechanics. They are much easier to work with. However, if you use them when things are moving close to the speed of light, your answers will be incorrect. They may not even be close to what actually happens. We say that Newton’s laws apply when velocities are much less than the velocity of light. When that’s the case (e.g. traffic going down a road) they work just fine – you’d be silly to use relativity to improve car safety – but when that’s not the case (e.g. physics of black holes) you’ll get things very wrong indeed. 

A trap for a modeller is to forget where the realm of applicability actually is. In the rush to make approximations and simplifications, just where the boundary is between reasonable and not reasonable can be forgotten. I’ve been reminded of this this week, while working with some models of the electrical behaviour of the brain. Rather than go into the detail of what that problem was, here’s a (rather simpler!) example I can across some time ago now. 

I was puzzling over some predictions made in a scientific paper, using a model. It didn’t quite seem right to me, though I struggled for a while to put my finger on exactly what I didn’t like about what the authors had done. Then I saw it. There were some complicated equations in the model, and to simplify them, they’d made a common mathematical approximation: 1/(1+x) is approximately equal to 1-x.  That’s a pretty reasonable assumption so long as x is a small number (rather less than 1). We can see how large it’s allowed to get by looking at the plot here. The continuous blue line shows y = 1/(1+x); the dotted line shows 1-x.  (The insert is the same, at very small x). 

 

 

approximation_test.jpg

We can see for very small x (smaller than 0.1 or so) there’s not much difference, but when x gets above 0.5 there’s a considerable difference between the two. When x gets larger still (above 1) we have the approximation 1-x going negative, whereas the unapproximated 1/(1+x) stays positive. It’s then a completely invalid approximation. 

However, in this paper, the authors had made calculations and predictions using a large x. What they got was just, simply, wrong, because they were using the model outside the region where it was valid. 

This kind of thing can be really quite subtle, particularly when the system being modelled is complicated (e.g. the brain) and we are desperate to make simplifications and approximations. There’s a lot we can do that might actually go beyond what is reasonable, and a good modeller has to look out for this. 

Going down the plughole Marcus Wilson Jul 04

Being a father of an active, outdoor-loving two-year-old, I am well acquainted with the bath. Almost every night: fill with suitable volume of warm water, check water temperature, place two-year-old in it, retreat to safe distance. He’s not the only thing that ends up wet as he carries out various vigorous experiments with fluid flow. 

One that he’s just caught on to is how the water spirals down the plug-hole. Often the bath is full of little plastic fish (from a magnetic fishing game), and if one of these gets near the plug hole it gets a life of its own. It typically ends up nose-down over the hole, spinning at a great rate as it gets driven round by the exiting water. 

The physics of rotating water is a little tricky. There are two key concepts thrown in together; first the idea of circular motion  - which involves a rotating piece of water having a force on it towards the centre (centripetal force); second is viscosity – in which a piece of water can have a shear force on it due to a velocity gradient in the water. Although viscosity has quite a technical definition, colloquially, one might think of it as ‘gloopiness’ [Treacle is more viscous than water. The ultimate in viscosity is glass, which is actually a fluid, not a solid - the windows of very old buildings are thicker at the bottom than the top due to the fluid flow over tens or hundreds of years.] In rotational motion there’s a subtle interplay between these two forces which can result in the characteristic water-down-plughole motion. 

In terms of mathematics, we can construct some equations to describe what is going on and solve them. We find, for a sample of rotating fluid, that two steady solutions are possible. 

The first solution is what you’d get if all the fluid rotated at the same angular rate – the velocity of the fluid is proportional to the radius. This is what you’d get if you put a cup of water on a turntable and rotated it – all the water rotates as if it were a solid.

The second solution has the velocity inversely proportional to the radius – so the closer the fluid is to the centre, the faster it is moving. This is like the plughole situation where a long way from the plug hole the fluid circulates slowly, but close in it rotates very quickly. Coupled with this is a steep pressure gradient – low pressure on the inside (because the water is disappearing down the hole) but higher pressure out away from the hole. Obviously this solution can’t apply arbitrarily close to the rotation axis because then the velocity would be infinite. This is where vortices often occur. (Actually, Wikipedia has a nice entry and animations on this, showing the two forms of flow I’ve described above). 

A Couette viscometer expoits these effects as a way of measuring the viscosity of a fluid. Two coaxial cylinders are used, with fluid between them. The outer is rotated while the inner one is kept stationary, and the torque required enables us to calculate the viscosity of the liquid.

 

Threshold concepts bite back Marcus Wilson Jun 20

Long story cut short: I’m currently writing a paper on a piece of work I presented at the (fairly) recent conference on Threshold Concepts, that was hosted here at Waikato. In order to do this, I’m needing to learn a new language, namely that of qualitative research. 

Qualitative Research is not something that comes naturally to a physicist. The most obvious problem is that it requires a paradigm shift – from the positivist approach that underlies most of science, and particularly physics, to the (ahem) social constructivism that is common-place in the qualitative literature. I need help. 

So, yesterday, under cover of darkness*, with heavy coat and thick scarf wrapped around my face, I sneaked into the library, passed by the familiar  ’Q’ section and headed across the corridor** to raid the ‘H’ section***. I knew my target – I’d already searched the on-line library catalogue in the safety of my office – so it was a quick mission. Get in there, grab the books, get them issued (on the self-service kiosk, certainly not the front desk lest I be recognized for what I was – a scientist carrying subversive literature) and get out of there before any of my colleagues, or worse still, any of my students, spotted me. Catching a positivist (or p******ist, as they’re refered to in the social science literature) raiding the ‘H’ section would be sure to inflame cross-disciplinary tensions so discretion was absolutely paramount.  Mission safely accomplished, I returned to the safety of EF-link block.  

However, my mission has hardly begun. The next step is to decode the language. The words might be English, but they’re written in some kind of secret code known only to practioners of social constructivism. Fortunately, my wife Karen has come across such writing before and is familiar with teasing out some of the hidden meanings in the language. With some tuition, and hard work, I’ve begun to make a little sense of this writing. It is a hard and frightening task – there is so much that is just utterly alien to me. I feel that there must be some underpinning concept behind it that I just haven’t grasped, that makes it so troublesome – if I get it – if I discover what that secret code really is – it will all fall into place and at last I’ll be able to see what qualitative research really is about. 

But one thing I do know, is that I’m dealing with a threshold concept here. And there’s the deep irony. In order for me to support my paper on threshold concepts, I need to get into the qualitative research literature, and this in itself is a threshold concept to me. The introductory chapter of one of the books, which explicitly states that it is for people with no familiarity with qualitative research, is still intractable to me. Why? The words are English, the sentences aren’t long, but somehow it appears to draw from hidden knowledge that I am not familiar with.  I just don’t get it – the sense isn’t there – the concepts are so troubling. I’m sure then the very notion of ‘qualtiative research’ is, to someone trained as a scientist, a threshold concept in itself. And I’m not yet over that threshold. Not completely, anyway. . 

So, to close, I’m still grappling with this stuff. But perhaps the greatest impact is that I now have some idea of what my students are going through when they complain “I just don’t get it” when dealing with what I feel is the blatantly obvious

 

*OK, so it was actually about 9.30 in the morning, but that doesn’t sound as dramatic. 

**One might actually say ‘crawled under the barbed wire laid long the border’: This metaphor has echos of Glen S. Aikenhead (1996) Science Education: Border Crossing into the Subculture of Science, Studies in Science Education, 27:1, 1-52.

***The Q and H refer to US Library of Congress coding of books – Q is (broadly) where physics lives, H is where the qualitative research methods books hang out, looking menacing.

Managing ignition timing Marcus Wilson Jun 13

I’ve just been at a great lecture by Peter Leijen as part of our schools-focused Osborne Physics and Engineering Day.   He’s an ex-student of ours, who did electronic engineering here at Waikato – and graduated just a couple of years ago.  He now works in the automotive electronics industry. That’s an incredibly quickly growing industry. So much of a car’s systems are now driven by electronics, not mechanics. Being a car ‘mechanic’ now means being a car ‘electronic engineer’ just as much as it does being a mechanic. 

One interesting piece of electronics is the ignition timing system. The mechanism that produces the spark in the cylinders from a 12 volt battery is really old and standard technology – one uses a step-up transformer and kills the current to the primary coil by opening a switch – the sudden drop in current creates a  sudden reduction in magnetic flux in the transformer, and these collapsing flux lines cutting the secondary coil create a huge voltage, enough for the spark plug to spark. That really is easy to do. The tricky thing is getting it to spark at the right time. 

One needs the fuel/air mix in the cylinder to be ignited at the optimum time, so that the resulting explosion drives the piston downwards. Ignite too early, while the compression is going on, and you’ll simply stop the piston rather than increasing the speed of its motion. Apply too late, and you won’t get the full benefit of the explosion. It’s rather like pushing a child on a swing – to get the amplitude of the motion to build, you need to push at the optimum time – this is just after they’ve started swinging away from you. 

All this is complicated by the fact that the explosion isn’t instantaneous. It takes a small amount of time to happen. That means, at very high revolution rates, one has to be careful as to exactly when you make the ignition. It has to be earlier than at lower rates, particularly if the throttle setting is low, because the explosion takes a significant proportion of the period of the oscillation.  This is called ‘ignition advance’.  

On newer cars, this is done electronically. A computer simply ‘looks up’ the correct angle of advance for the rpm and the throttle setting of the car, and applies the outcome. The result: a well running, efficient engine, using all the power available to it. Or so you might think.

But here’s the revelation from Peter: car manufacturer’s can deliberately stuff up the timing. Why do they want to do that? Well, there’s a market for selling different versions of the otherwise same car. The high-end models have performance and features (and price tag) that the low-end models don’t have. There’s status in buying the high-end model (if you’re the kind of person who cares about that – and the fact that these things sell says, yes, there are such people), but, alternatively, if that extra couple of horsepower doesn’t bother you, you can get the lower-spec model for a lower price. Now, the manufacturers have worked out that making lots of different versions of the otherwise same car is inefficient. It’s far easier to have a production line that fires out identical cars. So how do you achieve the low-end to high-end specification spectrum? Easy. You build everything high-end, and then to produce  low-end cars deliberately disable or tinker with the features so they don’t work or don’t perform so well. That is, make the car worse. 

Ignition timing is one example, says Peter. There are in fact companies who will take your low-end car and un-stuff-up your electronics for you – in effect reprogramme it to do what it should be doing. In other words, turn your low-end car back into a high-end one (which is how it started out life) without you having to pay the premium that the manufacturer would place on it for not stuffing it up in the first place. 

Who said free market economics resulted in the best outcome for consumers?

 

When whizz-bang isn’t whizz-bang enough Marcus Wilson Jun 11

A couple of weeks ago saw the University of Waikato Open Day. (Acually, two days). There were some fantastic displays set up across the whole univerisity, with some exciting lectures and activities. With a dual-audience of would-be students and members of the public, our displays were meant to be eye-catching and fun, and I thought they were. There were some good whizz-bang displays, and some really great whizz-whizz bang-bang displays and activities. I think nearly everyone had a good time there. (Naturally the Van de Graaff generator was its usual hit…)

However, when the feedback on the day(s) began to roll it, it became apparant that some displays were not as fun as I thought. At least, the audience didn’t think so. Too boring. I wonder whether this is because people have come to expect that whizz-bang-interactive-touch-it-yourself-excitement is the normal, basic thing to expect in science displays now.   Whizz-bang just doesn’t cut it – you need to be double whizz – double bang or you don’t get a look-in now. 

Is this down to ‘interactivity inflation’?  When I was very young,  the most exciting place in the world to visit was the Natural History Museum in London. Back in the late seventies it wasn’t really interactive – that came in slowly – there was lots of stuff in cabinets just to look at. But what it did have was a fossil skeleton of a diplodocus in the main entrance hall (yes, some entrance hall). It didn’t move, it didn’t grunt (or whatever diplodoci did), it just stood there looking, well, wow! – dinosaur-ish.  What more could you want. Further into the museum one found the whale skeleton suspended from the ceiling – again – wow! with the pickled contents of its stomach in a large glass jar.  At that time, a large jar of krill in formaldehyde was indeed exciting stuff. I loved it. 

Nowadays a jar of long-dead krill is simply silly. Yuck. Have we come to expect too much from our scientific displays? Or is it an example of the current generation’s requirement for things that can be instantly double-clicked, shared, downloaded, posted or liked.  Whatever, it certainly takes a lot of time and thought to put together something double-whizz double-bang.

And, finally, is WWBB what recruits future students anyway? Sure, it gets their attention. But does it maintain it over several years? I suspect not.   

P.S. I’ve just looked at the Natural History Museum Website.  (Obviously a thing that didn’t exist when I was 8.) The first thing on it: “Download the UK Tree Identification App.” What happened to taking the time to carefully learn different leaf and fruit shapes, bark texture, canopy shape etc? Who cares about that – just let the app do the work…

Network-wide options by YD - Freelance Wordpress Developer