AfroBrazilian, Prevention Even with excellent medical and nursing care, bedsores can be hard to prevent, especially among vulnerable patients.
Find a plausible theory for how some bits of the world behave, make predictions, test them experimentally. If not, you need to think again.
Scientific work is vastly diverse and full of fascinating complexities. Still, the recipe captures crucial features of how most of it has been done for the past few hundred years. Now, however, there is a new ingredient.
Computer simulation, only a few decades old, is transforming scientific projects as mind-bending as plotting the evolution of the cosmos, and as mundane as predicting traffic snarl-ups. What should we make of this scientific nouvelle cuisine?
While it is related to experiment, all the action is in silico — not in the world, or even the lab. It might involve theory, transformed into equations, then computer code. Or it might just incorporate some rough approximations, Coming through slaughtertabtab essay are good enough to get by with.
Made digestible, the results affect us all. As computer modelling has become essential to more and more areas of science, it has also become at least a partial guide to headline-grabbing policy issues, from flood control and the conserving of fish stocks, to climate change and — heaven help us — the economy.
But do politicians and officials understand the limits of what these models can do? Are they all as good, or as bad, as each other? If not, how can we tell which is which? Modelling is an old word in science, and the old uses remain. It can mean a way of thinking grounded in analogy — electricity as a fluid that flows, an atom as a miniature solar system.
Recall James Watson in using first cardboard, then brass templates cut in the shape of the four bases in DNA so that he could shuffle them around and consider how they might fit together in what emerged as the double-helix model of the genetic material.
Computer models are different. It is the dynamics that call for the computation. Somewhere in the model lies an equation or set of equations that represent how some variables are tied to others: In most systems, tracking such changes over time quickly overwhelms human powers of calculation.
Just turn your model, whatever it is, into a system of equations, let the computer solve them over a given period, and, voila, you have a simulation.
In this new world of computer modelling, an oft-quoted remark made in the s by the statistician George Box remains a useful rule of thumb: He meant, of course, that while the new simulations should never be mistaken for the real thing, their features might yet inform us about aspects of reality that matter.
To get a feel for the range of models currently in use, and the kinds of trade-offs and approximations model builders have to adopt, consider the certifiably top-notch modelling application that just won its authors the Nobel Prize for chemistry. We know how these reactions work in principle, but calculating the full details — governed by quantum mechanics — remains far beyond our computers.
What you can do is calculate the accurate, quantum mechanical results for the atoms you think are important.
As he explained in Nature in October: It is a powerful combination. But there are other fields where modelling benefits from checking back with a real, physical system. Aircraft and Formula One car designs, though tested aerodynamically on computers, are still tweaked in the wind-tunnel often using a model of the old-fashioned kind.
Marussia F1 formerly Virgin Racing likewise uses computational fluid dynamics to cut down on expensive wind-tunnel testing, but not as a complete substitute.
Nuclear explosion simulations were one of the earliest uses of computer modelling, and, of course, since the test-ban treaty ofsimulated explosions are the only ones that happen.
Still, aspects of the models continue to be real-world tested by creating extreme conditions with high-power laser beams. More often, though — and more worryingly for policymakers — models and simulations crop up in domains where experimentation is harder in practice, or impossible in principle.
And when testing against reality is not an option, our confidence in any given model relies on other factors, not least a good grasp of underlying principles. In epidemiology, for example, plotting the spread of an infectious disease is simple, mathematically speaking.
The equations hinge on the number of new cases that each existing case leads to — the crucial quantity being the reproduction number, R0. If R0 is bigger than one, you have a problem.
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Get it below one, and your problem will go away. It would be harder to model a totally new disease, but we would know the factors likely to influence its spread.As a member, you'll also get unlimited access to over 75, lessons in math, English, science, history, and more.
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