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One of the problems with exponential growth in a finite space is that you quickly run out of room. If this rate of increase in energy consumption continues, in a few years we’ll be straining at the limits of the energy
that we can provide.
It needn’t necessarily mean the apocalypse for us or for cryptocurrencies.
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to create a less energy-intensive blockchain. Still others argue that the 35 TWh figure, based on a single analysis, is too high, but
only by a factor of three or so.
Apocalypse aside, though, the energy use required by Bitcoin still seems so wasteful.
The distributed computing power of the blockchain computing network is now far, far in excess of that of the world’s top supercomputers ; by some estimates, hundreds of thousands of times more powerful. Technically speaking, the bitcoin mining network’s calculations are optimized for “hashing”—integer calculations that aren’t general-purpose floating point operations—but however you slice it, it’s an extraordinary amount of hardware, power consumption, and potential.
The SHA-256 algorithm that bitcoin miners use is designed to be
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, to prevent any individual from gaining centralized control of the ledger. But the result is that it is terribly wasteful. Emin Gün Sirer, who studies cryptocurrencies at Cornell University, says that it has to be this way: “It’s perverse. Had bitcoin been mined by doing something useful, then there would be a correspondence between useful work and the number of bitcoins you get… That creates a mental anchor point in people’s mind for how much a bitcoin should cost.”
There were some suggestions, like PrimeCoin , that would use its blockchain computing power to calculate chains of prime numbers, or Permacoin (PDF), that also asked for storage space to create data archives. The sad reality is that none of these coins are likely to harness anything close to the computing power dedicated to Bitcoin.
Naturally, I wish that things had transpired such that the computing power had been devoted—at least in part—to science. There have been extraordinary citizen science successes, such as climate@home, SETI@home, and Fold.it which take advantage of idle computing power from the users, as well as idle processing power in our brains in some cases .
Basic Quantitative Concepts
Ratios are simply expressions of one measure relative to another. There are several types of ratios that are frequently used in public health.
Consider a class that has 20 male students and 80 female students. We can think about this in several ways. We could express this simply as the ratio of men to women and write the relationship as 20:80 or 20/80. We can also simplify this by dividing both the numerator and the denominator by a number that divides evenly into both the numerator and the denominator. In this case, we could divide both by 20 to simplify this to a 1:4 ratio (or 1/4 ratio). This indicates that for every man, there are four women.
We could also consider this from the inverse perspective, i.e., the number of women relative to the number of men; in this case the ratio of women to men is 80/20 which is equivalent to 4 to 1, i.e., there are four women for every man.
A proportion is a type of ratio that relates a part to a whole. For example, in the class with with 20 men and 80 women, the total class size is 100, and the proportion of men is 20/100 or 20%. The proportion of women is 80/100 or 80%. In both of these proportions the size of part of the class is being related to the size of the entire class. The class above conveniently had a total size of 100, but this usually isn't the case.
If we go back to the information on mortality from bird flu that was presented on the previous page, it can be seen that there are several ways of thinking about this basic information.
The fact that 44 died and the other 79 lived could be expressed as a simple ratio , which compares the number who died to the number who survived. 44/79 or 44:79 would be two ways of expressing this simple ratio. The ratio of those who died relative to those who lived was 44 to 79.
simple ratioAlternatively, we might want to focus on the proportion who lived. In total, 123 people were infected, and 44 of these died. Therefore, the proportion who died was 44/123, which could be expressed as a decimal fraction (0.36) or as a percentage (36%). This proportion is referred to as the "case-fatality" rate, although strictly speaking, it is a proportion and not a rate.
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