You are made up of atoms, you occupy space and you have mass.
You know what that means?
You are made up of atoms, you occupy space and you have mass.
You know what that means?
Well, a few weeks ago I broke my toe, which meant that I was forced to sit in my room hour after hour and think about what to do. Luckily, I found something to keep me busy: writing a new math book. The book is called Math Concepts Everyone Should Know (And Can Learn) and was completed yesterday. It is now live on Amazon (click the cover to get to the product page) for the low price of $ 2.99 and can be read without any prior knowledge in mathematics.
I must say that I’m very pleased with the result and I hope you will enjoy the learning experience. The topics are:
– Combinatorics and Probability
– Binomial Distribution
– Banzhaf Power Index
– Hypothesis Testing
– Bijective Functions And Infinity
As already noted in this blog post, I recently conducted a survey on sleeping which also included the topic nightmares. The strongest effects on the occurrence of nightmares come from the variables age, depression and stress. While among younger people (age 36 or lower) roughly 50 % frequently have nightmares, the same is true for only 27 % of older people. A statistical test shows that this difference has a very strong statistical significance. Depression led the nightmare risk to rise from 24 % (no tendency for depression, self-reported) to 57 % (strong tendency for depression) and stress from 21 % (low stress level, self-reported) to 50 % (high stress level). Both increases were also statistically significant.
There were other variables that also led to an increase in the nightmare risk as well, though none of them as robust as age, depression and stress. The living conditions have a noticeable effect. A busy road increases the risk by 22 %, noisy family members or roommates by 26 % and noisy neighbors by 27 %. Also noteworthy are the effects of lifestyle. Frequently drinking stimulating beverages adds 15 % to the nightmare risk, smoking adds 16 %, alcohol adds 18 %, cannabis adds 21 % and eating late roughly 13 %. At this point, even a little before that, the differences went below the threshold for statistical significance. One more thing I can say is that the variables education, income and sleeping alone do not seem to have any effect at all on the risk of nightmares.
With all of this input I produced a predictor variable and a formula to compute the nightmare risk. Define the following variables:
L = 1/5*(Stim Beverages + Smoking + Alcohol + Cannabis + Eating Late)
C = 1/3*(Busy Road + Noisy Family + Noisy Neighbor)
M = 1/2*(Stress + Depression)
All individual points range from 1 (no) to 5 (yes). For example, when a person never drinks stimulating beverages such as Coke or Coffee, set Stim Beverages = 1, if a person heavily consumes stimulating beverages, set Stim Beverages = 5. This way you can produce the values of L, C and M. Once these values are known for a person, one may compute the predictor P:
P = 0.6*M + 0.3*C + 0.2*L + 0.5*(90/Age)
And then insert the predictor P into this formula, that was found by regression analysis, to calculate the nightmare risk of this person:
Risk = 0.802 – 0.720 / ( 1 + (P / 4.55)^5.12 )
Where the symbol ^ means “to the power of”. To asses the accuracy of the formula, I compiled the table below. It shows the predictor range with the corresponding nightmare risk as measured in the survey and as calculated from the formula (in the bracket) for the midpoint of the predictor interval.
2.0-2.5 … 10 % (10 %)
2.5-3.0 … 19 % (13 %)
3.0-3.5 … 17 % (19 %)
3.5-4.0 … 25 % (28 %)
4.0-4.5 … 41 % (38 %)
4.5-5.0 … 45 % (48 %)
5.0-5.5 … 61 % (57 %)
5.5-6.0 … 68 % (64 %)
So while there are deviations, the formula works quite well for the most parts.
Let me demonstrate the process of finding the nightmare risk using the formula, I’ll offer myself as the test subject. Here are my values for the variables. Remember that each variable ranges from 1, a strong no, to 5, a strong yes.
L = 1/5*(Stim Beverages + Smoking + Alcohol + Cannabis + Eating Late)
L = 1/5*(4 + 5 + 2 + 1 + 4) = 3.2
C = 1/3*(Busy Road + Noisy Family + Noisy Neighbor)
C = 1/3*(3 + 1 + 1) = 1.7
M = 1/2*(Stress + Depression)
M = 1/2*(4 + 4) = 4
P = 0.6*M + 0.3*C + 0.2*L + 0.5*(90/Age)
P = 0.6*4 + 0.3*1.7 + 0.2*3.2 + 0.5*(90/32) = 5
Risk = 0.802 – 0.720 / ( 1 + (P / 4.55)^5.12 )
Risk = 0.802 – 0.720 / ( 1 + (5 / 4.55)^5.12 ) = 0.53 = 53 %
So roughly 50/50, not that bad actually. I would say that puts me in the risk group, but luckily not the high-risk group.
Here I presented some basic results of my survey on sleeping. The analysis showed that the male respondents reported having been awake a maximum of 40.6 h, while for women this was 36.6 h, not an enormous difference, but statistically significant at p < 0.05. I also wanted to give you the breakdown for all of the respondents, male or female:
You can see that around 25 % (1 in 4) never made it past the 24 h mark. Roughly the same percentage (adding up the final four bars) made it past the 48 h mark and a brave 4.4 % (1 in 23) even past the 72 h mark. Feel free to use the comments section to tell me what the longest time you were awake was and what the circumstances were that made you stay up so long.
I conducted a paid survey via AYTM on the topic of sleeping. I was interested in finding out what variables (psychological, lifestyle, circumstance) have a noticeable effect on sleep related issues such as nightmares, sleep duration, time needed to fall asleep, etc … Now that I’ve got the raw data result, there’s a ton of relationships to analyze and that will take time. But I’ve already found a few neat statistically significant results, some to be expected, some rather surprising. I’ll publish them, as well as the results yet to be found, here on my blog in the coming weeks.
Geographic Region: US
Number of Respondents: 250
Males: 96 (38.4 %)
Females: 154 (61.6 %)
Minimum Age: 18
Median Age: 36
Maximum Age: 81
White-American: 163 (65.2 %)
African-American: 23 (9.2 %)
Asian-American: 17 (6.8 %)
Hispanic-American: 28 (11.2 %)
Other: 19 (7.6 %)
Here’s what I extracted from the data so far. Statistical significance was determined via a two-population Z-test. Notice the p-value. You can interpret it as the chance that the result came to be by random fluctuations rather than via a real effect. Hence, the lower the p-value, the more significant and reliable the result. A p-value of 0.05 roughly means that there’s a 1 in 20 chance that the result is just a random fluctuation, a value of 0.01 that there’s a 1 in 100 chance for the same. All of the results below are significant at p < 0.05, some even at p < 0.01.
By the way: if you’ve got your own data, you can let this great website do a two-population Z-test for you. Only works though if you’ve got the result in form of a percentage. To learn more about hypothesis testing, including how to perform a Z-test if the data is not given in form of a percentage, check out this great book by Leonard Gaston.
Hypothesis: Young people have more nightmares
Percentage of people with nightmares for people at median age or younger: 49.6 %
Number of respondents: 127
Percentage of people with nightmares for people older than median age: 26.8 %
Number of respondents: 123
The Z-Score is 3.706. The p-value is 0.0001. The result is significant at p < 0.01.
Correlation between age and probability for nightmares: Probability = 0.757 – 0.00958·Age
(Every year the chance for nightmares goes down by roughly 1 %)
Hypothesis: Young people are more light sensitive
Percentage of light sensitive people for people at median age or younger: 62.2 %
Number of respondents: 127
Percentage of light sensitive people for people older than median age: 49.6 %
Number of respondents: 123
The Z-Score is 2.0065. The p-value is 0.02222. The result is significant at p < 0.05.
Correlation between age and probability for light sensitivity: Probability = 0.756 – 0.00504·Age
(Every two years the chance for light sensitivity goes down by 1 %)
Hypothesis: Older people more frequently take naps
Percentage of people taking naps for people at median age or younger: 23.6 %
Number of respondents: 127
Percentage of people taking naps for people older than median age: 33.3 %
Number of respondents: 123
The Z-Score is 1.7009. The p-value is 0.04457. The result is significant at p < 0.05.
Correlation between age and probability for taking naps: Probability = 0.194 + 0.00232·Age
(Every four years the chance for taking naps goes up by 1 %)
Hypothesis: Females wake up more frequently at night
Percentage of males who frequently wake up at night: 51.1 %
N = 96
Percentage of females who frequently wake up at night: 64.3 %
N = 154
The Z-Score is 2.081. The p-value is 0.03752. The result is significant at p < 0.05.
Hypothesis: Males have a higher peak waking duration
Peak waking duration for males: 40.6 h
SEM: 1.76 h
Peak waking duration for females: 36.6 h
SEM: 1.33 h
The Z-Score is 1.8132. The p-value is 0.0349. The result is significant at p < 0.05.
Hypothesis: People with low income daydream more
Percentage of people with low income who frequently daydream: 40.1 %
N = 142
Percentage of people with high income who frequently daydream: 26.9 %
N = 108
The Z-Score is 2.1764. The p-value is 0.01463. The result is significant at p < 0.05.
Hypothesis: People with low income take longer to fall asleep
Time to fall asleep for people with low income: 31.4 min
SEM = 1.44 min
Time to fall asleep for people with high income: 22.9 min
SEM = 1.08 min
The Z-Score is 4.7222. The p-value is < 0.00001. The result is significant at p < 0.01.
Hypothesis: Depressed people have more nightmares
Percentage of people with nightmares for depressed people: 57.1 %
Number of respondents: 84
Percentage of people with nightmares for non-depressed people: 28.9 %
Number of respondents: 166
The Z-Score is 4.3308. The p-value is 0. The result is significant at p < 0.01.
Hypothesis: Depressed people daydream more
Percentage of depressed people who frequently daydream: 50.3 %
N = 84
Percentage of non-depressed people who frequently daydream: 26.5 %
N = 166
The Z-Score is 3.7392. The p-value is 9E-05. The result is significant at p < 0.01.
Hypothesis: Depressed people need more time to feel fully awake after a good night’s sleep
Time to feel fully awake for depressed people: 42.5 min
SEM: 2.70 min
Time to feel fully awake for non-depressed people: 28.9 min
SEM: 1.27 min
The Z-Score is 4.5580. The p-value is < 0.00001. The result is significant at p < 0.01.
Hypothesis: People who need more time to fall asleep also need more time to feel fully awake after a good night’s sleep
Time to feel fully awake for people who need less than 30 minutes to fall asleep: 29.9 min
SEM: 1.77 min
Time to feel fully awake for people who need 30 minutes or more to fall asleep: 37.1 min
SEM: 1.92 min
The Z-Score is 2.7572. The p-value is 0.002915. The result is significant at p < 0.01.
Correlation between time falling asleep (FAS) versus time to feel fully awake (FAW):
FAW = 24.7 + 0.317*FAS
(Every ten minutes additional time needed to fall asleep translate into roughly three minutes additional time required to feel fully awake after a good night’s sleep)
What are functions? I could just insert the standard definition here, but I fear that this might not be the best approach for those who have just started their journey into the fascinating world of mathematics. For one, any common textbook will include the definition, so if that’s all you’re looking for, you don’t need to continue reading here. Secondly, it is much more rewarding to build towards the definition step by step. This approach minimizes the risk of developing deficits and falling prey to misunderstandings.
So where do we start?
We have two options here. We could take the intuitive, concept-focused approach or the more abstract, mathematically rigorous path. My recommendation is to go down both roads, starting with the more intuitive approach and taking care of the strict details later on. This will allow you to get familiar with the concept of the function and apply it to solve real-world problems without first delving into sets, Cartesian products as well as relations and their properties.
Then let’s get started. For now we will think of a function as a mathematical expression that allows us to insert the value of one quantity x and spits out the value of another quantity y. So it’s basically an input-output system.
Can you give me an example of this?
Certainly. Here is a function: y = 2·x + 4. As you can see, there are two variables in there, the so-called independent variable x and the dependent variable y. The variable x is called independent because we are free to choose any value for it. Once a value is chosen, we do what the mathematical expression tells us to do, in this case multiply two by the value we have chosen for x and add four to that. The result of this is the corresponding value of the dependent variable y.
So I can choose any value for x?
That’s right, try out any value.
Okay, I’ll set x = 1 then. When I insert this into the expression I get: y = 2·1 + 4 = 6. What does this tell me?
This calculation tells you that the function y = 2·x + 4 links the input x = 1 with the output y = 6. Go on, try out another input.
Okay, can I use x = 0?
Certainly. Any real number works here.
For x = 0 I get y = 2·0 + 4 = 4. So the function y = 2·x + 4 links the input x = 0 with the output y = 4.
That’s right. Now it should be clear why x is called the independent variable and y the dependent variable. While you may choose any real number for x, sometimes there are common sense restrictions though, we’ll get to that later, the value of y is determined by the form of the function. A few more words on terminology and notation. Sometimes the output is also called the value of the function. We’ve just found that the function y = 2·x + 4 links the input x = 1 with the output y = 6. We could restate that as follows: at x = 1 the function takes on the value y = 6. The other input-output pair we found was x = 0 and y = 4. In other words: at x = 0 the value of the function is y = 4. Keep that in mind.
As for notation, it is very common to use f(x) instead of y. This emphasizes that the expression we are dealing with should be interpreted as a function of the independent variable x. It also allows us to note the input-output pairs in a more compact fashion by including specific values of x in the bracket. Here’s what I mean.
For the function we can write: f(x) = 2·x + 4. Inserting x = 1 we get: f(1) = 2·1 + 4 = 6 or, omitting the calculation, f(1) = 6. The latter is just a very compact way of saying that for x = 1 we get the output y = 6. In a similar manner we can write f(0) = 4 to state that at x = 0 the function takes on the value y = 4. Please insert another value for x using this notation.
Will do. I’ll choose x = -1. Using this value I get: f(-1) = 2·(-1) + 4 = 2 or in short f(-1) = 2. So at x = -1 the value of the function is y = 2. Is all of this correct?
Yes, that’s correct.
You just mentioned that sometimes there are common sense restrictions for the independent variable x. Can I see an example of this?
Okay, let’s get to this right now. Consider the function f(x) = 1/x. Please insert the value x = 1.
For x = 1 I get f(1) = 1/1 = 1. So is it a problem that the output is the same as the input?
Not at all, at x = 1 the function f(x) = 1/x takes on the value y = 1 and this is just fine. The input x = 2 also works well: f(2) = 1/2, so x = 2 is linked with the output y = 1/2. But we will run into problems when trying to insert x = 0.
I see, division by zero. For x = 0 we have f(0) = 1/0 and this expression makes no sense.
That’s right, division by zero is strictly verboten. So whenever an input x would lead to division by zero, we have to rule it out. Let’s state this a bit more elegantly. Every function has a domain. This is just the set of all inputs for which the function produces a real-valued output. For example, the domain of the function f(x) = 2·x + 4 is the set of all real numbers since we can insert any real number x without running into problems. The domain of the function f(x) = 1/x is the set of all real numbers with the number zero excluded since we can use all real numbers as inputs except for zero.
Can you see why the domain of the function f(x) = 1/(3·x – 12) is the set of all real numbers excluding the number four? If it is not obvious, try to insert x = 4.
Okay, for x = 4 I get f(4) = 1/(3·4 – 12) = 1/0. Oh yes, division by zero again.
Correct. That’s why we say that the domain of the function f(x) = 1/(3·x – 12) is the set of all real numbers excluding the number four. Any input x works except for x = 4. So whenever there’s an x somewhere in the denominator, watch out for this. Sometimes we have to exclude inputs for other reasons, too. Consider the function f(x) = sqrt(x). The abbreviation “sqrt” refers to the square root of x. Please compute the value of the function for the inputs x = 0, x = 1 and x = 2.
f(0) = sqrt(0) = 0
At x = 0 the value of the function is 0.
f(1) = sqrt(1) = 1
At x = 1 the value of the function is 1.
f(2) = sqrt(2) = 1.4142 …
At x = 2 the value of the function is 1.4142 … All of this looks fine to me. Or is there a problem here?
No problem at all. But now try x = -1.
Okay, f(-1) = sqrt(-1) = … Oh, seems like my calculator spits out an error message here. What’s going on?
Seems like your calculator knows math well. There is no square root of a negative number. Think about it. We say that the square root of the number 4 is 2 because when you multiply 2 by itself you get 4. Note that 4 has another square root and for the same reason. When you multiply -2 by itself, you also get 4, so -2 is also a square root of 4.
Let’s choose another positive number, say 9. The square root of 9 is 3 because when you multiply 3 by itself you get 9. Another square root of 9 is -3 since multiplying -3 by itself leads to 9. So far so good, but what is the square root of -9? Which number can you multiply by itself to produce -9?
Hmmm … 3 doesn’t work since 3 multiplied by itself is 9, -3 also doesn’t work since -3 multiplied by itself is 9. Looks like I can’t think of any number I could multiply by itself to get the result -9.
That’s right, no such real number exists. In other words: there is no real-valued square root of -9. Actually, no negative number has a real-valued square root. That’s why your calculator complained when you gave him the task of finding the square root of -1. For our function f(x) = sqrt(x) all of this means that inserting an x smaller than zero would lead to a nonsensical result. We say that the domain of the function f(x) = sqrt(x) is the set of all positive real numbers including zero.
In summary, when trying to determine the domain of a function, that is, the set of all inputs that lead to a real-valued output, make sure to exclude any values of x that would a) lead to division by zero or b) produce a negative number under a square root sign. Unless faced with a particularly exotic function, the domain of the function is then simply the set of all real numbers excluding values of x that lead to division by zero and those values of x that produce negative numbers under a square root sign.
I promise we will get back to this, but I want to return to the concept of the function before doing some exercises. Let’s go back to the introductory example: f(x) = 2·x + 4. Please make an input-output table for the following inputs: x = -3, -2, -1, 0, 1, 2 and 3.
This was an excerpt from “Math Dialogue: Functions“, available on Amazon for Kindle.
The following is an excerpt from my book “Introduction to Stars: Spectra, Formation, Evolution, Collapse”, available here for Kindle. Enjoy!
A vast number of written recordings show that people watching the night sky on 4 July 1054 had the opportunity to witness the sudden appearance of a new light source in the constellation Taurus that shone four times brighter than our neighbor Venus. The “guest star”, as Chinese astronomers called the mysterious light source, shone so bright that it even remained visible in daylight for over three weeks. After around twenty-one months the guest star disappeared from the night sky, but the mystery remained. What caused the sudden appearance of the light source? Thanks to the steady growth of knowledge and the existence of powerful telescopes, astronomers are now able to answer this question.
The spectacular event of 4 July 1054 was the result of a supernova (plural: supernovae), an enormous explosion that marks the irreversible death of a massive star. Pointing a modern telescope in the direction of the 1054 supernova, one can see a fascinatingly beautiful and still rapidly expanding gas cloud called the Crab Nebula at a distance of roughly 2200 parsecs from Earth. At the center of the Crab Nebula cloud lies a pulsar (Crab Pulsar or PSR B0531+21) having a radius of 10 km and rotating at a frequency of roughly 30 Hz.
Image of the Crab Nebula, taken by the Hubble Space Telescope.
Let’s take a look at the mechanisms that lead to the occurrence of a supernova. At the end of chapter two we noted that a star having an initial mass of eight solar masses or more will form an iron core via a lengthy fusion chain. We might expect that the star could continue its existence by initiating the fusion of iron atoms. But unlike the fusion of lighter elements, the merging of two iron atoms does not produce energy and thus the star cannot fall back on yet another fusion cycle to stabilize itself. Even worse, there are several mechanisms within the core that drain it of much needed energy, the most important being photodisintegration (high-energy photons smash the iron atoms apart) and neutronization (protons and electrons combine to form neutrons). Both of these processes actually speed up the inevitable collapse of the iron core.
Calculations show that the collapse happens at a speed of roughly one-fourth the speed of light, meaning that the core that is initially ten thousand kilometers in diameter will collapse to a neutron star having a radius of only fifteen kilometers in a fraction of a second – literally the blink of an eye. As the core hits the sudden resistance of the degenerate neutrons, the rapid collapse is stopped almost immediately. Because of the high impact speed, the core overshoots its equilibrium position by a bit, springs back, overshoots it again, springs back again, and so on. In short: the core bounces. This process creates massive shock waves that propagate radially outwards, blasting into the outer layers of the star, creating the powerful explosion known as the (collapsing core) supernova.
Within just a few minutes the dying star increases its luminosity to roughly one billion Suns, easily outshining smaller galaxies in the vicinity. It’s no wonder then that this spectacular event can be seen in daylight even without the help of a telescope. The outer layers are explosively ejected with speeds in the order of 50,000 kilometers per second or 30,000 miles per second. As time goes on, the ejected layers slow down and cool off during the expansion and the luminosity of the supernova steadily decreases. Once the envelope has faded into deep space, all that remains of the former star is a compact neutron star or an even more exotic remnant we will discuss in the next section. Supernovas are relatively rare events, it is estimated that they only occur once every fifty years in a galaxy the size of the Milky Way.
At first sight, supernovae may seem like a rather destructive force in the universe. However, this is far from the truth for several reasons, one of which is nucleosynthesis, the creation of elements. Scientists assume that the two lightest elements, hydrogen and helium, were formed during the Big Bang and accordingly, these elements can be found in vast amounts in any part of the universe. Other elements up to iron are formed by cosmic rays (in particular lithium, beryllium and boron) or fusion reactions within a star.
But the creation of elements heavier than iron requires additional mechanisms. Observations indicate that such elements are produced mainly by neutron capture, existing atoms capture a free neutron and transform into a heavier element, either within the envelope of giant stars or supernovae. So supernovae play an important role in providing the universe with many of the heavy elements. Another productive aspect of supernovae is their ability to trigger star formation. When the enormous shock wave emitted from a supernova encounters a giant molecular cloud, it can trigger the collapse of the cloud and thus initiate the formation of a new cluster of stars. Far from destructive indeed.
The rapid collapse of a stellar core is not the only source of supernovae in the universe. A supernova can also occur when a white dwarf locked in a binary system keeps pulling in mass from a partner star. At a critical mass of roughly 1.38 times the mass of the Sun, just slightly below the Chandrasekhar limit, the temperature within the white dwarf would become high enough to re-ignite the carbon. Due to its exotic equilibrium state, the white dwarf cannot make use of the self-regulating mechanism that normally keeps the temperature in check in main sequence stars. The result is thus a runaway fusion reaction that consumes all the carbon and oxygen in the white dwarf within a few seconds and raises the temperature to many billion K, equipping the individual atoms with sufficient kinetic energy to fly off at incredible speeds. This violent explosion lets the remnant glow with around five billion solar luminosities for a very brief time.
Simulation of the runaway nuclear fusion reaction within a white dwarf that became hot enough to re-ignite its carbon content. The result is a violent ejection of its mass.
The following is an excerpt from my e-book “Introduction to Stars: Spectra, Formation, Evolution, Collapse” available here for Kindle.
When you look at the spectrum of the heat radiation coming from glowing metal, you will find a continuous spectrum, that is, one that does not have any gaps. You would expect to see the same when looking at light coming from a star. However, there is one significant difference: all stellar spectra have gaps (dark lines) in them. In other words: photons of certain wavelengths seem to be either completely missing or at least arriving in much smaller numbers. Aside from these gaps though, the spectrum is just what you’d expect to see when looking at a heat radiator. So what’s going on here? What can we learn from these gaps?
Spectrum of the Sun. Note the pronounced gaps.
To understand this, we need to delve into atomic physics, or to be more specific, look at how atoms interact with photons. Every atom can only absorb or emit photons of specific wavelengths. Hydrogen atoms for example will absorb photons having the wavelength 4102 A, but do not care about photons having a wavelength 4000 A or 4200 A. Those photons just pass through it without any interaction taking place. Sodium atoms prefer photons with a wavelength 5890 A, when a photon of wavelength 5800 A or 6000 A comes by, the sodium atom is not at all impressed. This is a property you need to keep in mind: every atom absorbs or emits only photons of specific wavelengths.
Suppose a 4102 A photon hits a hydrogen atom. The atom will then absorb the photon, which in crude terms means that the photon “vanishes” and its energy is transferred to one of the atom’s electrons. The electron is now at a higher energy level. However, this state is unstable. After a very short time, the electron returns to a lower energy level and during this process a new photon appears, again having the wavelength 4102 A. So it seems like nothing was gained or lost. Photon comes in, vanishes, electron gains energy, electron loses energy again, photon of same wavelength appears. This seems pointless, why bother mentioning it? Here’s why. The photon that is created when the electron returns to the lower energy level is emitted in a random direction and not the direction the initial photon came from. This is an important point! We can understand the gaps in a spectrum by pondering the consequences of this fact.
Suppose both of us observe a certain heat source. The light from this source reaches me directly while you see the light through a cloud of hydrogen. Both of us are equipped with a device that generates the spectrum of the incoming light. Comparing the resulting spectra, we would see that they are for the most part the same. This is because most photons pass through the hydrogen cloud without any interaction. Consider for example photons of wavelength 5000 A. Hydrogen does not absorb or emit photons of this wavelength, so we will both record the same light intensity at 5000 A. But what about the photons with a 4102 A wavelength?
Imagine a directed stream of these particular photons passing through the hydrogen cloud. As they get absorbed and re-emitted, they get thrown into random directions. Only those photons which do not encounter a hydrogen atom and those which randomly get thrown in your direction will reach your position. Unless the hydrogen cloud is very thin and has a low density, that’s only a very small part of the initial stream. Hence, your spectrum will show a pronounced gap, a line of low light intensity, at λ = 4102 A while in my spectrum no such gap exists.
What if it were a sodium instead of hydrogen cloud? Using the same logic, we can see that now your spectrum should show a gap at λ = 5890 A since this is the characteristic wavelength at which sodium atoms absorb and emit photons. And if it were a mix of hydrogen and sodium, you’d see two dark lines, one at λ = 4102 A due to the presence of hydrogen atoms and another one at λ = 5890 A due to the sodium atoms. Of course, and here comes the fun part, we can reverse this logic. If you record a spectrum and you see gaps at λ = 4102 A and λ = 5890 A, you know for sure that the light must have passed through a gas that contains hydrogen and sodium. So the seemingly unremarkable gaps in a spectrum are actually a neat way of determining what elements sit in a star’s atmosphere! This means that by just looking at a star’s spectrum we can not only determine its temperature, but also its chemical composition at the surface. Here are the results for the Sun:
– Hydrogen 73.5 %
– Helium 24.9 %
– Oxygen 0.8 %
– Carbon 0.3 %
– Iron 0.2 %
– Neon 0.1 %
There are also traces (< 0.1 %) of nitrogen, silicon, magnesium and sulfur. This composition is quite typical for other stars and the universe as a whole: lots of hydrogen (the lightest element), a bit of helium (the second lightest element) and very little of everything else. Mathematical models suggest that even though the interior composition changes significantly over the life time of a star (the reason being fusion, in particular the transformation of hydrogen into helium), its surface composition remains relatively constant in this time.
I’m happy to announce my new e-book release “Introduction to Stars – Spectra, Formation, Evolution, Collapse” (126 pages, $ 2.99). It contains the basics of how stars are born, what mechanisms power them, how they evolve and why they often die a spectacular death, leaving only a remnant of highly exotic matter. The book also delves into the methods used by astronomers to gather information from the light reaching us through the depth of space. No prior knowledge is required to follow the text and no mathematics beyond the very basics of algebra is used.
If you are interested in learning more, click the cover to get to the Amazon product page:
Here’s the table of contents:
Spectrum and Temperature
Gaps in the Spectrum
The Life of a Star
From Protostar to Star
Main Sequence Stars
Giant Space Onions
The Death of a Star
Slicing the Space Onion
Sources and Further Reading
Enjoy the reading experience!
Understanding functions is vital for anyone intending to master calculus or learning mathematical physics. For those who have never heard of the concept of the function, here’s a quick introduction. A function f(x) is a mathematical expression, you can think of it as an input-output system, establishing a connection between one independent variable x and a dependent variable y. We “throw” in a certain value of x, do what the mathematical expression f(x) demands us to do, and get a corresponding value of y in return. Here’s an example of this:
The expression on the right tells us that when given a certain value of x, we need to multiply the square of x by 2, subtract 3x from the result and in a final step add one. For example, using x = 2, the function returns the value:
So this particular function links the input x = 2 to the output y = 3. This is called the value of the function f(x) at x = 2. Of course, we are free to choose any other value for x and see what the function does with it. Inserting x = 1, we get:
So given the input x = 1, the function produces the output y = 0. Whenever this happens, a value of zero is returned, we call the respective value of x a root of the function. So the above function has one root at x = 1. Let’s check a few more values:
The first line tells us that for x = 0, the value of the function lies on y = 1. For x = -1 we get y = 6 and for x = -2 the result y = 15. What to do with this? For one, we can interpret these values geometrically. We can consider any pair of x and y as a point P(x / y) in the Cartesian coordinate system. Since we could check every x we desire and calculate the corresponding value of y using the function, the function defines a graph in the coordinate system. The graph of the above function f(x) will go through the points P(2 / 3), P(1 / 0), P(0 / 1), P(-1, 6) and P(-2 / 15). Here’s the plot:
Graph of f(x) = 2x² – 3x + 1
You can confirm that the points indeed lie on the graph by following the x-axis, as usual the horizontal axis, and determining what distance the curve has from the x-axis at a certain value of x. For example, to find the point P(2 / 3), we move, starting from the origin, two units to the right along the x-axis and then three units upwards, in direction of the y-axis. Here we meet the curve, confirming that the graph includes the point P(2 / 3). Make sure to check this for all other points we calculated. Of course, to produce such a neat plot, we need to insert a lot more than just six values for x. This uncreative work is best done by a computer. Feel free to check out the easy-to-use website graphsketch.com for this, it doesn’t cost a thing and requires no registration. Note that the plot also shows a second root at x = 0.5, the point P(0.5 / 0). Let’s make sure that this value of x is a root of our function f(x) by inserting x = 0.5 and hoping that it produces the output y = 0:
As expected. This was all very mathematical, but what practical uses are there for functions? We can use them to establish a connection between the value of one physical quantity and another. For example, through experiments or theoretical considerations we can determine a function f(p) that links the air pressure p to the air density D. It would allow us to insert any value for the air pressure p and calculate the corresponding value for the air density D, which can be quite useful. Or consider a function f(v) that establishes a connection between the velocity v of an object and the frictional forces F it experiences. This is extremely helpful when trying to determine the trajectory of the object, yet another function f(t) that specifies the link between the elapsed time t and the position x of the object. Just to give you one example of this, the function:
connects the elapsed time t (in seconds) with the corresponding height h (in meters) for an object that is dropped from a 22 m tall tower. According to this function, the object will have reached the following height after t = 1 s:
Insert any value for t and the function produces the object’s location at that time. In this case we are particularly interested in the root. For which value of the independent variable t does the function return the value zero? In other words: after what time does the object reach the ground? We could try inserting several values for t and hope that we find the right one. A more promising approach is setting up the equation f(x) = 0 and solving for x. This requires some knowledge in algebra.
Subtracting 22 on both sides leads to:
Divide by -4.91:
And apply the square root:
For this value of t the function f(t) becomes zero (due to inevitable rounding errors, not perfectly though). The rounding errors are also why I switched from the “is equal to”-sign = to the “is approximately equal to”-sign ≈. You should do the same in calculations whenever rounding a value.
Graph of f(t) = -4.91t² + 22
As you can see, functions are indeed quite useful. If you had trouble understanding the algebra that led to the root t = 2.12, consider reading my free e-book “Algebra – The Very Basics” before continuing with functions.
This was an excerpt from my e-book Math Shorts – Exponential and Trigonometric Functions
I’m on a roll here … another math book, comin’ right up … I’m happy to announce that today I’m expanding my “Math Shorts” series with the latest release “Math Shorts – Exponential and Trigonometric Functions”. This time it’s pre-calculus and thus serves as a bridge between my permanently free e-books “Algebra – The Very Basics” and “Math Shorts – Derivatives”. Without further ado, here’s the blurb and table of contents (click cover to get to the product page on Amazon):
Before delving into the exciting fields of calculus and mathematical physics, it is necessary to gain an in-depth understanding of functions. In this book you will get to know two of the most fundamental function classes intimately: the exponential and trigonometric functions. You will learn how to visualize the graph from the equation, how to set up the function from conditions for real-world applications, how to find the roots, and much more. While prior knowledge in linear and quadratic functions is helpful, it is not necessary for understanding the contents of the book as all the core concepts are developed during the discussion and demonstrated using plenty of examples. The book also contains problems along with detailed solutions to each section. So except for the very basics of algebra, no prior knowledge is required.
Once done, you can continue your journey into mathematics, from the basics all the way to differential equations, by following the “Math Shorts” series, with the recommended reading being “Math Shorts – Derivatives” upon completion of this book. From the author of “Great Formulas Explained” and “Statistical Snacks”, here’s another down-to-earth guide to the joys of mathematics.
Table of contents:
1. Exponential Functions
1.3. Basics Continued
1.5. A More General Form
2. Trigonometric Functions
2.1. The Sine Function
2.3. The Cosine Function
2.7. Sine Squared And More
2.8. The Tangent Function
3. Solutions to the Problems
Have fun reading!
There is a major problem with reliance on placebos, like most vitamins and antioxidants. Everyone gets upset about Big Science, Big Pharma, but they love Big Placebo.
– Michael Specter
A Little White Lie
In 1972, Blackwell invited fifty-seven pharmacology students to an hour-long lecture that, unbeknownst to the students, had only one real purpose: bore them. Before the tedious lecture began, the participants were offered a pink or a blue pill and told that the one is a stimulant and the other a sedative (though it was not revealed which color corresponded to which effect – the students had to take their chances). When measuring the alertness of the students later on, the researchers found that 1) the pink pills helped students to stay concentrated and 2) two pills worked better than one. The weird thing about these results: both the pink and blue pills were plain ol’ sugar pills containing no active ingredient whatsoever. From a purely pharmacological point of view, neither pill should have a stimulating or sedative effect. The students were deceived … and yet, those who took the pink pill did a much better job in staying concentrated than those who took the blue pill, outperformed only by those brave individuals who took two of the pink miracle pills. Both the effects of color and number have been reproduced. For example, Luchelli (1972) found that patients with sleeping problems fell asleep faster after taking a blue capsule than after taking an orange one. And red placebos have proven to be more effective pain killers than white, blue or green placebos (Huskisson 1974). As for number, a comprehensive meta-analysis of placebo-controlled trials by Moerman (2002) confirmed that four sugar pills are more beneficial than two. With this we are ready to enter another curious realm of the mind: the placebo effect, where zero is something and two times zero is two times something.
The Oxford Dictionary defines the placebo effect as a beneficial effect produced by a placebo drug or treatment, which cannot be attributed to the properties of the placebo itself and must therefore be due to the patient’s belief in that treatment. In short: mind over matter. The word placebo originates from the Bible (Psalm 116:9, Vulgate version by Jerome) and translates to “I shall please”, which seems to be quite fitting. Until the dawn of modern science, almost all of medicine was, knowingly or unknowingly, based on this effect. Healing spells, astrological rituals, bloodletting … We now know that any improvement in health resulting from such crude treatments can only arise from the fact that the patient’s mind has been sufficiently pleased. Medicine has no doubt come a long way and all of us profit greatly from this. We don’t have to worry about dubious healers drilling holes into our brains to “relieve pressure” (an extremely painful and usually highly ineffective treatment called trepanning), we don’t have to endure the unimaginable pain of a surgeon cutting us open and we live forty years longer than our ancestors. Science has made it possible. However, even in today’s technology-driven world one shouldn’t underestimate the healing powers of the mind.
Before taking a closer look at relevant studies and proposed explanations, we should point out that studying the placebo effect can be a rather tricky affair. It’s not as simple as giving a sugar pill to an ill person and celebrating the resulting improvement in health. All conditions have a certain natural history. Your common cold will build up over several days, peak over the following days and then slowly disappear. Hence, handing a patient a placebo pill (or any other drug for that matter) when the symptoms are at their peak and observing the resulting improvement does not allow you to conclude anything meaningful. In this set-up, separating the effects of the placebo from the natural history of the illness is impossible. To do it right, researchers need one placebo group and one natural history (no-treatment) group. The placebo response is the difference that arises between the two groups. Ignoring natural history is a popular way of “proving” the effectiveness of sham healing rituals and supposed miracle pills. You can literally make any treatment look like a gift from God by knowing the natural history and waiting for the right moment to start the treatment. One can already picture the pamphlet: “93 % of patients were free of symptoms after just three days, so don’t miss out on this revolutionary treatment”. Sounds great, but what they conveniently forget to mention is that the same would have been true had the patients received no treatment.
There are also ethical consideration that need to be taken into account. Suppose you wanted to test how your placebo treatment compares to a drug that is known to be beneficial to a patient’s health. The scientific approach demands setting up one placebo group and one group that receives the well-known drug. How well your placebo treatment performs will be determined by comparing the groups after a predetermined time has passed. However, having one placebo group means that you are depriving people of a treatment that is proven to improve their condition. It goes without saying that this is highly problematic from an ethical point of view. Letting the patient suffer for the quest of knowledge? This approach might be justified if there is sufficient cause to believe that the alternative treatment in question is superior, but this is rarely the case for placebo treatments. While beneficial, their effect is usually much weaker than that of established drugs.
Another source of criticism is the deception of the patient during a placebo treatment. Doctors prefer to be open and honest when discussing a patient’s conditions and the methods of treatment. But a placebo therapy requires them to tell patients that the prescribed pill contains an active ingredient and has proven to be highly effective when in reality it’s nothing but sugar wrapped in a thick layer of good-will. Considering the benefits, we can certainly call it a white lie, but telling it still makes many professionals feel uncomfortable. However, they might be in luck. Several studies have suggested that, surprisingly, the placebo effect still works when the patient is fully aware that he receives placebo pills.
One example of this is the study by Kaptchuk et al. (2010). The Harvard scientists randomly assigned 80 patients suffering from irritable bowel syndrome (IBS) to either a placebo group or no-treatment group. The patients in the placebo group received a placebo pill along with the following explanation: “Placebo pills are made of an inert substance, like sugar pills, and have been shown in clinical studies to produce significant improvement in IBS symptoms through mind-body self-healing processes”. As can be seen from the graph below, the pills did their magic. The improvement in the placebo group was roughly 30 % higher than in the no-treatment group and the low p-value (see appendix for an explanation of the p-value) shows that it is extremely unlikely that this result came to be by chance. Unfortunately, there seems to be a downside to the honesty. Hashish (1988) analyzed the effects of real and sham ultrasound treatment on patients whose impacted lower third molars had been surgically removed and concluded that the effectiveness in producing a placebo response is diminished if the patient comes to understand that the therapy is a placebo treatment rather than the “real” one. So while the placebo-effect does arise even without the element of deception, a fact that is quite astonishing on its own, deception does strengthen the response to the placebo treatment.
Results of Kaptchuk et al. (2010)
Let’s explore some more experimental studies to fully understand the depth and variety of the placebo effect. A large proportion of the relevant research has focused on the effect’s analgesic nature, that is, its ability to reduce pain without impairing consciousness. Amanzio et al. (2001) examined patients who had undergone thoracotomy, a major surgical procedure to gain access to vital organs in the chest and one that is often associated with severe post-operative pain. As handing out sugar pills would have been irresponsible and unethical in this case, the researchers found a more humane method of unearthing the placebo effect: the open-hidden paradigm. All patients received powerful painkillers such as Morphine, Buprenorphine, Tramadol, … However, while one group received the drug in an open manner, administered by a caring clinician in full view of the patient, another group was given the drug in a hidden manner, by means of a computer-programmed drug infusion pump with no clinician present and no indication that the drug was being administered. This set-up enabled the researchers to determine how much of the pain reduction was due to the caring nature of the clinician and the ritual of injecting the drug. The results: the human touch matters and matters a lot. As can be seen from the graph below, every painkiller became significantly more effective when administered in an open fashion. Several follow-on studies (Benedetti et al. 2003, Colloca et al. 2004) confirmed this finding. This demonstrates that the placebo effect goes far beyond the notorious sugar pill, it can also be induced by the caring words of a professional or a dramatic treatment ritual.
The fact that the human touch is of major importance in any clinical treatment, placebo or otherwise, seems pretty obvious (though its power in reducing pain might have surprised you). Much less obvious are the roles of administration form and treatment ritual, something we shall further explore. For both we can use the following rule of thumb: the more dramatic the intervention, the stronger the placebo response. For example, several studies have shown that an injection with salt-water is more effective in generating the placebo effect than a sugar pill. This is of course despite the fact that both salt-water and sugar pills do not have any direct medical benefits. The key difference lies in the inconveniences associated with the form of delivery: while swallowing a pill takes only a moment and is a rather uncomplicated process, the injection, preparation included, might take up to several minutes and can be quite painful. There’s no doubt that the latter intervention will leave a much stronger impression. Another study (Kaptchuk et al. 2006) came to the exciting conclusion that a nonsensical therapeutic intervention modeled on acupuncture did a significantly better job in reducing arm pain than the sugar pill. While the average pain score in the sham acupuncture group dropped by 0.33 over the course of one week, the corresponding drop in the sugar pill group was only 0.15. Again the more dramatic treatment came out on top.
The experimental results mentioned above might explain why popular ritualistic treatments found in alternative medicine remain so widespread even when there are numerous studies providing ample proof that the interventions lack biological plausibility and produce no direct medical benefits. Despite their scientific shortcomings, such treatments do work. However, this feat is extremely unlikely to be accomplished by strengthening a person’s aura, enhancing life force or harnessing quantum energy, as the brochure might claim. They work mainly (even solely) because of their efficiency in inducing the mind’s own placebo effect. Kaptchuk’s study impressively demonstrates that you can take any arbitrary ritual, back it up with any arbitrary theory to give the procedure pseudo-plausibility and let the placebo effect take over from there. Such a treatment might not be able to compete with cutting-edge drugs, but the benefits will be there. Though one has to wonder about the ethics of providing a patient with a certain treatment when demonstrably a more effective one is available, especially in case of serious diseases.
Don’t Forget Your Lucky Charm
This seems to be a great moment to get in the following entertaining gem. In 2010, Damish et al. invited twenty-eight people to the University of Cologne to take part in a short but sweet experiment that had them play ten balls on a putting green. Half of the participants were given a regular golf ball and managed to get 4.7 putts out of 10 on average. The other half was told they would be playing a “lucky ball” and, sure enough, this increased performance by an astonishing 36 % to 6.4 putts out of 10. I think we can agree that the researchers hadn’t really gotten hold of some magical performance-enhancing “lucky ball” and that the participants most likely didn’t even believe the story of the blessed ball. Yet, the increase was there and the result statistically significant despite the small sample size. So what happened? As you might have expected, this is just another example of the placebo effect (in this particular case also called the lucky charm effect) in action.
OK, so the ball was not really lucky, but it seems that simply floating the far-fetched idea of a lucky ball was enough to put participants into a different mindset, causing them to approach the task at hand in a different manner. One can assume that the story made them less worried about failing and more focused on the game, in which case the marked increase is no surprise at all. Hence, bringing a lucky charm to an exam might not be so superstitious after all. Though we should mention that a lucky charm can only do its magic if the task to be completed requires some skill. If the outcome is completely random, there simply is nothing to gain from being put into a different mindset. So while a lucky charm might be able to help a golfer, student, chess player or even a race car driver, it is completely useless for dice games, betting or winning the lottery.
Let’s look at a few more studies that show just how curious and complex the placebo effect is before moving on to explanations. Shiv et al. (2008) from the Stanford Business School analyzed the economic side of self-healing. They applied electric shocks to 82 participants and then offered them to buy a painkiller (guess that’s also a way to fund your research). The option: get the cheap painkiller for $ 0.10 per pill or the expensive one for $ 2.50 per pill. What the participants weren’t told was that there was no difference between the pills except for the price. Despite that, the price did have an effect on pain reduction. While 61 % of the subjects taking the cheap painkiller reported a significant pain reduction, an impressive 85 % reported the same after treating themselves to the expensive version. The researchers suspect that this is a result of quality expectations. We associate high price with good quality and in case of painkillers good quality equals effective pain reduction. So buying the expensive brand name drug might not be such a bad idea even when there is a chemically identical and lower priced generic drug available. In another study, Shiv et al. also found the same effect for energy drinks. The more expensive energy drink, with price being the only difference, made people report higher alertness and noticeably enhanced their ability to solve word puzzles.
This was an excerpt from my Kindle e-book Curiosities of the Mind. To learn more about the placebo effect, as well as other interesting psychological effects such as the chameleon effect, Mozart effect and the actor-observer bias, click here. (Link to Amazon.com)
The idea that music can make you smarter became very popular in the mid-nineties under the name “Mozart effect” and has remained popular ever since. The hype began with the publication of Rauscher et al. (1993) in the journal Nature. The researchers discovered that participants who were exposed to the aforementioned Mozart sonata performed better on the Stanford-Binet IQ test than those who listened to verbal relaxation instructions or sat in silence.
This revelation caused armies of mothers and fathers to storm the CD stores and bombard their children with Mozart music. One US governor ordered the distribution of Mozart CD’s by hospitals to all mothers following the birth of a child. Not surprisingly, marketers eagerly joined the fun (with increasingly ridiculous claims about the effect of music on intelligence) to profit from the newly-found “get-smart-quick” scheme. What got lost in the hype however was the fact that Rauscher et al. never found or claimed that exposure to Mozart would increase your IQ in general. Neither did they claim that the performance on an IQ test is a reliable indicator of how smart a person is. All they demonstrated was that exposure to an enjoyable musical piece led to a temporary (< 15 minutes) increase in spatial reasoning ability, not more, not less. Despite that, the study suffered the fate all studies are bound to suffer when they fall into the hands of the tabloid media, politicians and marketers: the results get distorted and blown out of proportion.
By the way: I wonder if mothers and fathers would have been just as eager to expose their children to Mozart had they known about some of the less flattering pieces written by the brilliant composer, among them the canon in B-flat major titled “Leck mich im Arsch” (which translates to “Kiss my Ass”) and the scatological canon “Bona Nox!” which includes the rather unsophisticated line “shit in your bed and make it burst”. These are just two examples of the many obscene and sometimes even pornographic pieces the party animal Mozart wrote for boozy nights with his friends. One can picture the young composer and his companions sitting in a flat in Vienna singing obscene songs after downing a few bottles of wine while concerned mothers cover their children’s ears, cursing the young generation and their vile music. That’s the side of Mozart you won’t get to hear in orchestral concerts.
But back to the topic. So whatever happened to the Mozart effect? Hype aside, is there anything to it? The thorough 1999 meta-analysis of Mozart effect studies by Chabris came to the conclusion that the popularized version of the effect is most certainly incorrect. Listening to Mozart, while no doubt a very enjoyable and stimulating experience, does not permanently raise your IQ or make you more intelligent. However, said meta analysis, as well as the 2002 Husain et al. study described above, did find a small cognitive enhancement resulting from exposure to Mozart’s sonata. The explanation of the enhancement turned out to be somewhat sobering though.
In the original study, Rauscher et al. proposed that Mozart’s music is able to prime spatial abilities in a direct manner because of similarities in neural activation. Further discussion and experiments showed that such a direct link is rather unlikely though, especially in light of the results of Nantais and Schellenberg (1999). In this study participants performed a spatial reasoning task after either listening to Mozart’s sonata or hearing a narrated story. When the reasoning task was completed, the participants were asked which of the two, Mozart’s piece or the story, they preferred. The result: participants who preferred the sonata performed better on the spatial reasoning task after listening to the piece and participants who preferred the story performed better on the test after hearing the story. However, participants who listened to Mozart’s music and stated that they would have preferred the story instead did not show the cognitive improvement. Overall the researchers found no benefit in the Mozart condition. All of the above implies that the enhancement in performance is a result of exposure to a preferred stimulus rather than a direct link between Mozart and cognition. It seems that the Mozart effect is just a small part of a broader psychological phenomenon that goes a little something like this: experiencing a preferred stimulus, be it a musical piece, a narrated story or a funny comic book, has a positive effect on arousal and mood and this in turn enhances cognitive abilities.
This was an extract from my Kindle e-book Curiosities of the Mind. Check it out if you interested in learning more about the psychological effects of music as well as other common psychological effects such as the false consensus bias, the placebo effect, the chameleon effect, …
After about six months of work and fun, my new Kindle book is finally ready and … surprise, it’s not math! This time we’ll be delving deep into the ultimate scientific challenge: the mind. Here’s the blurb and the Table of Contents:
Over the past centuries, science has successfully unlocked many secrets of the universe. We understand atoms and molecules, cells and organs, stars and galaxies and have managed to use this newly-found knowledge to build fantastic machines that can bring us from A to B at mind-blowing speeds and cutting-edge medication that can alleviate pain almost instantly. Yet, the grandest secret the universe has to offer remains a perplexing mystery: the mind.
Despite the incredible scientific progress, we still find ourselves wondering what it is, where it is and how it works. This book cannot hope to give you a full answer, but it will provide you with fascinating hints on the workings of this mysterious machine by discussing the strange curiosities manifested in it. You will get to know the most common psychological effects and biases, learn about illuminating experiments from the field of social psychology and find out a lot about yourself and the people around you in the process. For a detailed table of contents, check out the Look Inside feature.
Table of Contents:
– Placebo Effect
A Little White Lie
Don’t Forget Your Lucky Charm
Poor Little Albert
– Chameleon Effect
Just A Harmless Fly
Now 10 % More Likable
It’s All About Empathy
– Childhood Amnesia
A Fundamental Part Of Being Human
The Nature Of Early Memories
Parents, Culture And Gender
The Self And Autobiographical Memories
– IKEA Effect
Labor Leads To Love
High Expectations versus Low Rewards
Of Origami Frogs and Men
– False Consensus Effect
Bungee Jumping Enthusiasts Are Biased
What Would You Do?
A Powerful Defense Mechanism
Friends – A Biased Sample Of Mankind
– Actor – Observer Bias
Everybody’s To Blame Except You
Fidel Castro’s Army Of Students
A Matter Of Culture And Vision
Let’s Talk Later
– Dunning-Kruger Effect
Unskilled And Unaware
Learn To Be Better
Statistical Artifacts And Task Difficulty
– Music and the Mozart Effect
Tones and Overtones
Music And Your Mind
Say No to Marketers
Mean and Standard Deviation
Regression to the Mean
Click here to get to the product page on Amazon.
(This is an excerpt from The Book of Forces – enjoy!)
The forces we have discussed so far are well-understood by the scientific community and are commonly featured in introductory as well as advanced physics books. In this section we will turn to a more exotic and mysterious interaction: the Casimir force. After a series of complex quantum mechanical calculations, the Dutch physicist Hendrick Casimir predicted its existence in 1948. However, detecting the interaction proved to be an enormous challenge as this required sensors capable picking up forces in the order of 10^(-15) N and smaller. It wasn’t until 1996 that this technology became available and the existence of the Casimir force was experimentally confirmed.
So what does the Casimir force do? When you place an uncharged, conducting plate at a small distance to an identical plate, the Casimir force will pull them towards each other. The term “conductive” refers to the ability of a material to conduct electricity. For the force it plays no role though whether the plates are actually transporting electricity in a given moment or not, what counts is their ability to do so.
The existence of the force can only be explained via quantum theory. Classical physics considers the vacuum to be empty – no particles, no waves, no forces, just absolute nothingness. However, with the rise of quantum mechanics, scientists realized that this is just a crude approximation of reality. The vacuum is actually filled with an ocean of so-called virtual particles (don’t let the name fool you, they are real). These particles are constantly produced in pairs and annihilate after a very short period of time. Each particle carries a certain amount of energy that depends on its wavelength: the lower the wavelength, the higher the energy of the particle. In theory, there’s no upper limit for the energy such a particle can have when spontaneously coming into existence.
So how does this relate to the Casimir force? The two conducting plates define a boundary in space. They separate the space of finite extension between the plates from the (for all practical purposes) infinite space outside them. Only particles with wavelengths that are a multiple of the distance between the plates fit in the finite space, meaning that the particle density (and thus energy density) in the space between the plates is smaller than the energy density in the pure vacuum surrounding them. This imbalance in energy density gives rise to the Casimir force. In informal terms, the Casimir force is the push of the energy-rich vacuum on the energy-deficient space between the plates.
(Illustration of Casimir force)
It gets even more puzzling though. The nature of the Casimir force depends on the geometry of the plates. If you replace the flat plates by hemispherical shells, the Casimir force suddenly becomes repulsive, meaning that this specific geometry somehow manages to increase the energy density of the enclosed vacuum. Now the even more energy-rich finite space pushes on the surrounding infinite vacuum. Trippy, huh? So which shapes lead to attraction and which lead to repulsion? Unfortunately, there is no intuitive way to decide. Only abstract mathematical calculations and sophisticated experiments can provide an answer.
We can use the following formula to calculate the magnitude of the attractive Casimir force FCS between two flat plates. Its value depends solely on the distance d (in m) between the plates and the area A (in m²) of one plate. The letters h = 6.63·10^(-34) m² kg/s and c = 3.00·10^8 m/s represent Plank’s constant and the speed of light.
FCS = π·h·c·A / (480·d^4) ≈ 1.3·10^(-27)·A / d^4
(The sign ^ stands for “to the power”) Note that because of the exponent, the strength of the force goes down very rapidly with increasing distance. If you double the size of the gap between the plates, the magnitude of the force reduces to 1/2^4 = 1/16 of its original value. And if you triple the distance, it goes down to 1/3^4 = 1/81 of its original value. This strong dependence on distance and the presence of Plank’s constant as a factor cause the Casimir force to be extremely weak in most real-world situations.
a) Calculate the magnitude of the Casimir force experienced by two conducting plates having an area A = 1 m² each and distance d = 0.001 m (one millimeter). Compare this to their mutual gravitational attraction given the mass m = 5 kg of one plate.
b) How close do the plates need to be for the Casimir force to be in the order of unity? Set FCS = 1 N.
Inserting the given values into the formula for the Casimir force leads to (units not included):
FCS = 1.3·10^(-27)·A/d^4
FCS = 1.3·10^(-27) · 1 / 0.0014
FCS ≈ 1.3·10^(-15) N
Their gravitational attraction is:
FG = G·m·M / r²
FG = 6.67·10^(-11)·5·5 / 0.001²
FG ≈ 0.0017 N
This is more than a trillion times the magnitude of the Casimir force – no wonder this exotic force went undetected for so long. I should mention though that the gravitational force calculated above should only be regarded as a rough approximation as Newton’s law of gravitation is tailored to two attracting spheres, not two attracting plates.
Setting up an equation we get:
FCS = 1.3·10^(-27)·A/d^4
1 = 1.3·10^(-27) · 1 / d^4
Multiply by d4:
d4 = 1.3·10^(-27)
And apply the fourth root:
d ≈ 2·10^(-7) m = 200 nanometers
This is roughly the size of a common virus and just a bit longer than the wavelength of violet light.
The existence of the Casimir force provides an impressive proof that the abstract mathematics of quantum mechanics is able to accurately describe the workings of the small-scale universe. However, many open questions remain. Quantum theory predicts the energy density of the vacuum to be infinitely large. According to Einstein’s theory of gravitation, such a concentration of energy would produce an infinite space-time curvature and if this were the case, we wouldn’t exist. So either we don’t exist (which I’m pretty sure is not the case) or the most powerful theories in physics are at odds when it comes to the vacuum.
(This is an excerpt from The Book of Forces)
All objects exert a gravitational pull on all other objects. The Earth pulls you towards its center and you pull the Earth towards your center. Your car pulls you towards its center and you pull your car towards your center (of course in this case the forces involved are much smaller, but they are there). It is this force that invisibly tethers the Moon to Earth, the Earth to the Sun, the Sun to the Milky Way Galaxy and the Milky Way Galaxy to its local galaxy cluster.
Experiments have shown that the magnitude of the gravitational attraction between two bodies depends on their masses. If you double the mass of one of the bodies, the gravitational force doubles as well. The force also depends on the distance between the bodies. More distance means less gravitational pull. To be specific, the gravitational force obeys an inverse-square law. If you double the distance, the pull reduces to 1/2² = 1/4 of its original value. If you triple the distance, it goes down to 1/3² = 1/9 of its original value. And so on. These dependencies can be summarized in this neat formula:
F = G·m·M / r²
With F being the gravitational force in Newtons, m and M the masses of the two bodies in kilograms, r the center-to-center distance between the bodies in meters and G = 6.67·10^(-11) N m² kg^(-2) the (somewhat cumbersome) gravitational constant. With this great formula, that has first been derived at the end of the seventeenth century and has sparked an ugly plagiarism dispute between Newton and Hooke, you can calculate the gravitational pull between two objects for any situation.
(Gravitational attraction between two spherical masses)
If you have trouble applying the formula on your own or just want to play around with it a bit, check out the free web applet Newton’s Law of Gravity Calculator that can be found on the website of the UNL astronomy education group. It allows you to set the required inputs (the masses and the center-to-center distance) using sliders that are marked special values such as Earth’s mass or the distance Earth-Moon and calculates the gravitational force for you.
Calculate the gravitational force a person of mass m = 72 kg experiences at the surface of Earth. The mass of Earth is M = 5.97·10^24 kg (the sign ^ stands for “to the power”) and the distance from the center to the surface r = 6,370,000 m. Using this, show that the acceleration the person experiences in free fall is roughly 10 m/s².
To arrive at the answer, we simply insert all the given inputs into the formula for calculating gravitational force.
F = G·m·M / r²
F = 6.67·10^(-11)·72·5.97·10^24 / 6,370,000² N ≈ 707 N
So the magnitude of the gravitational force experienced by the m = 72 kg person is 707 N. In free fall, he or she is driven by this net force (assuming that we can neglect air resistance). Using Newton’s second law we get the following value for the free fall acceleration:
F = m·a
707 N = 72 kg · a
Divide both sides by 72 kg:
a = 707 / 72 m/s² ≈ 9.82 m/s²
Which is roughly (and more exact than) the 10 m/s² we’ve been using in the introduction. Except for the overly small and large numbers involved, calculating gravitational pull is actually quite straight-forward.
As mentioned before, gravitation is not a one-way street. As the Earth pulls on the person, the person pulls on the Earth with the same force (707 N). However, Earth’s mass is considerably larger and hence the acceleration it experiences much smaller. Using Newton’s second law again and the value M = 5.97·1024 kg for the mass of Earth we get:
F = m·a
707 N = 5.97·10^24 kg · a
Divide both sides by 5.97·10^24 kg:
a = 707 / (5.97·10^24) m/s² ≈ 1.18·10^(-22) m/s²
So indeed the acceleration the Earth experiences as a result of the gravitational attraction to the person is tiny.
By how much does the gravitational pull change when the person of mass m = 72 kg is in a plane (altitude 10 km = 10,000 m) instead of the surface of Earth? For the mass and radius of Earth, use the values from the previous example.
In this case the center-to-center distance r between the bodies is a bit larger. To be specific, it is the sum of the radius of Earth 6,370,000 m and the height above the surface 10,000 m:
r = 6,370,000 m + 10,000 m = 6,380,000 m
Again we insert everything:
F = G·m·M / r²
F = 6.67·10^(-11)·72·5.97·10^24 / 6,380,000² N ≈ 705 N
So the gravitational force does not change by much (only by 0.3 %) when in a plane. 10 km altitude are not much by gravity’s standards, the height above the surface needs to be much larger for a noticeable difference to occur.
With the gravitational law we can easily show that the gravitational acceleration experienced by an object in free fall does not depend on its mass. All objects are subject to the same 10 m/s² acceleration near the surface of Earth. Suppose we denote the mass of an object by m and the mass of Earth by M. The center-to-center distance between the two is r, the radius of Earth. We can then insert all these values into our formula to find the value of the gravitational force:
F = G·m·M / r²
Once calculated, we can turn to Newton’s second law to find the acceleration a the object experiences in free fall. Using F = m·a and dividing both sides by m we find that:
a = F / m = G·M / r²
So the gravitational acceleration indeed depends only on the mass and radius of Earth, but not the object’s mass. In free fall, a feather is subject to the same 10 m/s² acceleration as a stone. But wait, doesn’t that contradict our experience? Doesn’t a stone fall much faster than a feather? It sure does, but this is only due to the presence of air resistance. Initially, both are accelerated at the same rate. But while the stone hardly feels the effects of air resistance, the feather is almost immediately slowed down by the collisions with air molecules. If you dropped both in a vacuum tube, where no air resistance can build up, the stone and the feather would reach the ground at the same time! Check out an online video that shows this interesting vacuum tube experiment, it is quite enlightening to see a feather literally drop like a stone.
(All bodies are subject to the same gravitational acceleration)
Since all objects experience the same acceleration near the surface of Earth and since this is where the everyday action takes place, it pays to have a simplified equation at hand for this special case. Denoting the gravitational acceleration by g (with g ≈ 10 m/s²) as is commonly done, we can calculate the gravitational force, also called weight, an object of mass m is subject to at the surface of Earth by:
F = m·g
So it’s as simple as multiplying the mass by ten. Depending on the application, you can also use the more accurate factor g ≈ 9.82 m/s² (which I will not do in this book). Up to now we’ve only been dealing with gravitation near the surface of Earth, but of course the formula allows us to compute the gravitational force and acceleration near any other celestial body. I will spare you trouble of looking up the relevant data and do the tedious calculations for you. In the table below you can see what gravitational force and acceleration a person of mass m = 72 kg would experience at the surface of various celestial objects. The acceleration is listed in g’s, with 1 g being equal to the free-fall acceleration experienced near the surface of Earth.
So while jumping on the Moon would feel like slow motion (the free-fall acceleration experienced is comparable to what you feel when stepping on the gas pedal in a common car), you could hardly stand upright on Jupiter as your muscles would have to support more than twice your weight. Imagine that! On the Sun it would be even worse. Assuming you find a way not get instantly killed by the hellish thermonuclear inferno, the enormous gravitational force would feel like having a car on top of you. And unlike temperature or pressure, shielding yourself against gravity is not possible.
What about the final entry? What is a neutron star and why does it have such a mind-blowing gravitational pull? A neutron star is the remnant of a massive star that has burned its fuel and exploded in a supernova, no doubt the most spectacular light-show in the universe. Such remnants are extremely dense – the mass of several suns compressed into an almost perfect sphere of just 20 km radius. With the mass being so large and the distance from the surface to the center so small, the gravitational force on the surface is gigantic and not survivable under any circumstances.
If you approached a neutron star, the gravitational pull would actually kill you long before reaching the surface in a process called spaghettification. This unusual term, made popular by the brilliant physicist Stephen Hawking, refers to the fact that in intense gravitational fields objects are vertically stretched and horizontally compressed. The explanation is rather straight-forward: since the strength of the gravitational force depends on the distance to the source of said force, one side of the approaching object, the side closer to the source, will experience a stronger pull than the opposite side. This leads to a net force stretching the object. If the gravitational force is large enough, this would make any object look like a thin spaghetti. For humans spaghettification would be lethal as the stretching would cause the body to break apart at the weakest spot (which presumably is just above the hips). So my pro-tip is to keep a polite distance from neutron stars.
When it comes to using antimatter for propulsion, getting sufficient amounts of the exotic fuel is the biggest challenge. For flights within the solar system, hybrid concepts would require several micrograms of antimatter, while pure antimatter rockets would consume dozens of kilograms per trip. And going beyond the solar system would demand the production of several metric tons and more.
We are very, very far from this. Currently around 10 nanograms of anti-protons are produced in the large particles accelerators each year. At this rate it would take 100 years to produce one measly microgram and 100 billion years to accumulate one kilogram. However, the antimatter production rate has seen exponential growth, going up by sixteen orders of magnitude over the past decades, and this general trend will probably continue for some time.
Even with a noticeable slowdown in this exponential growth, gram amounts of anti-protons could be manufactured each year towards the end of the 21st century, making hybrid antimatter propulsion feasible. With no slowdown, the rate could even reach kilograms per year by then. While most physicists view this as an overly optimistic estimate, it is not impossible considering the current trend in antimatter production and the historic growth of liquid hydrogen and uranium production rates (both considered difficult to manufacture in the past).
There is still much to be optimized in the production of antimatter. The energy efficiency at present is only 10-9, meaning that you have to put in one gigajoule of pricey electric energy to produce a single joule of antimatter energy. The resulting costs are a staggering 62.5 trillion USD per gram of anti-protons, making antimatter the most expensive material known to man. So if you want to tell your wife how precious she is to you (and want to get rid of her at the same time), how about buying her a nice anti-matter scarf?
Establishing facilities solely dedicated to antimatter production, as opposed to the by-product manufacturing in modern particle accelerators, would significantly improve the situation. NASA experts estimate that an investment of around 5 billion USD is sufficient to build such a first generation antimatter factory. This step could bring the costs of anti-protons down to 25 billion USD per gram and increase the production rate to micrograms per year.
While we might not see kilogram amounts of antimatter or antimatter propulsion systems in our lifetime, the production trend over the next few decades will reveal much about the feasibility of antimatter rockets and interstellar travel. If the optimists are correct, and that’s a big if, the grandchildren of our grandchildren’s grandchildren might watch the launch of the first spacecraft capable of reaching neighboring stars. Sci-fi? I’m sure that’s what people said about the Moon landing and close-up pictures from Mars and Jupiter just a lifetime ago.
For more information, check out my ebook Antimatter Propulsion.
I’m very excited to announce the release of my latest ebook called “Antimatter Propulsion”. I’ve been working working on it like a madman for the past few months, going through scientific papers and wrestling with the jargon and equations. But I’m quite satisfied with the result. Here’s the blurb, the table of contents and the link to the product page. No prior knowledge is required to enjoy the book.
Many popular science fiction movies and novels feature antimatter propulsion systems, from the classic Star Trek series all the way to Cameron’s hit movie Avatar. But what exactly is antimatter? And how can it be used accelerate rockets? This book is a gentle introduction to the science behind antimatter propulsion. The first section deals with antimatter in general, detailing its discovery, behavior, production and storage. This is followed by an introduction to propulsion, including a look at the most important quantities involved and the propulsion systems in use or in development today. Finally, the most promising antimatter propulsion and rocket concepts are presented and their feasibility discussed, from the solid core concept to antimatter initiated microfusion engines, from the Valkyrie project to Penn State’s AIMStar spacecraft.
Section 1: Antimatter
An Explosive Mix
Proton and Anti-Proton Annihilation
Sources of Antimatter
Getting the Fuel
Section 2: Propulsion Basics
Conservation of Momentum
♪ Throw, Throw, Throw Your Boat ♫
So What’s The Deal?
May The Thrust Be With You
Specific Impulse and Fuel Requirements
Section 3: Antimatter Propulsion Concepts
Solid Core Concept
Plasma Core Concept
Beamed Core Concept
Antimatter Catalyzed Micro-Fission / Fusion
Antimatter Initiated Micro-Fusion
Section 4: Antimatter Rocket Concepts
You can purchase “Antimatter Propulsion” here for $ 2.99.
Exponential functions have the general form:
with two constants a and b (called base). It’s quite common to use Euler’s number e = 2.7182… as the base and the exponential function expressed as such:
with two constants a and c. Converting from one form to the other is not that difficult, just use ec = b or c = ln(b). Here’s how it works:
As for the plot, you should keep two special cases in mind. For b > 1 (which corresponds to c > 0 in case of base e), the function goes through the point P(0,a) and goes to infinity as x goes to infinity.
(Exponential function with b > 1 or c > 0. For example: f(x) = 8·3x)
This is exponential growth. When 0 < b < 1 (or c < 0) this turns into exponential decline. The function again goes through the point P(0,a), but approaches zero as x goes to infinity.
(Exponential function with 0 < b < 1 or c < 0. For example: f(x) = 0.5x)
Here’s how the differentiation of exponential functions works. Given the function:
The first derivative is:
For the case of base e:
You should remember both formulas. Note that the exponential functions have the unique property that their first derivative (slope) is proportional to the function value (height above x-axis). So the higher the curve, the sharper it rises. This is why exponential growth is so explosive.
If exponential functions are combined with power or polynomial functions, just use the sum rule.
(This was an excerpt from the FREE ebook “Math Shorts – Derivatives”)