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What does it mean to divide a fraction by a fraction?
What does it mean to divide a fraction by a fraction?
This lesson from Virtual Nerd clearly explains the meaning of this.
What does it mean to divide a fraction by a fraction?

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Detecting genetic disorders with 3d face scans
It is possible to detect numerous types pf genetic disorders with 3d face scans
How do we make measurements of the face?

How can we automate such measurements and make them accurate?

(Facial recognition technology will change the way we live, The Economist)
Detecting genetic disorders with 3d face scans
Johan at the Phineas Gage Fan Club writes:
Following on from last week’s post on smile measuring software, The Scotsman (via Gizmodo) reports on the work by Hammond and colleagues at UCL, who are developing 3d face scans as a quick, inexpensive alternative to genetic testing.
This is not as crazy as it sounds at first since it is known that in a number of congenital conditions, the hallmark behavioural, physiological or cognitive deficits are also (conveniently) accompanied by characteristic appearances.
The classic example of this is Down syndrome, which you need no software to recognise. More examples appear in the figure above, where you can compare the characteristic appearances of various conditions to the unaffected face in the middle.
Hammond’s software can be used to identify 30 congenital conditions, ranging from Williams syndrome (a sure topic of a future post) to Autism,
Detecting genetic disorders with 3d face scans


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Diagnostically relevant facial gestalt information from ordinary photos
Rare genetic disorders affect around 8% of people, many of whom live with symptoms that greatly reduce their quality of life. Genetic diagnoses can provide doctors with information that cannot be obtained by assessing clinical symptoms, and this allows them to select more suitable treatments for patients. However, only a minority of patients currently receive a genetic diagnosis.
Alterations in the face and skull are present in 30–40% of genetic disorders, and these alterations can help doctors to identify certain disorders, such as Down’s syndrome or Fragile X.
Extending this approach, Ferry et al. trained a computer-based model to identify the patterns of facial abnormalities associated with different genetic disorders. The model compares data extracted from a photograph of the patient’s face with data on the facial characteristics of 91 disorders, and then provides a list of the most likely diagnoses for that individual. The model used 36 points to describe the space, including 7 for the jaw, 6 for the mouth, 7 for the nose, 8 for the eyes and 8 for the brow.
This approach of Ferry et al. has three advantages. First, it provides clinicians with information that can aid their diagnosis of a rare genetic disorder. Second, it can narrow down the range of possible disorders for patients who have the same ultra-rare disorder, even if that disorder is currently unknown. Third, it can identify groups of patients who can have their genomes sequenced in order to identify the genetic variants that are associated with specific disorders.
from Quentin Ferry et al, eLife 2014;3:e02020
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This App Uses Facial Recognition Software to Help Identify Genetic Conditions
A geneticist uploads a photo of a patient’s face, and Face2Gene gathers data and generates a list of possible syndromes
… Face2Gene, the tool Abdul-Rahman used, was created by the Boston startup, FDNA. The company uses facial recognition software to aid clinical diagnoses of thousands of genetic conditions, such as Sotos syndrome (cerebral gigantism), Kabuki syndrome (a complicated disorder that features developmental delay, intellectual disability and more) and Down syndrome.
This App Uses Facial Recognition Software to Help Identify Genetic Conditions, Smithsonian Magazine
Related resources
How phenotypes lead to genotypes (infographic?)
Scientific journal articles
Detecting Genetic Association of Common Human Facial Morphological Variation Using High Density 3D Image Registration, Shouneng Peng et al, PLoS Comput Biol. 2013 Dec; 9(12)
Uses of imaginary numbers
What are imaginary numbers?

Elsewhere in math class you have learned about the definition and use imaginary numbers.
This resource is specifically about the usefulness and meaning of imaginary numbers.
It assumes that you already know what imaginary numbers are and how to use them.
But sure, since you, here’s a good refresher – Ask Dr. Math: What is an imaginary number? What is i?
And here’s another explanation: Better Explained: A Visual, Intuitive Guide to Imaginary Numbers

Are they “real” in some sense?
In what sense are imaginary numbers just as real as “real” numbers? People used to say the same thing about fractions! People argued that either something is a number or it isn’t – how can one possibly have part of a number?
Later, people said the same thing about irrational numbers.
And for quite a long time, people said the same thing about the number 0 – people argued that there couldn’t possible be a number without value.
Yet today everyone agrees that fractions, irrational numbers, and zero are all “real.”
How it possible that people didn’t “believe in” those numbers before, but they do now? Because we introduce people to these numbers and show how they all work together in a well-defined, useful system (“mathematics”.)
So the same could be true for imaginary numbers – what if we showed people how imaginary numbers filled in a gap in our math system?
Consider this function 𝑓(𝑥) = 𝑥2 + 1
Here is this function’s plot in the real x-y plane:

Now according to the Fundamental Theorem of Algebra we should have n-roots for n-th degree polynomial. Yet when we consider the graph for this function it doesn’t appear to intersect the x-axis right.
Well, the thing is, we are not seeing it correctly and have not included a fundamental set of numbers : Complex Numbers which have both real and imaginary part but don’t get confused yet as both the parts are quite real.
The below GIF plots the the function in the complex plane The vertical axis that comes out of the paper is the imaginary axis, NOT the Z-axis.
The explanation in the paragraphs above comes from math.stackexchange

from “Imaginary Numbers are Real,” Welch labs
How can one show that imaginary numbers exist? In the same way that people showed that fractions exist. Exactly the same argument shows that imaginary numbers exist:
How can one show that imaginary numbers really exist?
Here’s a great video showing how imaginary numbers can be thought of as just as real as other numbers:
Imaginary numbers are not some wild invention, they are a natural part of our number system.
How are imaginary numbers used?
I. Alternating current circuits
“The handling of the impedance of an AC circuit with multiple components quickly becomes unmanageable if sines and cosines are used to represent the voltages and currents.”
“A mathematical construct which eases the difficulty is the use of complex exponential functions. “
.
II. Engineering – damped oscillators
Many objects have simple harmonic motion, aka oscillation. Objects move back and forth, and the “pull back” force is related to how far the object is pulled from the center.
This motion doesn’t last forever. Due to friction, the motion slowly dampens, or dies away, over time. This is called damped oscillation.
There are mechanical vibrations in any structures, such as bridges, overpasses, tunnel walls, and floors of shopping malls and buildings.
Here’s a practical example of a problem that requires imaginary numbers in math to produce an engineering solution:
“An existing mid-rise office building included a gymnasium on the second floor. Floors above the gym level were occupied as offices by different tenants. Vibration complaints were reported by the tenants on the fourth floor at two different locations.
In essence, vibrations generated at second floor were traveling up through the columns and producing unacceptable vibrations at the fourth floor. The task was to verify the reported vibration complaints analytically, and then propose vibration mitigation measures.”

Vertical vibration transmission from a gym, Floor Vibration Expert, Boston, MA
Here is an (exaggerated) analysis of how oscillation in bridge structures.

The same is true for studying a plucked violin or guitar string,

And of course the same kind of analysis is used for studying damped oscillations in car shock absorbers, pendulums, bungee jumping, etc.
The engineering of any of these involves equations that use imaginary numbers.
See Real World Example: Oscillating Springs (Math Warehouse)
III. Useful in some parts of Economics

Image from St. Lawrence University, Mathematics-Economics Combined Major
“Complex numbers and complex analysis do show up in Economic research. For example, many models imply some difference-equation in state variables such as capital, and solving these for stationary states can require complex analysis.”
and
“The application of complex numbers had been attempted in the past by various economists, especially for explaining economic dynamics and business fluctuations in economic system
In fact, the cue was taken from electrical systems. Oscillations in economic activity level gets represented by sinusoidal curves The concept of Keynesian multiplier and the concept of accelerator were combined in models to trace the path of economic variables like income, employment etc over time. This is where complex numbers come in.”
{This explanation by sensekonomikx, Yahoo Answers, Complex numbers in Economics}
Why use imaginary math for real numbers?
Electrical engineers and economists study real world objects and get real world answers, yet they use complex functions with imaginary numbers. Couldn’t we just use “regular” math?

Image from Imaginary Numbers Are Real, Welch Labs
Answer:
Imaginary numbers transform complex equations in the real X-Y axis into simpler functions in the “imaginary” plane.
This lets us transform complicated problems into simpler ones.
Here is an explanation from “Ask Dr. Math” (National Council of Teachers of Mathematics.)


Also
We sometimes just use imaginary numbers because they can be easier to use: Engineers and physicists use the complex exponential 𝑒𝑗𝜔𝑡 instead of sines and cosines.
Why? This notation makes differential equations much easier to deal with.
That’s why we use imaginary numbers when studying electrical impedance.
Why is impedance represented as a complex number rather than a vector?
Other examples of real world uses
http://hyperphysics.phy-astr.gsu.edu/hbase/electric/impcom.html
Careers That Use Complex Numbers, by Stephanie Dube Dwilson
Imaginary numbers in real life: Ask Dr. Math
Imaginary numbers, Myron Berg, Dickinson State Univ.
The universe physically seems to run on complex numbers
If we look only at things in our everyday life – objects with masses larger than atoms, and moving at speeds far lower than the speed of light – then we can pretend that the entire word is made of solid objects (particles) following more or less “common sense” rules – the classical laws of physics.
But there’s so much more to our universe – and when we look carefully, we find that literally all of our classical laws of physics are only approximations of a more general, and often bizarre law – the laws of quantum mechanics. And QM laws follow a math that uses complex numbers!
When you have time, look at our intro to the development of QM and at deeper, high school level look at what QM really is .
Scott Aaronson writes about a central, hard to believe feature of quantum mechanics:
“Nature is described not by probabilities (which are always nonnegative), but by numbers called amplitudes that can be positive, negative, or even complex.”
He points out that this weird reality seems to be a basic feature of the universe itself
“This transformation is just a mirror reversal of the plane. That is, it takes a two-dimensional Flatland creature and flips it over like a pancake, sending its heart to the other side of its two-dimensional body.
But how do you apply half of a mirror reversal without leaving the plane? You can’t! If you want to flip a pancake by a continuous motion, then you need to go into … dum dum dum … THE THIRD DIMENSION.
More generally, if you want to flip over an N-dimensional object by a continuous motion, then you need to go into the (N+1)st dimension.
But what if you want every linear transformation to have a square root in the same number of dimensions? Well, in that case, you have to allow complex numbers. So that’s one reason God might have made the choice She did.”
– PHYS771 Quantum Computing Since Democritus, Lecture 9: Quantum. Aaronson is Professor of Computer Science at The University of Texas at Austin.
Imaginary Numbers May Be Essential for Describing Reality
A new thought experiment indicates that quantum mechanics doesn’t work without strange numbers that turn negative when squared.
Charlie Wood, Quanta Magazine , 3/3/2021
A group of quantum theorists designed an experiment whose outcome depends on whether nature has an imaginary side. Provided that quantum mechanics is correct — an assumption few would quibble with — the team’s argument essentially guarantees that complex numbers are an unavoidable part of our description of the physical universe.
“These complex numbers, usually they’re just a convenient tool, but here it turns out that they really have some physical meaning,” said Tamás Vértesi, a physicist at the Institute for Nuclear Research at the Hungarian Academy of Sciences who, years ago, argued the opposite. “The world is such that it really requires these complex” numbers, he said.
Read Imaginary numbers could be needed to describe reality, new studies find, Ben Turner, Live Science, 12/21/2021
Quantum theory based on real numbers can be experimentally falsified, Marc-Olivier Renou et al. Nature volume 600, pages625–629 (2021)
Testing real quantum theory in an optical quantum network, Phys. Rev. Lett. Zheng-Da Li, et al.
Are negative probabilities real?
In 1942, Paul Dirac wrote a paper “The Physical Interpretation of Quantum Mechanics” where he introduced the concept of negative energies and negative probabilities:
“Negative energies and probabilities should not be considered as nonsense. They are well-defined concepts mathematically, like a negative of money.”
The idea of negative probabilities later received increased attention in physics and particularly in quantum mechanics. Richard Feynman argue that no one objects to using negative numbers in calculations: although “minus three apples” is not a valid concept in real life, negative money is valid.
Similarly he argued how negative probabilities as well as probabilities above unity possibly could be useful in probability calculations.
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Wikipedia, Negative Probabilities, 3/18
John Baez ( mathematical physicist at U. C. Riverside in California) writes
The physicists Dirac and Feynman, both bold when it came to new mathematical ideas, both said we should think about negative probabilities. What would it mean to say something had a negative chance of happening?
I haven’t seen many attempts to make sense of this idea… or even work with this idea. Sometimes in math it’s good to temporarily put aside making sense of ideas and just see if you can develop rules to consistently work with them. For example: the square root of -1. People had to get good at using it before they understood what it really was: a rotation by a quarter turn in the plane. Here’s an interesting attempt to work with negative probabilities:
• Gábor J. Székely, Half of a coin: negative probabilities, Wilmott Magazine (July 2005), p.66–68
He uses rigorous mathematics to study something that sounds absurd: half a coin. Suppose you make a bet with an ordinary fair coin, where you get 1 dollar if it comes up heads and 0 dollars if it comes up tails. Next, suppose you want this bet to be the same as making two bets involving two separate ‘half coins’. Then you can do it if a half coin has infinitely many sides numbered 0,1,2,3, etc., and you win n dollars when side number n comes up….
… and if the probability of side n coming up obeys a special formula…
and if this probability can be negative whenever n is even!
This seems very bizarre, but the math is solid, even if the problem of interpreting it may drive you insane.
By the way, it’s worth remembering that for a long time mathematicians believed that negative numbers made no sense. As late as 1758 the British mathematician Francis Maseres claimed that negative numbers “… darken the very whole doctrines of the equations and make dark of the things which are in their nature excessively obvious and simple.”
So opinions on these things can change. By the way: experts on probability theory will like Székely’s use of ‘probability generating functions’. Experts on generating functions and combinatorics will like how the probabilities for the different sides of the half-coin coming up involve the Catalan numbers.
Learning standards
Massachusetts Mathematics Curriculum Framework 2017
Number and Quantity Content Standards: The Complex Number System
A. Perform arithmetic operations with complex numbers.
B. Represent complex numbers and their operations on the complex plane.
C. Use complex numbers in polynomial identities and equations.
Common Core Mathematics
High School: Number and Quantity » The Complex Number System
CCSS.MATH.CONTENT.HSN.CN.A.1
Know there is a complex number i such that i2 = -1, and every complex number has the form a + bi with a and b real.
CCSS.MATH.CONTENT.HSN.CN.A.2
Use the relation i2 = -1 and the commutative, associative, and distributive properties to add, subtract, and multiply complex numbers.
CCSS.MATH.CONTENT.HSN.CN.A.3
(+) Find the conjugate of a complex number; use conjugates to find moduli and quotients of complex numbers.
Resources
The Black Swan, Nassim Taleb
In his book The Black Swan, Nassim Taleb develops two ideas, Mediocristan and Extremistan, to help explain his Black Swan Theory.
Mediocristan is where normal things happen, things that are expected, whose probabilities of occurring are easy to compute, and whose impact is not terribly huge. The bell curve and the normal distribution are emblems of Mediocristan. Low-impact changes have the highest probabilities of occurring, and huge, wide-impact changes have a very small probability of occurring.
Bell curve describing Mediocristan
Examples: Nature is full of things that follow a normal distribution. Height of humans is a simple example. If you take a few hundred people, and take their average height, there is no human whose height would significantly disrupt the average if added to the sample. Height/weight of people, or life expectancy, are from Mediocristan.
Properties: In Mediocristan, nothing is scalable, everything is constrained by boundary conditions, time, the limits of biological variation, the limits of hourly compensation, etc. Because of such constraints and the limits of our knowledge, random variation of attributes exists in Mediocristan, and can be usefully described by Gaussian probability models. In such “orderly” randomness models, probability distributions are such that no single instantiation of the value of an attribute can greatly affect the sum of all values in the distribution. Even the most extreme attribute values do not materially affect the mean value of a distribution, because the more extreme any value is, the more improbable it is that the extreme value will actually occur in nature.
Exstremistan is a different beast. In Extremistan, nothing can be predicted accurately and events that seemed unlikely or impossible occur frequently and have a huge impact.
Examples: In Extremistan, a single new observation can completely disrupt the aggregate. Imagine a room full of 30 random people. If you asked everyone their salary and calculated the average, the odds are the average would seem pretty reasonable. However, if you added Bill Gates to the room and then calculated the average salary, your average would jump up by a huge margin. One observation had a disproportionate effect on the average. This is Exstremistan. Things like book sales, whether a movie becomes a hit, or a viral video on the internet all have similar characteristics, and therefore reside in Extremistan.
Properties: A winner takes all competitions. As in: a small number of individuals or companies win everything. More inequality and less social justice are inevitable. Actions by individuals and small groups generate increasingly extreme results. As in: “eventually, one man might be able to declare war on the world and win.” Systemic events, both negative and positive, will occur at a high frequency, faster and with more extreme outcomes than ever before.
[Taleb’s central critique of bell curves is that they are often applied to areas that are subject to the dynamics of Extremistan, even though it only accurately describes Mediocristan.]
Source
https://assaadmouawad.wordpress.com/2011/11/11/mediocristan-vs-extremistan/
Nassim Nicholas Taleb, author of the bestselling book, The Black Swan, divides the world into 2 countries: Mediocristan and Extremistan. Looks like these two countries have completely different laws governing them. What are these laws? And how are they different? Let’s look at these questions in this article.
Mediocristan: Let’s start with Nassim’s favorite thought experiment. Assume that you round up a thousand people randomly selected from the general population and have them stand next to each other in one stadium. Imagine the heaviest person you can think of and add him to the sample. Assuming he weighs three times the average, between 400 and 500 pounds, he will represent a very small fraction of the total weight of the entire population (in this case about half a percent). In Mediocristan, when your sample is large, no single instance will significantly change the aggregate or the total. So who all belong to Mediocristan? Things like height, weight, income of a baker or a prostitute, car accidents, mortality rates, IQ etc.
Strange country of Extremistan: Now, let’s turn to the same people whom we lined up in a stadium and add up their net worth. Add to them net worth of Bill Gates which according to wikipedia is $58 billion. Now ask the same question: How much of the total wealth would he represent? 99.9 percent? Indeed, all others would represent no more than a rounding error for this net worth. For someone’s weight to represent such a share, he would need to weigh fifty million pounds! Same thing can be observed about book sales of randomly selected authors and adding J. K. Rowling to the list . In Extremistan, inequalities are such that one single observation can disproportionately impact aggregate, or the total. Nassim calls such events/things black swans. Matters that belong to Extremistan are: wealth, book sales per author, name recognition as a “celebrity”, speakers of a language, damage caused by earthquake, deaths in war, sizes of companies, financial markets etc.
How does this help? Nassim observes that the law of averages or the bell-curve statistics works well in Mediocristan. When friends from Mars will visit earth, they can check a small sample of people and learn a lot about people from Mediocristan. However, if you try to apply bell-curve to Extremistan it can get you in trouble. Let’s say you want to cross a river during your wildlife trek and you ask the local villager, “How deep is the river?” Villager says, “On an average 4 feet”. Now, in Extremistan, you don’t know whether it is: 4 feet +/- 1 foot or 4 feet and in one or two places 50 feet deep. Thanks to Satyam scam and the money I lost in a single day, I didn’t take time to understand what a black swan means. Next time you apply bell curve statistics to your decision (such as stock purchase), ask whether you are applying the right law in the right land.
Source
http://www.catalign.in/2009/01/black-swan-and-laws-of-mediocristan-vs.html
In his remarkable book, “The Black Swan”, Taleb describes at length the characteristics of environments that can be subject to black swans (unforeseeable, high-impact events).
When we make a forecast, we usually explicitly or implicitly base it on an assumption of continuity in a statistical series. For example, a company building its sales forecast for next year considers past sales, estimates a trend based on these sales, makes some adjustments based on current circumstances and then generates a sales forecast. The hypothesis (or rather assumption, as it is rarely explicit) in this process is that each additional year is not fundamentally different from the previous years. In other words, the distribution of possible values for next year’s sales is Gaussian (or “normal”): the probability that sales are the same is very high; the probability of an extreme variation (doubling or dropping to zero) is very low. In fact, the higher the envisaged variation, the lower the probability that such variation will occur. As a result, it is reasonable to discard extreme values in the forecasts: no marketing director is working on an assumption of sales dropping to zero.
Now, the assumption that a Gaussian-shaped curve’s fit with a potential distribution of outcomes will be the best fit is just that: an assumption. It is based simply on observation of the past. Never before have our sales dropped by 20%, 50% let alone 100%. 10, 20 or 30 years of data can confirm this (observation of the past on a large number of data). But this is only an observation of the past, not a law of physics.
Now, if we reason theoretically, not historically, on sales trends, we must recognize that there are many situations in which sales can vary widely. A sudden boycott of our products, for example (Danish dairy products in the Middle East after the Muhammad cartoons), a tidal wave in Japan, which deprives us of an essential supplier, a technological breakthrough that makes our products obsolete (NCR in 1971), the collapse of the Euro, etc. Suddenly deprived of oxygen, our sales are collapsing.
This is the black swan. The reason is simple: sales, like many statistical series, do not follow a Gaussian distribution. The probability of a large variation may be relatively low, but the reality is that in fact it cannot be calculated, because the distribution is unknown and cannot be estimated (this is what economist Frank Knight calls true uncertainty). We can thus be in a year in which the extreme value radically changes the historical distribution. We are in the domain of “fat tails”, ie unlike normally distributed series, high values can have a high probability of occurring. …
source https://silberzahnjones.com/2011/11/10/welcome-to-extremistan/
A black swan is an unpredictable, rare, but nevertheless high-impact event. The concept is easily demonstrated and well known but naming these events as “black swans” was popularised by Nassim Nicholas Taleb in his book of the same name, which was described in The Sunday Times as one of the 12 most influential books since the Second World War.
http://rationalwiki.org/wiki/Black_swan
Emmy Noether
Amalie Emmy Noether (1882 – 1935) was a German mathematician known for her landmark contributions to abstract algebra and theoretical physics. She was described by Pavel Alexandrov, Albert Einstein, Hermann Weyl, and Norbert Wiener as the most important woman in the history of mathematics. In physics, Noether’s theorem explains the connection between symmetry and conservation laws.

Our related articles
https://kaiserscience.wordpress.com/physics/mathematics/symmetry/
External articles
In her short life, mathematician Emmy Noether changed the face of physics. ScienceNews.Org
http://www.thephysicsmill.com/2014/03/09/international-womens-day-spotlight-emmy-noether/
Scientific calculators < $10
This might seem amazing, but today one can purchase scientific calculators for less than $10.
http://budgetlightforum.com/node/34682

Learning Standards
MASSACHUSETTS CURRICULUM FRAMEWORK FOR MATHEMATICS
CONCEPTUAL CATEGORY: Number and Quantity
Calculators, spreadsheets, and computer algebra systems can provide ways for students to become better acquainted with these new number systems and their notation. They can be used to generate data for numerical experiments, to help understand the workings of matrix, vector, and complex number algebra, and to experiment with non-integer exponents.
Guiding Principle 3: Technology Technology is an essential tool that should be used strategically in mathematics education. Technology enhances the mathematics curriculum in many ways. Tools such as measuring instruments, manipulatives (such as base ten blocks and fraction pieces), scientific and graphing calculators, and computers with appropriate software, if properly used, contribute to a rich learning environment for developing and applying mathematical concepts. However, appropriate use of calculators is essential; calculators should not be used as a replacement for basic understanding and skills. Elementary students should learn how to perform the basic arithmetic operations independent of the use of a calculator.4 Although the use of a graphing calculator can help middle and secondary students to visualize properties of functions and their graphs, graphing calculators should be used to enhance their understanding and skills rather than replace them. Teachers and students should consider the available tools when presenting or solving a problem. Students should be familiar with tools appropriate for their grade level to be able to make sound decisions about which of these tools would be helpful.
Use appropriate tools strategically. Mathematically proficient students consider the available tools when solving a mathematical problem. These tools might include pencil and paper, concrete models, a ruler, a protractor, a calculator, a spreadsheet, a computer algebra system, a statistical package, or dynamic geometry software. Proficient students are sufficiently familiar with tools appropriate for their grade or course to make sound decisions about when each of these tools might be helpful, recognizing both the insight to be gained and their limitations. For example, mathematically proficient high school students analyze graphs of functions and solutions generated using a graphing calculator. They detect possible errors by strategically using estimation and other mathematical knowledge. When making mathematical models, they know that technology can enable them to visualize the results of varying assumptions, explore consequences, and compare predictions with data. Mathematically proficient students at various grade levels are able to identify relevant external mathematical resources, such as digital content located on a website, and use them to pose or solve problems. They are able to use technological tools to explore and deepen their understanding of concepts.
Data needs an interpretation to have meaning
Lesson: “Data has no meaning without a physical interpretation”
Content objectives:
1. SWBAT to identify trends in data (apparent linear plots; apparent linear data plus noise; and simple harmonic motion.)
Thesis: raw data doesn’t tells us anything physical phenomenon. We always first need to know what physical phenomenon we are analyzing, before we can interpret it.
Tier III vocabulary: Simple harmonic motion
Launch: Students are given graph paper, and data. Plot the given data points, and connect the dots in a way that they think is logical.
Question: Justify why you connected the dots in that way. Why not in some other way?
Direct Instruction/guided practice
Teacher instructions
Create a sine wave. I do so here using Desmos – desmos.com/calculator/xxmkiptej7
I modified this function to be Y = 4•sin(1.5x)
Don’t tell the students yet. This sine wave is a position versus time graph of any object in the real world undergoing simple harmonic motion.
Y-axis can be interpreted as height; X-axis is time.
Let’s get some data points from this function. Draw a straight line across it, from upper right to lower left.
The line will intersect the sine wave at many points.
Overlay some semi-transparent graph paper on top of this, and plot these points. Or, as I have done here, do it on an app. In this example we have seven data points.
Give the students the coordinates for these points but do not show them the graph! Just give them the data. Ask them to interpret it, plot it, and hypothesize about what the data could mean.
Tag six more points from the sine wave, that are not on the original straight line.
Here I chose some data points that we could sample from actual motion, if we happened to be sampling at just the right time interval.
Again, give students these coordinates without showing them the graph. Ask them to interpret it, plot it, and hypothesize about what the data could mean.
If one were to plot only these points then they would appear as a straight line.
A naïve reading of the raw data would lead one (mistakenly) to believe that we are studying some kind of linear phenomenon.
If one were to plot only these points then they would appear as a straight line.
A naïve reading of the raw data would lead one (mistakenly) to believe that we are studying some kind of linear phenomenon.
Very few students will quickly see that these points fit a sine curve. They will have all sorts of answers
When we are done with all of these examples, then we can show them the original sine curve; show them each of these graphs, and how all the different data came from the same data set/phenomenon.
Part A: Justify your choice: What real world motion would produce such a function? Think-Pair-Share
After the discussion, the teacher reveals what produces such data: SHM, Simple Harmonic Motion:
Summative question, tying this all together:
Why couldn’t most students plot the data correctly, even after the final data points were added?
Answer: Unless you know what kind of phenomenon you are studying, you have no idea whether the data is supposed to be linear, harmonic, exponential, etc. Data – by itself – has no meaning without a physical interpretation.
Closure: Query multiple students: Where do you experience SHM in your own life?
Possible answers: Moving back-and-forth on a swing, pendulum of a clock, automobile suspension system
Something more to think about:

Image from https://m.xkcd.com/2048/
Learning standards
A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas (2012)
Dimension 1: Scientific and Engineering Practices: Practice 4: Analyzing and Interpreting Data.
“Once collected, data must be presented in a form that can reveal any patterns and relationships and that allows results to be communicated to others. Because raw data as such have little meaning, a major practice of scientists is to organize and interpret data through tabulating, graphing, or statistical analysis. Such analysis can bring out the meaning of data—and their relevance—so that they may be used as evidence.”
The Quantum Thermodynamics Revolution
As physicists extend the 19th-century laws of thermodynamics to the quantum realm, they’re rewriting the relationships among energy, entropy and information.
Natalie Wolchover, Senior Writer, Quanta Magazine, May 2, 2017
https://www.quantamagazine.org/quantum-thermodynamics-revolution/
In his 1824 book, Reflections on the Motive Power of Fire, the 28-year-old French engineer Sadi Carnot worked out a formula for how efficiently steam engines can convert heat — now known to be a random, diffuse kind of energy — into work, an orderly kind of energy that might push a piston or turn a wheel. To Carnot’s surprise, he discovered that a perfect engine’s efficiency depends only on the difference in temperature between the engine’s heat source (typically a fire) and its heat sink (typically the outside air). Work is a byproduct, Carnot realized, of heat naturally passing to a colder body from a warmer one.
Carnot died of cholera eight years later, before he could see his efficiency formula develop over the 19th century into the theory of thermodynamics: a set of universal laws dictating the interplay among temperature, heat, work, energy and entropy — a measure of energy’s incessant spreading from more- to less-energetic bodies. The laws of thermodynamics apply not only to steam engines but also to everything else: the sun, black holes, living beings and the entire universe. The theory is so simple and general that Albert Einstein deemed it likely to “never be overthrown.”
Yet since the beginning, thermodynamics has held a singularly strange status among the theories of nature.
“If physical theories were people, thermodynamics would be the village witch,” the physicist Lídia del Rio and co-authors wrote last year in Journal of Physics A. “The other theories find her somewhat odd, somehow different in nature from the rest, yet everyone comes to her for advice, and no one dares to contradict her.”
Unlike, say, the Standard Model of particle physics, which tries to get at what exists, the laws of thermodynamics only say what can and can’t be done. But one of the strangest things about the theory is that these rules seem subjective. A gas made of particles that in aggregate all appear to be the same temperature — and therefore unable to do work — might, upon closer inspection, have microscopic temperature differences that could be exploited after all. As the 19th-century physicist James Clerk Maxwell put it, “The idea of dissipation of energy depends on the extent of our knowledge.”

In recent years, a revolutionary understanding of thermodynamics has emerged that explains this subjectivity using quantum information theory — “a toddler among physical theories,” as del Rio and co-authors put it, that describes the spread of information through quantum systems. Just as thermodynamics initially grew out of trying to improve steam engines, today’s thermodynamicists are mulling over the workings of quantum machines. Shrinking technology — a single-ion engine and three-atom fridge were both experimentally realized for the first time within the past year — is forcing them to extend thermodynamics to the quantum realm, where notions like temperature and work lose their usual meanings, and the classical laws don’t necessarily apply.
They’ve found new, quantum versions of the laws that scale up to the originals. Rewriting the theory from the bottom up has led experts to recast its basic concepts in terms of its subjective nature, and to unravel the deep and often surprising relationship between energy and information — the abstract 1s and 0s by which physical states are distinguished and knowledge is measured. “Quantum thermodynamics” is a field in the making, marked by a typical mix of exuberance and confusion.
“We are entering a brave new world of thermodynamics,” said Sandu Popescu, a physicist at the University of Bristol who is one of the leaders of the research effort. “Although it was very good as it started,” he said, referring to classical thermodynamics, “by now we are looking at it in a completely new way.”
Entropy as Uncertainty
In an 1867 letter to his fellow Scotsman Peter Tait, Maxwell described his now-famous paradox hinting at the connection between thermodynamics and information. The paradox concerned the second law of thermodynamics — the rule that entropy always increases — which Sir Arthur Eddington would later say “holds the supreme position among the laws of nature.” According to the second law, energy becomes ever more disordered and less useful as it spreads to colder bodies from hotter ones and differences in temperature diminish. (Recall Carnot’s discovery that you need a hot body and a cold body to do work.) Fires die out, cups of coffee cool and the universe rushes toward a state of uniform temperature known as “heat death,” after which no more work can be done.
The great Austrian physicist Ludwig Boltzmann showed that energy disperses, and entropy increases, as a simple matter of statistics: There are many more ways for energy to be spread among the particles in a system than concentrated in a few, so as particles move around and interact, they naturally tend toward states in which their energy is increasingly shared.
But Maxwell’s letter described a thought experiment in which an enlightened being — later called Maxwell’s demon — uses its knowledge to lower entropy and violate the second law. The demon knows the positions and velocities of every molecule in a container of gas. By partitioning the container and opening and closing a small door between the two chambers, the demon lets only fast-moving molecules enter one side, while allowing only slow molecules to go the other way. The demon’s actions divide the gas into hot and cold, concentrating its energy and lowering its overall entropy. The once useless gas can now be put to work.
Maxwell and others wondered how a law of nature could depend on one’s knowledge — or ignorance — of the positions and velocities of molecules. If the second law of thermodynamics depends subjectively on one’s information, in what sense is it true?

A century later, the American physicist Charles Bennett, building on work by Leo Szilard and Rolf Landauer, resolved the paradox by formally linking thermodynamics to the young science of information. Bennett argued that the demon’s knowledge is stored in its memory, and memory has to be cleaned, which takes work. (In 1961, Landauer calculated that at room temperature, it takes at least 2.9 zeptojoules of energy for a computer to erase one bit of stored information.) In other words, as the demon organizes the gas into hot and cold and lowers the gas’s entropy, its brain burns energy and generates more than enough entropy to compensate. The overall entropy of the gas-demon system increases, satisfying the second law of thermodynamics.
The findings revealed that, as Landauer put it, “Information is physical.” The more information you have, the more work you can extract. Maxwell’s demon can wring work out of a single-temperature gas because it has far more information than the average user.
But it took another half century and the rise of quantum information theory, a field born in pursuit of the quantum computer, for physicists to fully explore the startling implications.
Over the past decade, Popescu and his Bristol colleagues, along with other groups, have argued that energy spreads to cold objects from hot ones because of the way information spreads between particles. According to quantum theory, the physical properties of particles are probabilistic; instead of being representable as 1 or 0, they can have some probability of being 1 and some probability of being 0 at the same time. When particles interact, they can also become entangled, joining together the probability distributions that describe both of their states. A central pillar of quantum theory is that the information — the probabilistic 1s and 0s representing particles’ states — is never lost. (The present state of the universe preserves all information about the past.)
Over time, however, as particles interact and become increasingly entangled, information about their individual states spreads and becomes shuffled and shared among more and more particles. Popescu and his colleagues believe that the arrow of increasing quantum entanglement underlies the expected rise in entropy — the thermodynamic arrow of time. A cup of coffee cools to room temperature, they explain, because as coffee molecules collide with air molecules, the information that encodes their energy leaks out and is shared by the surrounding air.
Understanding entropy as a subjective measure allows the universe as a whole to evolve without ever losing information. Even as parts of the universe, such as coffee, engines and people, experience rising entropy as their quantum information dilutes, the global entropy of the universe stays forever zero.
Renato Renner, a professor at ETH Zurich in Switzerland, described this as a radical shift in perspective. Fifteen years ago, “we thought of entropy as a property of a thermodynamic system,” he said. “Now in information theory, we wouldn’t say entropy is a property of a system, but a property of an observer who describes a system.”
Moreover, the idea that energy has two forms, useless heat and useful work, “made sense for steam engines,” Renner said. “In the new way, there is a whole spectrum in between — energy about which we have partial information.”
Entropy and thermodynamics are “much less of a mystery in this new view,” he said. “That’s why people like the new view better than the old one.”
Thermodynamics From Symmetry
The relationship among information, energy and other “conserved quantities,” which can change hands but never be destroyed, took a new turn in two papers published simultaneously last July in Nature Communications, one by the Bristol team and another by a team that included Jonathan Oppenheim at University College London. Both groups conceived of a hypothetical quantum system that uses information as a sort of currency for trading between the other, more material resources.
Imagine a vast container, or reservoir, of particles that possess both energy and angular momentum (they’re both moving around and spinning). This reservoir is connected to both a weight, which takes energy to lift, and a turning turntable, which takes angular momentum to speed up or slow down. Normally, a single reservoir can’t do any work — this goes back to Carnot’s discovery about the need for hot and cold reservoirs. But the researchers found that a reservoir containing multiple conserved quantities follows different rules. “If you have two different physical quantities that are conserved, like energy and angular momentum,” Popescu said, “as long as you have a bath that contains both of them, then you can trade one for another.”
In the hypothetical weight-reservoir-turntable system, the weight can be lifted as the turntable slows down, or, conversely, lowering the weight causes the turntable to spin faster. The researchers found that the quantum information describing the particles’ energy and spin states can act as a kind of currency that enables trading between the reservoir’s energy and angular momentum supplies. The notion that conserved quantities can be traded for one another in quantum systems is brand new. It may suggest the need for a more complete thermodynamic theory that would describe not only the flow of energy, but also the interplay between all the conserved quantities in the universe.
The fact that energy has dominated the thermodynamics story up to now might be circumstantial rather than profound, Oppenheim said. Carnot and his successors might have developed a thermodynamic theory governing the flow of, say, angular momentum to go with their engine theory, if only there had been a need. “We have energy sources all around us that we want to extract and use,” Oppenheim said. “It happens to be the case that we don’t have big angular momentum heat baths around us. We don’t come across huge gyroscopes.”

Popescu, who won a Dirac Medal last year for his insights in quantum information theory and quantum foundations, said he and his collaborators work by “pushing quantum mechanics into a corner,” gathering at a blackboard and reasoning their way to a new insight after which it’s easy to derive the associated equations. Some realizations are in the process of crystalizing. In one of several phone conversations in March, Popescu discussed a new thought experiment that illustrates a distinction between information and other conserved quantities — and indicates how symmetries in nature might set them apart.
“Suppose that you and I are living on different planets in remote galaxies,” he said, and suppose that he, Popescu, wants to communicate where you should look to find his planet. The only problem is, this is physically impossible: “I can send you the story of Hamlet. But I cannot indicate for you a direction.”
There’s no way to express in a string of pure, directionless 1s and 0s which way to look to find each other’s galaxies because “nature doesn’t provide us with [a reference frame] that is universal,” Popescu said. If it did — if, for instance, tiny arrows were sewn everywhere in the fabric of the universe, indicating its direction of motion — this would violate “rotational invariance,” a symmetry of the universe. Turntables would start turning faster when aligned with the universe’s motion, and angular momentum would not appear to be conserved. The early-20th-century mathematician Emmy Noether showed that every symmetry comes with a conservation law: The rotational symmetry of the universe reflects the preservation of a quantity we call angular momentum. Popescu’s thought experiment suggests that the impossibility of expressing spatial direction with information “may be related to the conservation law,” he said.
The seeming inability to express everything about the universe in terms of information could be relevant to the search for a more fundamental description of nature. In recent years, many theorists have come to believe that space-time, the bendy fabric of the universe, and the matter and energy within it might be a hologram that arises from a network of entangled quantum information. “One has to be careful,” Oppenheim said, “because information does behave differently than other physical properties, like space-time.”
Knowing the logical links between the concepts could also help physicists reason their way inside black holes, mysterious space-time swallowing objects that are known to have temperatures and entropies, and which somehow radiate information. “One of the most important aspects of the black hole is its thermodynamics,” Popescu said. “But the type of thermodynamics that they discuss in the black holes, because it’s such a complicated subject, is still more of a traditional type. We are developing a completely novel view on thermodynamics.” It’s “inevitable,” he said, “that these new tools that we are developing will then come back and be used in the black hole.”
What to Tell Technologists
Janet Anders, a quantum information scientist at the University of Exeter, takes a technology-driven approach to understanding quantum thermodynamics. “If we go further and further down [in scale], we’re going to hit a region that we don’t have a good theory for,” Anders said. “And the question is, what do we need to know about this region to tell technologists?”
In 2012, Anders conceived of and co-founded a European research network devoted to quantum thermodynamics that now has 300 members. With her colleagues in the network, she hopes to discover the rules governing the quantum transitions of quantum engines and fridges, which could someday drive or cool computers or be used in solar panels, bioengineering and other applications. Already, researchers are getting a better sense of what quantum engines might be capable of. In 2015, Raam Uzdin and colleagues at the Hebrew University of Jerusalem calculated that quantum engines can outpower classical engines. These probabilistic engines still follow Carnot’s efficiency formula in terms of how much work they can derive from energy passing between hot and cold bodies. But they’re sometimes able to extract the work much more quickly, giving them more power. An engine made of a single ion was experimentally demonstrated and reported in Science in April 2016, though it didn’t harness the power-enhancing quantum effect.
Popescu, Oppenheim, Renner and their cohorts are also pursuing more concrete discoveries. In March, Oppenheim and his former student, Lluis Masanes, published a paper deriving the third law of thermodynamics — a historically confusing statement about the impossibility of reaching absolute-zero temperature — using quantum information theory. They showed that the “cooling speed limit” preventing you from reaching absolute zero arises from the limit on how fast information can be pumped out of the particles in a finite-size object. The speed limit might be relevant to the cooling abilities of quantum fridges, like the one reported in a preprint in February. In 2015, Oppenheim and other collaborators showed that the second law of thermodynamics is replaced, on quantum scales, by a panoply of second “laws” — constraints on how the probability distributions defining the physical states of particles evolve, including in quantum engines.
As the field of quantum thermodynamics grows quickly, spawning a range of approaches and findings, some traditional thermodynamicists see a mess. Peter Hänggi, a vocal critic at the University of Augsburg in Germany, thinks the importance of information is being oversold by ex-practitioners of quantum computing, who he says mistake the universe for a giant quantum information processor instead of a physical thing. He accuses quantum information theorists of confusing different kinds of entropy — the thermodynamic and information-theoretic kinds — and using the latter in domains where it doesn’t apply. Maxwell’s demon “gets on my nerves,” Hänggi said. When asked about Oppenheim and company’s second “laws” of thermodynamics, he said, “You see why my blood pressure rises.”

While Hänggi is seen as too old-fashioned in his critique (quantum-information theorists do study the connections between thermodynamic and information-theoretic entropy), other thermodynamicists said he makes some valid points. For instance, when quantum information theorists conjure up abstract quantum machines and see if they can get work out of them, they sometimes sidestep the question of how, exactly, you extract work from a quantum system, given that measuring it destroys its simultaneous quantum probabilities. Anders and her collaborators have recently begun addressing this issue with new ideas about quantum work extraction and storage. But the theoretical literature is all over the place.
“Many exciting things have been thrown on the table, a bit in disorder; we need to put them in order,” said Valerio Scarani, a quantum information theorist and thermodynamicist at the National University of Singapore who was part of the team that reported the quantum fridge. “We need a bit of synthesis. We need to understand your idea fits there; mine fits here. We have eight definitions of work; maybe we should try to figure out which one is correct in which situation, not just come up with a ninth definition of work.”
Oppenheim and Popescu fully agree with Hänggi that there’s a risk of downplaying the universe’s physicality. “I’m wary of information theorists who believe everything is information,” Oppenheim said. “When the steam engine was being developed and thermodynamics was in full swing, there were people positing that the universe was just a big steam engine.” In reality, he said, “it’s much messier than that.” What he likes about quantum thermodynamics is that “you have these two fundamental quantities — energy and quantum information — and these two things meet together. That to me is what makes it such a beautiful theory.”
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Emergent phenomenon
Thomas T. Thomas writes:
From our perspective at the human scale, a tabletop is a flat plane.

but at the atomic level, the flat surface disappears into a lumpy swarm of molecules.
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Aficionados of fractal imagery will understand this perfectly: any natural feature like the slope of a hill or shore of a coast can be broken down into smaller and smaller curves and angles, endlessly subject to refinement. In fractal geometry, which is driven by simple equations, the large curves mirror the small curves ad infinitum.
The emergent property is not an illusion… The flatness of the tabletop is just as real—and more useful for setting out silverware and plates—than the churning atoms that actually compose it. The hill and its slope are just as real—and more useful for climbing—than the myriad tiny angles and curves, the surfaces of the grains of sand and bits of rock, that underlie the slope.
Emergent property works on greater scales, too. From space the Earth presents as a nearly perfect sphere, a blue-white marble decorated with flashes of green and brown, but still quite smooth. That spherical shape only becomes apparent from a great distance. Viewed from the surface, it’s easy enough for the eye to see a flat plane bounded by the horizon and to focus on hills and valleys as objects of great stature which, from a distance of millions of miles, do not even register as wrinkles.
Emergent properties come into play only when the action of thousands, millions, or billions of separate and distinct elements are perceived and treated as a single entity. “Forest” is an emergent property of thousands of individual trees. The concept of emergent properties can be extremely useful to describe some of the situations and events that we wrestle with daily.
The Human Condition: Emergent Properties, Thomas T. Thomas, 8/11/2013
also
NOVA ScienceNow Emergence, PBS
Examples
Conway’s game of life
https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life
http://emergentuniverse.wikia.com/wiki/Conway%27s_Game_of_Life
http://www.scholarpedia.org/article/Game_of_Life
http://www.conwaylife.com/
BOIDS: Birds flocking
Boids Background and Update by Craig Reynolds
http://www.red3d.com/cwr/behave.html
http://www.emergentmind.com/boids
Coding: 3 Simple Rules of Flocking Behaviors: Alignment, Cohesion, and Separation
https://en.wikipedia.org/wiki/Flocking_(behavior)
Classical physics
Classical physics is an emergent property of quantum mechanics
TBA
External links
Online Interactive Science Museum about Emergence
How Complex Wholes Emerge From Simple Parts Quanta magazine
Learning Standards
2016 Massachusetts Science and Technology/Engineering Curriculum Framework
Appendix VIII Value of Crosscutting Concepts and Nature of Science in Curricula
In grades 9–12, students can observe patterns in systems at different scales and cite patterns as empirical evidence for causality in supporting their explanations of phenomena. They recognize that classifications or explanations used at one scale may not be useful or need revision using a different scale, thus requiring improved investigations and experiments. They use mathematical representations to identify certain patterns and analyze patterns of performance in order to re-engineer and improve a designed system.
Next Gen Science Standards HS-PS2 Motion and Stability
Crosscutting Concepts: Different patterns may be observed at each of the scales at which a system is studied and can provide evidence for causality in explanations of phenomena. (HS-PS2-4)
A Framework for K-12 Science Education
Scale, proportion, and quantity. In considering phenomena, it is critical to recognize what is relevant at different measures of size, time, and energy and to recognize how changes in scale, proportion, or quantity affect a system’s structure or performance…. The understanding of relative magnitude is only a starting point. As noted in Benchmarks for Science Literacy, “The large idea is that the way in which things work may change with scale. Different aspects of nature change at different rates with changes in scale, and so the relationships among them change, too.” Appropriate understanding of scale relationships is critical as well to engineering—no structure could be conceived, much less constructed, without the engineer’s precise sense of scale.
Dimension 2, Crosscutting Concepts, A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas (2012)
http://necsi.edu/guide/concepts/emergence.html
Boolean logic
How to program in Scratch, using Boolean logic
- Boolean operators include:
- AND, OR, NOT, < , = , >
- In other words, is one sprite touching some other thing? The answer by definition must be true or false.
- In other words, is one sprite touching something of a certain color? The answer by definition must be true or false.
- In other words, is a certain key being pressed?
- In other words, is the mouse being used?
- Why use Boolean operators?
- To focus a search, particularly when your topic contains multiple search terms.
- To connect various pieces of information to find exactly what you’re looking for.
- https://sites.google.com/a/onalaskaschools.com/tech/boolean-search-tools
- .
- https://ircutp.wordpress.com/utp-irc-faqs/boolean-operators/
A Boolean block is a hexagonal block (shaped after the Boolean elements in flowcharts)
The block contains a condition. The answer to the condition will be either true or false.
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It’s important to determine if a statement (expression) is “true” or “false”.
Ways to determine TRUE and FALSE are prevalent in all kinds of decision making.
A mathematically precise way of asking if something is TRUE or FALSE is called a Boolean operation.
It is named after George Boole, who first defined an algebraic system of logic in the mid 19th century.
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Boolean data is associated with conditional statements. For example, the following statement is really
a set of questions that can be answered as TRUE or FALSE.
IF (I want to go to a movie) AND (I have more than $10) THEN (I can go to the movie)
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We can combine several “boolean” statements that have true/false meaning into a single statement
using words like AND and OR, and NOT).
“If I want to go to the movie AND I have enough money, then I will go to the movie.”
BOTH conditions have to evaluate to true (have to be true) before the entire expression is true.
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Some terms you already learned in math are really Boolean operators
Less than < [ ] < [ ] > Equal to < [ ] = [ ] > Greater than < [ ] < [ ] >
For example: (The height of a building) < 20 meters
For any building we look at, this statement will either be true or false.
Go through what each Boolean block does (page 68)
External links
Books
Book “Adventures in Coding”, Eva Holland and Chris Minnick, Wiley, 2016. Pages 50-59
Learning Standards
Massachusetts
Computational Thinking 6-8.CT.c.2 Describe how computers store, manipulate, and transfer data types and files (e.g., integers, real numbers, Boolean Operators) in a binary system.
CSTA K-12 Computer Science Standards
CT.L2-14 Examine connections between elements of mathematics and computer science
including binary numbers, logic, sets and functions.
CPP.L2-05 Implement problem solutions using a programming language, including: looping behavior, conditional statements, logic, expressions, variables, and functions.








