Inside a red-bricked building on the north side of Washington DC, internist Shantanu Nundy rushes from one examining room to the next, trying to see all 30 patients on his schedule. Most days, five of them will need to follow up with some kind of specialist. And odds are they never will. Year-long waits, hundred-mile drives, and huge out of pocket costs mean 90 percent of America’s most needy citizens can’t follow through on a specialist referral from their primary care doc.

But Nundy’s patients are different. They have access to something most people don’t: a digital braintrust of more than 6,000 doctors, with expert insights neatly collected, curated, and delivered back to Nundy through an artificial intelligence platform. The online system, known as the Human Diagnosis Project, allows primary care doctors to plug into a collective medical superintelligence, helping them order tests or prescribe medications they’d otherwise have to outsource. Which means most of the time, Nundy’s patients wait days, not months, to get answers and get on with their lives.

In the not-too-distant future, that could be the standard of care for all 30 million people currently uninsured or on Medicaid. On Thursday, Human Dx announced a partnership with seven of the country’s top medical institutions to scale up the project, aiming to recruit 100,000 specialists—and their expert assessments—in the next five years. Their goal: close the specialty care gap for 3 million Americans by 2022.

In January, a single mom in her 30s came to see Nundy about pain and joint stiffness in her hands. It had gotten so bad that she had to stop working as a housekeeper, and she was growing desperate. When Nundy pulled up her chart, he realized she had seen another doctor at his clinic a few months prior who referred her to a specialist. But once the patient realized she’d have to pay a few hundred dollars out of pocket for the visit, she didn’t go. Instead, she tried get on a wait list at the public hospital, where she couldn’t navigate the paperwork—English wasn’t her first language.

Now, back where she started, Nundy examined the patient’s hands, which were angrily inflamed. He thought it was probably rheumatoid arthritis, but because the standard treatment can be pretty toxic, he was hesitant to prescribe drugs on his own. So he opened up the Human Dx portal and created a new case description: “35F with pain and joint stiffness in L/R hands x 6 months, suspected AR.” Then he uploaded a picture of her hands and sent out the query.

Within a few hours a few rheumatologists had weighed in, and by the next day they’d confirmed his diagnosis. They’d even suggested a few follow-up tests just to be sure and advice about a course of treatment. “I wouldn’t have had the expertise or confidence to be able to do that on my own,” he says.

Nundy joined Human Dx in 2015, after founder Jayanth Komarneni recruited him to pilot the platform’s core technologies. But the goal was always to go big. Komarneni likens the network to Wikipedia and Linux, but instead of contributors donating encyclopedia entries or code, they donate medical expertise. When a primary care doc gets a perplexing patient, they describe their background, medical history, and presenting symptoms—maybe adding an image of an X-ray, a photo of a rash, or an audio recording of lung sounds. Human Dx’s natural language processing algorithms will mine each case entry for keywords to funnel it to specialists who can create a list of likely diagnoses and recommend treatment.

Now, getting back 10 or 20 different doctors’ takes on a single patient is about as useful as having 20 friends respond individually via email to a potluck invitation. So Human Dx’s machine learning algorithms comb through all the responses to check them against all the project’s previously stored case reports. The network uses them to validate each specialist's finding, weight each one according to confidence level, and combine it with others into a single suggested diagnosis. And with every solved case, Human Dx gets a little bit smarter. “With other online tools if you help one patient you help one patient,” Komarneni says. “What’s different here is that the insights gained for one patient can help so many others. Instead of using AI to replace jobs or make things cheaper we’re using it to provide capacity where none exists.”

Komarneni estimates that those electronic consults can handle 35 to 40 percent of specialist visits, leaving more time for people who really need to get into the office. That’s based on other models implemented around the country at places such as San Francisco General Hospital, UCLA Health System, and Brigham and Women’s Hospital. SFGH’s eReferral system cut the average waiting time for an initial consult from 112 days to 49 within its first year.

That system, which is now the default for every SFGH specialty, relies on dedicated reviewers who get paid to respond to cases in a timely way. But Human Dx doesn’t have those financial incentives—its service is free. Today, though, by partnering with the American Board of Medical Specialities, Human Dx can now offer continuing education and improvement credits to satisfy at least some of the 200 hours doctors are required to complete every four years. And the American Medical Association, the nation’s largest physician group, has committed to getting its members to volunteer, as well as supporting program integrity by verifying physicians on the platform.

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It’s a big deal to have the AMA on board. Physicians have historically been wary of attempts to supplant or complement their jobs with AI-enabled tools. But it’s important to not mistake the organization’s participation in the alliance for a formal pro-artificial intelligence stance. The AMA doesn’t yet have an official AI policy, and it doesn’t endorse any specific companies, products, or technologies, including Human Dx’s proprietary algorithms. The medical AI field is still young, with plenty of potential for unintended consequences.

Like discrepancies in quality of care. Alice Chen, the chief medical officer for the San Francisco Health Network and co-director of SFGH’s Center for Innovation in Access and Quality, worries that something like Human Dx might create a two-tiered medical system, where some people get to actually see specialists and some people just get a computerized composite of specialist opinions. “This is the edge of medicine right now,” Chen says. “You just have to find the sweet spot where you can leverage expertise and experience beyond traditional channels and at the same time ensure quality care.”

Researchers at Johns Hopkins, Harvard, and UCSF have been assessing the platform for accuracy and recently submitted results for peer review. The next big hurdle is money. The project is currently one of eight organizations in contention for a $100 million John D. and Catherine T. MacArthur Foundation grant. If Human Dx wins, they’ll spend the money to roll out nationwide. The alliance isn’t contingent on the $100 million award, but it would certainly be a nice way to kickstart the process—especially with specialty visits accounting for more than half of all trips to the doctor’s office.

So it’s possible that the next time you go in for something that stumps your regular physician, instead of seeing a specialist across town, you’ll see five or 10 from around the country. All it takes is a few minutes over lunch or in an elevator to put on a Sherlock Holmes hat, hop into the cloud, and sleuth through your case.

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A young, fit US soldier is marching in a Middle Eastern desert, under a blazing summer sun. He’s wearing insulated clothing and lugging more than 100 pounds of gear, and thus sweating profusely as his body attempts to regulate the heat. But it’s 108 degrees out and humid, too much for him bear. The brain is one of the first organs affected by heat, so his judgment becomes impaired; he does not recognize the severity of his situation. Just as his organs begin to fail, he passes out. His internal temperature is in excess of 106 degrees when he dies.

An elderly woman with cardiovascular disease is sitting alone in her Chicago apartment on the second day of a massive heatwave. She has an air conditioner, but she’s on a fixed income and can’t afford to turn it on again—or maybe it broke and she can’t afford to fix it. Either way, she attempts to sleep through the heat again, and her core temperature rises. To cool off, her body’s response is to work the heart harder, pumping more blood to her skin. But the strain on her heart is too much; it triggers cardiac arrest, and she dies.

Such scenarios could surely happen today, if they haven’t already. But as the world warms due to climate change, they’ll become all too common in just a few decades—and that’s according to modest projections.

This is not meant to scare you quite like this month’s cover story in New York magazine, “The Uninhabitable Earth.” That story was both a sensation and quite literally sensational, attracting more than two million readers with its depiction of “where the planet is heading absent aggressive action.” In this future world, humans in many places won’t be able to adapt to rising temperatures. “In the jungles of Costa Rica, where humidity routinely tops 90 percent, simply moving around outside when it’s over 105 degrees Fahrenheit would be lethal. And the effect would be fast: Within a few hours, a human body would be cooked to death from both inside and out,” David Wallace-Wells writes. “[H]eat stress in New York City would exceed that of present-day Bahrain, one of the planet’s hottest spots, and the temperature in Bahrain ‘would induce hyperthermia in even sleeping humans.’”

These scenarios are supported by the science. “For heat waves, our options are now between bad or terrible,” Camilo Mora, a geography professor at University of Hawaii at Manoa, told CNN last month. Mora was the lead author of a recent study, published in the journal Nature, showing that deadly heat days are expected to increase across the world. Around 30 percent of the world’s population today is exposed to so-called “lethal heat” conditions for at least 20 days a year. If we don’t reduce fossil-fuel emissions, the percentage will skyrocket to 74 percent by the year 2100. Put another way, by the end of the century nearly three-quarters of the Earth’s population will face a high risk of dying from heat exposure for more than three weeks every year.

This is the worst-case scenario. Even the study’s best-case scenario—a drastic reduction in greenhouse gases across the world—shows that 48 percent of humanity will be exposed regularly to deadly heat by the year 2100. That’s because even small increases in temperature can have a devastating impact. A study published in Science Advances in June, for instance, found that an increase of less than one degree Fahrenheit in India between 1960 and 2009 increased the probability of mass heat-related deaths by nearly 150 percent.

And make no mistake: Temperatures are rising, in multiple ways. “We’ve got a new normal,” said Howard Frumkin, a professor at the School of Public Health at the University of Washington. “I think all of the studies of trends to date show that we’re having more extreme heat, and we’ve having higher average temperatures. Superimposed on that, we’re seeing more short-term periods of extreme heat. Those are two different trends, and they’re both moving in the wrong direction.” Based on those trends, the US Global Change Research Program predicts “an increase of thousands to tens of thousands of premature heat-related deaths in the summer … each year as a result of climate change by the end of the century.” And that’s along with the deaths we’ve already seen: In 2015, Scientific American noted that nine out of the ten deadliest heat waves ever have occurred since 2000; together, they’ve killed 128,885 people.

In other words, to understand how global warming wreaks havoc on the human body, we don’t need to be transported to some imagined dystopia. Extreme heat isn’t a doomsday scenario but an existing, deadly phenomenon—and it’s getting worse by the day. The question is whether we’ll act and adapt, thereby saving countless lives.

There are two ways a human body can fail from heat. One is a direct heat stroke. “Your ability to cool yourself down through sweating isn’t infinite,” said Georges Benjamin, executive director of the American Public Health Association. “At some point, your body begins to heat up just like any other object. You go through a variety of problems. You become dehydrated. Your skin dries out. Your various organs begin to shut down. Your kidneys, your liver, your brain. As gross as this may sound, you in effect, cook.” (So maybe Wallace-Wells wasn’t being hyperbolic after all.)

Heat death can also be happen due to a pre-existing condition, the fatal effects of which were triggered by high temperature. “Heat stress provokes huge amounts of cardiovascular strain,” said Matthew Cramer of the Institute of Exercise and Environmental Medicine. “For these people, it’s not necessarily that they’ve cooked, but the strain on their cardiovascular system has led to death.” This is much more common than death by heat stroke, but is harder to quantify since death certificates cite the explicit cause of death—“cardiac arrest,” for instance, rather than “heat-related cardiac arrest.”

In both scenarios, the body’s natural ability to cool itself off through sweating has either reached its capacity or has been compromised through illness, injury, or medication. There are many people who have reduced capacity for sweating, such as those who have suffered severe burns over large parts of their bodies. Cramer, who studies heat impacts on burned people, says 50,000 people suffer severe burn injuries per year in America, and the World Health Organization considers burns “a global public health problem,” with the majority of severe burn cases occurring in low- and middle-income countries.

Bodies that are battling illness or on medication may also struggle with heat regulation. Diuretics tend to dehydrate people; anticholinergics and antipsychotics reduce sweating and inhibit heat dissipation. An analysis of the 2003 heat wave in France that killed 15,000 people suggested that many of these deaths could have been avoided had people been made aware of the side effects of their drugs. As for illnesses, “Anything that impairs the respiratory or circulatory system will increase risk,” said Mike McGheehin, who spent 33 years as an environmental epidemiologist at the Centers for Disease Control and Prevention. “Obesity, diabetes, COPD, heart disease, and renal disease.” Kidney disease, mental illness, and multiple sclerosis. The list goes on and on.

This summer has presented many opportunities for bodies to break down from heat. Temperature records, some more than a century old, have been broken across California, Nevada, Utah, Idaho and Arizona. (Speaking of Arizona, it’s been so hot there that planes can’t fly.) And it’s not just America. Last month, Iran nearly set the world record for highest temperature ever recorded. The May heatwave that hit India and Pakistan set new world records as well, including what the New York Times called “potentially the hottest temperature ever recorded in Asia”: 129.2 degrees Fahrenheit. Worldwide, 2017 is widely expected to be the second-hottest year, after 2016, since we began keeping global average temperature records in 1880.

These trends have public health professionals concerned about how people are going to deal with the heat when it comes their way. “Clearly this is one of the most important problems we’re going to see from a public health perspective,” Benjamin said. “This is not a tomorrow problem. It’s a significant public health problem that we need to address today.”

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It’s a public health problem especially in cities, says Brian Stone, a professor at Georgia Tech’s City and Regional Planning Program. “Our fundamental work shows that larger cities are warming at twice the rate of the planet,” he said, describing a phenomenon known as urban heat islands, where built-up areas tend to be hotter than surrounding rural areas, mainly because plants have been replaced by heat-absorbing concrete. Global warming is making that phenomenon worse. “We’re really worried about the rate of how quickly we’re starting to see cities heat up,” Stone said.

According to Stone’s analysis, the most rapidly warming city is Louisville, Kentucky, followed by Phoenix, Arizona, and Atlanta, Georgia. But he’s less concerned about cities like Phoenix, which already have infrastructure to deal with brutally high temperatures, than he is about Chicago, Buffalo, and other cities in the northern United States that have really never had to deal with extreme heat. That is precisely why the Chicago heat wave of 1995 that killed 759 people was so deadly. According to the Chicago Tribune, the city was “caught off guard,” and had “a power grid that couldn’t meet demand and a lack of awareness on the perils of brutal heat.”

In other words, Stone and others say, excessive death rates are not always due to just extreme temperatures, but unusual temperatures. People are more likely to die when they are confronted with temperatures they don’t expect and thus aren’t prepared for. That’s why officials in cities not experiencing heat-related extremes need to improve emergency response systems, now. “Those people have got to start thinking in term of, ‘two years ago we had four hot days, the year after we had eight hot days,’” Benjamin said. “Public health systems should be put in place to respond to prolonged heat waves. Emergency cooling centers where people can go should be built. Identify where the people who are most socially isolated live.” Absent preventative action, heat-related deaths in New York City could quintuple by the year 2080, according to recent research.

Some cities have already started to prepare. Stone recently completed a heat adaptation study for Louisville that includes not only emergency management planning but also ways the city can prevent itself from getting so hot (by improving energy efficiency and installing green roofs, for instance). But as for now, he said, it’s rare to see a city actually adopt policies supportive of heat management. “We do see flooding adaptation plans—New York City has one, and New Orleans has one—but heat adaptation planning is a very new idea, in the US and really around the world,” he said. “It takes a lot to convince a mayor that a city can actually cool itself down. It’s not intuitive.”

The good news is that humans adapt to heat, both physiologically (through acclimatization) and socially (with air conditioning, for instance). That will continue, according to the US Global Change Research Program, which states with very high confidence that adaptation efforts in humans “will reduce the projected increase in deaths from heat.”

But there’s a limit to this. “There’s no way to adapt to heat that’s more than a certain amount,” Frumkin said. “And socially, there’s always going to be people we miss, who don’t have access to air conditioning.” McGeehin noted those people will likely be poor, elderly, and minority populations. “It’s a quintessential public health problem in that it impacts the most disenfranchised of our society. Young, healthy, middle-class people will largely be left alone,” he said.

Air conditioners also have limits, especially in cities where blackouts can occur. “It is inevitable,” Stone said, that large cities will see blackouts during future heat waves. “The number of blackouts we see year over year is increasing dramatically,” he said. “Whether that’s caused by the heatwave or just happens during the heatwave doesn’t really matter…. The likelihood of an extensive blackout during a heatwave is high, and getting higher as we add more devices and stressors to the grid.”

It’s a “cruel irony,” Frumkin said, that as the world gets hotter, we need more air conditioning, and thus consume more electricity. And if that electricity comes from fossil fuel sources, it will create more global warming, which in turn will increase the demand for air conditioning. The answer, he said, is to “decarbonize the electric grid.” But that’s easier said than done, especially when the Trump administration is devoted to increasing the use of fossil fuels to support the country’s electrical grid.

As with many other efforts to fight climate change, though, cities don’t need Washington’s help to take action on heat adaptation. “Cities can manage their own heat islands on their own, and that’s where we most need to be focused,” Stone said. But that will require convincing elected leaders that extreme heat is big a threat as, say, rising seas—and one that can’t be addressed with something as obvious as a sea wall. That’s the challenge, says McGeehin: “Heat as a major natural disaster is mostly overlooked in this country.” It’s a quiet killer, and perhaps more lethal because of it.

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As I understand it, the whole point of cooking a turkey is to take it at some temperature and then increase it to a higher temperature. Sure, maybe there's something about family togetherness in there, but really, Thanksgiving is all about thermal transfer. The USDA recommends a minimum internal temperature of 165°F (74°C). I guess this is the minimum temperature to kill all the bad stuff in there—or maybe it is the lowest temperature that it can be and still taste great.

Either way, if you want to increase the temperature of the turkey you need to add energy. Perhaps this energy comes from fire, or an oven or even from hot oil—but it needs energy. But be careful. There is a difference between energy and temperature. Let me give you an example.

Suppose you put some leftover pizza in the oven to heat it up. Since you don't want to make a mess, you just rip off a sheet of aluminum foil and put the pizza on that and then into the oven. The oven is set to 350 degrees Fahrenheit so that after 10 minutes, both the pizza and the foil are probably close to that temperature. Now for the demonstration. You can easily grab the aluminum foil without burning yourself, but you can't do the same to the pizza. Even though these two objects have the same temperature, they have different amounts of thermal energy.

The thermal energy in an object depends on the object's mass, the object's material and the object's temperature. The change in thermal energy for an object then depends on the change in temperature.

In this expression, m is the mass of the object and the variable c is the specific heat capacity. The specific heat capacity is a quantity that tells you how much energy it takes to one gram of the object by 1 degree Celsius. The specific heat capacity of water is 4.18 Joules per gram per degree Celsius. For copper, the specific heat capacity is 0.385 J/g/°C (yes, water has a very high specific heat capacity).

But what about turkey? What is the energy needed to heat up 1 gram of turkey by 1°C? That is the question I want to answer. Oh sure, I could probably just do a quick search online for this answer, but that's no fun. Instead I want to calculate this myself.

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Here is the basic experimental setup. I am going to take a turkey breast (because I am too impatient to use the whole turkey) and put it in a known amount of hot water. I will then record the change in temperature of the water and the change in temperature of the turkey. Of course, this will have to be in an insulated container such that all of the energy that leaves the water will go into the turkey.

With the change in temperature of the water, I can calculate (based on the known specific heat capacity of water) the energy lost. Assuming all this energy goes into the turkey, I will then know the increase in energy of the turkey. With the mass and change in turkey temperature, I will have the specific heat capacity of a turkey.

Just to be clear, I can set the changes in energy to be opposite from each other and then solve for the specific heat capacity of the turkey. Like this.

OK, it's experiment time. I am going to start with 2,000 mL (2 kilograms) of hot water and add it to a foam box with my turkey breast. I will monitor both the temperature of the water and the turkey. Oh, the turkey has a mass of 1.1 kilograms. Here's what this looks like (without the box lid).

I collected data for quite a while and I assumed that the water and the turkey would reach an equilibrium temperature—but I was wrong. Apparently it takes quite a significant amount of time for this turkey to heat up. Still, the data should be good enough for a calculation.

Hopefully it's clear that the red curve is the hot water and the blue is for the turkey. From this plot, the water had a change in temperature of -21.7°C and the turkey had +27°C. Putting these values along with the mass of the water and turkey, I get a turkey specific heat capacity of 6.018 J/g/°C. That's a little bit higher than what I was expecting—but at least it is in the ballpark of the value for water. But overall, I'm pretty happy.

But what can you do with the specific heat capacity for a turkey? What if you want to do a type of sous-vide cooking in which the turkey is placed in a vacuum-sealed bag and then added water at a particular temperature? Normally, the temperature of the water is kept at some constant temperature. But what if you want to start with hot water and cold turkey and then end up with perfect temperature turkey? In order to do this, you could calculate the starting mass and temperature of water that would give you the best ending turkey temperature. I will let you do this as a homework assignment.

Of course there is another way to cook a turkey. You could drop it from some great height such that it heats up when it lands. Oh, wait—I already did this calculation.

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It's almost always the last topic in the first semester of introductory physics—angular momentum. Best for last, or something? I've used this concept to describe everything from fidget spinners to standing double back flips to the movement of strange interstellar asteroids.

But really, what the heck is angular momentum?

Let me start with the following situation. Imagine that there are two balls in space connected by a spring. Why are there two balls in space? I don't know—just use your imagination.

Not only are these balls connected by a spring, but the red ball has a mass that is three times the mass of the yellow ball—just for fun. Now the two balls are pushed such that they move around each other—just like this.

Yes, this is a numerical calculation. If you want to take a look at the code and play with it yourself (and you should), here it is. If you want all the details about how to make something like this, take a look at this post on the three body problem.

When we see stuff like these rotating spring-balls, we think about what is conserved—what doesn't change. Momentum is a good example of a conserved quantity. We can define momentum as:

Let me just make a plot of the total momentum as a function of time for this spring-ball system. Since momentum is a vector, I will have to plot one component of the momentum—just for fun, I will choose the x-coordinate. Here's what I get.

In that plot, the red curve is the x-momentum of the red (heavier) ball and the blue curve is for the yellow ball (yellow doesn't show up in the graph very well). The black line is the total momentum. Notice that as one object increases in momentum, the other object decreases. Momentum is conserved. You could do the same thing in the y-direction or the z-direction, but I think you get the idea.

What about energy? I can calculate two types of energy for this system consisting of the balls and the spring. There is kinetic energy and there is a spring potential energy:

The kinetic energy depends on the mass (m) and velocity (v) of the objects where the potential energy is related to the stiffness of the spring (k) and the stretch (s). Now I can plot the total energy of this system. Note that energy is a scalar quantity, so I don't have to plot just one component of it.

The black curve is again the total energy. Notice that it is constant. Energy is also conserved.

But is there another conserved quantity that could be calculated? Is the angular velocity conserved? Clearly it is not. As the balls come closer together, they seem to spin faster. How about a quick check, using a plot of the angular velocity as a function of time.

Nope: Clearly, this is not conserved. I could plot the angular velocity of each ball—but they would just have the same value and not add up to a constant.

OK, but there is something else that can be calculated that will perhaps be conserved. You guessed it: It's called the angular momentum. The angular momentum of a single particle depends on both the momentum of that particle and its vector location from some point. The angular momentum can be calculated as:

Although this seems like a simple expression, there is much to go over. First, the L vector represents the angular momentum—yes, it's a vector. Second, the r vector is a distance vector from some point to the object and finally the p vector represents the momentum (product of mass and velocity). But what about that "X"? That is the cross product operator. The cross product is an operation between two vectors that produces a vector result (because you can't use scalar multiplication between two vectors).

I don't want to go into a bunch of maths regarding the cross product, so instead I will just show it to you. Here is a quick python program showing two vectors (A and B) as well as A x B (you would say that as A cross B).

You can click and drag the yellow A vector around and see what happens to the resultant of A x B. Also, don't forget that you can always look at the code by clicking the "pencil" icon and then click the "play" to run it. Notice that A X B is always perpendicular to both A and B—thus this is always a three-dimensional problem. Oh, you can also rotate the vectors by using the right-click or ctrl-click and drag.

But now I can calculate (and plot) the total angular momentum of this ball-spring system. Actually, I can't plot the angular momentum since that's a vector. Instead I will plot the z-component of the angular momentum. Also, I need to pick a point about which to calculate the angular momentum. I will use the center of mass for the ball-spring system.

There are some important things to notice in this plot. First, both the balls have constant z-component of angular momentum so of course the total angular momentum is also constant. Second, the z-component of angular momentum is negative. This means the angular momentum vector is pointing in a direction that would appear to be into the screen (from your view).

So it appears that this quantity called angular momentum is indeed conserved. If you want, you can check that the angular momentum is also conserved in the x and y-directions (but it is).

But wait! you say. Maybe angular momentum is only conserved because I am calculating it with respect to the center of mass for the ball-spring system. OK, fine. Let's move this point to somewhere else such that the momentum vectors will be the same, but now the r-vectors for the two balls will be something different. Here's what I get for the z-component of angular momentum.

Now you can see that the z-component for the two balls both individually change, but the total angular momentum is constant. So angular momentum is still conserved. In the end, angular momentum is something that is conserved for situations that have no external torque like these spring balls. But why do we even need angular momentum? In this case, we really don't need it. It is quite simple to model the motion of the objects just using the momentum principle and forces (which is how I made the python model you see).

But what about something else? Take a look at this quick experiment. There is a rotating platform with another disk attached to a motor.
What happens with the motor-disk starts to spin? Watch. (There's a YouTube version here.)

Again, angular momentum is conserved. As the motor disk starts to spin one way, the rest of the platform spins the other way such that the total angular momentum is constant (and zero in this case). For a situation like this, it would be pretty darn difficult to model this situation with just forces and momentum. Oh, you could indeed do it—but you would have to consider both the platform and the disk as many, many small masses each with different momentum vectors and position vectors. It would be pretty much impossible to explain with that method. However, by using angular momentum for these rigid objects, it's not such a bad physics problem.

In the end, angular momentum is yet another thing that we can calculate—and it turns out to be useful in quite a number of situations. If you can find some other quantity that is conserved in different situations, you will probably be famous. You can also name the quantity after yourself if that makes you happy.

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On Monday night, residents of the Los Angeles neighborhoods of Westwood, Los Feliz, Silver Lake, and parts of the San Fernando Valley experienced a mild earthquake—a magnitude 3.6. Most people slept through the temblor and no damage was reported.

But a select group of 150 LA residents got a text alert on their mobile phone a full eight seconds before the quake hit at 11:10 pm—enough time for people to drop, cover, and hold on. Along with a pinned location of quake's epicenter, the text gave its magnitude and intensity, the number of seconds left before the shaking, and instructions on what to do. The system detects an earthquake's up-and-down p-wave, which travels faster and precedes the destructive horizontal s-wave, and converts that signal into a broadcast warning.

Other parts of the world have similar systems—but accessible to a wider population. On Tuesday afternoon, Mexico City sirens blared a few seconds before a magnitude 7.1 earthquake struck the capital, flattening hundreds of buildings and killing at least 200 people. When an 8.1 magnitude quake hit on September 7 off the coast of Mexico, the SASMEX alert system collecting data from sensors along Mexico’s western coast gave residents more than a minute’s warning from sirens and even news reports on radio and TV. A complementary smartphone app is used by millions of Mexicans. And Japan also has a sophisticated earthquake text-alert system, giving tsunami and earthquake warnings to the entire nation.

So why is the US earthquake system stuck in beta mode with only a lucky few getting an earthquake heads-up? The LA residents received their early warning as part of a pilot study conducted by the US Geological Survey and Santa Monica-based Early Warning Labs. But experts say lack of money and bureaucratic inertia has stymied the USGS ShakeAlert warning system, despite a decade of promises and positive trial runs.

The USGS has only installed about 40 percent of the 1,675 sensors it needs to protect seismically vulnerably areas of the West Coast in Los Angeles, the San Francisco Bay Area, and Seattle, says Doug Given, who coordinates the ShakeAlert system at the USGS Pasadena office.
“We still don’t have full funding,” says Given. “We are on a continuing resolution through December 8 and are operating at the level of last year’s budget."

ShakeAlert costs a measly $16 million each year to build and operate, but the USGS has only been given $10 million each year. The Trump administration's proposed budget had zeroed-out the entire ShakeAlert program, but dozens of lawmakers from San Diego to Seattle protested. A House committee blocked the cuts in July, but the final budget document is still awaiting passage.

The promise of ShakeAlert—which goes beyond the smartphone app tested by those LA residents—has already been shown in many ways. The system gives automated early warnings to slow BART trains in the Bay Area and protect California oil and gas refinery operations. ShakeAlert will even automatically put NASA’s deep space telescope in Goldstone, California into a safe mode. A few luxury condo buildings in Marina del Rey, Calif., and Santa Monica College have also purchased a commercial version of the ShakeAlert warning, which piggybacks off the USGS sensors but offers a direct signal to the building that slows elevators inside.

But getting a widespread text alert system up and running for the millions of Californians (and Oregonians and Washingtonians) is a tougher sell. The engineers and scientists working on the project have to be confident there won’t be false alarms that would weaken the warning’s credibility.

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They are also dealing with a bottleneck from US phone companies who haven’t been able to embed the warning signal into existing wireless networks, according to Josh Bashioum, founder and principal investigator of Early Warning Labs. “Unfortunately, the way our telcos are set up, they aren’t fast enough to deliver an early warning,” Bashioum says.

The providers don't have the ability to send an automated text message to the millions of people living in Southern California, for example, that could also override all the other signals that phones are processing at the same time. These texts have to go out in the narrow window between the detection of the p-wave and the arrival of the potentially deadly s-wave, or they aren't any good. Then again, Japanese cell companies have figured it out.

The USGS and Bashiouim have been meeting with the cell providers to push the effort, but Given expects it won’t happen for another three to five years. In the meantime, he hopes to at least get more seismic sensors in the ground so that scientists can alert first responders when a big quake hits. “The closer your [seismic] station is to the earthquake, the quicker you are going to recognize it detect it and send the alert,” Given says. “Given that we don’t know where the earthquake is going to occur, we have to have sensors all over the potential area of coverage.”

Sure, he could put a lot more sensors along the San Andreas fault, which has the highest odds of another quake. But that won't stop other quakes from hitting. For now, residents who live near seismic zones will have to make do with a real-time warning, and hope their building is up to code.

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Marathon wisdom told you it was too rainy, too slippery, and too warm for fast times at this morning’s Berlin Marathon, but Eliud Kipchoge refused to be overcome, either by the conditions or by his competitors. He won a race against perhaps the strongest field assembled in the past decade, even after a surprise attack by a debutant marathoner, Guye Adola, threatened to spoil his day. Kipchoge eventually missed the world record by 35 seconds, finishing in 2:03:32—a miraculous time in the circumstances. In both the fact and the manner of his victory, he has laid to rest any debate about who is the best marathon runner of this generation.

Berlin woke up in a cloud. In the forested Tiergarten, where the race starts, it was 57 degrees—significantly too hot for the fastest times—and the air was thick and moist. The official weather forecast said it was 99 percent humidity, but it’s hard to imagine how they missed that final one percent. The air was like soup. Humidity is a problem for elite athletes.

If the atmosphere was thick, so was the sense of expectation. As the three star athletes—Eliud Kipchoge, who ran 2:00:25 in Nike’s Breaking2 experiment earlier this year; Wilson Kipsang, the only man ever to win New York, London, and Berlin; and Kenenisa Bekele, world and Olympic record holder in 5,000 and 10,000 meters, and last year’s Berlin winner—warmed up in front of the start line, they betrayed their states of mind. Bekele looked tight with nerves as he stretched out his arms above his head, while Kipsang and Kipchoge ran some fast sprints and smiled easily to the crowd. Kipsang’s grin cracked briefly when the starter announced his rival, Kipchoge, as “the world’s best marathon runner.”

Thick Air and Slippery Turns

From the start, Kipchoge, wearing a white singlet, black half-tights, and red shoes, tucked in behind the three elite pacers, who had been asked to lead the fastest athletes to halfway in a previously unthinkable split time of 60 minutes and 50 seconds. The rain soon became intense, and it became obvious that nobody was going to run so fast for the first half. Simply turning a corner required care and concentration. Every time the lead pack did so, they slowed considerably. As the rain intensified, Gideon Kipketer, the rangy pacemaker (and Kipchoge’s training partner) screwed his face up into the weather.

The lead pack, which included not just the three big names but the Ethiopian debutant Adola and the Kenyan Vincent Kipruto, made halfway in 61:30, a second or two outside world record pace. In the conditions, it was an excellent split. The weather also started to lift a little, and Kipchoge looked increasingly comfortable.

Bekele, though, was dropped from the lead pack at halfway, unable to live with the pace. He did not finish the race. By 17 miles, only one pacemaker had survived—Sammy Kitwara. He dropped out at the 30-kilometer (18.6 mile) mark, and so—to everyone’s surprise—did Wilson Kipsang, clutching his stomach.

Almost everyone was suffering. Not only was the road slippery, but the athletes’ clothes were sticking to the skin, and—most importantly—all the runners would have found it hard to regulate their temperature. One of the limiting factors in marathon running is an athlete’s ability to dissipate the heat generated while synthesizing the energy needed to run so fast. Mostly, body heat is lost through sweating. But, the thicker and warmer the air, the harder that process becomes.

For the final seven and a half miles, it was Kipchoge, the master, versus Adola, the newcomer. Adola, who is taller and has a scruffier gait, seemed relaxed, and Kipchoge looked actively irritated by the close attention the Ethiopian was paying him. Kipchoge asked Adola more than once to move either in front or behind him. Adola continued as he was, shoulder to shoulder with the senior man. As they jostled, the world record drifted away. At the 35-kilometer (21.7 mile) marker, Kipchoge was around six seconds outside world record pace. But, oddly, it was at this moment that Kipchoge began to smile. Battle was joined.

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Race to the Finish

At around 23 miles, Adola attacked, opening a gap of 10 meters and moving to the other side of the road as if to accentuate the distance between he and Kipchoge. The Kenyan responded, and seemed to be reeling Adola in, but the Ethiopian pressed again. Even as the world record drifted toward impossibility, nobody who was watching the race cared. This was thrilling sport, a true duel. With two miles to go, Kipchoge seemed visibly to muster reserves of energy for a final attempt to break Adola, and at the final drinks station at 40 kilometers (24.8 miles), he caught him, and then blew past him.

Kipchoge finished with a kick. When he crested the line, he looked as happy as a lottery winner. He hugged his coach, Patrick Sang, and saluted the crowd. Sang is not normally given to hyperbole, but his pride, minutes after the race had ended, was uncontainable.

“In these terrible conditions, two-oh-three is amazing,” Sang told me. “There was the mental challenge, the physical challenge, the environmental challenge… He is one of the great runners.”

I’d go a step further. Eliud Kipchoge has never broken the world record, but I’ve now watched four races in which he was in shape to do so—the London Marathon of 2016, which he won in 2:03:05, the Rio Olympic Marathon which he won in 2:08:44, the Breaking2 race at Monza which he won in 2:00:25, and today’s Berlin Marathon. In each case, he would have ripped chunks out of the world record in perfect conditions. But he has either been running on a slow course, or in slow conditions, and the title of world-record holder has evaded him. That’s marathon racing. In this sport, you have to be good and lucky.

Kipchoge may never break the world record now. The years, and the marathons, are piling up. He would never admit this, but it’s possible his chance has come and gone. In the final reckoning, it won’t matter. Nobody who watched Kipchoge win those four races could be in any doubt of his superiority. Today’s race was a reminder not just of his physical talents but of his mental fortitude. World record or no world record, he is the greatest.

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