Rescue of soccer team in Thailand Cave

In July 2018, the world held its breath as an international team of cave divers endeavored to rescue 12 boys and their soccer coach stranded deep in a flooded cave in Thailand. Follow the harrowing operation and discover the scientific ingenuity that made the rescue possible. Hear how rescuers explored every option—from pumping water, to drilling a new exit, to ultimately cave diving with the children through the treacherous, flooded passages. (Premiered November 14, 2018)

Follow the harrowing operation to rescue 12 boys stranded in a flooded cave in Thailand.

Thai Cave Rescue

NOVA Wonders Can We Build a Brain? Hero

Artificially intelligent machines are taking over. They’re influencing our everyday lives in profound and often invisible ways. They can read handwriting, interpret emotions, play games, and even act as personal assistants. They are in our phones, our cars, our doctors’ offices, our banks, our web searches…the list goes on and is rapidly growing ever longer. But how does today’s A.I. actually work—and is it truly intelligent? And for that matter, what is intelligence? The world’s brightest computer programmers are trying to build brighter machines by reverse-engineering the brain and by inventing completely new kinds of computers, with exponentially greater speed and processing power. NOVA Wonders looks at how far we’ve come and where machines are headed as their software becomes ever more…cerebral. How close are we from a world in which computers take over—from diagnosing cancer to driving our cars to targeting weapons? If we place more and more of our lives under the control of these artificial brains, what are we putting at risk?

How does today’s artificial intelligence actually work—and is it truly intelligent?

Can We Build a Brain?

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A two-year analysis of nearly a quarter of a million Twitter users reveals how Americans’ social lives are taking a toll on their sleep schedules.

Scientists Track “Social Jet Lag” Sleeping Habits With Twitter Data

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Tech & Engineering

Koch Foundation

Draper

There’s a lot going on ‘under the hood’ of a self-driving car.
To match human drivers, an automated vehicle needs to learn how to handle three distinct tasks: seeing everything around the car, understand what it’s seeing, and planning the car’s path.
Three Tasks Driverless Cars Need to LearnPublished: October 18, 2019Chris Gerdes: Trying to develop an automated vehicle that can do everything that a human driver can do is a huge problem, and it requires an awful lot of interlocking pieces. Narrator: To even match human drivers, a self-driving car needs to learn to handle not just one, but three distinct tasks nearly flawlessly. The first: seeing everything that’s around the car; second, understanding what it’s seeing; and third, planning the car’s path and controlling its acceleration, braking, and steering along the way.First, seeing: most self-driving cars rely on cameras, radar and LIDAR. Each has weaknesses. Cameras work poorly at night, radar doesn’t distinguish very well between different types of objects, and LIDAR can fail completely in rain, snow or fog. But even when sensors are working nearly perfectly, there’s still the second big challenge: understanding what they are seeing.For us, taking in sensory information and making best guesses about the real world are second nature. For a computer, making meaning out of visual data is fiendishly difficult. In each case, it’s the task called “perception.”Amnon Shashua: The human brain is an expert in perception, but for machines, it’s not natural. So this is one big challenge, that if you want to drive autonomously, you need to perceive the world just like humans do. Narrator: Until recently, no computer could come close, but since 2010, a big leap forward in a branch of A.I. called “machine learning” has come a long way to closing the gap.Daniela Rus: Machine learning, it’s about, uh, giving machines the ability to look at data, identify patterns and make predictions. Narrator: Even if the car can see and make sense of its surroundings with unmatched perfection, it could still be a lethal hazard if it fails to handle its third crucial task: planning; yet another daunting challenge, because the planning software has to anticipate what’s likely to happen, then plot the car’s pathway and speed, ready to change both in a split second if the sensors spot trouble.Just like the car’s perception software, its planning software also needs to be trained.

There’s a lot going on ‘under the hood’ of a self-driving car.

Three Tasks Driverless Cars Need to Learn

Hear from two survivors of a tragic bridge collapse.
In August of 2018, an 800-foot section of Genoa, Italy&#x27;s Ponte Morandi Bridge collapsed, killing 43 people. Two survivors reflect on the event.
Survivors Recount Bridge Collapse in Genoa, ItalyPublished: October 16, 2019Onscreen: On August 14, 2018, something terrible happened to the Polcevera Bridge in Genoa, Italy.Witness: (Translated from Italian) Oh, God! Oh, God!Narrator: A massive 800-foot section of the bridge, carrying four lanes of traffic, collapses. Gianluca’s truck plummets 100 feet.Gianluca Ardini: (Translated from Italian) I felt that I had landed, that I had arrived. I wasn’t moving anymore. I turn and see my colleague with his head on the wheel. At that moment I thought he had fainted or something. When I heard the sirens I touched him and said, “Come on. Let’s get rescued.” But I got no reply, and I realized he wasn’t alive.Narrator: Twenty-seven vehicles fall with the bridge. This is Davide Capello’s S.U.V. It falls 130 feet and lands inside a cavity formed by a section of the bridge.Davide Capello: (Translated from Italian) I got out of my car myself, climbed over the debris and got to a safe area. It looked like the apocalypse.Narrator: Across from Davide’s car, trapped inside the remains of the collapsed bridge tower, rescuers make a shocking discovery. Hanging inside this truck is Gianluca Ardini.Ardini: (Translated from Italian) I was screaming and screaming in order to get found. And I could feel the truck moving, and I panicked again, as I thought, “Man, what if it falls?”Narrator: Firefighters begin the precarious job of rescuing him from the mangled cab. It takes 40 minutes to cut Gianluca free and carry him to safety.

Hear from two survivors of a tragic bridge collapse.

Survivors Recount Bridge Collapse in Genoa, Italy

What is a single-point bridge failure?
In a single-point bridge failure, one component breaks and brings down an entire structure. See how this happened 52 years ago to West Virginia’s Silver Bridge.
How West Virginia&#x27;s Silver Bridge Fell in 1967Published: October 16, 2019Onscreen: The Silver Bridge, West Virginia, 1967Narrator: It was nicknamed the Silver Bridge because of its shiny coat of aluminum paint. But unknown to the people that used it, its design contained a serious flaw.On December 15, 1967, Peggy Huber was driving towards the Silver Bridge.Peggy Huber: Traffic was heavy. It was Friday and near Christmastime. I got behind a dump truck. And it was moving so slow it stopped right at the light where the bridge was, and I was behind it. And I had reached down to turn the radio dial, and I heard this awful noise, and when I looked up, the bridge was just like a TINKERTOY®, waving, and it just fell. It just sounded like a bunch of metal. It was very loud.Jack Fowler: It collapsed, I think, at 4:58, so it was a busy time. There was 31 vehicles that fell.Narrator: The collapse killed 46 people. It remains the worst road bridge disaster in American history.To find out what had caused the bridge to fail, investigators search the riverbed for wreckage.Fowler: They brought in a whole bunch of divers, and they would bring pieces up, and of course they would examine it, to see if it would fit in any way.Narrator: The bridge was an eyebar suspension bridge. Giant bars, up to 55 feet long, with a hole called an eye at each end, were linked together with steel pins. Crucially, each link of the suspension chain contained only two eyebars.Fowler: And they finally found one of the eyebars that was broken, split. And when they examined it, they found that there had been a hairline fracture in it.Narrator: Nearly 40 years of corrosion in the steel had triggered a fracture that ripped the eyebar from its pin. The weight of the bridge proves too much for the remaining eyebar, pulling it with an extreme force that tears the link apart. The failure of this single point triggers an unexpected domino effect, causing the entire bridge to collapse.In the United States alone, around 18,000 bridges have been identified as “fracture critical.” This means if one component fails, part or all of the bridge would probably collapse.

What is a single-point bridge failure?

How West Virginia's Silver Bridge Fell in 1967

<div class="rich-text">On December 26, 2013, a <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4309665/">two-year-old boy</a> named Emile Ouamouno fell ill in the village of Meliandou in Guinea, West Africa. For two days, his tiny body was wracked with fever as he vomited and passed black stool. By December 28, he was dead.Within weeks, his sister, mother, and grandmother were, too—<a href="http://b/">the first casualties</a> of what would eventually become thousands. The <a href="https://www.cdc.gov/vhf/ebola/history/distribution-map.html">largest Ebola outbreak in history</a> had begun.It would be many months, however, before a team of researchers would <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4309665/">pinpoint the probable source</a> of the epidemic: a colony of Angolan free-tailed bats (Mops condylurus) that had roosted in a hollow cola tree less than 200 feet from Emile’s home. The locals called them lolibelo, or flying mice, for their distinctive smell and long tails. They were common targets for children at play, who would rouse them from sleep with sticks and roast them as snacks.</div>
 

 
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 <div class="rich-text">By the time ecologists and veterinarians arrived in Meliandou in April 2014, the tree in question had been burned and the bats were long gone. But the winged, mouse-sized mammals are still considered some of the likeliest candidates for Ebola’s so-called animal reservoirs, maintaining the virus in the wild before it makes each of its fateful hops into humans.The disease’s devastating trajectory is a familiar one. Ebola, like SARS, Lyme disease, HIV, and <a href="https://www.cdc.gov/onehealth/basics/zoonotic-diseases.html">most of the other infections</a> known to plague people, got its start in another species. It’s in these creatures that pathogens can also hide between epidemics, biding time before they re-emerge.By the time most reservoir species are identified, the pathogens in question have already spilled over into people. In the wake of an outbreak, that leaves just one course of action: mitigation—the frantic attempt to halt the collapse of a line of dominos after they’ve already begun to fall.For <a href="https://www.caryinstitute.org/science/scientific-staff/our-scientists/dr-barbara-han">Barbara Han</a>, a disease ecologist at the Cary Institute, this reactive approach isn’t enough. “The fact is, you’ve already waited until people got sick,” she says. “You don’t want to hand out umbrellas after the rain falls. You want to forecast the rain before it starts.”To get ahead of the curve, researchers need better tools that can predict these outbreaks before they happen, says <a href="https://www.ucl.ac.uk/biosciences/people/dr-david-w-redding">David Redding</a>, a disease ecologist at University College London. That means searching for the ecological and epidemiological patterns that precede spillovers—the harbingers of outbreaks that flicker to life in the wild, then trickle into humans.Most of these warning signs aren’t readily discernible by human brains alone. So scientists like Han and Redding have turned to computational models that can scour gobs of ecological and demographic data in their stead, hunting for clues to where the next infectious leak might spring.
 <figure class="richtext-image full-width"><img alt="14904890281_c1b0eb1f01_o.png" class="richtext-image full-width" height="600" width="800"
 src="/wgbh/nova/media/images/14904890281_c1b0eb1f01_o.width-800.png"/><figcaption>The border between Guinea and Liberia, during the 2014 Ebola outbreak. Frequent boat travel, as well as other modes of transport, can help Ebola virus hop from one country to another. Image Credit: CDC Global, flickr<div class="credit"></div></figcaption>
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 <h3>Into the wild, and back out again</h3>Out in the wild, infection is a given—a reality Barbara Han, who got her scientific start in animal ecology, became intimately familiar with while tracking <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0016675">fungal pathogens</a> in amphibians.But the bugs that lurk in wildlife don’t always stay there.Of the <a href="https://www.cdc.gov/niosh/topics/emerginfectdiseases/default.html">new and emerging infectious diseases</a> documented by the Centers for Disease Control (CDC) over the past couple decades, <a href="https://www.cdc.gov/onehealth/basics/zoonotic-diseases.html">75 percent</a> are zoonotic, or capable of spreading from animals to humans. And while scientists have amassed a good deal of data on animal reservoirs over the years, they’ve long struggled to uncover the crucial commonalities among them—the traits that make a species ideally suited to pass a pathogen to people.That’s why Han has turned to a tool that could accomplish what human researchers can’t on their own. Several years ago, she and her team <a href="http://www.pnas.org/cgi/doi/10.1073/pnas.1501598112">trained a computer model</a> to pick out new rodent species with high disease-carrying potential, based on the traits they shared with 217 previously identified <a href="https://www.cdc.gov/rodents/diseases/index.html">carriers of disease</a>. Han compares the approach to Pandora’s strategy for recommending songs: An algorithm learns the trends that dictate musical taste or vulnerability to infection, then offers up a comparable band or animal that hasn’t been considered before. Using this tactic, Han’s model scanned through the 2,277 rodent species that exist worldwide and homed in on 58 not previously designated as reservoirs.The list was diverse, spanning much of the rodent family tree. But its members did seem to have a couple things in common, like brief lifespans, early sexual maturity, and large numbers of offspring. “These rodents basically have a ‘live fast, die young’ approach to life,” Han says. It’s possible they prioritizing reproduction over other resource-heavy pursuits like, say, an ironclad immune system, she says. But unlike rats and mice, long-lived, slow-maturing humans have more to lose by ignoring infections.
 <figure class="richtext-image full-width"><img alt="iStock-1151879070.jpg" class="richtext-image full-width" height="556" width="800"
 src="/wgbh/nova/media/images/iStock-1151879070.width-800.jpg"/><figcaption>A northern grasshopper mouse (Onychomys leucogaster), one of several species pinpointed by a machine learning model trained to detect rodents at risk of carrying infectious diseases. Image Credit: Weber, iStock<div class="credit"></div></figcaption>
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 Predictions aren’t guarantees. And it’s likely that many of these disease-carrying candidates will never harbor a problematic pathogen at all. But when done well, “modeling studies are great for hypothesis generation—they demonstrate what could happen,” says <a href="https://www.cdc.gov/media/spokesperson/damon/index.html">Inger Damon</a>, Director of the CDC’s Division of High-Consequence Pathogens and Pathology.And in certain instances, they seem spot on, Han says. While her team’s paper was being prepped for publication, <a href="https://www.sciencedirect.com/science/article/pii/S1383576913000366?via%3Dihub">two</a> of the <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3578753/">voles</a> on their computer-generated list were confirmed to harbor parasites.A similar story has played out in other animal groups, too. In 2016, Han and her colleagues published a <a href="https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0004815">list of bat species</a> that could play host to filoviruses, the group that includes Ebola. Less than a year later, a team of virus hunters uncovered <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5382765/">filoviruses</a> lurking in China’s fruit bats, including a couple species from Han’s paper.Around the same time, a colleague at Columbia University phoned Han, bursting with excitement: He’d discovered <a href="https://www.nature.com/articles/s41564-018-0227-2/">a new ebolavirus</a> in Sierra Leone. It wasn’t yet clear if the virus could cause disease in humans, but it had been detected in two types of bats. One of them was the Angolan free-tailed bat—another species high on Han’s list of potential reservoirs. The same species suspected of infecting Emile Ouamouno years before.<h3>Bridging the divide</h3>Reservoirs aren’t reservoirs until they’re tapped. For a disease to jump into a human population, it needs access—a region where infected animals and people overlap.At University College London, David Redding and ecologist <a href="https://www.ucl.ac.uk/biosciences/people/prof-kate-jones">Kate Jones</a> have taken their own computational approach to uncover the dynamics of infection at these ports of entry. Their newest model, described in a <a href="https://www.nature.com/articles/s41467-019-12499-6">paper</a> published today in the journal Nature Communications, is what Redding calls a hybrid approach, borrowing from both ecology and epidemiology to predict areas at high risk of Ebola spillover and subsequent outbreak in Africa.“We know where animal hosts are,” he says. (In Ebola’s case, that almost certainly means bats, and possibly <a href="https://www.cdc.gov/vhf/ebola/about.html">great apes and duikers</a>—a type of antelope—as well.) “And we also know where people are. Where you have both, you have likely contact, and risk of disease.”
 <figure class="richtext-image full-width"><img alt="13717624625_23eab06a54_o.jpg" class="richtext-image full-width" height="600" width="800"
 src="/wgbh/nova/media/images/13717624625_23eab06a54_o.width-800.jpg"/><figcaption>Workers in Guinea, West Africa, shortly after Ebola was confirmed to have hit the region. Image Credit: EU Civil Protection and Humanitarian Aid, flickr<div class="credit"></div></figcaption>
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 That might sound simple enough. But a bevy of other variables make dynamics of a spillover far more complex, Redding says. Land use, for instance, can have a big impact on a reservoir’s range, and how much the members of different populations mix. On the human side, the size of an ensuing outbreak depends on <a href="https://www.theguardian.com/global-development/2019/aug/16/congo-ebola-outbreak-reaches-second-province-south-kivu-woman-dead">connectivity</a>—how easy it is for people to physically get around and mingle with others—and regional wealth, which often dictates the amount of money allocated to health care.From the virus’ perspective, “the ideal situation would probably include a human population situated in a forested area with animal hosts, near a big transportation hub, near a big city,” Redding says. “That’s where you would expect large outbreaks to occur.”</div>
 

 
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 <div class="rich-text">While these are many of the critical variables that affect zoonotic disease, Damon says, there are always more variables to consider. Only some spillovers turn into outbreaks. And the likelihood of that transition can hinge on aspects of human behavior that Redding’s model didn’t capture, like the prevalence of funerary practices that may increase contact with infected bodies, she says.By definition, computational modeling will always be a bit reductionist, says <a href="http://www.sadieryan.net/">Sadie Ryan</a>, a disease ecologist at the University of Florida. Programs have to accurately and efficiently capture the complexities of the real world with a limited set of data. That’s a huge challenge—and a high stakes one, she says. “If you’re doing massive spatial computational simulations without real information, you’re just making video games.”But models like these, which take animals, humans, and their environments into account, effectively capture the “biological realism of these spillover events,” Ryan says.
 <figure class="richtext-image full-width"><img alt="large (1).jpg" class="richtext-image full-width" height="502" width="800"
 src="/wgbh/nova/media/images/large_1.width-800.jpg"/><figcaption>The largest Ebola outbreak in history may have begun when an Angolan free-tailed bat (Mops condylurus) passed the virus to a toddler in Meliandou, Guinea, West Africa in 2013. Children in the village often roused bats from tree hollows to play with cook eat them. Image Credit: Jakob Fahr, iNaturalist<div class="credit"></div></figcaption>
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 In its current iteration, Redding’s model has proven powerful. With the data it was fed, it correctly identified several areas that had already experienced Ebola outbreaks, such as the Democratic Republic of Congo (DRC), Gabon, and regions in West Africa hit by the epidemic that began in Meliandou.When the simulation originally ran in 2018, it also flagged several other regions—including Nigeria, Ghana, Rwanda, and Kenya—that, at the time, had been mostly untouched by the virus. In the months since, two of its outbreak predictions in the DRC have come true.<h3>Cloudy with a chance of infection</h3>West Africa’s Ebola epidemic ended in June of 2016. In the two and a half years after Emile Ouamouno fell ill in Meliandou, at least 28,646 people had been infected and at least 11,323 had died—more than <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5394636/">all previous Ebola outbreaks combined</a>.The virus has since <a href="https://www.msf.org/drc-ebola-outbreak-crisis-update">re-emerged</a>. And with so many available hiding places in the wild, it’s likely to do so again, Redding says.This is where outbreak predictions can be powerful, Han says. They can inform where health care resources are diverted next, or how ecologists and conservationists can protect and monitor (rather than villainize) reservoir species in their natural habitats, she says.Acting on the numbers churned out by these models, however, is another issue entirely, Damon says. Predicting spillover isn’t the same as preventing it—a process that requires increased surveillance, or an infusion of resources that can quash outbreaks before they have a chance to grow.
 <figure class="richtext-image full-width"><img alt="15751491634_130b8c764b_o.jpg" class="richtext-image full-width" height="600" width="800"
 src="/wgbh/nova/media/images/15751491634_130b8c764b_o.width-800.jpg"/><figcaption>An infection control supervisor (left) demonstrates proper hand washing techniques during a field supervision visit to a small clinic in N&#x27;Zérékoré, Guinea. Image Credit: Lindsey Horton, CDC Global, flickr<div class="credit"></div></figcaption>
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 These interventions will become increasingly complicated to execute in a rapidly changing world, Ryan says. As temperatures rise and habitats disappear, reservoir species—among many others—will be forced to uproot and adopt new behaviors, rejiggering their potential to transmit disease. “Climate change impacts literally everything,” says Han, who’s now collaborating with researchers at NASA to incorporate climate data into her team’s predictions.In the case of Ebola, one trend may already be clear: The worse climate change gets, the more outbreaks we’ll have, Redding says. Projecting into the year 2070, his team’s simulations show that warmer, wetter conditions will raise the risk of spillovers across the African continent. Some of these effects can be mitigated by reducing carbon emissions and increasing <a href="https://www.un.org/sustainabledevelopment/climate-change/">sustainable development</a>, Redding says—but only if the world takes action soon.“This is about getting the concept of intervention out there ahead of time,” Ryan says. “If this is how the future is unfolding, let’s be there before it happens. And let’s be ready.”</div>
 

 
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Mathematical modeling and AI can pick out patterns preceding epidemics that human brains can’t readily discern.

To predict the next infectious disease outbreak, ask a computer

<div class="rich-text">Around the turn of the 18th century, English philosopher John Gough stumbled upon the weirdness of rubber.When stretched, the elastic material felt hotter, then cooled below baseline upon release—observations that Gough, who’d lost his eyesight as a child, made with his lips.The temperature difference wasn’t huge (though Gough did go on to <a href="https://web.archive.org/web/20110607134244/http://www.chem.yale.edu/~chem125/goodyear/Gough.html">publish his results in 1805</a>). And though prototypes of <a href="https://gizmodo.com/guy-tries-to-build-a-mini-fridge-with-rubber-bands-and-1785687275">elastically powered refrigerators</a> have popped up in recent years, they’re less energetically efficient than those that run on vapor compression, a fossil-fuel-guzzling method found in kitchens and air conditioners worldwide.Materials like rubber, it might seem, aren’t destined to cool the foods and office spaces of the future.</div>
 

 
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 <div class="rich-text"><a href="https://www.researchgate.net/profile/Zunfeng_Liu">Zunfeng Liu</a>, a materials scientists at Nankai University in China, thinks differently. In a <a href="https://science.sciencemag.org/cgi/doi/10.1126/science.aax6182">paper</a> published today in the journal Science, he and his colleagues make the case that the natural properties of rubber can still be leveraged to achieve some serious chill—just not in a way that researchers have considered before.A stretch, Liu says, is great for cooling. But what’s even better is a twist: a motion that can contort material without hogging the extra space that stretched fibers tend to consume. Liu and his team, a group co-led by the University of Texas at Dallas’ <a href="https://profiles.utdallas.edu/ray.baughman">Ray Baughman</a>, have found that twisting and untwisting strands of rubber, as well as other everyday items like fishing line and sewing thread, can also make temperatures plunge—with double the cooling efficiency of simple stretch and release.“This is a beautiful piece of experimental work,” says <a href="http://www.physics.hmc.edu/~gerbode/wiki/">Sharon Gerbode</a>, a soft-matter physicist at Harvey Mudd College who was not involved in the study. “It gives us a new way of approaching the extremely energy-intensive process of refrigeration...just by saying, ‘Let’s twist this and see what happens.’”The team has already applied their twisted technique to something that’s, well, pretty cool: a simple “twist fridge” that can lower the temperature of a teaspoon or so of water by around 10 degrees Fahrenheit in a single, 30-second cycle, Liu says.That’s not much water, and the technology is still in its infancy. But with more tinkering, Baughman says, the team’s discovery could someday provide an alternative to traditional cooling systems, which, in addition to consuming fossil fuels, produce <a href="https://e360.yale.edu/features/cooling_a_warming_planet_a_global_air_conditioning_surge">gobs of greenhouse gases</a> and account for <a href="http://www.iifiir.org/userfiles/file/publications/notes/NoteTech_29_EN.pdf">about 20 percent</a> of global electricity consumption.
 <figure class="richtext-image full-width"><img alt="iStock-667236998.jpg" class="richtext-image full-width" height="533" width="800"
 src="/wgbh/nova/media/images/iStock-667236998.width-800.jpg"/><figcaption>When materials like rubber are coiled up and released, their temperatures drop. Image Credit: MovieAboutYou, iStock<div class="credit"></div></figcaption>
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 The scientific oomph behind the breakthrough boils down to a couple concepts in thermodynamics. Because rubber is a polymer, it’s made up of subunits that manifest as long chains, which, when left limp, tangle as haphazardly as loose headphone wires. A stretch or twist, however, tugs these strands into alignment, making them more orderly.Rubber’s initial state of disarray teems with energy—energy that has to be released when its molecules get organized. That energy is released as heat, temporarily warming the elastic and its surroundings. When it’s released from contortion, rubber slips back into a more energetic jumble. This time, the molecules absorb heat from their surroundings and funnel that energy into resuming a state of chaos, allowing the rubber and whatever’s nearby to cool.Rubber’s cool factor has been known for centuries. But so far, the research on it has pretty much been limited to stretch, Liu says. “We just wondered if there would be additional benefit if we exerted twist.”
 <figure class="richtext-image full-width"><img alt="wang191011.jpg" class="richtext-image full-width" height="443" width="800"
 src="/wgbh/nova/media/images/wang191011.width-800.jpg"/><figcaption>Natural rubber fibers in various stages of twist: twisted, partially coiled, fully coiled, and fully supercoiled. Image Credit: Wang et al. Science, 2019<div class="credit"></div></figcaption>
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 And so, using fibers suspended between motorized attachment points, Liu, Baughman, and their colleagues twisted rubber until it coiled, then supercoiled, or entered an extra-wound-up state. After the warmed up material returned to room temperature, the researchers released it, and found its temperature dropped by up to 28 degrees Fahrenheit, besting another elastic that had been stretched to six times its length. The two strategies could also be combined: Rubber fibers that were stretched and twisted cooled off by up to 30 degrees Fahrenheit.Though there are some variations at the molecular level, the same broad “energy in, energy out” principles applied for other substances, including fishing line and sewing thread, Liu says, though neither material could top rubber’s cooling ability.These tests were mostly proof of principle, Baughman explains. “People don’t usually think of fishing line as elastic,” he says. “But if you exert so much twist that it coils...it can do some heating and cooling. We were amazed.”</div>
 

 
 <div class="ad"></div>
 

 
 <div class="rich-text">“Even with relatively widespread and cheap materials...they can induce this big effect, and with such a simple motion,” says <a href="http://www.mit.edu/~sborisk/">Svetlana Boriskina</a>, a mechanical engineer at the Massachusetts Institute of Technology who was not involved in the study. “Just knowing that is practical and useful.”The best-performing material the team tested, Liu says, was nickel titanium (also known as nitinol) wire, an elastic alloy that’s been used in medical devices and implants. A single nitinol wire twisted up could produce a cooling effect of 31 degrees Fahrenheit; adding three more wires to the mix eked out another 7 or so degrees of chill. In a final experiment, the team bundled up three of these wires in a small chamber and flowed water over them. As the wires untwisted inside the tiny refrigerator, they cooled, bringing down the temperature of the surrounding fluid as well.Nitinol isn’t as cheap as rubber, Liu says, but it’s hardier: While elastics lose their resilience fairly quickly, the metal alloy retained its cooling powers through 1,000 cycles of twisting and untwisting.
 <figure class="richtext-image full-width"><img alt="Supercoiled rubber fiber.JPG" class="richtext-image full-width" height="534" width="800"
 src="/wgbh/nova/media/images/Supercoiled_rubber_fiber.width-800.jpg"/><figcaption>Study author Zunfeng Liu holds a super-coiled rubber fiber in his laboratory. Image courtesy of Zunfeng Liu, Nankai University<div class="credit"></div></figcaption>
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 To power an actual refrigerator or air conditioner long-term, though, a material would probably need to withstand several million cycles of twisting, says <a href="https://mse.umd.edu/clark/faculty/722/Ichiro-Takeuchi">Ichiro Takeuchi</a>, a materials scientist at the University of Maryland who wasn’t involved in the study. It’s also unclear how other prototypes would fare when scaled up to a larger size.Still, Borskina, Gerbode, and Takeuchi all praise the study for its elegant and creative approach. In their first published pass, Gerbode points out, the researchers achieved greater cooling efficiencies than what’s typical for vapor compression systems. “That’s pretty impressive,” she says.In the world of refrigeration, the twist may still be the new kid on the block, Baughman says. But it’s already established itself as a pretty cool contender, he says. “This could create enormous savings for the world.”</div>
 

 
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A new refrigeration technique harnesses the ability of rubber and other materials to cool down when released from a tight twist.

Refrigerators of the future may be inspired by the weird physics of rubber

<div class="rich-text">Self-driving cars are being tested on public roads around the world every day and have already sparked debate due to concerns about their safety, cost, and impact on America&#x27;s infrastructure. This month, NOVA is hosting three events that will dive into how autonomous vehicles work, how they may change the way we live, and whether we will ever be able to entrust them with our lives. If you can&#x27;t join an event in person, make sure to tune in to the live streams on Facebook.<a href="/pages/videopages/look-whos-driving/">Look Who&#x27;s Driving</a> airs on Wednesday, October 23 at 9/8c p.m. on PBS.</div>
 

 
 <div class="rich-text"><h2>Lunch Hour Live: NOVA Investigates Self-Driving Cars</h2>Thursday, October 17, 2019 | 12:00 - 1:00 p.m.Location: WGBH Studio at the Boston Public Library 700 Boylston Street Boston, MA 02116Science Correspondent for PBS NewsHour, Miles O&#x27;Brien, sits down with Dr. Bobbie Seppelt from MIT and Dr. Sam Anthony from Perceptive Automata to discuss NOVA&#x27;s new documentary, &quot;Look Who&#x27;s Driving,&quot; exploring how self-driving cars work, how close we are to the large-scale development of them and if we&#x27;ll ever be able to trust AI with our lives. <a href="https://www.facebook.com/WGBHBoston/">Watch the conversation live on the WGBH Facebook page.</a></div>
 

 
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 <div class="rich-text"><h2>Look Who&#x27;s Driving Film Screening &amp; Panel Discussion</h2>Friday, October 18, 2019 | 7:00 - 9:00 p.m. | <a href="https://computerhistory.org/events/look-whos-driving/#register">RSVP Here</a>Location: Computer History Museum 1401 N. Shoreline Blvd MountainView, CA 94043How can we train artificial intelligence to be better than humans at making life-and-death decisions? How do self-driving cars work? How close are we to large-scale deployment of them? Join us for a special screening of this fascinating documentary followed by our panel of pioneering company leaders and academic experts who will tackle not just these technical issues, but some of the potential economic and social implications. <a href="https://www.facebook.com/computerhistory/">This panel discussion will be streamed live on the Computer History Museum Facebook page.</a> To reserve your ticket, <a href="https://computerhistory.org/events/look-whos-driving/#register">visit this link</a>.<h2>Wireless Connectivity: The 6th Sense of Self-Driving Vehicles</h2>Tuesday, October 22, 2019 | 6:00 - 7:00 p.m. | <a href="https://www.eventbrite.com/e/wireless-connectivity-the-6th-sense-for-self-driving-vehicles-tickets-75003065187">RSVP Here</a>Location: WGBH Studio at the Boston Public Library 700 Boylston Street Boston, MA 02116Can wireless connectivity make self-driving cars safer? At this month&#x27;s NOVA Science Café, find out how Worcester Polytechnic Institute professor Dr. Alex Wyglinski is using wireless connectivity to facilitate vehicle-to-vehicle communication and create a comprehensive situational awareness of the road environment for self-driving vehicles. <a href="https://www.facebook.com/wgbhforum/">Watch the conversation live on the WGBH Forum Network Facebook page</a>. To reserve your seat, <a href="https://www.eventbrite.com/e/wireless-connectivity-the-6th-sense-for-self-driving-vehicles-tickets-75003065187">visit this link</a>.</div>
 

 
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This month, NOVA is hosting three events that will dive into how autonomous vehicles work, how they may change the way we live, and whether we will ever be able to entrust them with our lives.

October Events: "Look Who's Driving" Screenings & Panel Discussions

Can new engineering techniques help prevent deadly bridge collapses?

Topic: Tech & Engineering

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