Lisa Cuchara, Quinnipiac University

On hot summer days, few things are more refreshing than a dip in the pool. But have you ever wondered if the pool is as clean as that crystal blue water appears?

As an immunologist and infectious disease specialist, I study how germs spread in public spaces and how to prevent the spread. I even teach a course called “The Infections of Leisure” where we explore the risks tied to recreational activities and discuss precautions, while also taking care not to turn students into germophobes.

Swimming, especially in public pools and water parks, comes with its own unique set of risks — from minor skin irritations to gastrointestinal infections. But swimming also has a plethora of physical, social and mental health benefits. With some knowledge and a little vigilance, you can enjoy the water without worrying about what might be lurking beneath the surface.

The reality of pool germs

Summer news headlines and social media posts often spotlight the “ick-factor” of communal swimming spaces. These concerns do have some merit.

The good news is that chlorine, which is widely used in pools, is effective at killing many pathogens. The not-so-good news is that chlorine does not work instantly – and it doesn’t kill everything.

Every summer, the Centers for Disease Control and Prevention issues alerts about swimming-related outbreaks of illness caused by exposure to germs in public pools and water parks. A 2023 CDC report tracked over 200 pool-associated outbreaks from 2015 to 2019 across the U.S., affecting more than 3,600 people. These outbreaks included skin infections, respiratory issues, ear infections and gastrointestinal distress. Many of the outcomes from such infections are mild, but some can be serious.

Germs and disinfectants

Even in a pool that’s properly treated with chlorine, some pathogens can linger for minutes to days. One of the most common culprits is Cryptosporidium, a microscopic germ that causes watery diarrhea. This single-celled parasite has a tough outer shell that allows it to survive in chlorine-treated water for up to 10 days. It spreads when fecal matter — often from someone with diarrhea — enters the water and is swallowed by another swimmer. Even a tiny amount, invisible to the eye, can infect dozens of people.

Another common germ is Pseudomonas aeruginosa, a bacterium that causes hot tub rash and swimmer’s ear. Viruses like norovirus and adenovirus can also linger in pool water and cause illness.

Swimmers introduce a range of bodily residues to the water, including sweat, urine, oils and skin cells. These substances, especially sweat and urine, interact with chlorine to form chemical byproducts called chloramines that may pose health risks.

These byproducts are responsible for that strong chlorine smell. A clean pool should actually lack a strong chlorine odor, as well as any other smells, of course. It is a common myth that a strong chlorine smell is a good sign of a clean pool. In fact, it may actually be a red flag that means the opposite – that the water is contaminated and should perhaps be avoided.

How to play it safe at a public pool

Most pool-related risks can be reduced with simple precautions by both the pool staff and swimmers. And while most pool-related illnesses won’t kill you, no one wants to spend their vacation or a week of beautiful summer days in the bathroom.

These 10 tips can help you avoid germs at the pool:

• Shower before swimming. Rinsing off for at least one minute removes most dirt and oils on the body that reduce chlorine’s effectiveness.

• Avoid the pool if you’re sick, especially if you have diarrhea or an open wound. Germs can spread quickly in water.

• Try to keep water out of your mouth to minimize the risk of ingesting germs.

• Don’t swim if you have diarrhea to help prevent the spread of germs.

• If diagnosed with cryptosporidiosis, often called “crypto,” wait two weeks after diarrhea stops before returning to the pool.

• Take frequent bathroom breaks. For children and adults alike, regular bathroom breaks help prevent accidents in the pool.

• Check diapers hourly and change them away from the pool to prevent fecal contamination.

• Dry your ears thoroughly after swimming to help prevent swimmer’s ear.

• Don’t swim with an open wound – or at least make sure it’s completely covered with a waterproof bandage to protect both you and others.

• Shower after swimming to remove germs from your skin.

Lisa Cuchara, Professor of Biomedical Sciences, Quinnipiac University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Andrej Prša, Villanova University

Stars are the fundamental building blocks of our universe. Most stars host planets, like our Sun hosts our solar system, and if you look more broadly, groups of stars make up huge structures such as clusters and galaxies. So before astrophysicists can attempt to understand these large-scale structures, we first need to understand basic properties of stars, such as their mass, radius and temperature.

But measuring these basic properties has proved exceedingly difficult. This is because stars are quite literally at astronomical distances. If our Sun were a basketball on the East Coast of the U.S., then the closest star, Proxima, would be an orange in Hawaii. Even the world’s largest telescopes cannot resolve an orange in Hawaii. Measuring radii and masses of stars appears to be out of scientists’ reach.

Enter binary stars. Binaries are systems of two stars revolving around a mutual center of mass. Their motion is governed by Kepler’s harmonic law, which connects three important quantities: the sizes of each orbit, the time it takes for them to orbit, called the orbital period, and the total mass of the system.

I’m an astronomer, and my research team has been working on advancing our theoretical understanding and modeling approaches to binary stars and multiple stellar systems. For the past two decades we’ve also been pioneering the use of artificial intelligence in interpreting observations of these cornerstone celestial objects.

Measuring stellar masses

Astronomers can measure orbital size and period of a binary system easily enough from observations, so with those two pieces they can calculate the total mass of the system. Kepler’s harmonic law acts as a scale to weigh celestial bodies.

Think of a playground seesaw. If the two kids weigh about the same, they’ll have to sit at about the same distance from the midpoint. If, however, one child is bigger, he or she will have to sit closer, and the smaller kid farther from the midpoint.

It’s the same with stars: The more massive the star in a binary pair, the closer to the center it is and the slower it revolves about the center. When astronomers measure the speeds at which the stars move, they can also tell how large the stars’ orbits are, and as a result, what they must weigh.

Measuring stellar radii

Kepler’s harmonic law, unfortunately, tells astronomers nothing about the radii of stars. For those, astronomers rely on another serendipitous feature of Mother Nature.

Binary star orbits are oriented randomly. Sometimes, it happens that a telescope’s line of sight aligns with the plane a binary star system orbits on. This fortuitous alignment means the stars eclipse one another as they revolve about the center. The shapes of these eclipses allow astronomers to find out the stars’ radii using straightforward geometry. These systems are called eclipsing binary stars.

More than half of all Sun-like stars are found in binaries, and eclipsing binaries account for about 1% to 2% of all stars. That may sound low, but the universe is vast, so there are lots and lots of eclipsing systems out there – hundreds of millions in our galaxy alone.

By observing eclipsing binaries, astronomers can measure not only the masses and radii of stars but also how hot and how bright they are.

Complex problems require complex computing

Even with eclipsing binaries, measuring the properties of stars is no easy task. Stars are deformed as they rotate and pull on each other in a binary system. They interact, they irradiate one another, they can have spots and magnetic fields, and they can be tilted this way or that.

To study them, astronomers use complex models that have many knobs and switches. As an input, the models take parameters – for example, a star’s shape and size, its orbital properties, or how much light it emits – to predict how an observer would see such an eclipsing binary system.

Computer models take time. Computing model predictions typically takes a few minutes. To be sure that we can trust them, we need to try lots of parameter combinations – typically tens of millions.

This many combinations requires hundreds of millions of minutes of compute time, just to determine basic properties of stars. That amounts to over 200 years of computer time.

Computers linked in a cluster can compute faster, but even using a computer cluster, it takes three or more weeks to “solve,” or determine all the parameters for, a single binary. This challenge explains why there are only about 300 stars for which astronomers have accurate measurements of their fundamental parameters.

The models used to solve these systems have already been heavily optimized and can’t go much faster than they already do. So, researchers need an entirely new approach to reducing computing time.

Using deep learning

One solution my research team has explored involves deep-learning neural networks. The basic idea is simple: We wanted to replace a computationally expensive physical model with a much faster AI-based model.

First, we computed a huge database of predictions about a hypothetical binary star – using the features that astronomers can readily observe – where we varied the hypothetical binary star’s properties. We are talking hundreds of millions of parameter combinations. Then, we compared these results to the actual observations to see which ones best match up. AI and neural networks are ideally suited for this task.

In a nutshell, neural networks are mappings. They map a certain known input to a given output. In our case, they map the properties of eclipsing binaries to the expected predictions. Neural networks emulate the model of a binary but without having to account for all the complexity of the physical model.

We train the neural network by showing it each prediction from our database, along with the set of properties used to generate it. Once fully trained, the neural network will be able to accurately predict what astronomers should observe from the given properties of a binary system.

Compared to a few minutes of runtime for the physical model, a neural network uses artificial intelligence to get the same result within a tiny fraction of a second.

Reaping the benefits

A tiny fraction of a second works out to about a millionfold runtime reduction. This brings the time down from weeks on a supercomputer to mere minutes on a single laptop. It also means that we can analyze hundreds of thousands of binary systems in a couple of weeks on a computer cluster.

This reduction means we can obtain fundamental properties – stellar masses, radii, temperatures and luminosities – for every eclipsing binary star ever observed within a month or two. The big challenge remaining is to show that AI results really give the same results as the physical model.

This task is the crux of my team’s new paper. In it we’ve shown that, indeed, the AI-driven model yields the same results as the physical model across over 99% of parameter combinations. This result means the AI’s performance is robust. Our next step? Deploy the AI on all observed eclipsing binaries.

Best of all? While we applied this methodology to binaries, the basic principle applies to any complex physical model out there. Similar AI models are already speeding up many real-world applications, from weather forecasting to stock market analysis.

Andrej Prša, Professor of Astrophysics and Planetary Science, Villanova University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Tracy Gleason, Wellesley College and Stephanie Madsen, McDaniel College

The coach, the specialized equipment, the carefully tailored exercise regimen – they’re all key to athletic performance. But imagination might be an unexpected asset when it comes to playing sports.

The idea that athletic achievement depends on the mind isn’t new. Sport psychologists have known for years that working with an athlete on their mental game – visualizing the skill, kinesthetically feeling the swing – has a positive impact on actual performance. But these mental simulations draw only upon mental imagery – seeing and feeling the physical goals in the mind’s eye. Imagination offers a much wider range of possibilities.

What if your game could be helped by an imaginary friend?

In a recent retrospective study of college students, we discovered that imagination comes in handy in athletics in ways that are surprisingly social. The creation of what we termed imaginary athletes – a person or being that a child imagined in the context of athletics – enabled and motivated athletic play, especially for children between the ages of about 6 and 12. Imaginary athletes also provided companionship during athletic play.

Remembering childhood imaginary athletes

The most basic form of an imaginary athlete might be a wall, fence or even tree that makes a good opponent in a pinch. For a child or adolescent practicing a sport alone, a surface that provides a ball return or a steady target for a throw gives opportunities for practice usually requiring other players.

Is it any wonder, then, if the branches of the tree start to resemble a wide receiver’s arms, or an invisible goalie emerges in front of the fence? Solitary play might be a lot more fun if a make-believe teammate could provide an assist, or an invisible coach could appear and shout instructions during practice.

The college students in our study reported that such support, even if imaginary, made them play a little longer or try a little harder as kids.

About 41% of our sample of 225 college students reported creating at least one imaginary athlete at some point in middle childhood or early adolescence. Most, but not all, of these beings fell into three categories based on their characteristics.

The first we called placeholders, such as ghost runners. They are typically generic, amorphous, imaginary teammates created by groups of children when not enough real players are available.

The second type functioned as what we named athletic tools. They helped kids focus on their performance and improve their skills, usually by providing a worthy competitor, sometimes based on an admired professional athlete. The skills of athletic tools were often just above those of the child, drawing out the desire to be better, stronger, faster.

Social relationships, our name for the third kind of imaginary athlete, primarily served emotional functions, relieving loneliness and providing the child or adolescent with a sense of belonging, safety or companionship as they engaged in their sport.

Students who remembered imaginary athletes differed from their peers in two ways. First, more men than women reported creating these imaginary beings, possibly owing to the greater investment in and importance of athletics among boys versus girls. Second, people with imaginary athletes scored higher than those without on a current-day measure of predilection for imagination, but they were not more likely to report having created a make-believe friend or animal as a child.

Imagination is a valuable power

Creating an imaginary other might seem like a quirky, perhaps even childish, addition to sports practice. But actually, this behavior is entirely logical. After all, imagination is the core of human thought. Without it, we couldn’t conceptualize anything outside of the present moment that wasn’t already stored in memory. No thinking about the future, no consideration of multiple outcomes to a decision, no counterfactuals, daydreams, fantasies or plans.

Why wouldn’t people apply such a fundamental tool of day-to-day thought in athletic contexts? Participation in sports is common, especially among school-age kids, and many college students in our study described drawing upon their imaginations frequently when playing sports, especially when doing so in their free time.

The creation of imaginary athletes is also unsurprising because it’s one of myriad ways that imagination enhances people’s social worlds throughout their lives. Above all else, social relationships are what matter most to people, and using imagination in thinking about them is common. For instance, people imagine conversations with others, particularly those close to them, sometimes practicing the delivery of bad news or envisioning the response to a proposal of marriage.

In early childhood, kids create imaginary companions who help them learn about friendship and other’s perspectives. And in adolescence, when people focus on developing their autonomy and their own identities, they create parasocial relationships that let them identify with favorite celebrities, characters and media figures. Even in older age, some widows and widowers imagine continued relationships with their deceased spouses. These “continuing bonds” are efforts to cope with loss through imaginary narratives that are fed by and extrapolate upon years of interactions.

At each point in their developmental trajectory, people might recruit imagination to help them understand, manage, regulate and enjoy the social aspects of life. Imaginary athletes are merely one manifestation of this habit.

Because so many children and adolescents spend a lot of time engaged in sports, athletics can be a major environment for working on the developmental tasks of growing up. As children learn about functioning as part of a group, forming, maintaining and losing friendships, and mastering a range of skills and abilities, imaginary athletes provide teammates, coaches and competitors tailored to the needs of the moment.

Of course, an imaginary athlete is but one tool that children and adolescents might use to address developmental tasks such as mastering skills or negotiating peer relationships. Children who aren’t fantasy-prone might create complex training regimens to practice their skills, and they might manage their friendships by talking through problems with others.

But some report that turning inward generated real athletic and social benefits. “I got confidence out of my [imaginary athletes],” reported one participant. “If I could imagine beating someone, and [winning], then I felt like I could do anything.”

Tracy Gleason, Professor of Psychology, Wellesley College and Stephanie Madsen, Professor of Psychology, McDaniel College

This article is republished from The Conversation under a Creative Commons license. Read the original article.