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Aquatic carnivorous plants with ultra-fast traps studied

Circa 2010


How do Utricularia, aquatic carnivorous plants commonly found in marshes, manage to capture their preys in less than a millisecond? A team of French physicists from the Laboratoire Interdisciplinaire de Physique has identified the ingenious mechanical process that enables the plant to ensnare any small, a little too curious aquatic animals that venture too closely. It is the reversal of its curvature and the release of the associated elastic energy that make it the fastest known aquatic trap in the world. These results are published on 16 February 2011 on the website of the journal Proceedings of the Royal Society of London B.

Utricularia are that capture small prey with remarkable suction . Utricularia are rootless plants formed of very thin, forked leaves on which wineskin-shaped traps, just a few millimeters in size, are attached. Only the flowers, standing on long stems, stick out of the water. The traps are underwater. When an aquatic animal (water fleas, cyclops, daphnia or small ) touches its sensitive hairs, the trap sucks it in, in a fraction of a second, along with water, which is then drained through its walls.

In order to understand the mechanical process involved, the researchers observed and recorded the extremely rapid movements of the capture phase with a . The scientists show that the trap door buckles, which reverses its curvature and allows it to open and close very rapidly, thus entrapping its prey. The suction time (less than a millisecond) is much shorter than was previously assumed.

DeepMind AI learns simple physics like a baby

Inspired by research into how infants learn, computer scientists have created a program that can pick up simple physical rules about the behaviour of objects — and express surprise when they seem to violate those rules. The results were published on 11 July in Nature Human Behaviour1.

Developmental psychologists test how babies understand the motion of objects by tracking their gaze. When shown a video of, for example, a ball that suddenly disappears, the children express surprise, which researchers quantify by measuring how long the infants stare in a particular direction.

Luis Piloto, a computer scientist at Google-owned company DeepMind in London, and his collaborators wanted to develop a similar test for artificial intelligence (AI). The team trained a neural network — a software system that learns by spotting patterns in large amounts of data — with animated videos of simple objects such as cubes and balls.

What If Physics IS NOT Describing Reality?

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Neils Bohr said, “It is wrong to think that the task of physics is to find out how Nature is. Physics concerns what we can say about Nature.” Well it turns out that if we pay attention to this subtle difference, some of the most mysterious aspects of nature make a lot more sense.

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Peter Tse — What Makes Brains Conscious?

Everything we know, think and feel—everything!—comes from our brains. But consciousness, our private sense of inner awareness, remains a mystery. Brain activities—spiking of neuronal impulses, sloshing of neurochemicals—are not at all the same thing as sights, sounds, smells, emotions. How on earth can our inner experiences be explained in physical terms?

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Peter Ulric Tse is Professor of Cognitive Neuroscience in the department of Psychological and Brain Sciences at Dartmouth College. He holds a BA from Dartmouth (1984; majored in Mathematics and Physics), and a PhD in Experimental Psychology from Harvard University (1998).

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Closer to Truth, hosted by Robert Lawrence Kuhn and directed by Peter Getzels, presents the world’s greatest thinkers exploring humanity’s deepest questions. Discover fundamental issues of existence. Engage new and diverse ways of thinking. Appreciate intense debates. Share your own opinions. Seek your own answers.

Dark matter: Our review suggests it’s time to ditch it in favor of a new theory of gravity

We can model the motions of planets in the Solar System quite accurately using Newton’s laws of physics. But in the early 1970s, scientists noticed that this didn’t work for disk galaxies —stars at their outer edges, far from the gravitational force of all the matter at their center—were moving much faster than Newton’s theory predicted.

This made physicists propose that an invisible substance called “dark ” was providing extra gravitational pull, causing the stars to speed up—a that’s become hugely popular. However, in a recent review my colleagues and I suggest that observations across a vast range of scales are much better explained in an alternative theory of gravity proposed by Israeli physicist Mordehai Milgrom in 1982 called Milgromian dynamics or Mond —requiring no invisible matter.

Mond’s main postulate is that when gravity becomes very weak, as occurs at the edge of galaxies, it starts behaving differently from Newtonian physics. In this way, it is possible to explain why stars, planets and gas in the outskirts of over 150 galaxies rotate faster than expected based on just their visible mass. But Mond doesn’t merely explain such rotation curves, in many cases, it predicts them.

Faster-Than-Light Travel Could Work Within Einstein’s Physics, Astrophysicist Shows

For decades, we’ve dreamed of visiting other star systems. There’s just one problem – they’re so far away, with conventional spaceflight it would take tens of thousands of years to reach even the closest one.

Physicists are not the kind of people who give up easily, though. Give them an impossible dream, and they’ll give you an incredible, hypothetical way of making it a reality. Maybe.

In a 2021 study by physicist Erik Lentz from Göttingen University in Germany, we may have a viable solution to the dilemma, and it’s one that could turn out to be more feasible than other would-be warp drives.

Using thermodynamic geometry to optimize microscopic finite-time heat engines

Stochastic thermodynamics is an emerging area of physics aimed at better understanding and interpreting thermodynamic concepts away from equilibrium. Over the past few years, findings in these fields have revolutionized the general understanding of different thermodynamic processes operating in finite time.

Adam Frim and Mike DeWeese, two researchers at the University of California, Berkeley (UC Berkeley), have recently carried out a theoretical study exploring the full space of thermodynamic cycles with a continuously changing bath temperature. Their results, presented in a paper published in Physical Review Letters, were obtained using geometric methods. Thermodynamic geometry is an approach to understanding the response of thermodynamic systems by means of studying the geometric space of control.

“For instance, for a gas in a piston, one coordinate in this space of control could correspond to the experimentally controlled volume of the gas and another to the temperature,” DeWeese told Phys.org. “If an experimentalist were to turn those knobs, that plots out some trajectory in this thermodynamic space. What thermodynamic geometry does is assign to each curve a ‘thermodynamic length’ corresponding to the minimum possible dissipated energy of a given path.”

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