 Well, good afternoon, everyone. With this being the last talk of the afternoon, I'm happy to say that it will be light, so that's good. In the Oulet Lab, where I worked together with Michael Sint-Huber, we studied the collective behavior of insect swarms. And to be more precise, mitch swarms. But because not a lot of people know what mitches are, usually say insect swarms, mitches are a group of insects that include many small species of flies. And in our lab, we have a big tank where we have a colony of mitches. And mitches are interesting because, unlike other collectively behaving animal groups, they do not show any alignment. They're just randomly moving about, as you can see on the right there, where I'm showing you the long-term trajectories and instantaneous positions. So that's precisely the reason why we're interested in them. We try to measure macroscopic properties of these swarms and then relate that to their individual movement. So the black square that you see below the swarm is called a swarm marker. And it serves the function of a nucleation point. So a single mitch will start to fly above such an object and then other mitches will join it. And in nature, it can be any contrasting object in the environment. In our experiments, we have this black square marker. The biological purpose of the swarming is to mate. So the swarm is mostly composed of males, and then females fly in, and then they get chased and they mate. In a previous study, Nick and his postdoc, Ray Knee, found out that they could manipulate the swarm by moving the marker. They split the marker in two and found that they could create two subswarms that were attracted to each other. And they found that the swarms were attracted because the centers of the subswarms were not aligned with the centers of the marker. So they were slightly closer to each other, which led them to believe that there was an attraction between the subswarms. So as if there is some elasticity. Now, they could not measure the elasticity because we don't know what the force is that the marker applies to the swarm. It's not in contact. It has some effect on the swarm, but we don't know exactly what force that is. Now, in this study, we are moving the marker back and forth like a rheological experiment. And what this allows us to do is we can circumvent the problem. We don't know what force we're applying or what strain. What we see is that a wave propagates upward to the swarm and by studying this wave, we can determine the viscoelastic properties of a mid swarm. Now, this is the experimental setup. As I said, we have a large tank in which we have this colony of midges. The blue bar is a linear stage, which we use to move the black marker in an oscillatory fashion. We have the midges on, I should mention that the species is caronomous riparius. We have them on an artificial day-night cycle, and they swarm on the dusk and dawn of this cycle. Well, the amplitude is 84 millimeters. We vary the frequency. We have not yet done an amplitude variation. That's what we're going to do next. And we use three cameras, and then we obtain stereomatch positions at the frame rate of 100 frames per second. And because the midges are sensitive to light, we use infrared light to light them up. So this is the typical response. So you see the marker in red moving back and forth, and those are the midges. And you can sort of see them moving left and right. If we face average the center of mass of the midges, we obtain the data on the right. So the red is the center of the marker and black the face average center of mass of the swarm. And we see, indeed, that it's oscillating back and forth with a slight phase shift and smaller amplitude. So here, again, is the face average center of mass, but in the bottom of the swarm. And then when we go up to higher layers, we see, actually, that the amplitude decays. And there's also a small shift in the phase, very small, but it's there. So we can fit these curves to obtain the phase difference and the amplitude. And then here, I'm showing you this fitted phase and amplitude on top of the video. So the blue dots are for the different layers, the phase average signal. So that's over full experiment 60 periods. And then you still see the midges behind. And so the signal that you see, the blue dots, is like a very long time effort. So on average, they show this behavior. And what you already can see here is actually that it looks like there's a wave traveling up through the swarm. Now, such a propagating shear wave with the amplitude of the oscillation of the wave perpendicular to the direction of movement can be written like that with the amplitude s. And then the movement direction is z. And we see that the amplitude decay comes from the exponential with minus k i z. And the phase change comes from the minus k r, k r z. The complex wave number k star is composed of k r and k i. And it's also related to g star, which is the complex shear modulus. And the complex shear modulus is composed of g prime and g double prime, the storage modulus and the loss modulus. The storage modulus is a measure for the elastic part of the material or the swarm and the stored energy. Well, the loss modulus is a measure for the dissipated energy or the viscous part. And we can rewrite g prime and g double prime in terms of k r and k i. And then you can already see that if we get the decay of the amplitude as a function of height and the phase change as a function of height, and we fit those, we can obtain k i and k r. And that's exactly what I've done here. So on the left, we see the amplitude as a function of height. And on the right, the phase change as a function of height. So that's the phase difference with respect to the marker. So the marker is phase zero and that's the swarm. And you see there is a small plateau there. So the bottom of the swarm seems to react more uniformly to the marker. But then a little bit higher up, we see a decay of the amplitude and the change of the phase. So when we fit these, we obtain k i and k r and we calculate g prime and g double prime. So here I'm showing you for different frequencies, the loss modulus and the storage modulus. And interestingly, the storage modulus is negative, which is not on physical but uncommon. The loss modulus is linear increasing and also the wave speed, which is simply the frequency over k r, which is linearly increasing. So what this suggests, the wave speed is that the speed of information transferred to the swarms of the order of 1 meter per second. And that's also roughly the average velocity of a midge in the swarm. So that sort of makes sense. I have fitted the loss modulus and storage modulus with these two functions, the blue and the red one. And that's from a model that is based, a viscoelastic model that's based on a spring with elasticity e, a viscous dashpot with viscosity eta, and a mass. So this is the typical Voigt model, but then with a mass added. And what this mass does is it adds some inertia to the system. You can see that through the parameter i rho, it allows g prime to be negative. And the fit actually shows very nicely that then we have a constant elasticity, constant viscosity for the swarms of functional frequency. The fact that there is some inertia is probably linked to the swarm is active. So midges have a certain time before they react. So they're moving in a certain direction. The markings moving back. And they're like, oh, wait. Oh, it's moving. And then they come. So that's where sort of an effective inertia comes from. And then to conclude, the midge swarm's viscoelasticity is an emergent property. It demonstrates that the midge swarm is not simply a group of insects flying above the mark, which might as well have been the possibility that they're all individually looking at the mark and saying, well, we're here because of this marker. But it actually shows that layers higher up in the swarm, they're not looking at the marker. They're looking at midges below them. It can be seen as a bulk measure for the collectivity of the system. Possibly if the system was not behaving collectively, there would not be this measure. And then it tells us about information propagation. So we have the speed of the information. And we have a measure for how much information is lost. That's the loss modulus. And then we have some non-lossy information, the storage modulus. And what we will do next is vary the amplitude to see if there is a linear regime, non-linear regime. And we're intending to have two markers. And then oscillate those to see if we can get maybe a similar assistee from the attraction between two swabs. And with that, I'd like to thank you.