 presentation and our next speaker is from Spain and his name is Antonio Lasanta. Antonio, please start. Hi. Hi. Can you see my presentation? Yeah, yeah. We have your resume. Can you hear me? Okay. No. Yes. So Julia, please mute your microphone. Yeah. Please go ahead. Okay. Thank you for this opportunity. I'm very happy to be here because this is a very dynamic and nice style of conference. I like this. Okay. I will start to present my work but before I would like to thank my collaborator. The first one is Raul, our former chairman. After Raul, we have Franti Covegar Valle, Michelangelo Pérez Campaño, Anandre Santos, Aurora, Manuel, Antonio Sada. Antonio, I cannot see your slide. I see only a dark screen. No. Is that my problem? No. Yeah. Yes. Yes. It's okay now. Okay. That's right. No. Bigger. Okay. No. I don't know but I think you have, yeah, other rules have the same problem. I think when you go to full screen. Yeah. I'll try this other one. This one? Can you see this? It's okay now. No? It's okay. Yeah. Okay. And this is my collaborator. This is my publication. Okay. I first will start using a definition of what is a memory effect. I think the main point is a possible definition. After, I will show some sample. Very interesting. I think I'm very amazing in samples. And after that, I will present my own work about two memory effects with names. Those are callback effects and PEMBA effects. Okay. What is a memory effect? Okay. A definition would be memory connote, the ability to encode and access and erase signatures past history in the state of assistance. This is a definition from this very nice report of this memory formation in Mars. Okay. This is a thing that you can understand easily. My first example, a shared memory effect in a morphosol. Okay. This is based on this report. If you have an ensemble of Leonard Jones articles, for example, you can try this sample to prepare the sample to have some properties or some memory. So you can try your system doing one cycle of training. You apply an strain to the right and strain to the left. If you do this once, you have this square displacement. If you do this twice, you have the green line. If you do this three times, you have the red one and so on. So we can learn from here that we can try our system, even a very simple system, such as a Leonard Jones mixture. Okay. This is the first one. The first well-known effect is the aging effect. You can, for example, you have a glass. Glass is a collection of Leonard Jones. Also, again, Leonard Jones articles, and you can fix a temperature. You wait with this temperature at C1. You wait this time as you obtain an aging. The name is aging effect. After you change again your temperature and your weight, and you obtain this curve. Rejuvenation. Okay. Finally, you change again thermostat to the first one temperature, and you obtain this curve. Okay. This aging. If you once you have done this procedure, you can go back with the temperature and obtain this curve. You have trained. Okay. You have include memory in your system. In a spin glass is a well-known effect. If you do this in a spin glass, this is theory. Okay. Simulation. And this is an experiment in this well-known paper. You can hot your system, wait for a while. At this point, and after, hot again your system, wait for a while, and so on. Okay. This is the white, the not filled point. Okay. You go in this direction. And if you go back, the same procedure back, without waiting, your system follow the same trajectory. Can you see the filled dots? So I think this is very interesting. We have another kind of memory effect. The third one, we can go to living matter and apply and apply a flux, a force in a red blue cell. And so you can see that the cell and the center of the cell, the concave place, because the cell is the concave. You can see how this is a strength, a change the position, and also the length. Okay. If you put out your force, the cell, back to the original form. So the red blue cell, fortunately, have memory. I think it's very, this happened when you, when the red blue cell go out the heart. Okay. After example, is the acting network, if you are pretty. Okay. I have a question. So what type of force do you apply in the red blood cell? When the flux, when the red blood cells are going out the heart, very strong pressure and strain is applied in the flux, okay, in the blood. So you are doing a compression, like a compression. Okay. Okay. Okay. So it's, it's not an experiment with a single red blood cell, but with a stream of red blood cells. Okay. Okay. I think I need a bit fast because I would like to, this is only to explain a general perspective memory, different memory effects. Okay. Okay. This is another experiment. You apply a strain in an acting network. So the, this acting network is distributed in a, in an optimal, in an optimal distribution in the sense. And so when you try again to apply this strain to this acting network, it is easier than the first one. You can see a little bit, but you have this disorder. And when you apply a compression, okay, you, you see that all the, the network is in more or less in the same direction. Okay. So you are applying some preparation or some procedure where the system remember something. How, how do you prepare the system or so on. Please make the presentation full screen. It's really difficult to read the phone. Okay. Because before you can see in the, in the, okay. Now can you see? Yes. Yes. Okay. You can see the last one. Can I ask one question? Yes. Are there results that you were showing in the previous slides, the results of experiments or are also theory or computation? Sorry, I can hear you. Is it okay now? Yes. The, the result, the results that you show in the previous few slides are the results of experiments or theory and computation? No, about the acting network as experiment, as experiment. All of them experiment. Okay. Yeah. Thanks. Okay. This is my last sample. This is a memory of affecting HIV virus. Okay. This is very interesting because this is also a coronavirus kind of virus. Okay. This is a very interesting paper where they studied that when you, when you went on antibody act on a HIV virus, this virus remember where this antibody act. So they have picked in, in these places where the virus act. So the virus mutes and remember where the antibody act. So the next time that the antibody act on this virus is innocuous. Nothing happened. Nothing happened. So the virus is very difficult to design a, a vacuum for example. And it's left. Sorry. Only five minutes left. Antonio, you have only five minutes. Oh, okay. Yes. Okay. Okay. So this can be modeled with a simply hoping model. You can define the minimum, the minimum, the model. And so you have a memory in the same, and you modify this minimum, minimum. And so you have a different structure of the area. So this is only a past abstract of the paper. Okay. And the last one is this associative memory that you can design a pastel. So the, you only can order the, the pieces in one in two or three or whatever kind of distribution. You can see here the one, two or three minimum of the, of the energy of the, of the entropy of the, of the system. Okay. This is our sample and not my example. Okay. What is the COVAX effect? Does the COVAX effect and the Mpemba effect are effects where you do some protocol or prepare the system in some way. Okay. This is the original experiment where you have a protocol, you hold the system, maintain the temperature constant and when at the, at the middle of the protocol of the heating protocol, you change the thermostat and you observe a bump. Okay. We will see this in another resort. This is more or less the same. You have, you can see those, those bumps. Okay. Okay. My interest, ground arm medium. What is a ground arm medium? It's a prototypical sample and only equilibrium system. Why I'm interesting, interested in ground arm medium because we can compute everything in the delivery. We have kinetic theory and we can compute sample coefficient, aerodynamic equation. And we also know how to simulate the system. Okay. You see more dynamic simulation. So we can try to study or understand some memory effects and to see if we have some elementary ingredients to understand, elementary ingredients to reproduce those memory effects. Okay. In the first case, the cobalt effect. So we can see our protocol. We have the system at one temperature with a thermostat. Our thermostat is a Gaussian noise teaching the particles. Okay. And we can see here the red card where the system is cooling and we stop the cooling and change the thermostat. Okay. And we have this hand, this bump in the temperature is in the figure. We have to cobalt effect in the same sense and direction of the heating or the cooling. Okay. That work by Paul and Trisa. Why can do this? That this happens because if we compute the hydrodynamic equation, we see, we can see that the temperature is not an autonomous equation. Depends also of this coefficient A2. What is this A2? This is the cartosis, the non-gassianity of the distribution function. Okay. So we can see that we can tune this kurtosis. Okay. We can tune this kurtosis and obtain some kind of memory effect. Okay. Because the temperature in us autonomous that depends on this kurtosis. So we can see effectively that we have this hand. This hand depends of the difference of the two kurtosis, the initial kurtosis A2 initial and the final kurtosis. Okay. This is the theoretical explanation in ground matter of this type of memory effect. What do you mean of this? But this is that the system remembers where are you coming from? Okay. We can see also in a other kind of download medium that we have friction between particles. So we can have this kind of cobalt effect that is a giant cobalt effect. Okay. And my last sample is this. I think it's the most interesting. It depends on the effect. What is the depends on the effect? It depends on the effect. It's an effect. Antonio, sorry. Please wrap up your presentation. Okay. Yeah. Thank you. Yeah. Okay. You have 30 seconds time still. Okay. No, it's okay. You can finish that here. Questions? I'm sorry, but maybe I have been very fast or too fast. Okay. Thank you. You finish your talk. Okay. So any questions? Okay. Let's thank or speak.