 Magandang araw mga kababayan. We're back in TV UP, TV UP's science innovation series. And our topic today is very exciting. It's complexity science. I'm Giselle Concepcion, a professor at the Marine Science IGWP Dilliman. I do research on marine drug discovery and development. And with me are my co-hosts, Fidel Nemenzo, professor of mathematics, majoring in number theory and coding theory, as is area of research. We have Benji Baliejo, professor at the Institute of Environmental Science and Meteorology. He's a biogeographer and ecologist. And we have two guests, our young professors and scientists from the Institute of Mathematics. Professor Joma Escaner, who is into differential equations and mathematical modeling and heads maths, finance, mathematics. And we have Associate Professor John Rob Bantang of the National Institute of Physics, who is an expert in complexity science. So I'd like to start this exciting discussion with the rich, diverse expertise of our guests by asking Fidel to explain to our audience what complexity science means. What does it cover? When you think of complexity science, it's really a framework for analyzing systems. My main question, what is common? What do things such as Facebook, social networks, traffic, ecosystems, even the finance market, what do they have in common? These are examples of complex systems. A complex system basically is a dynamical system of different parts interacting. But the behavior of the entire system is determined by the interaction of the parts. Actually, I wanted to ask John Rob, who is an expert in complex systems, what is the simplest explanation of what a complex system is? What is the difference between a complicated system and a complex system? Well, first, based on its term. When you say complex, it's the opposite of simple. It means that complex systems are actually composed of simple systems being brought together by something. And that something is exactly the point that you're trying to say, that there's interaction between these simple parts. And the thing in complex systems is that their behavior cannot be simply derived from the sum, total, or average of the individual parts that composes it. But instead, it is very dependent more into the interaction between the parts. In this case, according to the complexity science, quite a view, it's very difficult if you can even understand a complex system by simply looking at the parts. Rather, you have to look at the complex system as a whole, as one system. And here, the difficulty arises because the usual physical and mathematical techniques is very difficult to be applied to them. For example, thermodynamics does not follow. You cannot apply thermodynamics very simply to these systems. For example, when you talk about granular systems, it's another one. It's like when you look into rice, I think many of our audiences are familiar with it. When you try to remove the palae from the rice, usually you shake. And it turns out that shaking is actually the right way to do this. Because according to experimental evidences, the palae actually goes on the surface. And this cannot be described by the usual Navier-Stokes equation, differential equation. And our current models are still trying to explain how it happens. Ma'am, I would like to go back to the metaphor of palae. I wanted to discuss traffic jams, for example. But one of the key features they say about complex systems is that it is nonlinear. Can you explain what nonlinearity means? Well, from mathematics we know nonlinearity means that one state is mapped into two or more different states. And in case of traffic, for example, you can never say that one method in solving a traffic problem will affect the same today and tomorrow. Because exactly the response of the system or the mapping of the system is different. It can be mapped into many different states, nonlinear. So the response of the system may be totally different from the response today. That's what makes it very difficult. For example, when you have a jitney in a traffic situation and it park in one side of the road, you'll never know how far the effect is. Maybe it's far down the road in a jam because it started here and then everyone blocked the way, or maybe not. That's the nonlinearity there. So a complex system is inherently complicated, but not all complicated systems are actually complex. So to put it in a Venn diagram. How about, can you explain, people talk about the butterfly effect? I think you know this in nonlinearity. Can you explain that to the audience? The butterfly effect is an unusual adage when you talk about mathematical case. I think everyone will understand. Chaos is not like, you know, magulok, usually when you say about chaos. Ah, so chaotic, this is so chaotic. It's actually not the chaotic, the mathematical case is not that chaotic magulok as we have in Filipino. Rather it is more of a sensitivity to the initial condition. So it means that, say traffic is a chaotic system in a sense that it really is very dependent on the initial condition. So in your decision whether to park even for a moment or just park for maybe another minute will have a very different effect in the traffic condition. Not just in one part of the community, but probably in the entire metro. So this is an example of a butterfly effect that a very small effect can give or yield a very tremendous different effect in the future. So that was very interesting to me as a chemist and a biochemist. And because we are teaching, team teaching a course on biological and social structures. And I just finished my several lectures on thermodynamic and kinetics. Thermodynamics, very limited in terms of the path that change will take. Whereas kinetics captures that pathway of the change. So the way you describe it to me, it looks like there's lots of kinetic based equations in complexity science. And am I right, Joma, is that simply expressed by differential equations? Well usually when we do mathematical modeling, we make use of differential equations. So differential equations are equations that would involve derivatives or changes of quantities over time. So for example, changes in the population over time can be affected by different factors or different things as well. So that's how we work with it. So there's nothing totally esoteric about complexity science. It's still rooted in the fundamental physical, chemical principles that we know from way back. And that's why we start our course on biological structures that way. And I would imagine that your area of research, John Robb, is, well it covers so many disciplines. And you are involved in one ecosystem research with biologists and sociologists. And some of them are actually urban planning experts. So when you look at the development of a community, there are so many factors that you need to consider. There are economic factors, social and psychological factors, and even physical factors. For example, physical in the sense that how are they separated from each other? Whether they are separated by natural boundaries like rivers or mountains? Or how far they are from their source of livelihood? How arranged are their source of livelihood? For example, if you're doing farming and they use fertilizers, it will affect river systems because of the neutrification and other imbalance in emissions. And then it would also depend on economic factors like which part of the system or population system has more opportunities to sell their products or transport their products. So those who are near the shore are usually more economically progressive because of the availability of export, being able to communicate and interact economically with the neighboring cities. And so in the one ecosystem, we try to understand how the ecological and social and economic and social profile changes over time. For example, we know that in the provinces, we have several livelihoods. Some are fishing, others are agriculture, husbandry, even quarries, taking minerals from somewhere, sometimes even kainin. And the problem is, when you say that you kind of do kainin for a time, what will happen to the life of these families? So by understanding the profile of these communities and how they are connected to each other using a complex network approach and other agent-based modeling approach, then we hope to find a certain pattern that might point us out where could be the problem if we impose certain policies. Dear viewers, John Robb is describing an emerging interdisciplinary research program funded by UP in Abra de Ilogu, Negroes, Mindoro Occidental. And what's interesting about that area is there is among that community. And so you're talking about agent-based modeling. And so would you say that the LGU is one agent? Would you say that the community of Mangyans would be another agent? Is that what it means? Or is it livelihood as agents? It can be agent-based modeling is a very broad class of modeling type. But an agent would be defined by the research question. Let's say you're asking about the livelihood profile. The livelihood can be thought of as an agent. But if we're talking about the options that each of the family have or which livelihood they want to take whenever this one is less opportunistic or the other way around, then the family becomes the agent. So whoever is sort of deciding changing the state from, let's say, doing these two in another time doing another state, then that would be the agent. So in case of the policy, the policy is like, what can think of it as like the IC model magnets or you have magnets and each part of the magnet has either spin up or spin down state. Then the whole thing, when you put it in a field, an external field, let's say in another one net of field, then it will react. And the IC model gives a very interesting reaction. And when you apply it to agents, the agents have their own decisions. They have their local conditions that they feel and then they decide according to that. But there's also a global condition which we call the environment. And the environment will give in the boundary conditions, so to say. And that would be provided by the LGU, the national government. And so let's say, for example, there's a policy, maybe every June or July, no fishing. Yes. And that would be a boundary condition to the situation. I see. I think Jomar will be able to explain like, when you have a differential equation, put boundary conditions, then the differential equation would behave systemally differently. So that's very, very enlightening to me boundary conditions. That's something I can understand myself. Coming from biology and molecular and cellular complex systems. But I think Jomar will tell us also something that's biological, medical, infectious, because I understand you're also into infectious disease modeling, endemics, et cetera. So tell us about that, Jomar. One of the research that I'm doing would be on dengue modeling. So we'd like to look into the dynamics of how dengue disease spreads throughout the Philippines. Usually, we use compartmentalized models. So we have three compartments. Basically, there's susceptible. We have infected and infectious individuals. And there are those who are removed or what recovered glass. So that's the basic model that we use. So any changes in the susceptible population is affected by their interaction with an infectious. So it depends on the biting rate of the mosquito, but many of the vector population is affectivity, the resistance of each individual that changes them. So we also have the initial conditions. So we can see how the population of each human and mosquito population works. And from there, we can look at what parameters that affects the behavior or the development of each population. And from there, we can strategize or make out some policies that would control the spread of the disease. So... My job is to have a basic question. What are the data that we need to implement the mathematical model for dengue infection? Most of what we're going to look at is how many people are affected by the disease. And that's one of the problems that is for some researchers. Because the data that we get are those who are hospitalized only. So we don't really actually know how many are really infected. And that's why when we make certain models or certain differential equations, we have to consider that the data that we have are only those which are from the hospitals and not from the houses. So who are you talking about in Japanese encapalitis? Because it's an issue right now. There are a lot of faces. And you're also asking about clinical practitioners. Do you think they will post questions like that? I think we can work with that. With the enough data. Because we cannot look into the parameters. We cannot estimate the parameters. If we don't have enough data. And that's one of the problems here in the Philippines. Because data gathering is one of the issues here. The challenge is here in the Philippines and like in other countries. So the model is only as good as the data and the way you're able to complete the data. You can have a very perfect model. You can have a perfect differential equation. But if you don't have the data, you won't get the correct parameters. And you cannot predict or make any. And it's also the kind or level of data. So I'll just address dengue, which I know has four serotypes. Dengue one, two, three and four. And there's this phenomenon where you get infected with a second serotype. And your dengue becomes, you know, serious. There's a shock and hemorrhage. So unless you're able to get data at that level. You really can't interpret it meaningfully and come up with a good or accurate model. So my question is do you collaborate with epidemiologists, infectious disease, MDs? As of the moment, we don't have any epidemiologists or medical epidemiologists for now. But I had a conversation before. We started with a project, but I got this post for some time. I'm not sure if that particular project progressed. But at the very least we should try to get data during the rainy season. So now we have a lot of rain. So incidents of dengue tends to go up during the rainy season because of the mosquitoes in the breeding grounds, right? I think the problem with the gathering data is for the serotype. It's a practical reason. Because in the root level, if you're a patient, actually there's no advantage if you know the serotype. Practically. And the real problem is mine. Because in the end you have to pay for the testing, you know, the acid. And it's a very expensive one. But U.P. Manila has developed a kit to determine the antibody. Is that already, you know, deployed to all the localities? It's not antibody-based, it's nucleic acid-based. So it is in the process of being deployed by the new age. Once it's either free or, you know... But they introduced it to the public during the National Science and Technology this year in July. That's right. And it's supposed to be point of care. So it's a simple lump-based type of PCR amplification. So it's a nucleic acid-based. So it can be used in the LGUs, in the health centers. So that's one of the major investments of U.P. also. And in the U.S. team? Yes. So you have to connect with our denger researchers in the Manila. But maybe Janra can tell us a bit about your team in Abra the E-LOG. Because you've gathered data. I imagine, quite accurately, as a basis for your agent-based model, which you have published in a scientific journal. I think that you were talking about part of our team. Yes. Part of another team, we're in Manila. Yes. So this is with the purpose. Yes. And then you were able to publish a way to... I think that was about the addiction of the options that they have using game theory. So the team of cross is doing a game theory-based model. So in the game theory, this is another aspect of agent-based model. Especially if you're talking about, you know, models like human beings who decide more, you know, free will. Free will. Yes. Free will. In this case, it's like the idea of economic models. Actually, you know, Jan Nash is the one who came up with this topic of game theory. And in this game theory, you know that you're trying to optimize certain utility given options. And they combine this with their agent-based model. The other team were able, in the group, were able to check on all possibilities of the likelihood. So what likelihood is most likely to appear in this community whenever your policy is this? So you have introduced a certain perturbation in the system. Yes. And in their model, using agent-based model with game theory put in, they can predict which one will come out. Most likely everyone will go to Sare Sare store or everyone will go to fishing. So it depends. Okay. So would you say that at this stage of your research, there are already positive interventions. Do you have a plan, a game plan of how to improve the community of the Mangyans together with the reefs and the ridges? So that was the original name of the program. I think there are, we have some talks, especially with our wooden planning collaborators, that we can actually do some, you know, intervention. And based on the models, we can choose the best possible intervention. But because that's near the Verde Island Passage, which is the area of the highest marine biodiversity in the world, is ecotourism one of the options for that area? Ecotourism? I'm not really sure. But I think ecotourism is one of these options. It's just that I'm not, the plan is not yet that concrete. I'm sure there are plans to apply the models that we have to develop in order to decide on how to manage. We've been involved by the LGV. Yes, yes. Very important. How do you explain this? I'm sure words like complexity, game theory, reputation. This is very interesting. Helen, the head of this program. She's very, you know, eager to share our results with the LGU. And every time we have publication here, she always emails the results now. Hey, we have a result on this and then you do it in the back. But Helen told me the mayor and the wife, who's like a former mayor, they are UP graduates. And they're very, very open and supportive of the work that we are doing there. And I'd like to share at this point that Helen and the team have found a foreign partner that will try to set up an ecotourism program there. And they formed a group called Eco Resilience. Now, Benji here is our expert on ecology. So could you try to model how you think a place like Amra de Ilog should be developed as an ecotouristic site? I've only been there once on the way to the site of the archaeology studies program in San Jose. But, well, I think with complex systems, communities are complex, human communities are complex. And the environment is complex. So, genre was already brought out of the work policy because all of this work would not really mean much if we cannot implement the right policies. That is a major field of research in STS, Science, Technology and Society, emerging systems and complex systems. How it will translate to impact policy. One of the major challenges is this. What everybody has to think in terms of chances or probabilities. He said that the chance of winning the lotto. All of these different events will result in different outcomes. Unfortunately, the government, anybody who is on the policy side would like a sure outcome. And the challenge is how are you going to relate all of these different outcomes and come up with the best for policy. That's one of the research angles in what is called post-normal science. Because all the stakeholders would have to input their information in order to understand and manage the outputs of a complex phenomenon. So, that's what we call post-normal science. It's now being used especially for DRRM. For instance, just to give an example, one of our previous guest director, director Rene Solitun, essentially adopts the purpose because he has been talking to different communities about earthquake hazards. So, earthquakes are also complex things. We kept predicting, but the effects on communities is rather unimaginable for most people, but we have to get a grip on it. So, whatever the responses are, are being translated into some policies which he evokes as adopted in communicating the risks. So, I think in that sense, if in Avro de Ilo, since I really can't say much about it, just pass through it, I saw that there are a lot of different areas. So, that is an ecological risk. So, how will the ecological risk be quantified in the sense of a probability? And how shall we translate the probability easily manageable by the LGU? So, that's one of the biggest challenges. I'd like to say that it's a good point you're making. We used to think of science as a simple scientific method approach. You have a hypothesis, that's the rational part. Then you do some experiments, and that's, sorry, you do some observations with instruments or with your bare senses, and that's the empirical part. Then you do the experiments where you make interventions in the system, and that's the experimental part. And then you have an interplay of the three activities. But now, Fidel, because of the power of numbers and computation, you have the iterative and then the predictive part of science. So, can you tell us a bit more about how one can create these models that can more or less accurately predict what might happen in the future? And therefore, this should be useful in guiding communities to save lives, to improve their livelihoods, and also to protect, conserve the environment. How does this all originate, say, from number theory or coding theory? I listened to one of your lectures in our postgraduate course last year. Maybe I'll talk about mathematics in general, but regarding the construction of mathematical models, later on I'll ask Jomad to explain that because he's the mathematical modeler and basically a pure mathematician. We think of mathematics as the study of numbers and formulas. Actually, mathematics is the study of patterns that uses rules of logic, deductive thinking, etc. It's a way of ordering ideas, ordering patterns. In a sense, it's a logical system. But it's also a tool that is very applicable. We can think of mathematics in the abstract. It's like language. We can invent the terms and symbols, attach meanings to them, and define the rules that govern interactions between the symbols. And then you create a mathematical system. Those rules are called actions. But we can think of mathematics as an abstract system. But what is beautiful about mathematics is because it also provides a language for interpreting natural phenomena. As it's physics and chemistry, in fact, almost all areas of study right now use mathematics. So it's not only an abstract system of thought. It's also a tool that we use to model interpret phenomena. We do not know what the right interpretation is. But through science and mathematics, we always arrive at better and better interpretations of reality. So when we look at a physical phenomena, we find a representation of that physical phenomena into a system of mathematical symbols. Numbers, differential equations, et cetera. We manipulate this in order to be able to predict the behavior of that system. Then you have to test the model. You test the model. Making use of instruments that can get quantitative data. That's what we always emphasize with our students. You can try to do experiments, but if it is not mathematical or quantitative in nature, then your model cannot be very meaningful. It cannot be refined. So, Jomar will tell us more about this. Well, when you do usually experimentation, usually controlled conditions. Sometimes it's very expensive when you do experimentation. When you have the model, when you make a particular model, which is usually based on physics, physical phenomena, because historically, mathematics and science are really very closely fed. Mathematics was regarded the language of science then. So there were probably two schools of thought. There were those who are abstract in mathematics and those who would like to work with the applied mathematics, more practical mathematics. So, as an applied person, I'd like to look into changes regarding people, communities. Any quantity for that matter. And then we look into a particular model based on physical phenomena, physical law, or reaction. And then we do the simulation. So, it's less expensive. That's very important. Because in silico modeling, it's the cheaper way to design and experiment. And at this point, Fidel, I'm really missing one of our colleagues who just passed away. This is Baltazar Aguda, the former executive director of the Philippine Genome Center. So, Baltazar was a chemist, agricultural chemist by training, UPLB graduate. Then he moved to physical chemistry. Then he moved to computational chemistry, computational biology. Then finally, he did genetics and systems biology. And his particular area of research is another major disease area, which is cancer. And that interest I share with him. And let me just tell you why in silico modeling is so important. Also, that modeling based on materials and therefore chemistry, I say that's the most important discipline, chemistry. Because this is a material world. So, you've got to understand a phenomenon on the basis of the materials. Then I'll have to add the energy. Because materials will not behave in a certain way, interact or compete, cooperate or compete. If you do not know the energy inputs or the energy content of the material. So, I always say, Einstein is right. It's really matter and energy that would describe the material world. But anyway, going back to BALTZ and my interest in cancer, it's really a complex system. Because in the cell, which is really the example of a biological complex system that is comparable to an electric circuit network. In cancer, there are key proteins that are mutated by mutagens, environmental factors. There have been researchers who have identified, say, the 5 to 7 major hallmarks of cancer or the molecular pathways in cancer. And you will see this map of the cell. It's actually like a cartoon that shows you mutations in key proteins in these pathways, including extracellular signaling pathways, program cell death pathways. So then cell cycle pathways that govern cell DNA synthesis and then mitosis separation of the cell. So this is very, very complex. And they're all regulated by enzymes. Because you have chemical reactions in the cell and there's lots of enzymes. They're regulated by receptor proteins. So what happens in the cancer cell is if you apply one drug, the target's one pathway or one protein in that pathway, the cell is so smart. It's so complex. Before you know it, it's going to develop resistance to that drug. Why? Because there is cross-talking. There is cross-talking across parallel pathways. So the cell knows how to respond to the external stimulus, which is the drug that will kill it. So nowadays, the approach to cancer therapy is really multiple therapies or synergistic therapies to ensure what we call synthetic lethality. So there's a term now called synthetic lethality in drug research. And you need to target many pathways all at the same time. Ideally, or conceptually, the different targets that are critical in getting that cancer to cell to proliferate. It's a very interesting phenomenon as far as complexity is concerned because complexity is called robustness. So that the complex systems are very robust and as you have described it, you can think of the cell as a network of proteins. This is, I think, five or ten years ago that the entire network of a yeast cell, protein network of a yeast cell was invisible. And from there you can identify important proteins that is responsible for different pathways. And targeting one of these, two or three or three of these, just says that it's not guaranteed to work because there are redundant pathways. It's like traffic. It's like traffic that when you block one lane or block one street, vehicles will just try to use other ways. It's just like that. And cancer is very amazing. Exactly. So I think cancer is still the symbol of our age or the complexity of our age in terms of disease is the most serious disease and it's coming from the environment. So very little cancer is inherited. It's a genetic disease but it's not inherited from your parents. Only a few cases are when the mutated cells are the germ cells and they are passed on to the progeny to the children. Wow, this is a fantastic topic and we have five minutes to go. So maybe we need like a one minute statement from each of us but I'd like to do my statement to say that Fidel is organizing a system-wide, UP system-wide complexity study group or study center. It's about time we do this because there's so many people interested in complexity science. So Fidel, tell us a little bit more about that. Well, one thing we're learning today is that complex systems are everywhere. We've talked about communities in Arbid-Elog, epidemics, ecosystems, cancer. A lot of things around us can be modeled using complex networks. And because of their complexity we need a multiplicity of perspectives and disciplines studying these phenomena. For example, John Robus talking about the project in Arbid-Elog involving your a physicist, there's a biologist, there's an urban planner, there's a sociologist. Phenomenon right now are so interrelated that we need ideas coming from different disciplines. So the world is complex, many of our problems are complex and therefore the solutions will come from coming together of people from different disciplines. That is why we're identifying people who want to work together to look at phenomenon problems like this. So there will be physicists, there will be people in mass communication, mathematicians. We've already heard some interest from people in health sciences to come together. We have a platform where we can put our minds together and look at complex problems of society. So we still do not know exactly what form it will have, but it's about time that we get together people to look at things from a complex science perspective. At the same time, of course, we were talking about data. We need data people to because right now the currency of all science is data. But it's not enough that you have data, but it's important that you extract insights from that data. And this is where we want all these people to be contributing. But actually before we end, can I just go back to the general characteristics of complex systems. We heard the term linearity, not robustness, but other key features, emergent behavior, emergent behavior, self-organization. John, Rob, can you just give us a few lines about these very important characteristics of complex systems? Early on, we thought about that the complex systems are complex, being composed of simple things. And what is essential or rather significant in determining the eventual dynamics of the whole thing is the interactions. And it is in this interaction that certain other properties emerge. It's like how the entire let's say you have a political system, how the entire political system eventually comes out with a uniform opinion about one thing, saying yes to a vote, a decision, or no. That is also a complex dynamic in itself. This is what we call the opinion dynamics in the system of people. And when something emerges, it's just that you cannot study the emergent behavior by simply looking at the level of the individuals composing it. We just know that electrons are attracted to protons, and that electrons are moving around with protons in a nucleus of an atom. Still, it means more in order to study these chemical properties. So in a sense, chemical properties are emergent properties of those components that comprises atoms. Even molecules. For example, one would be baffled by the idea that by itself is an explosive, highly reactive chemical. Chlorine is highly reactive also. In fact, it is very dangerous to biological life. But to put them together, something amazing happens. And in itself, that's an emergent property that you cannot predict from the individual sojourn and the individual comment. We don't have time for this. We don't have a lot of birds. They move in such wonderful unison. They soar, they sweep down, they change direction without collisions. Without nobody, without a single, or maybe a single leader. Basically, this is a very nice example of a complex system that exhibits emergent behavior that is also self-organizing behavior. Like when you look at the birds during their migration. They usually do the delta formation. And you know, delta formation is actually the most efficient formation when you move very fast. Because the one on the front breaks the wing and all the others are more relaxed. They use less energy. And they do that automatically. Nobody tells them to do it. Let's go delta formation. But there's probably a leader. And there's a kind of a quorum system. That's interesting. They have no leader according to... No one assigns a leader. Anyone can be a leader. Yes, yes, yes. So it's every kind of sensitivity about it. Whoever is strongest among them becomes the one. It's what you call self-organization or self-assembly. So this local self-organization that you've seen a flock of birds is something that you can only see at the global level. If you look at the global level, for example, the behavior of each bird, you cannot predict this kind of self-organizing phenomenon. I think we need to talk about this at another show. And I like to ask Banji to talk about the STS. Your baby, now his baby. And it's really the basis for interdisciplinarity. And it really will support our complexity science studies. So Banji... All of these, you know, in the social, interaction of social and science, sighted science, actually complex. And its major relevance today, especially for climate change, with all these hurricanes and typhoons battering the state of Florida in America and Caribbean islands. In Manila. Risk itself is an emergent property of a complex system. That's been long recognized in STS studies ever since. Now, the big challenge now is how can we translate this to policy? Because policy is usually... Usually if you say there's policy, that's it. There's not much room for external order. There's not much wiggle room, as they say. But risk and other responses are not like that. So that's one big challenge for STS now. And it's very relevant for the Philippines, especially for disasters. That's why we have to put some effort in our STS program to be the important focus for research. And it's definitely interdisciplinary. We have to involve all of our expertise in the university. Not just in the Liman, but in other campuses too. Well, dear viewers, I think we'd like to hear from you about this show. One of the hallmarks of complex systems is feedback looping. So we're going to hear this from you. And you can imagine there's so many other topics that we could discuss in Science Innovation series of TVUP, including financial risks and risk-taking. And Jomar is an expert on that as well. So thank you very much, my co-hosts Fidel and Benji, and our young and very knowledgeable guests, Jomar and Jung Robb. And thank you, everyone, for being with us in TVUP's Science Innovation series. Thank you very much.