 So I guess you're all familiar with these types of graphs and headings that some people fear that they might lose their job actually that a huge amount of jobs is being lost due to automation, due to robots picking our jobs and so forth. So if you think about it, of course, it has some truth in it, right? We are facing technological progress. We are facing digitalization, automation, and this has an impact on the labor market, obviously, and also on the demand for certain occupations. For the skills we all need, it affects also very traditional jobs as nurses that have been mentioned before because a nurse nowadays has to have a very different sets of skills to be able to conduct her or his work than in the past. And this all has some repercussions on the labor market also policies and how we have to deal with it. And if you have a look to the graph on the right, I mean, a more recent phenomenon is, of course, the COVID-19 crisis, where we see that also has a huge effects on the labor market, but the very unbalanced effects. You see some of the sectors such as restaurants and bars and so on, they are basically shut down. So there's a huge amount of people who are unemployed now. Well, on the other hand, we also see sectors such as the retail online sectors who face a huge increase in demand for their products or services. So there's a huge unbalance and how nice would it be if we are able to move to transit some of these people who lost their jobs recently to some of the other sectors or occupations that are in great demand nowadays. And this is basically what we're working on since a couple of years now. We developed a new approach and basically a new tool to conduct skills-based labor market analytics and labor market policies. And we do that based on a unique data set in which we combine structured data from the lower labor market forecasts with unstructured data from labor market vacancies. And up to now, we are acquired a data set starting in 2012 with more than 17 million vacancies. So it's really the text, the job description, the candidate description that we are analyzing by means of text analytics, natural language processing techniques. So when we merged that with the labor market analytics, we of course, we apply linear optimization to be able to arrive at meaningful transitions. And this all has provided us with a set with the skills taxonomy of more than 6,000 skills that are actually that that we find on the Dutch labor market. And this also provides us means to give insights not only on trends with respect to sectors, occupation skills and so forth, but also to derive new insights when it comes to these job market transitions. So the transition possibilities from one occupation to another, not only within a certain sector, but even between sectors, which could be very helpful given the current ongoing labor market phenomena. And what we also are able to do to derive is some overlap and gap analysis on the level of the transition. I'll show you in a minute. So we can clearly give information on where is the gap if you switch from one occupation to another one. And this of course informs labor market policies that this could inform education institutions that could inform lifelong learning and so on and so forth. So this actually quite a powerful tool due to time constraints. I'll skip our skills dynamics because we can actually on the skill level provide information which could be interesting for education institutions, for example. What I want to focus on today is our occupation dynamics. And this brings us to what you see here now. This is our so called similarity score matrix is no, you don't have to be able to read it. It's just to give you an idea. It's a 400 424 by 424 matrix. And it actually compares any two jobs, any job pair we have in our data set based on international isco classifications. And it gives us information on how similar these two jobs are with respect to the knowledge, the abilities, the skills, also the level of education, the work experience, and so on and so forth that's needed in these two occupations. And you see the darker the rectangular, the more the similarity, the more they are alike actually, which could inform any transition as you might expect. And if we combine this information on the similarity of any two jobs with labor market information, that brings us to these to good fit labor market transitions, because of course, it doesn't make sense to make a transition from an occupation that is maybe shrinking to another occupation that shrinking, you might want to switch or to transfer to a sector or an occupation that is where you have a stable labor market perspective, of course. And if we do that, if we combine the similarities and the labor market data that brings us to these transitions for, for example, I'm sorry, they're not all translated into English. But for example, it shows the secretaries, they have some transition possibilities and the secretary is one of the most frequently mentioned examples of people that will be redundant due to automation. But you can also see in yellow or in orange, you see, for example, the call center employees, which could easily or might be replaced by chatbots at some point. Even for these people, we do find some transitions. And if you, if you move a level down from the more general labor market perspective, we actually arrive at these numbers. And this brings me back to the first slide where you saw this quote that 99% are afraid of or we are some people are afraid that 99% of the jobs being lost. This is really not true, at least if you apply a skills based approach. And that's what you can see from the upper right table, or sorry, the upper left table, where we see that we find that for only 1% of the occupations in the Netherlands, we do find no option whatsoever to to transit from from the current occupation to a different one. And if you have a look at actually for the majority, for the vast majority of jobs, we do find transition possibilities and actually quite a bunch of them we do find a lot. If you have a look at the upper right part now, you see that on average, any occupation in the Netherlands has 33 possibilities to transfer. And if we play around with the constraints, because some people might not want to switch from an occupation when they are, when they would earn less, then of course we can also loosen or tighten these restrictions. But on average, we do find very many possibilities. But of course you have to know where to find them, right? So what are these possibilities given my current occupation and especially given my current skill set? Maybe one short remark, because gender is an issue today, and of course also in general. The interesting thing is if you do these calculations on how all these changes affect women and men, you find very often in international literature that women are more affected, more negatively affected by these developments, especially by automation than men. And we do actually do, we don't find that. So for the most recent study, the monitor of last year, we found that it's even less, it's only one third of females that are affected negatively, that would have to make a transition to deliver market developments while in the past it was 50-50. So I think this is already a good takeaway for women here, because in the US, for example, it's different. The idea is that it's mostly the female occupations that will be replaced. We don't find that here. So but if we go again one step lower, then we can have a look at the transitions given a certain occupation. This is one example starting at the shrinking occupation, and I'm not sure whether you can read it. It's at least not translated into English, I'm sorry. But at the left in blue, you see the aircraft mechanic. An aircraft mechanic, a Flichtermotor. This is one of the foremost occupations that's affected by corona, because the whole sector is basically down. This is why this occupation is also shown in blue. So there are very many people who lost their jobs or are going to lose their job. And this is actually why we did this study for the employers of Schiphol. And they asked us, where could we find new jobs, suitable new jobs for our people? And this is actually what you can see in the middle of a column. These are all the possible transitions given our calculations. Also informs these aircraft mechanics about the expected salary they could earn, whether it's still at the same skill level, the new job or not. So this can already provide a lot of information. It can even provide a more longer term labor market perspective, if you have a look at the right column, which is actually the second transition possibility. So people could, because now maybe they already left their jobs, so they could make this transition to one of the jobs in the middle. But if they don't like it, or if they want to earn more, because often these transitions are also including labor salary increases, then they could make again another switch. But of course, given what I told you at the beginning, and we all know from the COVID crisis, you can also put it the other way around. So there are lots of sectors. So one of the examples is also the energy sector. This is not really related to corona, but this is more related to the energy transition, where we all have to deal with, because we have to become more sustainable with respect to the energy provision and distribution. So if you have a look at the energy sector, they don't know where, for quite a lot of their occupations, they don't know where to get the people from, where to find them. So they gave us a list of the most urgent shortage occupations. And this is one example. This is the block mason. And there we could actually see from our database, from our tool, where they could find people who already have a decent set of skills, competencies, and so on, to make this transition also in a feasible amount of time, right? Because you don't really win if you have people who would be able to fulfill this occupation in about five years. Patricia, sorry to say you're running out of time. So could you kindly wrap up? Sure, I will. Thank you. So all this brings us to this new idea of the labor market, which provides us with the tool to make it much more fluid in a way, right? So the redundant aircraft mechanic could become an electricity net technician. And if this person wants to do so, we can inform them on the specific gaps and overlaps. So what type of knowledge they still have to learn? Which skills do they have to acquire at what level? And what do they already know? Or have in what they, yeah, what do they already know? And what they have to learn still. So this is what I wanted to tell you. If you have more questions, feel free to contact me. Thanks.