 And all of you have very nice discussions in the breakout rooms. Unfortunately, we did not hear any feedback from several groups about which topic they are presenting. But we hope that there won't be any overlaps of the topics right fine. So what we want to do now is we want all of you to select one person who can volunteer from your group. And that person will have to briefly, very briefly. Doesn't have to be like, I mean, like two to three minutes, you can present what you want, the particular visualization and the data to action framework that you prepared. And then we'll have a discussion around it. We will comment. And also we can open other participants to comment as well. So without wasting much time, shall we start with group one? So who is volunteering from group one to present? OK, I'll put it here for group one. Could you please share your presentation? You can share. Thank you. Let me just share now. OK, OK, thank you, everyone. We couldn't start on time in group one. I think we had a lot of discussion as to what the indicator to choose from. But although it would be fully appropriate our framework here, but I'll quickly take you through it. And I'll just probably talk about the related actions. Because now we couldn't complete the table. So let me quickly share. We chose the second and third 90 indicators here. But we focused mainly on the workload coverage in this particular example. So the objective, I think, I wonder if you can all see our, you can clearly see the visualization. But viral load coverage is the very last bar on the graph here. It is the very last bar on the graph. And so the objective here was to, I think, is to measure the viral load coverage at an initial level against the target of 90% this year, target linear. So we thought that this is the objective is to measure viral load coverage against the 90% target. But this data is shown at an initial level. And our data source here, although we didn't go back into the data sharing screen to see exactly how the forms look, we just believe that this one could be from the viral load testing data that we capture on monthly basis. Because you see here, it is on the visualization, on the last half month. So we took an assumption that this is reported on monthly basis. And here, we believe that if we break our data set into denominator and numerator, so we believe that all people living with HIV who are eligible for viral testing that should compose our denominator while our numerator should be those people who actually test it for not really for HIV. So yeah, I said test it for HIV, but it has to say test it for viral load. So our denominator are people who are eligible for viral load testing. And our numerator should be people who actually test it for viral load. And on the related actions, I think we didn't complete that because of time. But we believe that the actions should be taken for facilities that are for coverage. That is way below the target. And if we look at our visualization, we can know that for all these indicators, or if you look at viral load coverage, this at 53.5, that is way below the expected 90% viral load coverage. So I think the consent parties should take action to ensure that the coverage is good here. But again, there may be a lot of reasons related to this. But now that we didn't have time to explore that, we really tend to approach some informative discussion on that one. I think that's all for Group 1. Thank you. Right. Thank you very much. Just keep on sharing. So we will give the opportunity for other groups to come in. So any comments you have on what the Group 1 has presented or the specific visualization, anything more you can add to what they were presenting? Anything you can add? We can add programmatic implications in related actions, because the achievement is below our target. So we can recommend whether is it due to the functionality of testing facilities or is it due to access to the testing labs? So we need to add some programmatic implications, I think, in related actions. Yeah, very good. So basically, like we have to write what you can do is you can go at a detail about actions. So if it is one thing you can do is you can have a cutoff value, it's just not that 90% trend line. But in case what to do if it is more than 100 or like you can have multiple, and for each of them, you can think all possible combinations for variations based on the numerator and the denominator as well. Thank you. The comments? OK, although we were presenting it, can I have a question? Yeah. OK, thank you. I just want to know on the indicator patch, like we put a viral load coverage. So I'm just trying to come to a real-life situation where we have a test with this one here or a graph, like this one, which is composed of many indicators. Can we put all of them there? And on the data source, put all of the narrative for the indicators that we find in the graph or what is the practice here? What is the practice can we have all the indicators written and then show the graph objectives? How do we put it? Thank you. I don't know if I'm clear. It is not totally clear to me. You mean like if you have multiple data items coming from different sources, whether to mention them in data source? So yeah. Yeah, so assuming if that is the question you have, yeah, definitely like say, for example, if your numerator comprises of multiple data items which are coming from multiple forms that you mentioned and also another thing that you have to take into account is if you are just looking at, say for example, if you're looking at a coverage indicator, which is formulated based on numerator may be coming from monthly period and denominator may be just annual population count or something like that. I feel even it is important for you to make that also as a node because otherwise what will happen is whoever who's trying to interpret, that person will have to make an assumption. So that's what we want to get rid of by having this data source or what most probably I think the data source would be the place that you can mention. The periodicity if it is relevant. Saurabh, you want to add anything? No, no specific comments. Right, okay. So I guess we can, yeah, thank you very much, Groupon. Nicely done. So maybe we'll give the chance to Group2 to present. Somebody from Group2, volunteers. Good morning. Good morning. This is Larry from Group2. So I will share my screen. But it's, let me just put a disclaimer. Right now it's an incomplete presentation, but... No problem. We all know like we didn't give you enough time. So no problem, please proceed. Okay, so we had agreed to cover the HIV ad coverage rate for the whole of training land. And I'm sorry, but then the visualization, as you can see, we tried to make the picture fit in there, but then you can barely make out what is being presented. But then I will walk you through the whole presentation. Now the objective of the visualization is to measure the performance of ad services delivery. So we figured high coverage would be indicative of a good service linkage between testing and the actual initiation on ART. And our data source is the HIV ART program monthly data set. The numerator we're using is the number of people who are actually on ART and the denominator is the number of people that tested HIV positive. So the related actions, we figured that if any of those districts records a rate which would be less than 60%, the administrator would be able to do that. The administrator is to check whether there's under reporting of initiations of people on ART. Also, it will be a good idea if they were to check the causes of leakages if they are actually leakages between testing and initiation. And well, the others, as I said, it's an incomplete presentation, but then if it happens to be in the blue, it will be greater than or equals to a hundred percent and it will be always best to maintain such efforts. The other legend sits 60% to 75, 75 to 89, 90 to 100%. It's always best to check whether there's any underreporting or if there are any opportunities for improvement. Thank you. That is group two's presentation. Right. Thank you, Larry. So any comments from others, other groups? Any comments from facilitators, Saurabh, Prabhuprishnan, and the other group members who are on ART, any comments from facilitators, Saurabh, Pramil? So while they prepare a few things, first thing is if it is more than a hundred for the actions, I mean, if it is a hundred percent, you can maintain the efforts, but if it is more than a hundred, then can you do something else? So in fact, this is why it may be, if you are thinking of some issue with data, if it is more than a hundred percent, then I feel it would be better if you put that under a separate legend, like I mean, more than a hundred, so that you don't touch the hundred mark because a hundred is okay, right? I mean, we are fine with it, but if it is a hundred and one, or maybe like you can even think, because in real life scenario, what happens is there may be some occasional discrepancies of data, like you may get, you may notice a hundred and one, a hundred and five things like that for a short span of time until your data is ready, like completed. So maybe some countries, like what they do is they tend to give some allowance for that and that's why they sometimes keep it, if it is more than a hundred, they know a hundred and twenty percent, then please, you need to check whether something is wrong. So that's one thing. And also like when you are commenting about the actions, you should always comment on the indicator and denominator both, right? Say for example, you can check whether ART initiation, there are issues, and also you can also check again whether there is some issue with the denominator, right? So that also is something that you can comment. Okay. The comments, yeah. Saurabh or Pramil or any other facilitators who's there? Okay, looks like there are no more comments. So thank you very much Larry and the group too for that presentation. So please stop sharing group three. Any volunteers? Hello, I will be presenting for... Yes, please share your screen. I'll just share my screen. Okay, so here you go. Yeah, we can see. The indicator that we have chosen is the HIV test possibility theory. So this is a bit straightforward. That's why I decided to use tables to visualize this. So the table is visualizing the HIV test possibility theory between two regions, the animal region and the protein region for the last 12 months. So the objective of visualization is first to compare the HIV test possibility theory in the two districts within the last 12 months. Here, measure the overall HIV test performance for you to be able to monitor HIV test progress in training line. So this is where we were able to get the data. The data source is HIV test group. The indicator is the HIV test possibility theory. The numerator HIV test positive denominator HIV test performed the period is the last 12 months. So regarding the related actions, we were not able to finish it, but this is what we were able to... This is what our group was able to identify. First one is if HIV positive, our positivity rate is greater than or equal to 50%. Review the numerator. If there are any anti-errors or outliers for the denominator, so target population plausible or it's the selected target population. Also, if the HIV positive case is greater than or equal to HIV test performed, we will divide the data with the source. So these are the action steps that we were able to identify. We are done in two months, we are done in two months. Right, thank you very much, Chan. Anybody who wants to comment on what was presented by group three? Are there groups? Yes, Chan. Yes, please proceed up. Are you? Yeah. Yes, please proceed, yes. Related actions are here, only data validation. So here, many parameters can be used. For example, the reader here shown, 16.5 and 12.5. Is it expected rate or it is higher than expected? It's just my observation, observation. Yes, so he was suggesting in case the test positivity, positive cases is more than test performed other than validating the data source. There are a few other actions you have to do. So yes, do it up. That's correct. Chanra, you wanted to comment? No. Yeah, sorry, okay. Anybody else? Right, so if not, I have some questions. Like first of all, what was the reason why you selected a pivot table for this visualization as opposed to maybe a chart or a map? Any reason why you decided on a pivot table? I think our group decided to make it more straight for instance, the previous team. So I think that's the reason why we selected a pivot table as a chart. Right, so okay, yeah, that's fair enough. But like, are there any inherent disadvantages of using pivot tables for visualization? So are there like major advantages? What do you think? Oh, like, I mean, I'm asking from the entire group three. My idea, I think if we present the visualize the event report, we can be the exact numerator and denominator and we can see and we can cross check that number on that. And we didn't show all categories, just only a few categories, region, animal, region, food region, so it's easy to visualize the table, I think. Yeah. Yeah, so you partly answered the next question I was going to ask. Like why you just selected test positives and test performed and not anything else? Yeah, so maybe what I understood is you wanted to give a better idea to the person who was seeing about the proper, I mean, like what constitutes this indicator. So they don't have to drill or maybe have a look at another visualization to understand how the numerators and denominators are performing because you can give the entire summary in one visualization. So that's the advantage of using the tabular visualization. But is there a disadvantage also by doing this visualization? Yeah, usually, as I saw, we can show as a column chart plus line, meaning that combine, combine together. But the default I saw your exercise visualize is the tab also that why we didn't change any of your format. We just copy and interpret of that visualize. OK. No, in case if you're just, I mean, not using a chart or map, we just want to proceed with the table. What additional modifications would you like to do in a table to make it more visually striking? Anything? I mean, anyone, any of you can think of a better some modification you can do this table? Hello. Yes, yes, Joe. I think another modification which can be done maybe to apply some more lenses which can make it a bit of eye catching. Yeah, exactly. I mean, rather than eye catching, it will be like it just strikes out the message. So maybe group three, another thing you could have done is you can apply a legend as he suggested to the table so that the background color changes, right? So if it is really less, you can make it red so that people I mean like because you don't have to just focus on the figures if you just put that. So that's the enhancement that you can do when you select a pivot table. Of course, you can take the advantages of pivot table and you can make it better by doing those site modifications. Right. Yeah, yeah, yeah. OK. So it's good that you took this visualization so that I could highlight this point. Any further comments, facilitators, participants, anyone? And one more thing like about the actions, always rather than data, I mean, I totally agree. You have to mention about actions related to data. You don't have to check whether there has been some any data entry errors or outliers. And target populations are correct. Like some public health interventions also you can mention, right? Assuming if these values, the data is correct. Then you also need to mention some public health interventions that you have to do if HIV test positive rate is really high, right? Because then you need to strengthen your preventive measures, right? And community awareness and things like that. So you can even categorize your actions based on what to do for data related or what to do as public health interventions, right? That's also something that you can think of. All right, thank you so much. Group 4. Thank you. Group 4, any volunteers? Yeah, good for our presentation on behalf of the audience. Please share your screen, yes. Sure, let me share my screen. Right, we can see. Good, please proceed. Yeah, for Group 4, we looked at the indicator HIV cascaded by gender last 12 months. We picked the charts on the column charts to the visualization using the column charts was what we picked. And for the objective of the visualization is for us to monitor and for monitoring HIV program effectiveness to test and then the treat policy. So in terms of the number of persons tested and number of persons that have been treated and as well as a number of people that have been retained in terms of them from those who are already been who are already on the air and see over a period of time and then in terms of the gender male female our source of data came from one. The HIV those who tested positive for HIV and persons who are living with HIV that are being enrolled newly on the ART and those also being retained over time. I mean, over the last 12 months within the within the treatment regime then also the gender because we needed to with the data for in terms of gender male female to be able to different get that visualization than the related action for us. What we looked at is if any gender HIV we're also looking at in terms of if any gender any of the gender that's tested positive is higher than so what we did within the group is that we kind of looked at and said, can we agree on them? A threshold, so okay, a threshold of one 25 within the last 12 months. So if any of the gender HIV positive is above one 25 and probably check for reasons why this is happening and then review the interventions that is ongoing in terms of treatments then also to monitor we also looked at monitoring the retention of persons living with HIV around the ART. If there is a retention, if there is a retention the number of persons who are being retained drops below a certain number within them within a month. In any of the gender we also looked at a situation where there needs to be an intervention there needs to be sorry, not intervene there needs to be like check within that why that is happening and see if there is anything that needs to be done in terms of looking at if on the public health perspective and also if those who are new on ART are less than 50% of those who tested positive will also feel that there needs to be a check on this to check for a medication and adherence if people are really adherent that if you test positive you need to be an ART so to check if people are actually adhering and all of that. Thank you. All right, thank you. So any comments from the participants, any groups, any other groups want to comment? Anything from the facilitators? Right, I have a few comments. The first thing is like now when you're just looking at this table, the data source maybe you can label it because like people may get confused like, okay like whether these are data elements or indicators because it all depends on how you configure your instance in general. So if your end users are mostly used to seeing you know like indicators more, they will always look at data elements indicator labels. So maybe you can just mention these are all data elements. So you can just put a header and mention these are data elements so that they know. And the other thing is like now this is an example where you have used data elements as opposed to indicators. So I mean, do you prefer this visualization of data element? Does it really highlight the message that you want to convey or the particular objective that you want to attain? Is a data element a better visualization or you would prefer to have an indicator instance? So like instead of like, what are your thoughts about that? Because I'm not saying which one, I mean, it is wrong or right, but what do you think about it, Rufo? I think indicators are better for the data source than the elements. I think you better describe, visualize what we want to, what we want to see. Yeah, the thing, I mean, it really depends on what you want to show. Say for example, if comparison of numbers between regions or like orders, if you just want to show it at national level, just like here in training land, you just want to show the numbers at national level, then it is perfectly fine. But if your objective is to go for a comparison, then you might have an issue. We were actually doing national and gender disaggregation. Yeah, exactly. So that's like, this is fine because like, you are just giving a national level figure of each of the values. Yeah. Then my next question is like, would you have opted for a map over visually, over a chart or a chart is just fine for the objective that you want to cater? For the gender disaggregation, which was our focus, I think the chart based column chart visualization, we picked it, we decide it was better because it clearly shows the disaggregation of people on HIV living with HIV on ART in respect to, with respect to gender disaggregation, like we can see the green and we can see people living with HIV tested positive. We can see that the females are more than the males. And then those who retain on ART females are also more. And we can be able to clearly see that females, the increasing rate in female positivity and the increasing adherence to female has a related to their drug and also initiation, drug initiation. So with respect to gender females, the values are hard in all cases. Right. So based on what you want to convey to the end user, right, gender disaggregation and the values at national level you try to highlight, this may be a better visualization as opposed to a map because then you may have troubles. I mean, like you are trying to convey multiple parameters here, right? But in maps, it's usually when you just highlight one factor, map may be a better thing. And also want to make a comparison across the areas, then maps are much suited. So for this one, yes, I agree with you. And then as a third action point, you mentioned PLHIV in your own ART less than 50%. So this less than 50% value can you interpret from this chart? Because what I mostly see is the, I mean, the Y-axis is data elements, right? The raw values. So the 50%. Okay. The 50% was something we actually just formulated to create a scenario. We created a 50% as a threshold that for instance, if people living with HIV knew on ART, it's less than 50%, then we should check for adherence or initiation. That means that we have a high number of positivity, but then we have far less people that have been initiated on the drug and even far less that are adhering to the drug. So the 50% was something we assumed we formulated just to make a conclusion. Right, so what I was actually saying is because from this visualization, obtaining that 50% is not straightforward, right? Here we have the Y-axis is numbers and not the percentage. No, no, no. Yeah. It wasn't based on the numbers on the Y and the X-axis. Like for instance, now we have a female positivity test in 137,000 plus. So we are saying 50% of that number. Okay. So yeah. Yeah, what I was actually trying to highlight is there is some calculation the end user will have to do, right? By looking at the visualizer. Yeah, yeah, yeah, yeah. We never had a sound or two. No, no, that's okay. So this is something you can think about, like are we allowing the end users to interpret it that way just by having a visual look at the chart or maybe we can be a bit more specific. I mean, something for you to think about. Right. Yeah. Thank you so much. Thank you. Shall we move to the group number five? Group five. Anybody from group five? Saurav, we assigned people to group five, right? Was there nobody? Yeah, the members were there, but then I tried asking the topic or the indicator and did not get a response. So I assume they were, I mean, the members were too silent. Yeah. But we don't mind if you can share the screen so that we can interpret. Here we go. Yes, please. Okay, I guess Anupam shared the screen. Are you preferring to stay silent or you can unmute and present? You are currently muted, Anupam, right? So anybody else from the group mind presenting what you have? Yes. Yes, now we can hear. Please. Hello, is my screen visible? Yes, it is visible. But it's not visible to me now actually. Yeah. Is it visible? Yeah, for us it's visible. I think... Yeah, okay, okay. So I just use a indicator for our RMLCHA. That is the skill delivery coverage for the last quarter. So I prefer it to be presented as a map. So the objective is to measure the number of deliveries being attended by the skilled manpower. And because we want to, I mean, because the coverage is indicative of access of trained manpower to the pregnant mothers and higher the coverage by skilled manpower, the lower will be the maternal neonatal deaths. So that is why it is important to measure this indicator. And the sources will be the RMLCHA quarterly DHS2 dataset and the numerator will be number of deliveries attended by skilled birth attendants and denominator will be number of live births. And related actions, according to the map, which is... So if the grid is red there, less than 40% is... We'll review whether the... I mean, numerator, if there is... I mean, different errors are the entry errors are there or we'll review denominator. If there is target population is correct or is the target population either correctly enrolled or are the districts enrolled under the RMLCHA program, maybe the districts which are having less than 40% coverage, they are enrolled in the RMLCHA program. And if the skilled delivery coverage is green, the darker portion, it was showing more than 75%. So we need to sustain the efforts and we need to strengthen them further. If it is light, green, light yellow or dark yellow, it means the coverage is less than, still less than 75%. But then we have to check whether there is underreporting or is skilled attendants available, are there skilled attendants available in the area? Are traditional birth attendants been trained in that area? And are people ready to get their deliveries from the newly trained birth attendants? Because many of the time people prefer deliveries from their traditional attendants only and they don't prefer the newly trained birth tender. So we would like to look into these points. Thank you. All right, thank you very much. It's, that's really complete. Any comments from others? So I don't know Pramil, you want to comment? No, I think to me this, this looks quite complete, yeah. From this visualization. Yeah. And I see the skilled delivery covering. So, there is the only indicator skilled delivery covering. What is the other one indicator? But what are the parameters? I cannot understand it, so. You were breaking. I couldn't get it. I couldn't get it. Can you explain what, what was he trying to complain? And what are the others, what are the other? I couldn't get it. What are the other? What are the other? What are you saying? But what are the other parameters? Field delivery covering and which are not, that means that are not field delivery coverage, right? Or other? Yes. Okay. Actually when we see deliveries, actually we see whether it is institutional or it is home-based. So many of the time in home-based deliveries we look for skilled delivery coverages. And because in institution, we obviously are getting skilled manpower is there. But in home deliveries and sometimes in the rural areas, so people don't get to go to the institutions for the delivery. So we would like to see what are the deliveries which are being conducted by the skilled birth attendance. So that is what we want to see in the area. In the presence. Hello. Yeah, okay. Just, I have just two comments. First thing is about the objective because this is the good coverage which is an indicator. So I was just wondering like, because you mentioned to measure the number of deliveries being attended by the skilled manpower. Maybe, I don't know whether you can revert to, because we are not showing the, because we may have another visualization which may be only show the deliveries, the data element. So I'm not sure whether you can rename it so that it's not the numbers that you are seeing. So you want me to write the proportion of deliveries being attended by skilled? No, no, no. Yeah, I'm just suggesting... Or the percentage. Yeah, I mean, maybe you can, I'm just mentioning few areas you can modify if it is written. So maybe you can mention proportion rather than numbers because we may have another visualization where you're actually showing the data element value. That is one. And the other thing... Maybe, maybe, maybe. Yeah. And also about coverage if it is less than 40, just like, I mean, some of the actions that you have suggested for this third category, I mean, between 75 to 40, those may still be a number, right? So right now it's about data related parameters which are there. So you can also add this public health related parameters also to that section. So those two things I had. Yes, yes. And then it's a very complete framework. Nicely done. Thank you, thank you. Right. Shall we move on to the last group, group six? Yes, please. I will be sharing my screen, just a minute. Please do, yes. Is it visible now? Yes, it is visible. Okay. For our indicator, we selected MR1 coverage. So our objective of the visualization was to measure the overall performance of delivering the MR1 vaccine. Here we felt that the MR1 vaccine coverage is indicative of the utilization of immunization services by the community. And so a high coverage indicates high utilization, whereas a low coverage indicates that there are a lot of dropout children. And hence that there is a low utilization of health services by the population. So that's why it is important. For the data source, it was the immunization program monthly DHIS2 data set. The numerator is the number of MR1 doses given to children less than one year. And the denominator is the total population of children less than one year of age. Because this first dose is typically given at nine months of age. That is why in both the numerator and denominator, we have the less than one year present. Then for the related actions, if MR1 coverage is greater than 125%, then the actions would be to review the numerator as well as to review the denominator and cross-check the data. If MR1 coverage is green, that is greater than 90%, then everything is great. And the directive would be to sustain the efforts. And now if MR1 coverage was less than 90%, then we would need to check for various aspects, whether there was underreporting, whether the community is aware of the importance of vaccination, whether any recent AFI has occurred in the community. And hence there is a resistance or reluctance in the community for vaccination. If there have been any significant stockout of the vaccine, that's leading to the low coverage. And then we would also have some programmatic actions for the immunization teams and the staff in the respective district's facilities. It would be to identify all the dropout children in their respective catchment areas and mobilize them for the next immunization session. And where necessary, they would be allowed to plan additional sessions for selected areas in a campaign mode in order to improve the MR1 coverage rapidly. That's all, thank you. Right, thank you very much. And I especially like the actions part where you have been really going into details. So it's really good group six. And obviously I know like all of you, in some of the groups who presented initially, they didn't have that much of time to prepare also. So I'm just being fair by all the groups. But this is really nice, the actions component. I have just one comment, but before that, anybody else who want to comment? Looks like no comments from participants. Yeah, because this is really complete nicely done. Just one comment I have, maybe in the objective, you can mention that you're trying to achieve a cross-district comparison, maybe delivering MR1 vaccine across district. Because otherwise, I mean, if we don't include one query I have is like, why we select a map? So maybe we even selected another type of visualization. So one reason maybe so that we can show this geographic comparison, because the map is really valid here because you are just only selecting one parameter, which is one indicator. So what you're trying to do, the map is the ideal. But maybe you can also, you try to do a cross-district. Sure, sure, thank you. I can modify the objective accordingly to measure the overall performance of delivering MR1 vaccine and to compare the MR1 coverage across districts. Or there's MR1 vaccine across district, something like that. Okay, thank you. Thank you so much. No problem, right? Any more comments from anyone? Right. Okay then, so thank you very much everyone. It was a really good effort from all the groups. I mean, I was initially thinking like we might not be able to do all these presentations given the short span of time we have. But like I suppose today we had really good collaboration between the years, between participants in your groups as well as the facilitators. So I hope it was a worthy effort. And I think we can conclude the section on interpretations. So the plan from here onwards, of course I have to tell the word of the day, but other than that, we have one other section that is remaining for the day, which is implementation considerations, which of course is a bit of a, I mean, sharing experience, which I can share the generic recommendation where we can also go for a brief discussion about issues that you have and your experience. And also we have one ungraded assignment, MCQ type assignment also for the interpretations. So what we can do now is like, we can take a short break and we can also give you some time to do the ungraded exercise. Whereas we can have maybe five, 10 minutes short break and start the presentation on the implementation considerations. Finish it often, give you time to do the ungraded exercise later. Anybody who opposed the latter idea that is like anyone who doesn't like the idea of doing the ungraded assignment last after my next presentation, just let me know. If not, I will do the presentation next on implementation considerations and we'll do the ungraded assignment last. Please let me know if anybody doesn't like that idea. All right, okay. Yeah, I know like most of you like that idea, but if anyone doesn't like it, fine, right, okay. So I guess the majority likes the idea of doing the implementation considerations so that you can do the ungraded exercise leisurely. But still, I think we need to take a small break. So what we will do is we will meet in exactly 10 minutes at 335 local time in Indian Street, we will meet again. And then at that time, I will mention the word of the day. Thank you.