Now let's take a quick look at two important structures on the Dorsal aspect we shall not focus on the individual muscles on the extensor aspect instead We will focus first on this space here This space that you see here is the anatomical snub box So anatomical snub box has got a medial boundary and it's got a lateral boundary And you can see them clearly here the medial boundary is formed by the abductor policies longest and the extensor policies brevis Abductor policies longest and the extensor policies brevis is forming the medial boundary and the lateral boundaries form at the extensor policies longest The proximal end you can feel the radial stellar process Distantly we can feel the base of the first metacarpal bone and on the floor We can feel the trapezius young and as I already mentioned Running through the floor is this structure the most important structure that is the deep branch of the radial artery Which courses through and which continues as the deep continues into the palm as the deep armor arch Okay, that's the one thing which I wanted to show you The next thing I wanted to show you Are the tendons which are running on the torso aspect of the wrist just like I showed you the tendons on the Palmer aspect of the wrist can you see the structure here obliquely going up this bands of fibers This is their extensor retinaculum It is going obliquely from the radius to the ulnar side and this as it crosses over It divides the whole dorsal aspect into six compartments like to so let's take a quick look the first compartment We have the abductor policies longest and the extensor policies brevis The second compartment gives passes to the extensor carpet idealist longest and extensor carpet idealist brevis The third compartment gives passes to the extensor policies longest The fourth compartment gives passes to these four tendons that extensor digitorum and the extensor indices The fifth compartment gives passes to the extensor digiti mini me and the sixth compartment gives passes to this portion That you see here and that is the extensor carpet alaris So these are the four six compartments under the extensor retinaculum Okay, having mentioned that let's come to the last part and that is what are the structures that we can see on the palm on the hand This because of this rigor mortis this hand has been frozen, but anyway, let's see what we can salvage from here What are the structures that we see on the hand? Straightaway, we notice first of all that this was as we had mentioned This was the pommel ligament and the pommel tin pommel's longest tendon pommel's longest tendon gets inserted onto the flexor retinaculum also known as the transverse carpal ligament and The flexor retinaculum is attached to the PC form and the hook of amide Loose capoid and the trapezius. So therefore it forms a tunnel which is known as the carpal tunnel and through the carpal tunnel travels The travels the main trunk of the medial nerve, which I mentioned Attaching to the flexor retinaculum.  So this is the power upon your roses What we can also see on the Lateral side, we can see this elevate group of muscles These are collectively the muscles of the theater evidence and on the ulnar side We can see these group of muscles. These are collectively the muscles of the Hypothenor eminence and incidentally a few slips of the power is brevis, which is superficial It is not part of the hypothenor eminence is also seen here. This is the hypothenor. The power is brevis What else we can see? We can see some of the fibers of the median nerve here The the metacarpal branches and they will divide into the common and the proper digital branches We can see one of the median nerve branches here and there are a few other branches which are visible on the other sides also I Would like to draw your attention to this Interocious muscle here, which I already mentioned in the first interspace that is between the metacarpal bone first and the second And I also told you the significance of that is that there's a small tunnel there Which gives passage to the deep branch of the radial artery which goes from the dorsal aspect and comes to the Palmer aspect and forms a deep camera deep Palmer arch So that's all For this two section Ladies and gentlemen, thank you very much for watching. If you have any questions or comments put them in the comment section below Thank you camera persons. Have a nice day


Okay, today we're going to be looking at flashing the system partition again. Last time we did it through ADB just by pushing an image using ADB in our custom recovery of twerp. But we're going to look at a different way of doing it. Again, I'm in my directory here. I have a twerp recovery image here that I will boot into. So let me ADB reboot boot loader. Again, this tutorial is for intermediate users. It isn't for beginners. I assume you already know how to unlock your boot loader and how to use custom recovery. And we're just looking at some more advanced things. So here we are at the boot loader screen. And at this point, we can use the fast boot program on our computer, which I've installed through my repositories here on Debian, to communicate using the fast boot protocols to the phone here. And so you don't need a recovery at all.  If you were to mess up all those partitions, as long as you didn't mess up the boot loader, you should be able to get to this screen and be able to fix things. Now we're going to trash the system a little bit more later on and fix some things. But right now we're just going to go in and we're going to trash the system partition again. So I'm going to go ahead and use fast boot to boot this image. So basically what this is, you can on most devices just simply flash a custom recovery. Some phones, it takes a little more messing around. But in either case, if your phone supports fast boot, you can just tell it to boot an image. And if it's a bootable Android image, it will boot it. So basically what happens here is we have the twerp image, which is the twerp, basically it's an operating system. We're copying that file to the phone, loading it to RAM, mounting it and running it. Much like if you had a live CD or a live USB of distro of Linux that ran from RAM. So here we are in here. So now I should be able to ADB into the shell. Again, I can go into dev, block devices, boot devices, or block boot devices by name. Again, I can list out the system partition with the dash L and see that it's pointing to this partition here. So partition 41, we'll want to remember that. At this point I'm in here, so I can just say mount the system link. Again, since I'm in the directory, I can just give it that. Otherwise I'd have to give it the full path. And we'll mount that to our system folder here. I will go to my system folder again. I can type in mount here to list all mounted partitions. And we can see that it is mounted here. And if we list out here, you can see the files. Just like last time, I'm going to go ahead and I'm going to remove everything in the system directory, basically wiping out the main system of Android on this phone. If I was to now type reboot, the phone will reboot, but it will, let's try to focus this a little better. There we go. It's going to get caught in a boot loop. We shouldn't get any further than this Google screen. If that sits there long enough, it will restart. And unfortunately, I know of no way to get anything to come up on the screen. To troubleshoot that, we'll look more into possible options for that in the future. Again, I'll just let this sit here for a minute so you can see that it's not booting. So you can believe me that it's truly a trashed system. It shouldn't take more than five or six seconds before the animation should start. So what I'm going to end up doing here is I'm just going to hold down the power button until the phone turns off. And as soon as it turns off, I'm going to hit the down volume button and hold it. And now we're back at this bootloader screen with Fast Boot enabled. So let me go ahead and clear the screen here. Again, we have our system image, which we created in a previous video. If you want to see how I got that, check out the previous video. This should be video three in the series. So check out video two. But again, last time we used ADB to push it, basically writing it like it was a file after mounting Torp, which is a Linux operating system. But now we're just working on Fast Boot here. So what we can do is we can say Fast Boot, Flash, and give it and say system and image. I'm going to go ahead and hit Enter. So real quick, let me explain a few things here. So we're using Fast Boot, the command on our computer to use the Fast Boot protocols to the phone through USB. We're telling it to flash a partition that is labeled system. So in this particular case, you have to already have a partition there that is labeled system. If not, you have to create that. We'll look a little more into that in a future video. Let's go ahead and flash this.  In this particular case, it's sending it in four chunks. Now, you notice we get this invalid sparse file format at header magic. When you get a stock ROM, usually the image file is split into multiple images. So the system partition is split into multiple images and you would load them up one at a time. And I believe that error is just basically telling you, okay, this is just one big file. That's not right. But then it just figures it out. It's smart enough to figure it out. And basically what it's doing is this is a three gig image and it has to load it all over, unlike when we did it through ADB where we basically pushed it through USB and was running it directly to the internal memory. This is loading it over to the phone, basically into RAM, writing it to the partition, then loading up the next section of it and doing the same thing for all four sections here. And obviously this phone, I'm not sure does it say on the screen here. I forget how much RAM this phone has. I'm going to guess three gigs of RAM. So I mean, if you don't want to push gigs and gigs to a phone that doesn't have that much RAM, it's going to overwrite it. So basically fast boot is smart enough here to split this up into sections. So it's going to divide it up, send over a section, write it to the drive, then send the next section over after removing that first section from RAM, writing it to the drive, so forth and so on. And it shouldn't take too long. So you can see here that it took 16 seconds to copy. It took less than a second to erase the system partition. 16 seconds to copy the first. It's about a half a gig here if you did the math. And then it took about 20 seconds to write it. Same here, 16 seconds to copy it over. And then to write the drive, it took 70 seconds. Now we're on the third part. It's copied over again. It takes about 16 seconds to copy over that half a gig. Write it. We're on the last one. It's the smallest. It shouldn't take very long. So again, this is a lot faster than it was, again, pulling from the phone. We can push to it faster. And I'm not really sure why that is. I guess the read speed is faster. I'm not even going to guess why. But here we go. It's writing the last little part of this. And as soon as it is, we can see we're already on the start option here on the screen. And my camera battery is about to die. So hopefully we get this done before that dies. And just writing that last little section. The suspense. Okay. There we go. So it tells you how many seconds it took. And at this point, all I have to do is click start. So the power button there, because the volume buttons go up and down on this particular phone to go through options. And the power button chooses that. We get the Google screen here. And everything went well in a couple seconds. There we go. We have our boot animation. Again, I feel like this camera is not focused. It's very hard to focus the cameras kind of on, or the phones kind of on angle.  I say that all the time, but there's only so much I can do. I want to work on real hardware for you and give you a visual. And there we go. We're right back to how we were. So that is another option as opposed to just using ADB and pushing it as a file to the drive. We were able to use our bootloader and fast boot tools to copy it over. So even if you don't have a recovery image and or if you got trashed or whatever, you don't need it because the whole point of fast boot is to fix your partitions and to flash partitions. So if you have those images and those images aren't corrupted, you should be able to flash them over as we just did. And you can do that for any of the partitions. The boot partition, the cache partition, the user data. That's a good question because on a lot of newer phones, it's encrypted by default. I'm not sure if it will copy it over. That's a good question. Anyway, just thinking there. Like I said, there's almost 40 partitions on this phone. So we'll look at how to pull ways of pulling each of those. Obviously, twerp can make a backup and restore those, but it doesn't hurt to make your own images in case for some reason something happens to twerp and it won't write those. You have to be able to fix stuff with fast boot if your partitions really get messed up. I do thank you for watching. Please visit FilmsByChris.com. This is Chris with a K. Check out the link in the description. As always, I hope that you have a great day.


We've got hepatitis E outbreak here, diarrhea, cases, it's just a lack of water, it's just a lack of everything, lack of washing, people having to walk too long so they're using the surface water. Skin diseases we have now on the rise, no the water is the cause of it already. We're increasing the water storage both on-site and at the boreholes so we can keep pumping, keep these boreholes running longer and the other main thing is increasing the network of tap stands especially the communities further out really making a tap stand in the middle of their community rather than in the middle of nowhere and to stop these people having to walk so far for water.


Okay, we'll go to the next speaker, Sarah Kelligan. Well, first, get the PowerPoint up. So, let me introduce Sarah first. Dr. Sarah Kelligan is currently senior researcher and project manager within the British Atmospheric Data Center, responsible for bit preparation, project management and technical research. She's the project manager for the EU-funded metaphor project and has previously managed the JISC-funded OJIMS project, which aims to develop journals for data publications. So, next step. She's currently data manager for the NERC flood risks and extreme events program at the BADC, and she will talk to us a step-by-step guide through the research data set creation, big data versus long-tail metadata, data centers, data repositories. So, lots of information. Sarah, the floor is yours. Thank you. Okay, and this is the point where I realized that I really needed to update my biography on the website. No worries. Okay, can everybody hear me at the back? Yes, can everybody see the thing? Yeah, plus or minus.  Ooh, that's better than I was expecting. Good. Okay, hands up anyone who's involved with data management on a regular basis. Okay, good, right. So, there will be, I'm covering an awful lot here, and I do have the tendency of talking very fast, so I will try to slow down. And if I say things that are very, very obvious to people, please forgive me, if this is all a bit kind of too simplistic for you, you can play Count the Cats and entertain yourself that way. Okay, so let's start off. So, just to give a brief introduction about myself, I work for one of the, I work for the British Atmospheric Data Center, which is a data center which is funded by the UK's Natural Environment Research Council. And our job is to take in all the research data that is produced by scientists funded by NERC in a variety of fields, primarily environmental sciences and geosciences, and we archive that data. And we have to do this because observational data is, we can't reproduce it. We don't have a time machine, we can't knit back to last week to do the same measurements in the same place again. Once we've measured it, that's it, that's our only chance. And these environmental data sets are really, really important. So that's our job. That is our entire reason for being, we take in data, we look after it, and we give it to other people.  Does the mouse appear? Yes, the mouse appears. So we've got to the organize and analyze data. And the way that this is done is crucial to be able to reproduce the science. But more often than not, that is done by the researcher, and that organization and analysis of data doesn't really get shown to anyone else ever, except possibly the collaborators. What gets communicated to the rest of the world is this communicate results bit. And we want to be able to change this, because as we all know, if your conclusions are based on faulty data, then your conclusions won't stand, they're rubbish. We need to be able to find the data. So, here's the research data lifecycle. I got told I had to show this, so here it is. So, as I said, researchers are used to dealing with these three green blobs here. They are used to creating data, they're used to processing it, and often that's all they need to do.  Preserving and giving access to data isn't something that researchers generally do. And that's where they tend to farm it out to data repositories or institutional repositories. And then we have the whole reusing data, which can be done by third parties, but can equally be done by the originating researcher ten years down the line when they've gone back to their data set to do some extra work on it and realized that they can't, well, they haven't documented it well enough for what they thought they'd remember, they can't. So, you're always going to get this question, what is a data set? Data sites have a definition, and their definition says it is recorded information regardless of the form or medium on which it may be recorded, including writings, films, sound recordings, pictorial reproductions, drawings, design, or other graphic representations, procedural manuals, forms, diagrams, workflow charts, equipment descriptions, data files, data processing, or computer program software, statistical records, and other research data. So, in other words, a data set is anything you can possibly think of, right? In my opinion, a data set is something that is the result of a well-defined process. It is, because I'm a scientist, I trained as a physicist originally, I think it has to be scientifically meaningful, it could be just as meaningful from an arts and humanities perspective. It has to be meaningful. And I maintain that it has to be well-defined. It has to be very, very clear what is part of the data set and what isn't part of the data set. If you've ever created a data set, and I know that some of you have, you know that it is hard work.   And then at the end of the day, you have a graph, and other people look at it and go, is that it? And people just don't get, unless you have actually gone through the process of creating a data set, they don't get what it is and how much work goes into it. And oftentimes, when people say, where's your data? I want to see your data. They don't actually want to see your raw data. They want to see your analysis or your graphs or your notes or something like that. Even then, they expect you to kind of explain it to them. So dealing with data is tricky. So I'm going to give you an example from my own personal experience because I didn't always work for the British Atmospheric Data Centre. When I started work as a researcher, fresh out from university, I got hired to work with a particular experiment. So basically, I was creating a data set, and it was a radio propagation data set, which means that there was a satellite, little sat F1, up there in geostationary orbit, and we were taking measurements of the beacons that it had on it. Because the beacons were at the radio frequencies that if you get a rain storm or a cloud in between your satellite and your receive path, your signal goes out. And that's not very good for if you're wanting to kind of use that particular frequency to broadcast, say, the FA cup final. People might get a bit annoyed. So we had an experiment. We put instruments out in the field. There was a receiver inside the port of Cabin, and those are receivers inside the port of Cabin. This is an example, just a graph, of one day's worth of raw data. It was a particularly horribly wet day. It rained a lot. And you can tell because the black line with all the jaggedy bits, that's the signal, and you can see it drops out all the time. Even then, at the bottom there, it drops out so much that we've lost the signal completely. That's the growth of data. These data sets span years. I had to take each day, run it through a number of programs, which were written by a colleague of mine, and he took that and turned it into that. And then, finally, at the end of it came out with some nice cumulative distribution curves. This process took four major steps, four different computer programs, and 16 intermediate files for each day of measurements. Each month's worth of collected data took me somewhere between a couple of days and a couple of weeks worth of effort, depending on how rubbish the weather was. And it wasn't something that I could put the computer on and set it going because the computer didn't know what it was looking for. It wasn't clever enough. It had to have a human's eyes on it. I spent far more of my life than I'd care to remember doing this. All right. This is how not to preserve your data. This is the IttleSat data archive on CDs on a shelf in my office, right? This is the point where I hold my hands up and say, yes, I did it wrong. Definitely wrong. This is what the process data set looks like on disk. There it is. A whole heap of file names. You might be able to guess that there might be a date in there somewhere, but who knows, and what the hell is a .000 extension when you get right down to it. Luckily, these ones happen to be ASCII text. So when you open them up, you can actually open them up in WordPad, but then you look at them and go, what are these again? I do have documentation. Here's the documentation, or some of the documentation. You can't read it, but you'll know that there's a bit of a flowchart thingy going on there, and there's software file names in the documentation. The IDL files that I used to create that were the software, I have absolutely no idea if they will run in current versions of IDL or not. There we go. Documentation, it can produce some mixed feelings, right? Because what everybody wants out of life is to be an expert, really. They want to be felt like they're important, they're indispensable, and sometimes in the cut and thrust of the economic climate, often knowing that you are indispensable, that you are the only person who understands this very important data set, that's really good by way of job security. So that is my example. This is my past. My data organization was horrible. It was bad. And I wasn't even preserving my data properly. As for giving access to it, I spent more months of my life than I care to remember. Spending time working with legal and contract people in the department trying to figure out non-disclosure agreements. I'm not a lawyer. I never wanted to be a lawyer.  Right? It was a pain. Sharing was difficult. And it wasn't... I was wet behind the years. I was a newly-made researcher. The head of my group was wary about the prospect of making it all open data. Because this was a few years ago when open data really hadn't made its way or made itself felt. But also because in theory at least these data sets could have been commercially valuable. We never managed to sell them but in theory they were valuable. And I felt like I didn't get the credit for all this work I put into sharing the data and documenting it and all the rest. But the very first publication that was based on this data didn't have my name on it at all. I had given this to a PhD student by request and we got named as a group in the acknowledgments. I didn't get a personal mention at all. My only consolation is the fact that that particular paper has had two citations since. So it's not exactly been that impactful. So the good news is I have seen the light after a bit of poking and being told to put your data on the BADC or else. The data is all on the BADC now which means I don't have to worry about it anymore. It is there. There are people there who will manage it for me. Okay. So any niches in the audience? No? Oh dear. This analogy could go really, really flat then. Yes, there's one. Okay, so my scarf. Because data sets are so varied, I kind of thought I'd come up with a really an analogy for why a scarf is like a data set. So you can think about data sets. So how's my scarf like a data set? The raw material, the wool it's made from, it doesn't contain any information. It's just all rolled up into a ball. But the process of knitting encodes the information into the scarf. So the scarf is the result of a well-defined process. It's very clear what the active knitting actually does. And it has a particular method used to create it. I need to be able to describe my scarf. I need to be able to find it. I need to store it properly so it doesn't get lost or corrupted. So it doesn't get eaten by mice or moths or anything like that. I keep information about it because I might want to recreate it someday in case I do lose it. And possibly the most important thing is I've put an awful lot of time and effort into making it. I'm very personally attached to this scarf. For a scarf think of something that you've created yourself. This could be a garden or it could be a document that you've written or it could be a Lego model or it could be any other sort of model. Something that you feel like you've personally invested and put a lot of time into creating. It's that sort of feeling that you get. Just like not all scarves are the same not all data sets are the same. Just because somebody says oh I've got a scarf don't assume that you know exactly everything about that scarf just because they say scarf. Because scarves come different shapes different sizes, different colors they're used for different purposes they're made in different ways and the thing here is data sets are like that. If somebody says oh I've got a data set what that data set looks like or contains. If in doubt talk to the person who has the data set ask the creator get the information off them. Right, measure data. So measure data is important because we need to be able to describe things. So the example here is that we need longitude and latitude about the planet. It's to be able to locate us on the surface of the planet so longitude and latitude are artificial you're not going to find big massive lines drawn along the lines of longitude and latitude but we are artificially put them there and we've arbitrarily put them there but we are using them as a consensus and they allow us to communicate about places on the earth and they were designed by those who needed to navigate the ocean so these are a useful feature and they can often act metadata can often act as a surrogate for the real thing. So here's my scarf again I have metadata about my scarf I have descriptive metadata it's a scarf, it's color, teal blue I've got the dimensions how big it is I've got it's locative metadata so it's either hanging around my neck or it's hanging on the door of my wardrobe and I can also assign a knitted object identifier to my scarf should I feel the need no matter what my husband might say I've also got information needed to be created so I've got information about the raw material, the specific type of wool that I use to create it and the color, the dialogue these are all important things if you're really getting into knitting geekery I've got the knitting needle size that I needed to create it the algorithm effectively the program that I use to create the pattern that is in the scarf, feather and fan stitch I've got the number of stitches I cast on how I started it, how I finished it how tightly I knit it, this is all the information that you'd need to go ahead and recreate this particular scarf metadata can get really complicated really quickly and there's lots of this is a metadata taxonomy that was written up by my boss Brian Lawrence and it goes into lots of detail about how you do it it can get even more complicated than that I'm not expecting anyone at the back to be able to read anything of that the most important thing I think for this is that when you are dealing with researchers if you show them documents like these they will need to go away and have a nice quiet lie down in a darkened room for a while because this stuff is complicated and most of the time the researchers don't need to know about it so what do we as data centers do here is the data curation life cycle courtesy of the digital curation center and you'll see there data is right at the heart of it and then you've got extra layers of stuff that goes on all around it and that's basically what we as the data centers do we look after the data we make sure that the metadata is there we curate and preserve it we migrate formats occasionally occasionally we even throw stuff out although that doesn't happen very often I have to say data repository workflows again these are complicated again don't show these to the researchers they don't really need to know about it because all we need to do is have a good relationship with the data producers because you want to be able to interact with them to get the metadata about your data set workflows are going to vary across all the different data centers and all the different libraries anyway everybody's got their own way of doing things what is important to remember though is that you really want to be in the situation where you've got good interaction with the people who are submitting the data what you don't want to have is what we call a data dumper where somebody goes here is all the data from my 50 year career in astrochemistry or something I'm retiring now bye and they go off and they leave you with half a billion files named my data and you have no idea what to do with them or what they are and the only thing you can do in that case is write them to disk and hope that sometime in the future somebody might find some money and deem it's important enough to actually go and do some data archaeology on this stuff so you will get people asking why should I bother putting my data into a repository has anyone ever had that sinking feeling as they've just clicked close on a document and they haven't saved their final changes yeah that's the reason why we don't want to lose data and they could say it's alright I'll just do regular backups yes that's true however so this dates back to 1700 BC the face to Stisk and I don't know how linear or how old linear is these pieces of information these documents have been preserved for thousands and thousands of years unfortunately we can't read them so it's not enough to store the files we need to be able to actively curate them to preserve the information on them it's no point storing something if you can't actually read it so big data versus long tail data alright so I'm going to give you an example of big tail data which is different from the Large Hadron Collider this is the climate fifth coupled model intercomparison project CMAP5 and this model brings together loads this project brings together loads and loads of climate models and the results of these climate models will feed into the next intergovernmental panel on climate change assessment report which then will inform governments and policy advisors all around the world what they should be doing to deal with the effects of climate change what we've got is we've got lots of distinct experiments to find with very different characteristics and these all influence the configuration of the climate models what they can do and how they can be interpreted and all these climate models run on massive supercomputers so climate models are getting more and more advanced as time goes by and they're getting finer scale there's more data coming out of them there's more stuff being looked at them for example in the mid 1970s dioxide, rain and the sun now we've got AR4 which was the previous one from this we've got atmospheric chemistry, interactive vegetation the sea, the ocean, all sorts so it's getting more and more complicated and we're getting more and more data so the BADC is a part of the earth system grid federation and we host some of the CMAP5 model archive we are talking about petabytes worth of data here this is big data it's lots there's lots of numbers up there there's 90,000 years worth of simulation 60 experiments, 20 modelling centres from around the world we are talking big numbers here not quite as big as the LHC but still pretty big handling at all is a job it's a major international collaboration you probably can't see but there's lots of inside that green circle there of different countries we've got Australia European countries, the United States China and Japan I think and it's funded by a wide variety of sources it's funded by the Framework 7 projects easiness and metaphor and US funding and other sources like NERC in the UK is funding it as well this is a big international collaboration and it needs major physical infrastructure networks, computers, all the rest of it we need to comprehensively collect all the information, all the metadata about these climate model runs and we need to make sure that they are also saved properly the metaphor project that was mentioned earlier the entire project was just about collecting the metadata for climate runs major distribution systems are social challenges as well as technical ones you've got to remember that you can easily computers pretty much do what you tell them to people can get a bit funny so CMAP 5 is an example of big data it's got lots of different participants lots of different technologies international community who are willing to work together to standardise and automate the data and metadata production and creation so going back to Scarves big data is industrialised and standardised data it is the industries that crank out 50,000 of exactly the same Scarves in exactly the same shape and exactly the same colour and they all have little tags sewn in on them to say who made them, what their ID number is and all that washing instructions big data has large groups of people involved and it also has methods for attribution and credit for data creation established, it is all wrapped up everybody knows what their job is industrial process everybody knows their roles the long tail data is my hand knitted scarf it is bespoke data and metadata creation methods, it is an individual researcher or a small group working away in an office somewhere creating stuff that works for them but may not work or probably won't work with the office next door to them should they actually want to want to share it and for the long tail data we're talking generally any accepted methods for attribution and credit for data creation which is why the people who do the long tail data creation are so protective about their data they feel like they have to hang on to it to get every single last scrap of publication worthy material they can get because if they don't get the credit for making their data open then why should they bother so I got asked to say a few words in the future role of the library and I'll start off by saying I work for a domain specific repository and I feel that we really do have it lucky we get to pick and choose what data to keep and what data to throw away we can ask for and we sometimes even get the detailed metadata that we need to to kind of make the data sets more interactive, more valuable, more standardized we can provide specific tools and services we can do visualizations, we can do server side processing and we can do all this because we can mandate things like file formats and standardized metadata schemes we can deal with big data, we have the industrial processes, we can do this libraries, you guys are going to need to pick up and manage and archive the long tail data where there might not be a domain repository and you're going to have to have a generalized, widely applicable system that can cover everything from astronomy to zoology and everything in the middle to the sides as well and you're going to have to be ready to deal with pretty much anything so I think, yeah, good luck the important thing is don't panic there's a lot of information out there about managing data some of it will work for what you're trying to do some of it won't but the important thing is don't feel like you're in isolation on this one come and talk to the people who've done this before or the people who are trying to do it and learn from other experiences the good experiences and the bad experiences it can be often as valuable to learn oh god, no, don't do it that way as it can be, yes, this is the way to do it and as I said, good luck so, summarise and maybe a few conclusions, I'm not entirely sure so we all know that data is important and it's getting more important well, it's always been important and we all are starting to realise just how important it is and by people, I mean not only other researchers both in the domain that the data is but also outside those particular domains but also people like politicians the UK's Chancellor of the Exchequer has gone on record saying how important data is if the politicians are talking about how important data is, it must be serious conclusions and knowledge are only as good as the data they're based on if your data set is rubbish your conclusions are going to be rubbish the only way you can find out if the conclusions are rubbish is if you can actually dig into the data and find out if they support them or not that's why we need open data we need shared data because the science is supposed to be reproducible and verifiable there's a great quote that I heard at a meeting which says that research without data isn't research, it's advertising and that's quite important it's up to us as researchers to care for the data we've got and ensure that the story of what we did to the data is transparent and researchers are going to need help from data management experts whether they're in domain specific repositories or in libraries because we're going to want to use the data again and we're going to want people to trust our results it's not an easy job but someone's got to do it thank you very much yes, thank you Sarah any questions at this point from the audience we've got all kinds of questions coming up let me come to you thanks Sarah, very nice talk just one question do you have any data about the claim that long-tailed data are only made by few people no, this is kind of based on my own personal experience it seems sensible to me because if you've got big collaborations they tend to be more there tends to be more people so therefore they tend to standardize it more because they have to share it with more people it would be interesting to analyze because actually looking we always hear for example about the large Hadron Collider and these big data that is produced there but these are a handful of people compared to the amount of researchers in the world it's a really tiny fraction of people so it would be interesting to look at I'm coming from Stoen so I can perhaps just ask a little bit so we do have a lot of people working on LHC and they do produce a lot of data however all the theoreticists they also produce a lot of plots, a lot of other things that they don't store in standardized formats at the moment so we do also produce lots of long-tailed data if you want an example of long-tailed data and failure in data curation you'll notice that the slides I had where I showed the images of the graphs that I plotted as a result of the data analysis you'll notice that they were photographs of hard copy printouts of plots because I didn't save or I couldn't find the original soft copy plots on my computer so yeah data management occurs for everything even the things you don't necessarily think you should be data managing do you have the feeling that in the flow with the big data that you are dealing with that this is a solved case or more or less a solved I think I think it's more or less solved case for that particular situation so CMIT 5 is just one measurement project or model project there are still people doing stuff with climate models that don't necessarily fit in with CMIT 5 but the key thing with CMIT 5 is that you've got the opportunity to have enough people together to get them to agree that this particular standardized way of collecting metadata is good for CMIT 5 but can also be used for other projects that aren't directly related with CMIT 5 so you've got a chance of standardizing a bit better and was it easier was it quite easy to do no it took a long time it took a long time so metaphor was three years three and a half million euro it was a big project and that was not to do any of the infrastructure that was so lead to create the web based questionnaire that used to collect the metadata about the model runs any more questions no well we have sorry Nadja thanks a lot Sarah that was great energetic talk I wonder if you could talk about DOIs when you assign them when researchers can be credited for their data at NERC in particular okay so NERC now has the ability to mint DOIs for datasets held in our archive and because we are using DOIs in a very particular way we are using them to give credit to the researchers for the work they've done but we also want to assign a DOI to a dataset that then becomes part of the scientific record so we have rules for ourselves that say that the dataset has to be complete and frozen before we can assign a DOI we also say that it has to be of good technical quality so the metadata has to be there and complete the files have to be in the right format now this is we're still at the early stages of assigning DOIs we've done it for a few datasets and we're hoping to roll it out for more legacy datasets we've also changed the NERC guidance for authors so when people apply for a NERC grant they will see text assuming they read the handbook they will see text about how you go about getting a DOI and how you go about using it the DOI thing is all in the context of the wider work that we're doing about data citation and publication and Fiona Murphy is going to be talking about data publication later on this afternoon yeah okay thank you Sarah so we have now a little break let's see what the time is at the moment


Hey everyone, this is Dan with another episode of my ASML video. ASML provides lithography equipment to semiconductor foundries and has the monopoly or near monopoly in a couple of very important markets. ASML reached the all-time high on September 14th, then it dropped 20% and started to recover on October 4th. It is now still 8% from its all-time high. When it started to recover, I bought on a dip and tweeted my subscribers about it. My shares went up 14% already. I am very bullish on ASML and believe it has the potential to double in two years. I will explain why in this video. First of all, let's review my price forecast that I published on September 15th, 2021. At the time, I predicted that ASML would be at $910 a share by the end of November 2021. During that time, the price was $889.33. As of today, the price is $830.02, it certainly went down. Will I be setting a new price target? I will talk about that in the next few minutes. I just want to let you know that I'm still very bullish about ASML. I will explain why. First, let's look at the price chart of ASML, which is the candlestick chart. I'm comparing ASML with SMH, which is the semiconductor ETF representing the semiconductor industry. Then I plotted here QQQ representing the movement of NASDAQ 100 and SPY representing the movement of SMP 500. This is a daily chart for one year. As we can see, ASML went up a very impressive 121%, whereas the semiconductor industry represented by SMH went up by 56% impressive, but not as good as ASML. And then QQQ went up by 42% and SPY up by 37%. Definitely, ASML has outpaced all these other indicators. If you look at the chart from September 1st until today, ASML is not performing as well. We see the dip here after September 14th, and of course, the broad market also went down as well as SMH, but ASML has gone down a lot more, and since October 5th, ASML has recovered, but it has not nearly recovered as much as the broad market or SMH. One of the reasons is because of this red candle here, which is October 20th. That's when ASML announced their quarterly earnings. I will talk about that in the next few minutes. This is a report from Investor Business Daily about ASML's quarterly earnings. It says that they earned the equivalent of $4.97 a share as opposed to the analyst expectation of $4.61, which is good because they beat the expectation. However, the sales was only $6.1 billion in this quarter as opposed to the expectation of $6.19 billion, so they missed the expectation by a little bit. To make this situation a little worse, the ASML management forecasted the sales of $5.87 billion, and the analyst's expectation was $6.12 billion, so the revised forecast came below the analyst's expectation. That's the reason why the stock price dipped. Why did the management lower their forecast for sales for 2021? That's because they said it was because of supply chain issues, component shortages. That's the main reason that I'm still bullish about ASML. The supply chain shortage is pretty much affecting everybody in the world. This is really not surprising that ASML suffered an earning dip. However, because of ASML's strong positions in the EUV and the DUV markets, the businesses are not going anywhere else. They are the monopoly or near monopoly in those markets. It's just a matter of delaying the revenues, but they're not losing the revenues in the next few years. This is a slide from my March 28 video where I did my deep diving on ASML for fundamental analysis. In that video, I provided information saying that ASML absolutely dominates the semiconductor lithography market with 100% of the market share in the EUV market and 88% market share in the DUV market. The EUV and DUV machines are needed by the semiconductor foundries, especially if any foundry want to make any semiconductor that's 5 nanometer or beyond. They will need the EUV equipment, and that's why ASML is well-positioned to benefit from the expansion of the semiconductor industry that's been happening in the last couple years, and it will continue at least for the next two, three years. This is from the recent presentation by ASML management to the stockholders with regard to their quarterly earnings for the third quarter of 2021. We can see here the EUV where they have the monopoly. The EUV equipment occupies 54% of their revenues, and these slices are the DUV-related equipment, and that occupies 43% of their revenues. Looks like their main customers are either in Taiwan or South Korea, and some customers in China, Japan, and the US. If you look at the world fat plant investments in the next few years, Intel announced that they would invest $20 billion in the US and probably up to $95 billion in Europe in the next few years, and then Taiwan Semiconductor announced recently that they would invest $100 billion for foundries in the next three years in Taiwan, US, or in Japan, and then Samsung announced that they would invest $300 billion through 2030, mostly in Korea and in the US. With all these investment activities, ASML would definitely benefit from providing equipment to these fat plants. If you like what you've seen so far, I'd like to encourage you to click the like, subscribe, and notification button. This will enable you to receive notification when I publish my next video. It will also encourage me to publish more videos like this in the future. Thank you very much. Let's continue. We have a lot to cover. Let's look at some of the financial figures represented with these charts. These are from the Better Investing website, stock selection guide database. If you look at ASML sales, ever since 2012, 2013, the sales has been going up very steadily along this straight line with a very steep slope going upward. EPS has been very impressive also. The EPS growth has been 23% since 2012. If you look at more recently, it's been 30% growth since 2016, and they have been aggressively buying back stock, which is another very bullish sign for the investors. The management forecasted that for 2021, the sales growth will be 35%. That's definitely a very positive number. Return on equity in the last six, seven years has been going up steadily. Debt to equity ratio, even though it's been going up a little bit in the last few quarters, but it's still under control because it's only 27%. The company definitely is very rich in cash, and that's why they're being aggressively buying back stock. Let's look at the valuation of the company. First of all, I looked at the trailing P ratio, forward P ratios, and peg ratios of the major semiconductor companies. ASML is here. They have trailing P of 53, forward P of 41, and peg ratio of 1.69. The P is a little bit on the high side compared to the other companies, but not as high as NVIDIA, for example, or NXP. The peg ratio of 1.69, again, is on the high side, but not the highest one. The reason why ASML can justify these high PE ratios and peg ratios is because it's being growing fast, and also it's got the monopoly or non-monopoly in the lithography market, and they have to track record to support those numbers. Then I use the assumption of 50 PE ratio, which is close to the trailing PE now 53, and then annual earning growth of 29.8%, which is a figure from Yahoo Finance, and their past five-year growth was 17.9, but the trailing 12 months for this year is 28. If you look at the EPS growth that we discussed a couple of minutes ago, since 2016 it's been 30%. Therefore, 29.8% PE growth is reasonable for the next few years, especially considering the worldwide semiconductor shortage, and the aggressive investment plans Samsung, Towing Semiconductor, and Intel have put in place. There will be a lot of investment activities. That means there will be a lot of sales generated from those activities for ASML. From the recent management forecast of $5.87 billion of sales for the fourth quarter, then I look at the historical net margin and came to the conclusion of approximately $6.7 billion of earnings for 2021, and then using the trailing 12-month figures, I calculated their estimated stop values for 2021 through 2025, and they range from $760 a share to $2,156 a share. As you can see, it will approach $1,600 a share in 2024, which is double the value of today, and usually the stock price will lead the actual valuation by up to a year if the company is looking very bullish, and that's why there's a good possibility that ASML can reach $1,600 a share in a couple of years. But for now, for the next few months, I have set the target of still $910 a share, the same target I had back in September, except I have stretched the time horizon to the end of February 2022 in light of the stock market reaction to ASML in the last couple of months, although it's recovering, so I'm very optimistic about the stock price reaching this particular target. Let's look at the other analysts' opinions. So today, the closing price went from the September 15th value of $889 to $830. My target is still $910 a share, except rather than expecting that price to be reached by the end of November, I expect the price to be reached by the end of February next year. Yahoo Business maintains their 1.9 out of 5 buy rating. Same high target. The average target went down a little bit from $861 to $843. Same low target of $720. Who's Nimalier maintains his buy rating, although the overall rating got downgraded a little bit from A to B. Still a pretty good rating. Quantitative rating downgraded from A to B and fundamental rating downgraded from B to C. TipRanks.com maintains its moderate buy rating. High target went down a little bit from $998 to $979. Average target went from $949 to $881 and low target $874 to $720. C&Money maintains its buy rating. High target went up actually from $975 to $1030. Medium target went up also $925 to $966. Low target went up $743 to $779. The Street.com lower the rating from buy A- to B. Still a pretty good rating and the target is lower from $1120 to $1067. Overall my target of $910 is between the high targets here and the medium targets. Let's look at the chart. This is the data chart for the last few months. As I mentioned I bought at this point. I saw the price coming down but I had been very bullish about our stock. I was just waiting for the start to recover and when it finally started to recover I took a chance and bought the first day of recovery. With the expectation that if it didn't sustain the recovery and start dropping again I would sell the shares quickly but fortunately it continued to recover. Actually I bought more shares after it dipped here and then I saw the shares that I bought here about 6 days later with about 3-4% gain but I continued to keep the shares I bought on October 5th. As of now I'm seeing 14% gain already on these shares. We can see that there is a very nice looking upward sloping channel that the price has been following since the beginning of October. As of now we are still about 7.8% from the all-time high. That means there is quite a bit of space for it to move up in the next few days. Here is an overbought situation here back in September and sure enough the price started to go down. Actually when I published my September video we were right around here and at that time I predicted that there would be a pullback pretty soon and sure enough it happened. The DMI is bullish and for MACD it's being bullish for the last 2-3 weeks. This is our lead chart. Also we see this nice upward sloping channel. Our SI is at the level where it's probably getting to be overbought so there might be a pullback for the next few hours but as we saw in the daily chart it's not overbought yet. So if there is any pullback it will probably just last for a few hours then hopefully the price will continue to march up. DMI bullish, MACD bullish in the last couple hours. Let's look at the support and resistance levels. I've drawn the Fibonacci retracement diagram. There are many ways to draw the Fibonacci retracement diagram. I like to draw it so that the different levels are validated by the historical price levels and if you look at this chart it's certainly lined up with some of the important historical levels in the last few weeks. For this chart the minimum point is right here on October 4th and the maximum point is here on September 14th. For support, the next level support will be at 807 which is the 50-day simple moving average, the blue dashed line here and then the next level down will be 799 which is the Fibonacci 50%. Next level support will be 778 which is this red line and it's Fibonacci 61% and it's also close to the 20-day simple moving average, the middle of the Bollinger Band. That will be a strong support point. And then the next level of support will be 749 which is the Fibonacci 78%. For resistance the next level will be 845 right here. It's the upper Bollinger Band and Fibonacci 23%. And then the next level up will be 887 Fibonacci 0% which is not the all-time high, that's the closing price of the day when the price reached the all-time high for intraday basis. And then the next level up of course will be the intraday all-time high of 895 and then the next level up will be 927 that's actually the Fibonacci extension 23%. What are my strategies? As I mentioned I bought ASMR on a dip on October 5th when the price started to recover and actually I still have shares that I bought more than a year ago that I'm holding for the long term and I've been shrink trading the rest of my shares. In general I will buy ASMR shares when the price bounces from a key support level like what I did on October 5th or when positive news breaks out and I will sell when it drops at a key resistance level or when adverse news breaks out. And I will tweet my subscribers when I buy or sell ASMR shares or when major news breaks out. I'd like to encourage you to subscribe to my Twitter account which is DanMarketL for example. On October 5th I tweeted that I bought more ASMR and BNTX shares. And then on October 20th I said I bought more shares on a dip. On October 26th I said I sold the shares I bought on October 20th, these. And meanwhile I was still keeping the shares I bought on October 5th. That's where I bought the shares, October 5th and then October 20th. After the earnings announcement I believe that the market overreacted to the decrease in earning forecasts by management. That's why I bought on a dip again. And then I sold on October 26th around here with about 3-4% gain. With this particular purchase I am still sitting on a 14% gain, which is certainly very nice. If you like what you've seen so far I'd like to encourage you again to click the like, subscribe and notification button. As usual I will very much appreciate your comments, questions and suggestions. Let me recap my price forecast. I expect the price to reach $910 a share by the end of February 2022. So today the price closed at $830.02. I'd like to remind you that I'm not a financial advisor. I share my stocks trading strategies and analyses for educational purpose only. If you want to buy or sell stocks you should definitely make your own decision and you should consult with your financial advisors before you do so. This wraps up my video for now. I will chat with you again in the next few days. In the meanwhile I'd like to wish you the very best of luck with your financial investments.


Okay, good morning everyone. Yes, you are correct mark mobile. This is one of those five sensors Really interesting and I have to research that some more to see if More stocks are going to be going to that Really not in favor of that So markets hired today. Man, did I predict this good or what? Apple gapped up as I said it that it would and The markets gonna fly Really really just can't even tell you what I saw there But I knew it would happen couldn't call trade an apple until I knew that it gap But I talked about it all week that the that it would get up. I was just spot-on. I mean just I just knew I just knew I knew that would get up and market will run with that So for those of you that killed, you know, the bullish calls that I had made when the market fell Whatever day that was Monday or Tuesday. I don't remember anymore. It was Monday Um, I don't I don't know if some of you got back into them or not, but you know Everything's gonna run No sound I'm talking I am speaking can people hear me barry says no sound. I'm up in the air I gotta be honest with you. I've I've got three minutes to decide I can't decide if I want to do this one here or The AMC time to decide So probably what I'm going to do is watch them both because I really can't decide And that's the truth Actually, this looks like this is the exact same thing too. It looks like this is another one of those five centers as well Just noticed it. Didn't even notice it till now Yeah, I gotta really look into that What is up with that? Definitely have to check on that. Um, okay. So amc is in the middle vgfv I mean vgfv is in the middle. I'm sorry amc is the main one here Both of these have five cent spreads. I think I I didn't realize b bgfv did but it does too Incremental there's nothing wrong with that. It just means it's going to have a wider spread So top on here amc and bgfv. I don't know which one I'm doing I will tell you that pxt is a very good gap as well I don't know what you're doing people. You're just gonna have to pay attention today. They're both good I just switched it I just switched and put vgfv out. I can't decide. It's really like a tie Okay, this is an open this is an open look Okay, there there. It's open. All right bgfv Me Is a five cent differentiation and I just heard a noise. I don't know if that's hot com or whatever just so you know I hear this goes I hope I don't lose you because I just heard a noise Okay, stop and this is going to be 940 Do not have the entry yet Can everybody hear me because I just heard a noise and I think there's something wrong with hot com Just heard a bleep Okay All right, let's do 9 by 40 Except I think this is gonna flip I quick flipped over to amc amc. Let's do 70 by 1615 70 by 15 amc 70 by 15 amc if it hits back, let's take it get in getting right now get in right now I think it's gonna hit getting getting in 15 16 15 is a stop for this amc. I took it I think it's gonna roll over. Hope I don't regret that here 16 to here. Yes. Yes. Yes. Yes I just didn't want that to break and then we didn't get it here be an amc if you want it stop 1615 I'll go look at the other one here. This is rolling over bgfd Okay, this is still probably gonna set up All right, let's just focus on this here. I did it and we made the right decision because it's going first here amc So glad I took it what that's so weird about these ones really gonna have to do more research on that We're seeing I'm seeing them more in 2017 that I've ever seen them You gotta have a stop at 1615. Sorry people Target on this is $15. Let's see where we can take it. That would be really really really good Now let me just look at this Well, this just went here. You could be in the bgfd as well In fact, you could be in this stop 940 originally 9 by 940 you could take a 10 by 40 if you want it bgfv I'm in this. I'm not going to do two you could do them both They're both going to hit do them both if you want them stops 940 on the other one on bgfv amc. I'm in And we got to watch it and I think this gets to 15 very easily Let's pay attention. I think it's going to go right down and then we'll look at the market Stop on this is 15 But you really should know the stop way before now this this is trade has been going here for three minutes Dollars is a crazy target. I'm just looking at something here. Okay. This one you could still take 9 by 40 bgfv I don't think it hit yet. Actually. I thought it was going to hit right in there and then I Went back to my trade. No, it did not hit yet wait till that hits before you do that That still looks good Here we're in this excellent amazing so happy Told you I had to pay attention today though bgfv 9 by 40 if that hits you can do it This one is already going And it's too late if you're not in it. This is the trade 935. Wow. Here we go here Did everybody get it? Actually, I'm not gonna hear Literally my hands on the button here 15 20 I'm not letting this bounce it all against me at all Here amc amc then watch bgfv if you did not get it. I gave you the call for that 9 by 40 if that one hits That's it. I'm out. I took it. I took it. I'm not gonna let this bounce against me. Do it everyone 20 cents from the target out I you know, this very well could go and break 15. I'm not going to take any chances. Nice move in there Good flush got a great entry did everything perfect. Do whatever you want. I'm not sitting through a bounce in it Give it a chance to break didn't do it. I still think this goes. It still looks great Yay 935 Hey, yeah, yeah here. All right, let's go to the other one for those of you that didn't get that All right, I'll watch this here. I'll call something else for the people that did not get anything this fell Big move in this look at that. You could have done that one really really nice jwn No, that's off hlf. No, it's a bust Here I will call something else for those of you that didn't get the ones This this thing really fell though. Look at how that one really went the pxd. See what soda did nothing here Okay, let me just look at these other ones here This is a small baby one Okay here. I'm gonna call this if it sets up I'm gonna call it pyro 1673 and amc loving your calls melissa what you're just loving my calls today or You just woke up yesterday and all of a sudden decided to love me You should be loving me all year All right, let's do I mean, I'm not doing this I what am I saying if you want to do this you can do it is an official call 10 by 40 30 cents And don't take it until that hits if you didn't get the other one This is holding holding holding but do not preempt this because it could blow over the number I like the weakness in it though 10 by 40 bgf v And if pxt sets up again, that could have a massive target. I just don't know if it's going to Let's look at the market Wow, so look at this market Wow apple the options trades in apple you better be in it now better be in it if you're not in it You're too late. You got two seconds to take the apple option get in it. Did everybody do it? This looks so good. This is gonna be a big one people In fact, let me look up right now because I have to just see here what this is because I just called it I called it not even knowing knowing knowing knowing knowing That it was gonna work bb uh I have so many stocks in my head. No, I can't think straight. I'm glad I'm out of a trade. I'm like All right, let me look at this I think it's still a great price So apple if you didn't do it. It's still an amazing price. Okay. Here we are on this What's everybody doing smooth trader? Are you gonna do it? If this hits is anybody else gonna do bgf v if it hits Don't take it until it does if it blows over the high will be off of it. I'll go to the pxd Mark mobile his biggest day ever and they've been increasingly getting larger because yesterday you said it was your biggest day ever And last week you said it was your biggest day ever yesterday made 800 dollars not today. What did you mean? That's the way it should look you should have a scale that goes up You should have a curve that's up. This actually looks like it's gonna flip Nobody better be in this yet because it really didn't hit yet Okay, this Looks like I had a good exit on this Here if you want to trade this is a good one right in here pxd. I'm sorry. I missed the perfect entry for this You could do it stop 147 20. Wow.  I'm sorry. I should have watched this one first I was trying to watch the other one. This looks like it's gonna go. I'm sorry here Well, I'll watch it still and see if it backs up The the the bgfv may not hit This is too late to get it now though. Sorry. Just saw it was two dollars stop This is a really good gap. This is actually my favorite gap except for the fact that the price point was like $1,300 good for you mark mobile, but man, you are killing it. You are really doing great Did anyone do this on your own? I did not call this here. I didn't call it here I was waiting for it to rally then all of a sudden it broke. This is good Here I'll keep watching it And this one did not trigger yet if it does it's okay 10 by 40 bgfv amc Was the one and I am glad that I am out And I'll call this if it sets up, but it may just be a goner This is really gonna roll over.    Why didn't you get the amc? Is anyone else going to do this if I call this one here? It's another good gap actually Here it is going to set up again What was that? Was that real? I don't know if that was real or not No, it looks like it was an invisible tick Okay, this is going to be a buck, but this is what it is 40 145 if it hits right in here stop 146 It's going to be a dollar, but the target really is first target It's worth it. So it's two hours down to the low of the day 145 stop 146 PXD if you want it smooth trader isn't going to miss it I mean isn't going to do it. Okay Chief said he'll do it 145 by 146 first targets 143 second target really Dream target today, and this is 140. That's not out of the woods. I mean, that's not even unrealistic actually So this is probably going to hit 145 by 146, it's a $1 stop and you have to give it that it's ready PXD Okay, let's look at everything else This man, did I have a good exit on this holy guacamole. It never got to 15 And this here just hit two. So did anyone do this? You could be in this here. It's actually rolling over BGFV stop is 940 Oh, no, it didn't hit it didn't hit 10. Sorry. I thought it did No, I was wrong 15 Looks like it's gonna though just wait till it hits 10 if you're not in it If you're in it, it has triggered actually on the one and the two I think this this this is gonna go though It's holding there's that noise again. Did anybody hear that noise again Does anyone hear that noise? It's making a noise my computer. Can you hear me or not? Maybe I need to reset it. I mean I'm done trading Mike is on and off. Yeah, it's hot. I'm done. Hang loose here.  I'll be right back


Nice to refer to your Mountain View Dodge. What do I have here? A beautiful 2020 Rebel. This Rebel is special because it's got the Eco diesel in it.  Love the sport hood on these things. They're awesome. Mud flaps, wheel locks on everything we do here at Mountain View. Power heated seats, heated steering wheel, 12 inch screen, 4x4 because it's the Rebel. Gorgeous, gorgeous, gorgeous. Automatic headlights, power pedals, lots of room in the back seat here for the kids or for the dogs. Nice and easy. Seats pull up, got lots of storage under there. Being the Rebel, you've got the rubber formats on this thing too as well. This one here has already got the tonic cover on it so you don't need to worry about that. Of course, it's got the backup camera, trailer tow, and your hitch and your backup sensors. So for your round, you're going to come to Mountain View Dodge. You're going to see Steve McFarland. Have a great day.