So, we've already seen a few different heuristics. We talked about this idea of a simulated annealing sort of approach. And with simulated annealing, what we were using sort of as our inspiration was metallurgy. This entire idea, let me get that second L in there. The entire idea is again, as metal is heating up, the molecules are moving all about, and then as we start to reduce the temperature by plunging it into water, those molecules that are bouncing around, almost like configurations, are solidifying themselves. And well, that's allowing us to now have sort of a hopefully best case local maxima, but or hopefully the global. The other approach that we talked about was this idea of a genetic algorithm. And the entire idea here was this was inspired by evolution, you know, survival of the fittest. Figurations are based on sort of the implementations of our forefathers and mothers and survival of the fittest was kicking in because at some point, hopefully we reach a pinnacle. And so the last one that we're going to look at is something known as Aunt Colony Optimization. Again, what we're looking at is this idea of biologically inspired algorithms, algorithms, items, items, because, you know, we see that this is how something in nature or something in science operates. And we're just saying, well, can we take that same similar behavior and use it as a problem solving approach? Because again, something has used this to solve problems. So can we do it as well? And that's where, again, we look at this idea of ants, the entire purpose of an ant, I'm working off of a grid structure just because it makes my life easier, is that it's going to be traversing its environment in search for food, right? Oh, I found food. So what I need to do now is I need to go back to my colony with this food. Ta-da. But that's not just, obviously, they're not just working off of a straight line. There might be nooks and crannies in a maze. And secondly, it's not just I bring the food back. It's also I tell all of my ant buddies, and that's where we sort of look at this idea of it being a multi-agent search. The entire idea is that those ants are going all about the environment to hopefully find food sources. And as they do, they make sure to tell their sort of ant buddies again. So to at least start with that. Then when we're looking at it from sort of some kind of larger environment's sake, you know, when we're dealing with one possible route, perfectly fine. You know, again, the ant sees, oh, this is the only way, I go to it. But more importantly, what we're looking at is this idea of, which do I take when I have a fork in the road? Should I go left? Should I go right? Should I go left? Should I go right? The entire idea here is we actually start to introduce a little bit of the math equation and I'll walk you through it as well. What we're looking at here with this fancy symbol is this is something known as ADA. And for the purpose of ADA, what we are looking at is, again, this idea of attractiveness to a particular move. Now the XY is just sort of in our case referring to what position or what tile that we want to go to. If this was P1, and this was P2, then what I'd be looking for was the ADA of P1, and the ADA, ADA of P2. And so, okay, we've just established, here's a number, here's what we're looking at. What does attractiveness sort of boil down to? Well, that's where, again, we're sort of looking at it from the how much further am I working off of? So if I'm kind of drawing out my Manhattan distance lines, right, again, ADA is just saying how much further? How much further am I to my goal? Now we'll see sort of that's not the only thing that we work off of in this heuristic, but it at least helps build sort of our stepping stones. Because the second thing that we need to worry about is something known as the trail level. So we just talked about, again, ADA. So again, this idea, ADA, this idea of how far we have to go. And then we also have new kind of design, Tau. And the entire idea with Tau, let's see, the best way to describe Tau or how many ants have taken this path. Because again, if we're looking at ants and behaving like ants for a moment, we'll see sort of pheromones a little later, but they're pooping out pheromones or they're depositing pheromones onto the ground. And so an ant, looking at the ground, it's seeing that the pheromones are there and it's like, oh, well, I know that, you know, my colleagues or prior generation of ants have gone this route. I'm going to go this route. And so that's just sort of what these two things are coming into. So again, Tau is referring to number of previous ants and ADA is referring to how much further. Now to just kind of walk through this a little bit more and just to kind of get a little bit better understanding of ant colony optimization. Again, as we're starting to look at this, we'll see that the ant sort of picks one of those routes and is again still kind of making its way to the food source, the ant in this case found the pathway. So this is a valid path to our goal. But this is also technically a valid path. You know, again, we see that this route gets me to my food. So it's also valid. So very similar to what we saw with a star. There's multiple ways I could get from RGU to Seattle. And so one other little thing that we could work off of is the fact that you may have some limitation of how many steps your ant could do.  The ant died that way. So no other sort of pheromones are being dropped. And again, we just never take that path. Either way, what happens is as we, as our ant finds the food, it takes the food, it does a fancy little animation. Look at that. That's the quality that you get in this video. But my point being is as it was doing that, it was depositing pheromones along the way. I found a good source. So I'm going to let you know as I go back home, this was a good source. And for our sake, I'm just going to go ahead and say, you know, we're going to make our trail level something super easy. Just a simple one. I dropped one pheromone on the ground. That just makes, you know, life a little easier for us. But again, we're talking about more than one ant. I'm only working off of one ant. But what if all the ants are dropping one pheromone as well? Well, in that case, we may see that, you know, on this first tile here, right? This one, every ant had to traverse. So it's obviously going to get, in our case, all five ants pheromones. But again, let's say that three went north and two went south. Oh, well, you can see pheromones are being deposited in a different amount here. One thing to take note of with this is you could have these pheromone levels be anything you want them to be. There are certain sort of recommendations that you can work off of. But they just are whatever we want. Again, I could have 20 ants, and they're all depositing one fifth of a pheromone. And that's just giving me another number. That's the important thing. This is just a number. And so is just this. So again, for our sake, just going to work off of one because it makes everyone's lives easier. So again, what we're looking at is this fact that, yes, I do want to have, you know, the pheromone levels. But specifically, I want to be able to identify something about exploration, right? Maybe this food source dies off. For whatever reason, that food source has been expended because, again, it's a finite resource. But maybe there is another food source right here, food. And so again, we would want to, even though this took a longer route for, say, this path to get to this food, it still may be a valid one later on for whatever reason. So we still want to have the ability that even though this is our shortest path, right, maybe it's not over here, but maybe if you were to take, let's see, if you were to take this route right here, where my head is apparently in the way, right, that would be where the food source, the next food source is. So how do we take all of this? Again, how do I take my towel? How do I take my ADA, right, X of Y? How do I do all this? You know, how? And that's where I will warn you. It's a lot of numbers and it's a lot of formulas, but we're gonna work our way through that. Here is the formula. In essence, what we're looking at is we're going to select a probability of moving to a specific direction. So this path one is gonna have some probability. So probability of path one, the probability of taking path two. That's all this symbol right here is looking at, is given this ant, so this ant, this path, right? That's all that's kind of working off of and there's the better picture of that. But you see that we've already sort of built these points out, I've introduced them. So again, we're looking at tau. Tau is saying how many ants came before us and ADA. ADA is also just saying how close are we to the kind of end goal. So again, we're just sort of taking and working off of these two things. So again, the next little piece here you can sort of see going on is some sigma. The sigma is again, just a some summation. What we mean by that is what we're dealing with is given a particular route. So let's say P one again, this is all for P one. Then that's divided, so P one is divided by sort of the routes of P one and P two. So okay, we're just starting to flesh this out a little bit more and you can see I have sort of an explanation here. How do I bring these value? How do I make these numbers? Tell me how to make these numbers. Super simple and we're gonna work off of here is just the inverse of the distance. So the distance is in this case, oh well before we jump to that, just to a little see these because I didn't show them before, alpha and beta, these are mostly just letting us have some kind of flexibility. If they are both equal to one, we're just saying that the trail level and the pheromone or the tract of the trail level, the pheromones and the attractiveness or how much further they're equal. But I could make one go higher or lower than the other and so I could add a little more weight to them.  For our sake, we're just gonna act like they don't exist. Why did you even bring them up if you weren't going to look at them, right? But that just sort of helps kind of convey what we're doing here. So again, if I'm looking at this right now, all I see is that I'm working off of a trail level of a one, right? I'm just arbitrarily saying that that's all that these are in our case. Okay, right? We've taken sort of that first step, but I still haven't worked off of sort of the eta, right? What's the distance? How much further? How do I translate that into a number? Again, we're just looking at this from the calculation of the inverse of the distance because at the end of the day, if you think about this, it's no different than just working off of a grid, right? A graph structure. I don't have to have all those tiles. This is now just some edge moving to another node, right? And so what we're gonna do is if this had a weight, if this had a weight of 10, we're gonna say that it has the inverse, so one-tenth. Now, same kind of concept's going on there. I'm going to work off of that second path and say that it had, that's my P2, that's my P1, P1, and what's the probability of taking P1? Again, what we're doing here is why we take the inverse is specifically we wanna promote the lowest cost. Again, thinking about this like it's a shortest path problem. I want sort of, if we look at sort of 10 and 14 by themselves, right? I want the bigger of the number, but the problem is the bigger of the number's the 14. By taking the inverse of these problems, suddenly my one-tenth is the bigger of the number and that's sort of how we are able to get shortest path, ta-da. Now, again, as I plug this in, luckily this is where sort of at the start, this is super easy, so one times one-tenth is just one-tenth, one times one-tenth, just one-tenth, one times one-fourteenth, just one-fourteenth. I take this, I plug this into my calculator or my program or I do this math by hand and I'm gonna get one-tenth, again, one-tenth over one-tenth plus one-fourteenth or in this case, 0.17143. I cut it off for the sake of visibility, but you can notice what that's going to give me is 0.58 or what we're saying here is that the probability, probability of going to path one is 58%, 58%. That's it, and we can do the exact same thing on the opposite end, so rather than it being the one-tenth on the top, if I'm looking at this now from what's the probability that this ant is gonna go to P2, well, again, one times anything is just gonna be whatever that thing is, so this just becomes one-fourteenth over one-tenth plus one-fourteenth, and that's what we're gonna see. Again, we take that one-fourteenth and that one-tenth plus one-fourteenth, and that's gonna spit out 0.41 or, roughly speaking, what we're saying is 42%. And you may be thinking, oh, of course, I could have just done the first one minus 58, oh, well, it's one minus 0.58, and we're done, but what happens if we were dealing with three possible pathways or even four possible pathways? This bottom calculation is still this path over the sum of all the paths there. This one is that path over the sum of all possible paths. This one is the sum of all possible paths. And so again, this is just allowing me to see what kind of frame or what's the proportion of this being the good path in relation to all the others. And so we look at this, and the same thing that we saw with our simulated annealing and our genetic algorithms, we roll some random number. We are controlling random. And if that random number, if we said something like 0.25, if that's less than our 0.58, congratulations, go the top way. If, however, it's above that, that's no longer the case, that's too big, well, that was a false statement, go to the next one or consider which pathway you end up taking. Okay, so there you go. But we have one other issue because as I take these, these numbers are just gonna get bigger, and these numbers are just gonna get bigger, and these numbers are just gonna get bigger. So the last thing that we have to deal with is also something about those pheromones. And specifically, we have something known as evaporation, pheromone evaporation. As sort of pheromones are just sort of chemicals laced on the ground, well, air's slowly getting rid of them. And as a result, we need to account for that. Because again, bad paths start getting taken less, and we want to start to erase those pathways from our place. So how we deal with this is again, we've got a bunch of stuff going on here, but specifically what we're looking at is when we're working off of sort of our first approach, how do we wanna initialize the pheromone level? So again, if you think about it, what's the old tau? Again, tau is just talking about trail level. Well, that means if no ant has touched this place, it's a zero, and zero times anything is just zero. And we'll get to this, don't worry, we'll get to what this is in a bit. And so we take that and we multiply it by some initial value. Or you just set it as one, either one works. But then we get into updating our search, and now I'll start to break this down because now tau may be, tau old may be one. And so I need to know what this is. Well, as you can sort of see, this value, if this is where, well, you're gonna hate Greek letters, this is row. It looks like a P, row. Good way to think about this is, again, it's just the pheromone evaporation coefficient. It's some value, what percent of, let me, what percent of pheromone evaporates? Again, we're just saying, all right, there's pheromones on the ground, I need to have them slowly go away. How much do we evaporate at each cycle in our search? So again, row, or I'm just gonna call it P because it looks like a P, right? All that is is just telling me in proportion. So if I said that the evaporation rate was 0.5 or 5%, then what's left over is 95% of whatever my old tau was, old. All right, so 95% of the old tau is left up and then we get this, another sigma. Oh, all right, well, again, sigma just means summation. So we just play this out a little bit more and specifically that sigma is looking at delta tau, but what it's actually really asking is how much pheromone was deposited in this round? So how much was just deposited? That's it. And we take all of that amount as you can sort of see here by M referring to number of ants and K referring to this ant or this given ant. So given all of our ants, how much pheromone was being dropped by them? So how do I calculate this? Well, guess what, it's more math. What we're looking at is this is where we officially say how much pheromone one ant drops. That's all Q is, Q is that constant value of assigning, again, how much we're working off of. And then we do kind of work off of a cost of path as well. So how much pheromone was it dropping along the pathway? But again, that's just sort of our kind of, that's how we do it, that's in a nutshell kind of what we're working off of. So again, just playing that out a little bit more.  Length of the LK is just what's the length of the path taken by this ant at this time. So again, I've gotten to the goal. What was the length it took? So if we're looking at this, I have two possible routes. I have a P one and I have a P two. What are the new pheromone values after my search? Now this is where I have to sort of fake some randomness and say, let's arbitrarily say one ant went up to the top and one ant went to the bottom. Just for our sake, you know, to simplify things. Rho or P, again, the evaporation we're gonna say is 5% and one ant drops one pheromone on the ground. Okay, well, again, we're working off of tau old. So again, this is just tau, that's tau of the top path or I'm calling it P one. Well, let me correct and sort of follow along with what I'm doing here. So this is again, just our tau of old for our path top. So there, that's all. Again, if you see, I just said that that's a one. That's all. So I plugged in that value. Then I took my row or P and said that I'm going to have 5% being evaporated or 95% is being kept from the last kind of iteration. So in this first little chunk here, right, I would end up with a 0.95. Then I'm adding in how much pheromone. So this is the sigma of the delta of the, it was of, what's the symbol again? Yeah, tau, tau, the tau of this path. So again, this is just saying, well, I had one ant, so q of one over the path. So again, you know, in this case, 0.95 plus 0.1 is going to give us a new trail level of 1.05. Okay, well, guess what? You do the exact same thing. So again, tau old, we get the same q, but again, the pathway is much further this time. Again, remember this is LK or distance. So again, oh, well, you know, one path. So 95% is still kind of being carried over. That's like a 0.07, I'm not going to draw it out. But you can see, oh, well, the pheromones on this pathway, it's no longer a 50-50 shot, it's a 1.05 versus a 1.02. Not a major difference, but again, we're only working off of an M of two ants. What happens if I expand it? Now my M is so much more, it's eight ants. And specifically, I'm saying that five ants are taking the top, three ants are taking the bottom. Okay, well, again, this portion right here does not change. It's again, just the evaporation. How much, how much pheromone stays? Again, it's just 95%. So we do that same song and dance again, that works. But notice again, we had more ants. So the sum of this Q, again, over the path, this is more, more ants went the top route versus the three ants less went here. And if you plug in this calculation, you can see it. I would get now a trail level of 1.45 versus a 1.164. Again, these trail levels are just telling me how many ants came here before me. And that's in our way, sort of telling us which path is gonna be sort of the more attractive of those paths. So again, we're not working too terribly much off of this idea of probability, but just so you can sort of see this and walk through it a little bit more, those numbers, those towels that we just calculated out are used in the next iteration. And so I do the same song and dance. Now, probability does have to kick in. So with p1, right, the probability, the probability of p1 becomes 1.45 over 1.10, 1.45 times 1.10 plus 1.161.14.  By all means, take a second to calculate that out for a moment. And that's, just to give you the warning, that's the type of question that you might see on a quiz or an exam type of thing. But again, there's a probability of going to the top. There's a probability of going to the bottom. You do this same song and dance and you generate sort of which ants go where. For my sake, because it just makes my life easier, I'll just say instead of five, now six ants because it had a higher probability. So, oh, well, six ants went this way and only two ants went the bottom way. Well, again, notice we're just updating the pheromone levels.  So again, this is my tau of top. This is my tau of bottom. So again, that 1.45, I wanna take 95% of that 1.45 and whatever that turns out to be, I move on. And I do the same thing with my bottom. So, 95% of 1.164 gets carried over plus, again, how many ants took this pathway this time? And as you can start to see, again, as more ants are taking that top route, that pheromone level, sorry, that tau level, that trail level is getting much bigger because, again, we're saying that more ants are taking this route, more pheromones are being deposited on this route. And guess what? We take these values, the 1.97 and the 1.24 and we plug those in. And so we get, again, so our tau of p1 or top and our tau of bottom, just to correct myself here. Top now. So again, same thing. What gets carried over? 95% of that. What gets carried over on the bottom one? 95% of that. How many ants were this way? Well, again, to make my life easier, seven ants went this time, only one ant went this time. And you can see that we continue to go over and over. As we make these new iterations or these new steps in the ant colony search, one pathway is starting to become heavy with pheromones and because of that, this bottom pathway you're gonna see has much less probability each time versus this top path. So just to plug it in, right? Just so we can all see this. So two, seven times one-tenth, 2.597 one-tenth plus 1.258 times one-fourteenth.  Let's see, 2.579 times one-tenth. I know that that one's an easy one, but you know, brain. So we're dealing with 2579. So there we are, 0.2579 over. Let me not eat that. Times 1.258 times one-over-fourteen. That's gonna generate a 3475, 3475. And if we calculated that out, so again, there we are, 0.258 times one-over-fourteen. 0.2579 divided by 0.3475, the probability of taking the top route is now 0.7421, 0.7421 or 74.2%. And guess what, you do the same song and dance on the bottom here, so this becomes, rather than it being, it would still be the bottom of 3475, but just to calculate out that bottom one again, let's see that. Let's see, that's 1.2858 times one-over-fourteen. So 0.8, 0.0898, 0.0899, again, it's rounding up, if you're looking at that number. We take that number, divided by that 0.3475, and what we should see is 2585, eight, so that is 0.2589. 86, or roughly speaking, 25.8%. What do you know, we add this up, we do the song and dance, and the probability's coming out to 100% again. And so again, we're just seeing that top path, this top path has higher probability as we work through it, and the bottom path is starting to slowly, slowly kind of become less probable. And as this happens, as you can see, we eventually are starting to settle that this is the better pathway. But again, I only worked off of five ants, so here's my cultural relevance meme of the year. What if I used eight million ants?


What up everybody? Happy Wednesday.  All right big. Thanks to this group right here on a Wednesday the fifth getting into picker team seven Patrick With quadruple last spot mojo that's why as those asterisks those stars next to his name 70% of the time last spot mojo hits a hundred percent of the time So thanks to him for closing out the break. Thanks everybody for Grouping together and making this group break happen. There's the fresh case of elements. I was looking when I grabbed this case And I was looking around. I don't think we have any more of these So the second half of this case This is pick your team seven. So the second half is already in the store and that'll be our last break of elements for For who knows how long forever maybe Maybe not forever, but at least for now until we get if we get more All right, so let's do I'm gonna do this here. All right. Well, I'll do this way so you can see two four six boxes on the bottom There's two four six boxes on the top So one two three for the top six four five six for the bottom six It's two. We're doing the top six two four six These will save for next time So you can see kind of mark them number eight in my chicken scratch there Keep these back here save them for next time And away we go So two autographs one memorabilia card per box on average five cards total Looking for the on-card metal autographs looking for some of these rare earth parallels elements How many people know their uh, their periodic table by heart? I did not Not even close. All right, there's Justin Jefferson For the Vikings there you go Jilo. Yeah, h2o is water, but where does h And oh fall on the periodic table is h number one or is o number one Don't know This one is 32 out of 54 though. That is for patrick and the vikings There's raiders edition of davante adams 42 out of 74 You think tungsten is number 35 Or is it not and it's making some some chemist? Or some major like driving driving him crazy tungsten's 89 Here's a triple and they're all colts johnathan taylor who I think is out this week Matt ryan and alec pierce Double check your lineups. That's for jeremy and the colts It's gonna fit into a 120 It's better into a 130 top loader 13 out of 75 on that one We got a breeze hall two out of 10 Nice proton patch autographs Two-color patch and auto And that will be for adam and his jets Not this toddler this toddler adam not worthy of breeze hall There you go breeze study hall Tungsten is 74. Yeah, so that that's going to drive someone crazy Wait, is tungsten wait is the periodic number of tungsten 74? Plaxico burst metal moments, right? That's I see what they did there So i'm assuming this is uh New york football giant edition of plasco burrows Wait, so the atomic number of tungsten is 74 and these cards are out of 74 Can I can I confirm that? flood New york football giants. Yes says flood yet. Wow. Great job panini I'm glad they I'm glad they paid attention to that detail He could have just as easily glossed over and been like whatever That's adam with the giants. He's got both new york teams gila remembers gila remembers that uh the potassium symbol is k And that annoyed him a lot and now when people text him and write k. He thinks of potassium Oh man, just potassium just living rent-free in your head gila. I think that's uh, it's probably latin I think Potassium might start with letter k in latin, which is why it might be like that We got nuclear lamar jacks. He may go nuclear this season Nice that's for the purple birds adam could be like erin judge Could bet on himself could make a ton of money We got Peyton manning colts edition. It's going to go to jeremy to 27 cobalt The atomic number 27 I like that There's david montgomery electric bring these cars to the science fair. That's not a bad idea gila if i'm a school teacher If i'm a chemistry teacher i'd bring cards like this And then be like hey remember guys in elements football the cobalt parallel is number to 27 That's the atomic number I'll bet i'll bet kids would pick up pick that up real quick nine out of 70 There is david montgomery electric relic for the bears. That'll be for greg and buffalo james cook for the bills We got rookie neon signs gold ink autograph on that window over the jersey Going to erin the buffalo bills five out of 50 And we got steel signatures tyler higby I like the ink right on the on the plate there 111 out of 199 Yeah, that would be a nice class, right? Hey, if if i'm a if i'm a school teacher if i'm a science teacher is just trying to get through to the kids Maybe that's what i do got deandre hawkins To 199 i don't think it goes up to 199 right the periodic table That'll be for erin and the cardinals And there's tungsten Justin herbert to 74 There's johnathan taylor two-color patch for jeremy and the colts Right, whatever that Is it the second peak? Or is it pikue? It's one of those three rookie neon signs tray mcbride Rookie auto for erin and the cardinals. I saw the sign and it opened up my eyes and I am happy now Living without you 11 out of 50 Steel signatures derrick mason Derrick mason jar going to erin and the titans That is 124 out of 199 Three boxes to go Right, it's i well i now i'm second guessing myself too. Is it pikue to to peak one's interest? Or is it your peaking one's interest or like a peak like a mountain Peak someone's interest. I think it's I think it's with the q. It's the one with the q There's mac jones 172 out of 199 Well I'm a i'm a bit of a writer by trade a little bit of journalism in college. So I have to get things right like that otherwise Other people make fun of you you are in class G. Hello. We're taking you to school xenon The atomic number for xenon has to be 54, right? It is it's 54. Good job panini. I like that drake london going to brody in the falcons Brody now you're never going to forget that xenon atomic number is 54 because it's drake london's out of 54 I got mac jones return of the mac We'll go to music class too mac mac return of the mac By mark morrison, I believe maybe a one hit wonder This goes to mark and the patriots mark with mac More patriots for mark pierre strong jr rookie neon science I like the gold ink on the window over the jersey five out of 99 And back here is steel signatures patrick sartain Uh one That goes to the dolphins that'll be for jonathan His son patrick sartan two jr. Is not too bad himself So that goes to jonathan and the dolphins Yeah, we're taking people to school today nancy Impress your friends brody next time next time uh, you're with your friends You can be like hey I'll give you 100 bucks if you know what the atomic number of xenon is And they won't get it unless they're nerds, but they won't get it Aidan hutchinson 76 out of 99 for the lions that's going to go to It's going to go to chad b with detroit or cat team mojo There's xenon again sky more reach for the sky There's a snake in my boot and goes to greg and the can city chiefs And that's super charge relic david bell cleveland cleveland this is for you patrick with the browns of his last spot mojo teams That is 14 out of 75 Two two more here. We got jonah smith 76 out of 125 Patriots to me for mark and We have rookie steel signatures karson strong Looks like either silver ink white outing silver ink. I think silver ink against the metal looks pretty cool Uh, that will be four The fly eagles fly erin miller Fly like an eagle That was the steve miller band erin miller erin steve miller With the eagles is that what is that why you got the eagles erin? I'm gonna fly like an eagle that's steve miller band right fly like an eagle I don't know the rest of the words Time keeps on slipping slipping slipping into the future All right final box We got one dale robinson Adam with the giants And we got karson strong again i'm gonna fly like an eagle That's americium what in the world is that? Well, the americium atomic number we know is Is 95 because it's cards out of 95. It's a synthetic Radioactive chemical element with a symbol am An atomic number 95 it is a trends Transuranic member of the Actinide series. Anyway, that goes to erin and the eagles. We got mac jones That will be for the patriots another dual relic for mark Uh, not no, there it is numbered. I'll say gotta be numbered 140 to 199 And a couple more here. We've got tony pollard 103 out of 125 for dallas. That's for greg and the boys And last but not least it's a packer. It's romeo dobs rookie steel signatures romeo dobs packers jonathan kent He's got a couple touchdowns on the season already Maybe maybe building maybe we'll continue to build a relationship with erin rogers on the field and Let's see where he ends up 125 out of 199. I actually have them stashed away on my fantasy team Maybe throw them in a flex spot every once in a while. I'm joe for jaspy's case breaks.com Thanks for going to school with us. I'll see you next time for the next break jaspy's case breaks.com. Bye. Bye


My name is Amit Dan from AmitDan.com You are watching one of my deep travel videos and today I am taking to the magnificent Luxor temple offering an irrespective of all the shadows and lights at night. Located in the heart of Luxor, the Luxor temple was built over hundreds of years by the new kingdom Farahus. Known as the place of the first creation, this is where during the Farahus annually re-enacted coronation ceremony the god Amun experienced rebirth. Some striking anomalies can be seen in the complex. One of the temple halls was converted into a Christian church and then a Coptic church during the Christian era. There after thousands of years, a 14th century mosque was built on the southern wall. To this day the mosque is carefully preserved and active for praying. There is an entrance leading to the mosque from outside without entering through the main temple complex. The gigantic statues and columns were so big and high that I felt tiny when I was taking pictures standing next to them. The vast Luxor temple has a great colossal colonnade hall which is 61 meter long and you see thousands of sandstone fragments with carved and painted details of hieroglyph, text and temple ritual scenes. This was the first time we visited a temple at night. I guess the tour company arranged to visit at night because of the Luxor temple's famous night spectacles when the lights lit up. Thank you for watching this video. There will be more Egypt videos coming up so I'll see you shortly. Thank you.


Statistics and Excel, scatter plots with car-related data. Got data? Let's get stuck into it with statistics and Excel. Desktop version of OneNote here continuing on with our theme of taking data and creating a pictorial representation of it, allowing us to get a different view, a different angle at that data, hopefully helping us to extract more meaning from the data than we otherwise would be able to do. Now in prior presentations our primary pictorial representations have been the box plot or box and whiskers and the histograms. Great tools for taking one type of data set and trying to get a feel of that data set, looking for example the midpoint and the spread of the data. However, note that usually those tools are used for one data set, not multiple data sets, although you can think about multiple data sets by having a box and whiskers side-by-side or histogram side-by-side or possibly graphing one histogram on top of another histogram in the same chart which could give you a nice pictorial representation. But now we want to think about a scatter plot type representation. When we think about a scatter plot representation we're usually looking at two sets of data and we're trying to determine if there's going to be a relationship. Do they move together, these two sets of data? Now remember that we're always kind of thinking as human beings if they move together there's a cause and effect kind of relationship. That's what we kind of jump to but we can't really, we have to be careful of jumping to that conclusion. With the statistics of course what we're doing is trying to say okay is there a statistical relationship that these two things move together and if there is then we can go on and try to determine if there's if one is causing the other ones. Now we're always keeping in mind whenever we whenever we explore these concepts is one the statistics we can say these two things are kind of happening in correlation to each other but then the question is is there is there one causing the other or is there some other factor that's causing both of them that we have to always keep in mind and we also have to keep in mind whether or not we're getting the cause and effect wrong the chicken and the egg problem which one came first which one is caught if there is a cause and effect relationship it's possible to get it backwards and those are two things that we have to be very cautious of. So remember the statistics just doing this the box plot or the scatter plot is going to help us to get a visual representation to see if they're kind of moving in alignment and then we would have to go from there to further determine if there's a you know a cause and effect relationship. Alright so here's our information we've got car related information miles per gallon there gallons cylinders displacement horsepower weight acceleration model and these different factors in a car like if you're thinking about purchasing a car clearly they could have impacts on each other. So for example you would think that if you had more cylinders in the car that that might lead to a higher horsepower. So you might say that there's kind of a you would think there might be a relationship to these types of things if you have more cylinders in the car you would think that that horsepower might go up and as horsepower goes up maybe maybe the miles per gallon isn't as efficient you would think right if you have a high horsepower car especially if you're driving it in the city where you have a lot of stop and go traffic then you would think that then that might be miles per gallon reduce the miles per gallon the weight of the car you would think the weight of the car might have an impact on the the miles per gallon of the car and so on acceleration similar kind of thing you think you would think that the weight of the car might have some impact on the acceleration so you can we can plot these some of these data points together you can imagine multiple different ways that you can plot these data points, these two sets of data at a time, two at a time on the x and y axis to try to determine if there's some kind of movement that is happening together. And then of course, you've got to think about, well, what, what is the chicken and the egg thing? What's the, what's the cause is one cause and the other, or they moving together because of some third factor that is involved, we'll talk more about that later. But for now, if we look at this data, we're going to make something like this, which is going to be our scatter plot. Now just logistically note that in Excel, it's it's what happens oftentimes, if you just want to have the easiest way to input the scatter plot, then you can just put these two sets of data side by side. So in this case, we've got the horsepower and the miles per gallon that we're going to be looking at. And so here's our data set. So we just copied them from here over here, you don't have to do that. But that's the easiest way for Excel to just automatically create the scatter plot. Pretty much correctly. So if you were to just highlight this data and create the scatter plot, then it would put on the x axis, the horsepower. So here's the horsepower on the x axis. The the x axis is traditionally what we would think of as the independent variable. And so and then we're saying that the dependent variable, the thing that is that is moving because of horsepower, and that thought process would be the miles per gallon. That's just how we normally think about it when we plot it. You could think about it the other way and you could try to read the graph as this was the this was the independent variable. And this was the dependent but traditionally, of course, we put on the x axis what we think of as the independent variable. So it's moving independently. And then the miles per gallon up here. So note, another classic example of this is like is like SAT scores, if you're in like a college type of situation. And you're trying to look at test scores that a bunch of students took before they went into college, and you're trying to see if the test scores have an impact on their outcome, their grade point averages in college. That's a classic type of example. And in that type of example, note that the people that we have all the test scores, we have all the SAT test scores, right? So so we have all the data, and then we have the data related to the grades. And we're trying to then plot to determine the depend the independent variable in that case would be the SAT scores. And then the dependent variable, we're trying to see if there's a relationship or if they move together would be on the y axis for the actual grade point averages, right? Here, we're saying, okay, the horsepower, in our case, we have the horsepower that we know all the data for the horsepower. And we're trying to say that is that driving is the relationship between the horsepower and the miles per gallon. So if we look at the horsepower, then this is sorted from lower from highest to lowest in the horsepower. So on the highest point of the horsepower, we're at the 230. So if we go all the way out here somewhere to the horsepower somewhere out here is the 230 on the x axis. And if I then move that on over to the to the y axis, the miles, the miles per gallon, you can see the miles per gallon is lower than some of the other points, which is kind of what you would expect a higher horsepower car has has the lower miles per gallon. If I go all the way down to the bottom of the chart somewhere around like 96 on the horsepower, you know, would be around 96 around here, that's somewhere in the middle somewhere in here. You can see that if I take these spots, they're higher on the miles per gallon, which is kind of what you would expect. So you have some one of a relationship, the and then the question is, is the horsepower the thing that's called causing the miles per gallon situation, or are they both moving in alignment with some third factor, which we'll again, we'll talk more about later. But you can see that there's there's somewhat of a relationship. It's not a perfect relationship, of course, because we're plotting all the points here. And then we're trying to see a trend, whether there's a trend that is happening. Now oftentimes, the next thing that we would do is create the trend line. So this you can do in Excel by by adding a trend line. Oftentimes, we're looking for a straight line. That's what we would like to typically do. We would like to create kind of a straight line if possible if there's a relationship that looks similar to a straight line, so that we get the function of the line. Now this is similar. When we saw like the histograms, and we said if we can create a curve of the histogram that we can make a function for that would be useful, because then we can use mathematical equations to make predictions based on the equation. You have a similar thing here, right? We know that this everything doesn't fall in on the line. But if we can create a line that approximates the data in some way, that would be great. Because then then we can then we can at least make an equation and we can make predictions on the equation, which aren't perfect. But there's some kind of correlation that seems to be happening here. Now if there wasn't a straight line, and you had some other kind of curved line, then again, what we would really like to see happen is to have some kind of line that we can put through the data that approximates the data set that we can make a formula for. Because again, if we have the formula, then we can do more approximations, the straight line would be the easiest formula to be dealing with typically. So when you see something like this, clearly, the more tight the dots are around the center line, the more correlation there is the more they are moving together. And that's different from the question of which is one cause and the other. But but the more they're moving together in that sense. So so that means that this this line will possibly give more more predictive value, the tighter the dots are together around the line. If the dots are nowhere near around the line, they're scattered all over the place. And you can't you know, they're basically randomly scattered. Well, then you don't have a very good correlation, you can try to draw a line in it. But that line is not going to give you very much predictive power. If you're trying to determine, you know, horsepower and then the miles per gallon type of thing, right? So we're trying to because there's not a big correlation. Now in Excel, note that you can enter the plot, you can add the line. And then you can display the the equation on the line if you so choose here. And of course, you can also add it's quite important to add the x and y axisies. Because you and that's more important oftentimes than the title here. Because the title of this, you can you can make the title we're comparing, you know, the miles per gallon gallon and the horsepower. But the important point is you could see that on the graph. The important point is well, which is on the x axis, and which is on, you know, the y axis, so we can see what's going on. Now, if you were to analyze this data, once you once you do this, you might start to think, well, why does this kind of happen like that? You know, over here, you've got like very low horsepower. And we're always I'm always looking at the horsepower on the x first because I'm thinking of it as independent. And then I'm looking at that and comparing that to the miles per gallon on the right. And it's like, well, on much better miles per gallon with these very low horsepower, and then somewhere around here when you get to this kind of mid range of the horsepower, you've got a bigger spread in the middle and but a tighter line. And then down here, we have this very high horsepower, you actually have a little bit, a little bit higher miles per gallon than some of these items down here, where the horsepower is lower, but you've got you've got a lower miles per gallon. So these these some of these don't really follow the trend. So you might try to figure that out and say, well, why exactly might that be? I don't know, I'm just kind of but you can kind of kind of throw out theories from that point and try to speculate what is actually happening with the graph. I mean, maybe for example, these very high horsepower cars are not being driven in more populated areas or something. And therefore, they're actually being a little bit more efficient on the miles per gallon, because they're driven maybe by the way they're supposed to be driven, and more wide open areas. Whereas, if you have some of these more higher horsepower cars that are somewhat in the middle, but are being driven in downtown LA, where they're where they're going to be moving, you know, to two miles an hour and a stop to traffic, then you would think that would be highly inefficient for a car. So I don't know, I'm just, you know, then you can kind of speculate and try to see, well, what is actually going on with this relationship. Now also note that if you think of the miles per gallon as the independent, you can kind of do that, but that's not traditionally how it's graphed. We usually think of the X as the independent, but you could say, well, what if the causal relationship, what if I assume the causal relationship is the is the other way around, you know, in some case, you know, you're going to say, well, if I have the the miles per gallon up here, then then if you can imagine, is that causing, you know, the horsepower to be to be here, right, to be lesser on the horsepower. And then when I when I say the miles per gallon are quite low, I could say if I imagine this to be the independent, I could say there's the independent, and then the dependent would be that there's a higher horsepower, right. So so obviously that's probably not exactly the case because you could imagine making a very inefficient miles per gallon, you know, gasoline, guzzling machine car, which still doesn't have a lot of horsepower. So but in any case, so there's so there's that one. Now we like we can look at another one here. This one is the miles per gallon and the acceleration. So in this case, we're imagining the independent variable being the miles per gallon here and then the acceleration being the dependent variable. So once again, we took the data from our data set. And you don't have to do it this way, but I copied the two components that are most relevant and put them next to each other. And I put what I'm going to say is on the x axis, which is usually the independent variable on the left, the dependent variable on the right. And then if I just highlight that data and input a scatter plot, excel will in the most part get it right. And so now you've got this relationship, which you've got this scattering here, which you can then create a line through. And then if you're close to that line, you can look at the formula for the line and possibly have some predictive power of that line. So here we have a similar thing. We've got the miles per gallon. If I have a low miles per gallon, then I result in and I'm always looking at the x first and the acceleration a little bit lower on the acceleration, right? And if I have the if I have the miles per gallon up here somewhere, miles per gallon, you know, way up here, then then the acceleration is actually a little bit higher. And so you can kind of see a relationship here. And again, the question would be, well, there's kind of they're kind of moving together is one causing the other or maybe there's a third factor involved and we'll talk more about those situations in the future. But for now, just for constructing the scatter plot, we're usually trying to compare two sets of data. We usually want to be graphing on the x axis, the independent item on the y axis, the dependent item in excel. That is easiest to do if you have your column of data for the independent or x on the left and you're dependent on the right, you can just select the data and import the graph. Once you do, I would usually remove the title unless you want to add the title as well, but then add the accesses because it's quite important for the scatter plot to know which is the x, which is the y axis, then it's often useful to input the regression line and and then I have the line, the trend line, and then you can also then put the formula in place. I think those are just some useful, a useful kind of format to be creating the scatter plot using the tool of excel.


Okay I'm going to talk about now about maps. So if you remember in previous versions what we were able to show in our maps was the coordinates for the TI's, coordinates for the enrollment, and coordinates for the events. Okay but now we are going to be able to show also attributes and other elements of which value type is coordinate. Okay so let me show you how it works. So in this case I'm going to select the malaria case, technosis treatment and investigation program. Okay so I'm going to select the map view and then because I have many many many TI's here so I'm going to just use my filter in order to only show the TI's that has been enrolled this month. So in this filter I have to click on this and then it will show you as you see I'm in Spain now the other TI's that has been, let me zoom in a little bit. Okay so I have these three TI's and right now they are only showing the TI coordinates. Okay this is the full behavior as the first the first time that you open the map is going to show up only the coordinates for the TI's but now you know that we have this bottom over here so well you know that in this bottom you can use different map layers. For instance we have the, if you would like to see the relationships of in this case and cases so I can click here and then it will show the relationships as well. This is not new but I always like to highlight this one because I don't know if many people know that now in the in the maps you can show relationships as well. So let me then, sorry, change these to the to show the attributes. As I said before if you have in your program if you have attributes or that element which value type is a coordinate then they are going to be show up as a map layer so you can select in this case the gps attribute and this is a color just for you to differentiate in the map where you are because you can display many things at the same time in the map so we are using this label over here too that you can configure as well in the server to show the color that represents the gps attributes. So I click on apply and I can see that I have two. Okay again these are not the I coordinates it's our attribute coordinates that can be for instance the location of the patient or it can be whatever you want.


Feather RP2040 Scorpio. It's an 8-channel Neopixel drive-in feather. We can use the Neopixel 8 library inside of Arduino and in circuit Python to animate Neopixel LEDs up to eight strips or rings of them. It's all using a PIO state machine. It really leaves the other processing free for you to do other stuff on the board such as read inputs or write to displays. Feather Scorpio there and that has the right angle headers and then I'm using one of these IDC ribbon cables to breakouts for IDC and this one is keyed so you can't put it in wrong. So all the white wires there are the data lines going into all of the eight Neopixel strips and then all the black wires there are connected to grounds. The power side of the Neopixel strips broken out to snap connectors here and then I have this running to one of our old DC breakouts. Feather RP2040 Scorpio.


Hi, I'm Darren Thomas, president of Thomas Media Group, host of the 22nd Annual Columbia Black Expo. Friends, we kick off another exciting week with this incredible endeavor, this incredible event that we've been fortunate to serve the Columbia Midlands community for the past 22 years. Our Power of Praise kicks off the Black Expo week. We partnered with the historic Bethel AME Church and we'll partner with the Young Adult Ministry as we allow other groups to participate, other faith-centered institutions, and we we rotate every year, but we kick off the week with expressions of faith, praise, prayer, and it's an incredible event and we're very grateful for the Bethel AME family to host this event at the historic Bethel AME Church downtown Columbia. And that's on Tuesday night, rolling into the Expo, the other Expo events. Our kickoff reception is on Thursday at the Holland's Library. We are excited that we have partnered with the University of South Carolina and they're justice for all exhibit. Friends, if you've not seen this incredible exhibit, which really tells the story about the civil rights movement in South Carolina, particularly here in Columbia, this exhibit is actually on location in the Holland's Library. From what started in March, it will be in place until August the 2nd. However, we're using this backdrop, we're showcasing this exhibit as a part of our kickoff reception for the 22nd annual Columbia Black Expo. We use this opportunity to thank our partners and all of our stakeholders in the Black Expo experience. Then on Friday, our 16th annual Columbia Charity Golf Tournament. Friends, we've got an incredible day of golf and fun. We have partnered with University of South Carolina football coach Will Muschamp, South Carolina State football legend Willie Jeffries. Those two gentlemen will serve as our hosts. We'll use this event to continue to raise funds for the Judge Matthew Perry Scholarship Fund. The first tee program in New this year is the Richland One School District, Richland Two School District backpack program for homeless students. It's a wonderful day of golf at Wood Creek Farms. A lot of exciting things. We've been fortunate to use this event to empower young people. In fact, we've got a young man that's headed to North Carolina Central University on scholarship. We have a young lady that's a part of this program that was a scholar athlete at Newberry College on golf scholarship as a result of her participation with first tee. This is one of the reasons why we continue to support this program. We have a lot of young folks that come out to participate. This is a great event, a great way to give back. If you're a golfer, this is an event you do not want to miss. Of course, later that night, the night annual Taste of Black Columbia. Friends, if you love food, if you're a foodie, as people refer to, you love good food, great music, great time, this is an incredible event. We'll have anywhere of 20 some chefs, caterers, restaurants. If you've never been to the taste, you enter into the South Carolina State Museum. You walk around, you sample foods, great entertainment all throughout the venue. We end the night with Grammy nominated Lettucey, who will be performing on the stage at the conclusion of the Taste of Black Columbia. Again, Friday night, 7.30 until 11 at the South Carolina State Museum. We roll into the big day, the next day, the 22nd annual Columbia Black Expo. Now what we're doing differently this year, friends, we're bringing back our exhibitors. As many of you might be aware, we've shifted the focus of Black Expo from the more entertainment to more of empowerment. We focus on the five pillars of economic empowerment, which are jobs, education, wealth, health, and entrepreneurship. If you ever read the book, The Five Pillars of Economic Growth, each of those pillars serve as a growth lever for any community. This is truly a family or an event, activities for the entire family. Of course, we have our speakers, we've got our youth areas. There's so much to be obtained at this wonderful event, our 22nd annual Columbia Black Expo. For more information, folks can go to our website, blackpagessouth, well, I'm sorry, blackexposouth.com for ticket information or just general information on the on the black for the Black Expo. That's blackexposouth.com.