Okay, we'll go ahead and get started now. Last time, we finished up with a little introduction to the solve command, so the solve command as we've seen allows us to solve equations. And today we're going to solve one equation again, one at a time. We'll do a few examples and I'll show you a couple of ways of getting around problems when you encounter equations that can't be solved in a closed form. And then we'll briefly talk about solving systems of equations and then we'll do several chemistry-related examples from chemical equilibrium, which is where you often have coupled polynomial equations that are easy to handle in Mathematica as we shall see. All right, so I want to begin by revisiting what we finished off with last time just to remind you of a couple of things. And so what we're going to do is we'll start with the general cubic equation, so solve. And then I'm going to put in A times x cubed plus B times x squared plus C times x and then plus D equals equal 0 and solve that for x. All right, so we saw last time that that gives us a general solution that takes up a lot of real estate on our screen. And what I also showed you then last time is that if you want you can have that evaluated for particular values of the parameters, A, B, C and D, by saying something like, give me the last result and use replacement rules to specify the constants A, say it goes to 1, B to 2, C to 3 and D to 4. So this will take those formulas that resulted from the solve command and then plug those numbers in. Okay, so we get nice exact forms. If we want numerical approximation we could say n percent. Oops, percent. Okay, and so there we have nice numerical values for our three roots. One of them is real and two of them are complex for this set of coefficients. Now, suppose we wanted to get straight to the answer without having to do these additional steps. Well, one thing we could do is we could combine this, that back down here, and then we could add the replacement rules to the end. So here to the end of the equation, all right, so we could say put that right here and if you let that rip you get the straight solution. Now, what if you wanted the numerical approximation straight away? Well, there's various ways you can do that. If you make any one of these numbers non-exact, not an integer, you'll get numerical answers. Okay, we've seen that before, so maybe that would be useful in the present case. There's another way to do it, so let me go ahead and put this, well, let's grab this guy here and put it back and then I'm going to make this back to an integer, so if we do that we get the exact numbers. There's a numerical version of the solve command, okay? And if you put in end solve, that's the numerical version, that will take you straight to the numerical answers. All right, now the numerical version, as we'll see in a minute, is useful for getting solutions, numerical solutions, when an exact solution doesn't exist, okay? So let's try one of those cases where the exact solution doesn't exist. So let's try this Quintic polynomial. It's going to be x to the fifth minus 2 times x cubed and then plus x and plus 5. We'll set that equal to 0, equal equal 0 and try to solve that for x and notice we get something out that doesn't look very useful. This is a sign that we were unable to get an exact solution. So when that happens, do not despair if you need, if numerical values will suffice and in this case they have to suffice, you could go ahead and put in end solve and then you see that you get out five roots, one of which is real and four are complex, okay? So the end solve command can be very nice when you don't have exact solutions. All right, so far what we've done is we've seen how to use the solve command for polynomial equations. It can be used for non-polynomial equations also. So we'll do a couple of examples here. So we can say solve and this time we'll put in the square root of a polynomial. So this will be 1 plus x plus x squared and we'll set that equal to 2 and solve for x and you see we get out two solutions. If you want numerical values you can say n percent, all right? Now, one thing that's useful to do is to see what is it exactly that we're doing here when we solve? What do these roots mean? What's their interpretation? Say a graphical interpretation. Well, the interpretation would be, so I basically have, if you like, two functions here. I have y equals square root of 1 plus x plus x squared and then I have y equals 2, so just a horizontal line and what the solve command is doing is telling me where those two curves cross, so where they're simultaneously true, okay? So let's have a look at that. This is kind of a useful thing if you're hunting around for roots, we'll see in a minute in cases especially where it's not so obvious what they should be. It's always nice to be able to do a graph to convince you that you have the right answer. So what I'm going to do now is I'm going to plot. We'll plot square root of 1 plus x squared and then I'm going to plot 2 and I'm going to use a curly bracket because this is a list of things that we're plotting and then I'm going to plot them over a range of x that spans those two roots. So we'll say we'll go from x equals minus 3 to x equals 2, all right? And the other thing I'm going to do here is I'm going to turn on the mouse so that when I run it over the graph, it will tell me where I am in terms of the coordinates of the graph, so that's kind of a useful thing to be able to check up close. So there's this command called dynamic and then we're going to put in their mouse position and then that has an argument graphics, okay? So you'll see what this does in just a second. Now if we enter this, we see the two curves, okay? So here's the square root of 1 plus x plus x squared and then here's 2, all right? And our roots should be where those two cross. That's what we get when we solve the equation. Now notice when I run my mouse over the graph, it tells me where I am in the graph. So I can go over here and see when I'm at this crossing over here, x is equal to about minus 2.3. So that corresponds to the first root, that crossing there and when I go over to this side, if I can get close, there you see x is about 1.3 and so that corresponds to the second root on the right hand side, okay? So this is kind of a useful thing to do and also it helps you to appreciate what you're actually doing when you solve these equations. All right, let's do another non-pollinomial equation. So this one's going to be solve x to the power 1 fourth equals equals 2 times x, all right? So you see we get two roots to that equation. One is the trivial solution where both sides of the equation equals 0 and then the other one is this 1 over 2 times the cube root of 2, okay? You could get numbers by doing n percent. All right, now let's try another non-pollinomial equation that doesn't look too challenging but it turns out it doesn't have an exact solution. So this is going to be e to the x equals x squared and we solve for x, all right? So notice when we do that we get the brown messages which is usually not a good sign and we get solutions but they're written in terms of this thing called product log so we can find out what that is by doing the question mark and it basically says that product log is what we asked the thing to do so that's not very helpful. So what about if we try n solve? We'll just put an n in front and we get some numbers, okay? So notice we got a real root and a complex root and but we also got this message and the message, you know, might make you be a little bit concerned. So here's the case where we definitely would like to make a plot and check. Suppose we're interested in this real root, we can check to see if in fact that is correct, all right? So we can plot e to the x and x squared, all right? And we should put these in curly braces and then we can go over a range of x that covers our root so let's say minus 1 to 0. Okay, so here you have it and you see where the curves cross it looks like it's right around minus 0.7 so in fact this root is trustworthy. Now, there's another command that can generally succeed when you have problematic cases so here in fact we saw that solve gave us something that didn't look very useful and solve produced a number but it has this sort of worrisome warning. There's another command that will definitely find roots but it does them one at a time and it needs an initial guess. Okay, and that command is called find root, okay? So it'll find one at a time. So the way that works is you say find root and then we're going to put in this same equation here, okay? And then what we do is in addition to specifying what we want to solve for we put in our guess, okay? So we've seen the plot here so we can say well I can see it's around minus 0.7 and then if we put that in that's a pretty good guess then we'll get a more precise answer and you see that we get the one that n solve gave us. Okay? So find root is in general you can consider it to be very, very good at finding the root without any superfluous information. That can be handy if you're looking for them one at a time. Okay, so let's see another example where find root might be useful. Okay, so this one is going, we'll try to solve this equation, another non-pollinomial equation which is going to be log of x equals x cubed, all right? Minus 2, all right? So let's try that one. So we try that one and we get this funny product log thing again. So what do we do? Well let's make a plot and then we'll sort of see approximately where the roots are and then we can use to find root to find them one at a time. So let's say plot curly log of x, x cubed minus 2 and I'm going to plot this between, what am I going to plot it between 0 and 2? So x goes from 0 to 2, all right? And if I do that I see that there ought to be two roots. One is over here at about a little more than 0.1 and then the other one's over here at x is about 1.3. So I can mouse this in here, this equation and use find root. So the first one, I'll say x is around 0.1, all right? So that gives me actually 0.135674 and then the second one I find, let's put in 1.3 this time and I get 1.31498, okay? So there you see, you have a very robust way of finding the roots when you have a decent guess. How decent does it have to be? Well, let's check. So suppose I put in here 0.5, which is not that close. I get the same answer. What if I put in 0.7? Now you see it finds the other root because it's closer, all right? So you do, if you have closely spaced roots, you may find the wrong one if you don't know something about your functions, which you can do by making graphs, okay? So this is a few examples on using solve, end solve and find root for a single equation. What happens if you have more than one equation? Well, no problem. You can use solve and end solve to find solutions. So let's do an example. So we're going to say solve and make a list. Y equals equals X plus 2. And Y equals equals X squared minus X squared plus 10. Okay, so here we have a line and we want to find out where it crosses an inverted parabola, all right? And then we're solving for X and Y. Okay, so notice we get now a list of two lists, okay? And the first list gives us the X and Y values corresponding to the first solution and the second one to the second solution. If you want numerical values, there's various ways of doing it. One way is to just use end solve, which I'll do here. All right, so there's your two solutions, each consisting of coordinates X and Y. Now, let's see what this means graphically, all right? So let's plot X plus 2 and minus X squared plus 10. So I need a curly bracket and we'll do this over values of X that span our two solutions. So let's say X goes from minus 4 to 4, all right? So there's the two curves, here's Y equals X plus 2 and here's the inverted parabola. And so what our solutions are are the X and Y values of this point and the X and Y values of this point, okay? So you can see in fact that the first solution is this one over here and then the second root is this one here, okay? So looking at the graphs helps you to appreciate what you're actually doing when you ask to solve those equations, okay? I'll do one more example and then we'll move into some chemistry examples that will be hopefully a little more interesting. So another thing you can do is you can generate solutions that contain non-numerical parameters. So, you know, for example, just variables to be determined. So here's a simple example. Say solve A times X plus B times Y equals equals C and then we'll have D times X plus B or E times Y equals equals F, curly and then we solve for X and Y and instead of getting out a numerical answer we get out solutions that contain the parameters A, B, C, D, E, F, okay? And that one's a pretty easy one that you could do easily by hand but it's just to illustrate that you can actually do this generally without having numerical values, okay? Now, can any of you give me an example of a class of problems that you encounter in chemistry that results in polynomial equations? I'll give you a hint. It starts with an E, equilibria, all right? So let's do an example to remind ourselves how that works, okay? So I'm going to go over to the chalkboard and then we'll do an equilibrium calculation. Actually, that's what we're going to do for the rest of the time today, all right? So the first example we are going to consider is we're going to have this reaction. So methane gas plus steam in equilibrium with carbon monoxide plus hydrogen, okay? And this is called the steam reforming reaction and it's one way that you might be able to take a fossil fuel like methane and turn it into hydrogen and then the hydrogen you could use to burn clean in a fuel cell. So this is potentially an important reaction. One of the main problems is it requires a lot of high temperature to be effective. And the reason is if you look at the equilibrium constant which would be, how do I write the equilibrium constant Kp for this reaction? The law of mass action. The pressure of carbon monoxide times the pressure of hydrogen cubed divided by the pressure of methane times the pressure of steam, okay? And so that thing equals 1.8 times 10 to the minus 7 and that's at 600 Kelvin, all right? So it's pretty small. All right, so what I want to do now is we'll solve a particular problem corresponding to some given initial pressures of methane, steam and carbon monoxide. Now, when we want to set up an equilibrium calculation what's one of the procedures that we've learned in general chemistry that helps us to write down the law of mass action in terms of some unknown concentration or pressure? We have a table that we make. Ice table, right, all right, so what is an ice table? Well, an ice table is we put in our various reactants and products and then we write down what's the initial values of our concentrations or pressures? We express the change in terms of an unknown and then we write down the equilibrium values and then we plug them into the law of mass action and we solve for the single unknown, all right? So the case we're going to consider here is we start with 1.4 atmospheres of methane, 2.3 atmospheres of steam, 1.6 atmospheres of CO and no hydrogen. And what we're going to want to know is what's the pressures of all the gases at equilibrium, okay? Now, the change we can express in terms of an unknown which I'll call X and since methane is going away, I'll put a minus sign here if the reaction goes forward. By the stoichiometry, H2O is the same and then CO is going to be plus X and then what should I put for hydrogen plus 3X because of the stoichiometry, okay? So then at equilibrium, I have 1.4 minus X, 2.3 minus X, 1.6 plus X and plus 3X, all right? And now I plug those into the law of mass action and so I have now we have to start with 1.6 plus X times 3X cubed divided by 1.4 minus X times 2.3 minus X and that's going to be equal to 1.8 times 10 to the minus 7. Now, what kind of equation is this? Well, the whole thing. It's a polynomial in X, correct? What order is it? It's fourth because we have a term that's got X cubed times X, all right? Now, normally we can't solve this by hand, exactly, right? You're laterals if you could, it would be pretty rough. So what would you normally do in general chemistry class if you wanted to do this problem? Well, you would see that you have a very small equilibrium constant, so you would imagine that the reaction's not going to go very far to the right. It's going to go to the right and produce a little bit of hydrogen. So you might say, well, that means then that the change X is going to be small. And you might say it's small compared to these initial pressures. So you would replace 1.6 plus X with 1.6, in other words, say X is negligible compared to 1.6 and you do the same here and here. You can't say X is zero here because then the whole thing is zero. And then you would have 3X quantity cubed equals that and you could solve that in your calculator. It turns out for this particular set of initial pressures that's going to work. We'll check it in just a minute. But suppose you had a situation where you couldn't make that assumption. Well, then it's nice to be able to have an equation solver because as you'll see in just a second, Mathematica will take care of this like that, all right? And then once we have it in, we can play around with the numbers and see under what situations the approximation might actually be worth using and what cases it's not. Okay, so let's go ahead and plug this in and then we'll play around with it a little bit, all right? So then what we need to do is I'm going to go ahead and say equation or let's make it sound chemical. Let's call it mass action equals curly and now I'm just going to plug that equation in, all right?  All right, so there's my mass action, expression and now I can say solve, whoops, bracket, mass action, 4x and because I have numbers with decimal points, I'm going to get a numerical answer. Okay, so I do that and notice this is a fourth order polynomial so I do get four solutions and two of these turn out to be real, okay? So even though I have this nice tool that allows me to solve that equation, I still have to know some chemistry in order to pick out the answer, all right? So first of all we know that pressures are not complex quantities so we can rule these two out and now we have a choice, well how about this one? No, why? I can't have negative pressure of hydrogen, right? Okay, so that means the answer is the last one, okay? So then let's go ahead and see how we can pull the root out and calculate the equilibrium pressures of all of our species. All right, so for example I can say the pressure of methane is equal to 1.4 minus x and then I have to pull out the value of the root that I want from that list and the way I do that, we saw this last time as I say slash dot, oh sorry, let's make this, let's set this first equal to roots because I want to be able to just refer to it that way, all right, so I'm just going to re-enter that and then down here I say roots, the fourth element of the list of roots, okay? And what that's going to do, it's going to grab that fourth root and it's going to take off the x arrow and the curly brackets and just give me a number, okay? So then I get 1.398 or 763 atmospheres, okay? And you could convert that to the correct significant figures, all right?  Pressure of H2 is going to be 3 times x, okay? So we can do it like that, all right? So there's the pressure of H2 at equilibrium, all right? So this example I hope is useful because first of all it shows you how to set up an equilibrium problem, solve it and then how to actually pull out the roots in a way that you can use them, okay? Now let's put on our chemist hats again and think a little bit about this problem and ask whether or not we actually really needed to solve this quartic polynomial. So notice that the root that we decided was the right one is 0.002, is 0.002 negligible compared to 1.6, 1.4, and 2.3? Generally speaking I think the answer is yes. It may not be if you want to be really super, super precise, okay? But at least according to the standards that we have in GCAM that would be perfectly fine, okay? And we can see that by just repeating the calculation, okay? So let's go ahead and pull this in, put it back and we're going to just compare the roots with and without this approximation. So now we're going to make the approximation. We have to leave this X here but we can get rid of this one and this one, okay? And we solve and notice to the precision of the problem three significant figures we got basically the same answer. Actually it's only two significant figures the way I've written it, okay? So we get the same answer either way. So in this case the approximation was definitely good and we could have gotten away without Mathematica. But what if we, let's try starting with much lower pressures, all right? So instead of 1.6, let's start with 0.016 and instead of 3 say 0.03 and instead of 1.4 say 0.014. So 100 fold less in each case, 0.023, all right? So let's go ahead and let that rip, all right? And now you see the root that corresponds to the answer we seek is this one which is 0.013. And I think you would agree with me that that is no longer negligible compared to the values, the initial values, all right? So in that case the approximation wouldn't work. And just to see what we would get if we did make the approximation let's just go down here and put the same numbers in, 0.03, 0.014. And 0.023, all right? And to notice the physically meaningful root, the positive real root here is quite different. We can compare 0.05 versus 0.01, okay? So the approximation here would have given us garbage. All right. So there you have a gas equilibrium problem. And now what I want to do is to solve a series of acid-base equilibria problems, all right? So we're going to start simple. We'll start with the GCAM approach. And then we'll work our way up to increasingly complicated acid-base equilibria problems, including coupled equilibria. All right, so I'm going to go back over to the board now. Any questions on this example? All right, so next we'll start with a simple single acid-base equilibrium problem. So the one we're going to do is acetic acid which I'll denote HAC, aqueous. So acetic acid reacts with water and then produces an equilibrium where we have the protonated water plus the conjugate base acetate ion, okay? And we remember from general chemistry that we can write down the law of mass action in terms of the acid dissociation constant as concentration of H3O plus times acetate divided by the concentration of the undissociated acid. And for acetic acid at room temperature that's 1.76 times 10 to the minus 5, all right? Now, suppose we want to calculate the pH for an acetic acid solution where the initial concentration is equal to 1.00 molar. What do we do? Well, we can write down our ice table, okay? So we start out with 1.00 and I can write a zero for that and a zero for that. Now, when I write down a zero here, what am I assuming? But isn't there H3O plus in water? There's H3O plus in water, right? And that is 10 to the minus 7 molar at room temperature. So when I write down zero here to make life easy, I'm assuming that that's going to be negligible. Now, in a little while, we'll see that what we can do when that's not negligible. But certainly in G chem, we assume that and for the problems you're asked to solve in G chem, that's usually a safe assumption, okay? So then we can say minus X plus X and plus X. So we have 1.00 minus X, X and X. And so then our problem to be solved is X squared over 1.00 minus X, all right? So that gives us a quadratic equation which we can solve using the quadratic formula if we need to. Or in cases like this, the equilibrium constant is 10 to the minus 5. We start with a big amount of acetic acid. We can assume that X is small compared to one molar and so this would give us X squared. All right, so then we just take the square root of the acid dissociation constant, we get X and then we can calculate the pH as minus log 10 X. All right? So let's go ahead and we'll do this problem. We'll set it up in Mathematica. We'll do the calculation and then we'll start playing around with it and see about this approximation, okay? That's what we'll do right now. Okay, so let's go ahead and put in the K a, 1.76 times 10 to the minus 5. And then I'm going to get the roots of the equation to be solved which is solve X squared divided by 1.0 minus X equals, equals K, solve for X. Actually, let's first do the approximation. All right, so we get rid of that, solve. Okay, so we get two roots. Obviously, the second one is the one we want. All right? And so now we can say give me the pH or I can, another way I can do is I can say CH3O, the concentration of H3O at equilibrium is equal to X slash dot roots second element. And then I can say pH equals minus log 10 of CH3O, okay? Oops, minus log 10. Sorry. So I must have, oh yes, I, this one here is a zero when it should be an O. All right, there we go, thank you. Okay, so there you get a pH, 2.377, is it acidic or basic? It's acidic, fairly acidic and great. But, you know, we did that in GCAM. So let's just try for fun, redoing it, but now we'll do it exactly. So I'm going to go ahead and mouse that in. And then I'll just put everything all together here in the same cell, all right? And now we're going to put back the minus X. So this is an exact solution. We enter and we see that out here to the third decimal place, well to the second one here, that the answers are the same. Okay, so you can see here that the approximation was good. And how good will depend on your criteria, how much precision you want to have. But certainly according to GCAM standards, that would be perfectly acceptable. Let's try another case. Let's reduce the concentration by a factor of 1,000, 0.001. And, okay, so we can go ahead and do that. And go ahead and do that. Okay, and you notice now the pH is not as low. We didn't have this concentrated solution, one millimolar as opposed to one molar. And now let's do the same thing here. Well, first of all, let's ask, how does this compare to the initial value? It's 10%. If you can tolerate that kind of imprecision, then it may be okay. But let's see how far off we are, say, on the pH, okay? Now you see there's a bit more error here, all right? You're off in the, basically, in the second place. If you go down further, you start to see very substantial errors, okay? Now, you know, one of the things they don't emphasize in GCAM, what the error is depends on what you're doing. What the tolerable error is depends on what you're doing. In general, though, this would not be considered very acceptable. Okay. Now, if you keep going down, now you're starting to get some pretty serious issues. So notice here, when we solve this one exactly, well, first of all, the pH is totally different. The approximation here would be garbage. But notice this number. This is the amount of H3O plus that's being produced by the very small amount of acetic acid that we put in. This is starting to approach the amount that would have already been present. Due to the water dissociation. And certainly, if you go down another notch here, you run into the problem now where you're producing only 10 times as much as would have been there from the auto-dissociation. And so, if you were really interested in what's happening here and being very accurate, you need to actually worry about the water auto-dissociation, all right? So now, we're going to solve that problem. And here's a case where Mathematica can be quite helpful because now we're going to have to solve a series of coupled equations, the coupling corresponding to the equilibria that are interconnected, all right? And this is something that those of you who have been in analytical chemistry, Chem 151, have had some experience with. All right. So I'm going to go back over to the board because we have to talk about how to set up this problem. Okay. Now, what do I mean by coupled equilibria? Well, what I mean is that now, if we want to include the water dissociation, in addition to this chemical equation, we have this one, okay? So these two both share a common species that's going to affect the equilibria, okay? Now, we're going to see how to solve this in a very, very general way that will apply to any, any of coupled equilibria like this. It doesn't have to be acetic acid. In fact, we can get rid of the Cs here. It can be just any acid and it's conjugate base, okay? And we're going to assume just for the heck of it, it doesn't, we don't have to be precise here, but we'll say that this guy may also be present as a salt, a sodium salt. So we may, in addition to having some acid in the solution, we may also dump in some of the salt of the conjugate base and just to make things more interesting and complicated. Okay. So now, let's think about how many unknowns we have if we want to understand what's the concentration or we want to know what's the concentration of all species present at equilibrium. How many unknowns do we have? We don't know that until we solve the problem. We don't know H3O plus. We don't know OH minus. We don't know A minus. And we do know sodium plus, but let's just keep it here for now. So these are all the species present at equilibrium. Now, if I have five unknowns, how many equations do I need to determine the solution for five unknowns? I need five. How many do I have so far? One. So we have to come up with four more equations. All right. Well, one of them's easy. We can say that sodium plus, because it's a spectator ion, its concentration is whatever we started out with the concentration of the sodium salt, okay, of the conjugate base. So I'll just call that little c sub b. So the concentration of conjugate base that we started out with, okay. What else do I know? Well, I know the equilibrium constant for this reaction. So I'll go ahead and write that over here. That's k water is equal to H3O plus times OH minus divided by, well, that's just it, no water in there. And that's equal to 1.00 times 10 to the minus 14 at room temperature. This is for acetic acid. All right. So now we have three equations. We need two more. Anybody from Chem 151 remember what to do next? Well, one thing we know is that the total charge has to be zero. And what that means is the concentrations, the sum of the concentrations of all the cations has to equal the sum of the concentrations of all the anions, right? Okay. So now I can say then that Na plus concentration plus H3O plus concentration is equal to OH minus concentration plus conjugate base concentration at equilibrium. Okay. Make sense? Okay. We need one more. This last one's a little tricky, but once you hear it, you'll be, oh, okay, I see. So let's have a look at this equation here. This equation here has an A on both sides. What's the A? It's whatever the molecular species is, in this case, it's written as an anion that makes up the conjugate base, or when it has a proton on it, it's the weak acid. This species A is conserved. It's not made or destroyed in the reaction, right? It either loses or gains a proton, but A is conserved. So we can write down a fifth equation that expresses the conservation of A, all right? And so if I say that the initial concentration of acid that I started with when I made my solution is Ca, all right? So that's the initial amount that I put in. And we already said that the initial concentration of our salt of the conjugate base is Cb, so that, this is all the A species that we put in it initially. And so we can say that that has to be the same as the concentrations of the A species at equilibrium. And so that means we can write Ha plus A minus, okay? So just to be clear, these are numbers that are given. We decide what the initial concentration of acid and salt of conjugate bases. So I might say, okay, I'm going to start out with 0.1 molar acetic acid and 0.1 molar sodium acetate, and I want to know what the pH of that buffer solution is, taking into consideration the water dissociation, all right? Now, so we have this equation here basically eliminates sodium. We don't need to have it because we've basically put it in here, all right? So we have this equation to solve, and we have, or that one to include in our solution, this one, this one, and this one, all right? Now, if we were going to solve this by hand, what would we do? You'd have to start combining the equations and eliminating variables, and then finally you'd get down to one unknown. So for example, the H3O plus concentration if you wanted to solve for the pH, and you'd get a polynomial equation for that, that you would either solve or use an approximation to simplify and solve. Okay? Well, the cool thing is we don't have to do that. So there's an additional Mathematica command that we can use where all we have to do is type in the four equations, and then there's a command called eliminate, which will allow us to eliminate all the variables we don't want, and it will give us the polynomial equation and the variable that we want, which we can then pipe in to solve, and then we get the answer. It's actually pretty cool, all right? So let's go ahead and do this problem for the general case where we make possibly a buffer, or we just have a weak acid solution, and we're going to solve for the pH. Sound like fun? I hope so. Okay. All right. So I want to emphasize to you what we're going to do now. This is the general solution to this problem. So you pick any salt of a conjugate base, a monoprotic acid, and any weak monoprotic acid, and we're going to have the solution for that, for any concentrations. Okay. So what I'm going to do now is I'm going to clear CH3O because we used that already, and then I think we're okay. All right. So now I'm going to make a list of our equations. Okay? And I'm going to write them in a way that they're easy to see. So I'm going to say equations, equations equals, return, and the first one I'm going to put in is the acid dissociation equilibrium. Okay? So I'm going to say Ka equals equals the concentration of H3O at equilibrium times concentration of OH minus, oh no, sorry, it should be A minus, so Ca minus divided by Ca. So this is the equilibrium concentration of H3O plus equilibrium concentration of the conjugate base, equilibrium concentration of the undissociated acid. All right? Then I'm going to put in a comma, and then I'm going to put in the water dissociation equilibrium, KW equals equals CH3O times COH, all right, comma, return. Now I'm going to put in the conservation of the A species equation, so that's the initial concentration of acid plus initial concentration of salt of conjugate base equals equals CHA plus Ca minus, comma, return. And then finally the equation that expresses the conservation of charge or neutralization condition. So that's the concentration of sodium, which is the initial concentration of conjugate base plus concentration of H3O plus. So all the pluses equals all the minuses. So that'll be Ca minus, sorry, lower case, and then plus COH, okay? Now I don't need a comma here. I can hit return, put a curly. I should have put a curly up here, okay? And then I'm going to put a semi-quolline, okay? So now you can see by typing them in this way, we have a nice tidy array of equations that's just easier to read than if we typed them out on one line, okay? Now this next step is a new command, but as you'll see, it's really cute, all right? So I'm going to do the following. I'm going to put in simplified. I'm going to define that as the equation that I'm going to get by eliminating all the variables here except for H3O. So it's basically going to combine all these equations and eliminate all the variables that I specify. So I say eliminate, oops, eliminate bracket, equations, and then I put in a list of all the things I want taken out. So I don't want Ca minus. I don't want Ca and I don't want CoH, okay? So those are variables that I'm getting rid of. I have four equations. I get rid of three unknowns. I'm going to have one equation and one unknown, all right?  We're going to have to clear some more stuff. You think it's K, yeah it's K, it's black there. Yeah, let's get rid of Ka here, all right? Okay, that's what I wanted. Now that's interesting, isn't it? So notice that the only unknown in here is CaH3O because I know I'm going to, I put in this concentration. I know Ka in principle. I know K water, all right, and I know this concentration. So the only unknown is CaH3O and what order equation do I have here? I have a cubic so I wouldn't want to solve this by hand. I would either have to use some approximations if I was in a regime of concentrations where that would be sensible or since I have it typed into Mathematica, I can get the general solution, all right? And the way I could do that is I could say roots equals solve, simplified for CaH3O, okay? And as we've already seen, a cubic gives quite complicated looking solutions, all right? So in fact, what you might normally want to do is put that in there, okay? And now we could actually evaluate CaH3O for a particular, so this is the totally general solution. For this problem, all right?   So let's go ahead and use these to actually do a problem where you need to worry about the water dissociation, all right? So what this is going to be is we're going to consider a 10 to the minus 5 molar solution of HCN. HCN has a Ka of 6.17 times 10 to the minus 10. So let's see what we would get if we use the approximation, the weak acid approximation and ignored the water dissociation, all right? So the concentration of H3O would be, for that case, it would be square root of the initial concentration, so 1.0 times 10 to the minus 5. And then times the Ka, so that would be 6.17 times 10 to the minus 10, okay? So this is the concentration of H3O plus if we use the usual weak acid approximation. Notice that says we have actually less H3O plus than comes from water, so obviously that's no good. Wouldn't you agree? All right, so let's go ahead and do it exactly. We have the solution already in our hands. We just need to plug the numbers in, all right? So what we can do is we can say CH3O, no, no, sorry. Let's have a look at what it is. So we say roots slash dot, no, sorry, we have to say CH3O. This is a little confusing. Yes, small c. All right, so we have CH3O and then slash dot, roots, and then now we're going to use replacement rules to put in the known numbers, all right? So slash dot, list. So our concentration of acid, I said, was 10 to the minus 5 initially. We did not put in any conjugate base, so I'll set that equal to zero. We said that Ka is equal to 6.17 times 10 to the minus 10. And we know at room temperature, K water is equal to 1.0 times 10 to the minus 14. Can you say K is equal to minus 10? Yeah, it should be minus 10, thank you. That would give a very strange result. Okay, now let's go ahead and enter that and notice, notice what? Well, we got three roots, okay, that's to be expected. Okay, I think that's okay. Now notice we got complex roots, okay? And it's really hard to tell, though, what to do here, right? Because these imaginary parts, they're small. So for all intents and purposes, we can assume that they're negligible. Now the thing I'm a little confused about, though, is that this doesn't exactly match my notes. So let's go ahead and do the pH equals minus log 10. Of CH3, well, let's just go ahead and put all this stuff in there. Okay, that makes more sense. All right, now we got three complex numbers, but it's pretty clear that this is one that is for all intents and purposes a real number because this is tiny, whereas these ones give substantial imaginary parts, okay? So, how can you get rid of these? So this thing is sort of within what you might call machine precision, and there's a nice command that will clean that up for you. And that command is called CHOP. CHOP will chop off numbers that fall within the range of numerical error in a calculation, okay? So if I say CHOP, bracket, and then bracket, you see now that this guy becomes real because that imaginary part was negligible. And so this has to be the correct solution, all right? So the pH of 10 to the minus 5 molar HCN, we predict to be about 6.9. Does that seem reasonable? Seems reasonable. We put in some acid, so we expect it to be acidic, right? But it's a very weak acid, and so we expect it to be very close to neutral, just below 7, and in fact it is. All right, last thing I want to do is now let's ask the question, instead of putting in just the acid, I'm going to make a buffer by putting in a salt sodium cyanide, okay? And in fact what I'm going to do is put in 10 to the minus 4 molar. What's going to happen to the pH? It's going to go down if I put in sodium cyanide. It's tricky, all right? So I'm putting in sodium cyanide. Cyanide is a weak base, correct? Sodium cyanide can hydrolyze water and make the solution basic. And so if I actually put in more sodium cyanide than I have HCN, you might expect that the solution is going to become basic. Now, based on what we've done here, how can I do that calculation? All we have to do is change one number here. We've got the general solution for this problem. We just changed CB, exactly. So let's put in 1 times 10 to the minus 4. And the answer will be right there. And there you have it, 9.5, basic. All right, so the point is we've solved this very general problem. You see that using Mathematica, treating coupled equilibria like this is a piece of cake. And we'll do another example next time involving a diprotic acid and amphoteric equilibria. And then we'll move on to a new subject, differential equations. So we'll see you on Thursday.


Do you ever wonder what happens when the police leave? Crime scene cleaners are private companies that handle the cleanup after the police are gone. Spalling Decon is one of the nation's largest cleanup companies handling the aftermath of homicides, suicides, decompositions, hoarding, and much more. These are our stories. We're on day two. If you guys look here, there's blood splatter. How long do we need to go on here? And then I would think he probably went that way because that's where the most blood traffic is. It's just a lot. We're on day two at the, I guess, I mean, it's another stabbing. We're here in Castlebury and today is a full cruise so right now we're just doing, running like a game plan down. We've got Fee with us, Nick, and Watt is finally back from vacation. He landed at what, two o'clock this morning. So he's really, he's really tired. So it's going to be an exciting day. Really, this is just kind of overwhelming because of the amount of contamination. So game plan is two focusing on floors, one gopher grabbing the flooring that's coming up, and one focusing on blood splatter because we noticed down the hallway and on the ceiling in the hallway, there is a decent amount of blood splatter as well there. So onward we go. I'm thinking it started here just because this was kind of isolated and just from the layout on the couch and stuff. I feel like someone was probably sitting right here, started here, probably got back to his feet here because there's a puddle. And then I would think he probably went that way because that's where the most blood traffic is. It's impossible to like, and then from the bathroom came back out this way and that's why this part's so scattered and then out the door is what I would have seen from what I see. It's just a lot. So you're going to start in that corner there. You're going to do this here.  Blood splatter. And then we also have to kind of wipe down the furnishings and things like that because they want to keep all furnishings. So here we go. It's a very bloody mess but it will get done eventually. It's difficult. At least it looks like it's going to be difficult anyways with as little pry bars as we have. This is a huge job. This floor is so hard to take off. We have a lot of work here. This is a lot of work too. Doing is just scrubbing the blood splatter off and then I'll go back through and I think Nick is pushing me out of my corner there. He's like, take your time. I'm here all day. I'm sad. So sad. So if you guys look here, this is why we're removing all the flooring because it actually seeps down in. So we're going to have to treat the flooring underneath it here because it got all under the glue. I'm trying to just let this sit. I still have a little bit more blood splatter in this one spot here but I want my sanitizer to kind of sit on permanent. Kind of soak it in so that I can just kind of pop it off and wipe everything down and it should be good to go there. If you guys look here, there's blood splatter along here, along here. A small bit on the ceiling as well. The secret indication that this is actually there, the stabbing can take place. Kind of the whole work your way from top to bottom but because the extent of this job and there's so many people here, I have one person being a gopher, two working on the flooring and I'm focusing on blood splatter just because it's super, super strategic. Very detail oriented so I don't want one of the movies to take a chance on missing something. I'll stay on the hallway. Do you need a hand? I always do. It's like legit, your hand. Just mirror check. Baby hand syndrome. Makes everything look good. Your strong arm. My strong arm. Grandma's strong arm. I love this tool. When you're... Enquires or to violence. I mean I joke around like I'm a cut people. Everything starts to blend together when you're in the room.  It's getting better than I was thinking but this kind of floor is hard to take off. The problem is going to be the glue.  Fiona got me sick. Nick got Fiona sick. Fiona got me sick. We were having like a menage à trois. Yeah. It's really hot. Other than that, it's fine. Sweat that's about to run into my eyes in a mile. It's going to bother me soon though. That's icing our boots before we leave this place. What I'm doing here is just double checking that we got all the blood splattered. So I'm using indicated to spray. That's how I know. It's actually sticking to the glue so we're going to have to parlay and scrape it rather than it just coming up like any other flooring and we just have to vacuum up and then we would be able to seal the floor. This one, of course there's always that where we have to hand scrape everything and I'm going to have to go in and have to vacuum. That's one scraping. So enjoy your KSMR of vacuuming. Scrub the wire. Everywhere. The blood is in the glue. It's mixed. So it's so hard to remove. So we are going to clean. We're going to scrub.  V, you want to grab the hepper back and have that going as we're walking our way back so that way it's getting sucked up and clean all at one time. Maybe you want to hit see if you can hit some more spots that I was finding. Thank you. That's it guys. On to the next.


Okay, we're back here live in San Francisco, California's Oracle Open World 2012. It is packed, the streets are closed, this is SiliconANGLE.com's theCUBE, our flagship telecast where we go out to the events and extract a signal from the noise. And boy, there's a lot of noise here at Oracle Open World. They've got the streets are closed, 50,000 people, Oracle's waving their hands, jumping up and down. Cloud is here, big data's here. I'm John Furrier, the founder of SiliconANGLE.com, excited to be here. I'm joined by my co-host. I'm Dave Vellante at Wikibon.org. We're here with Sean Belvins, who is the executive vice president and general manager of Opera Solutions, a big data company. We found out about Opera when we did our big data study. Jeff Kelly quantified the marketplace and he did a little section on pure plays and Opera was one of the top two pure play, big data companies. Sean, we met at SAP Sapphire. Welcome back to theCUBE. I'm glad to be here. Thank you for the tremendous lift we got out of explaining the value and impact that we bring at Sapphire. How are you guys, a special debt? All right, well, we were excited because when we met you, we were like, okay, new person to theCUBE, and theCUBE's great. Extract the ceiling from the noise, get the big ideas out there, but you really had a value proposition that was at the beginning of, not the beginning of, in midstream of the big data revolution. Obviously SAP had huge success with HANA, that messaging, and I don't think they took advantage of the big data positioning as much. It kind of took more mobility, but you were talking about stuff that was so relevant. Now looking back, it feels like a decade since SAP, and the market has completely tipped over and completely validated around big data. So a couple of things. One is you have news here. You've worked with SAP, that's where we met you. Tell us what's happening here at Oracle. Then we want to go into some of the landscape changes in the marketplace. Absolutely, so first and foremost, we are announcing here at this event a partnership with Oracle around their big data appliance and exolytic stack. So they have two core capabilities that we like to say makes the conversation of the machine flow. So the essence of what we do is not analytics, it's taking all of the sources of big data inside and outside the four walls of your business, applying them in a signal hub, which is a collection of science models from our 230 scientists team in a productized, delivered as a subscription service way. So we're not backing a bus up, we're not bringing a bunch of consultants. This machine takes that big data, runs the science models against it and produces a set of best actions. It literally speaks and says if you do this, you can raise your top line or bottom line profit. Here's where the savings are, here's the actions to take with your customers, here's how you should price, here's the fraud and risk in your business. And so what happens with the Oracle announcement is that conversation between the machine and the person speeds up. It doesn't become a stuttering machine, it becomes a flowing machine where you can get real-time complex event processing. You don't have to wait for the machine to do all the scraping across the web of all these sources and then run those models. We can actually stage the models right inside of the exolytics box and then the big data appliance box with Hadoop brings all of those sources and loads them in lightning fast speeds. So it just makes for the machine speaking much faster and we're doing some work together in their solution center to really validate that to Oracle customers. Okay, so basically the signal layer is what you're laying on top of exolytics. And that's really most of the big data IP that you're bringing, right? I mean, the exolytics platform, other than I guess package with Hadoop, but essentially it's a block of infrastructure that you guys are leveraging. You got it, that's exactly right.  You need the Xbox or the PlayStation and it's really just a box of chips and really fast IO and graphics, but you don't have the game. So Opera's released a signal hub suite, which is not only that layer, but a set of applications that we can talk about that are those video games on top of that console box. And the difference I think with Oracle is that they didn't take the SAP route, they didn't re-architect. So the stuff that's in their structure plays out of the box with the heritage of the other architecture that you find. So customers can deploy the big data stack from Oracle and then use our signal hubs immediately on top of that. John, what's your perspective on the legacy companies, the dinosaurs some people call them, SAP, Oracle. You don't necessarily associate those guys with big data. You do associate Opera with big data and obviously Cloud Air and Hortonworks and companies like that. What's the role of these large companies? Is it you work with them because they got customers? I mean that's one obvious choice, where are they in the whole big data landscape in your view and why do you sort of gravitate toward these big whales? Well we're working across the ecosystem, we're essentially agnostic in that we work with whatever we find with the customer because big data is data that you can't control, it's unstructured, it's outside your four walls, you don't own it, right? So what we're finding is that inside the four walls there's a triangulation of the data they have across all of the purpose build applications in ERP and CRM and things like that. So they have the, if you will, the keys or some of the metadata that we would say, okay we want to know who your customer is but they can't, they don't have the capability to see what that customer's doing 23.9 hours of the day, they're not engaging with this company. So what are they buying, what are they looking at, what other things are they wanting to do? So for a very large airline for example, we use this technology and now when you come to their site, we know that you've been looking at Bermuda and you rent a Hertz car and you stay at the Marriott and you like French food and you like to play golf so it makes you an offer on this airline based on what you've actually been looking at before you came to their site tied with the data that they have. So we take the heritage of the data in these systems and marry it to what we consider to be the other 95% of data that they simply don't have any capability to even get to. So Signalhub's, the best way to say it, is Signalhub brings the 95% of data, you don't have any capability to leverage and mix it with the 5% that you have spent hundreds of millions of dollars to do transactional process. And that data source comes from where? Is that something that you recommend? Is that something that the customer brings to the table? That's a heck of a point. The reality is in this new world, the application is not a database schema and it's not Java code, the data is the application. So I'm looking for fading and attrition or I'm looking for elasticity and pricing. The data informs the application and the science models, the secret of our 230 scientists. Google's got 300, we've got 230. All of ours are pointed in different domain in industry and commercial space. And we take those models and when someone says this is where I want that missile to land, we go from an outcome base and then we say okay, we need these 50 terabyte or larger sources, Facebook, Twitter, social, unstructured, internal, wherever it comes from, runs that models through and that's what informs the best action. And that best action is when the machine doesn't come back with a bunch of numbers, it comes back and says pick up the phone, call Sally Smith because she's in danger of canceling that subscription online. So without naming names, some very large weight management places, dating services, we can tell you when these people are fading and they're getting ready to cancel a subscription so that you can take action. And the way we do that has to do with understanding behavior, sentiment, time series, clustering, segmentation. All these models have to get stacked in such a way because if you don't, the other approach is that you buy a tool from a vendor like SAS and you buy a skill set and he works it and you get a single digit two or 3% lift from statistical analysis. We're not talking about that. We're talking about a model where the machine, it writes a script and says, when you call Sally Smith, here are the five things you need to say to her and here's the offer you need to make to her. So much more measurable productivity in fact. Oh, completely, completely. Sean, so you know, most people out there that either in business, they get an iPad, they go, wow, I see the real time web and we talked about this at Sapphire where you're traveling out real time mobility. They see the Siri on the iPhone. Wow, magic. That's magic's happening. So that brings up the whole low latency. We all know that. We've been talking about this slash, et cetera. And Larry's talking about here at the keynote here about the performance and SAP same thing. It's all about performance in memory. The machines are getting faster and faster and faster. And that's great. That's good for business, good for everyone. However, we're data jockeys like you. We understand data. And when you have the ability to scale infinitely or somewhat infinitely, bad data scales just as good as good data. So explain to the folks out there this concept of why this ontology or why having some experts in the verticals matter because our philosophy is big data is all about low latency value. And that it's not just storing data because if you don't clean the data, bad data scales just as fast as good data. So when you have a lot of bad data, you're spending more times figuring out how to make it good if you just do your homework on the front end and get those seeds. Because that's really kind of what you guys are doing. We call it signal, right? And a signal is, you know, first of all, you don't own the data. You can't control the structure. You can't store enough of it. You have to derive a signal, which is a pattern of behavior relevant to the outcome you're looking for. So signals are informed by the use case or the scenario. So when we do for a very large retailer, I'll give you a perfect example. This retailer is up against the Walmart's and the targets of the world, but they're especially retailer, right? And they have to price a skew and that skew can't be priced higher than a target or a Walmart, right? So they have to get that pricing exactly right to be competitive. What we're finding is in real time, you can go into these other retailers and you can see for a geographic location when that skew is now sold out. So I go to that site and I say, I can't get Tickleby Elbow anymore because it's not available in this area. So at that point, the other store can raise their margin because they're the only one that has the inventory for that period of time. So they have to lower their price when it's in stock locally and then they can raise it when they're the only ones that have it, right? And that's the kind of situational fluency that you can't build an application fast enough for. You have to look at the data models and you're finding signals like they're out everywhere in the 10 mile radius. We're the only one that has it so we can actually get more margin for this. And then conversely, we can't be elastic here because it's across the street for $20. How do you compare and contrast to say Palantir, which has been getting a lot of buzz. They're coming in saying we can architect this big data operating system for application development. Is it similar to that, different? Very different and here's why. There's, as I said, the Palantir model and the other models are essentially consulting models. They have not industrialized and productized this offering in a way that allows us to do this in weeks instead of months. So they would do a project and they would say, we'll give you a 15% lift. And the problem with that is it costs you 8% to 10% to get the 15% lift and the time to benefit is months. We have got these signal libraries built in the Signal Hub for these Signal Suite applications for the modules that we have so that you're talking about an eight to 12 week proposition. The case I just gave you with the airline was 55 days start to finish. They pay us a flat subscription fee just like Salesforce or any other SaaS app. So they pay a subscription in for a cloud based front end that spits out those best actions. So it's a completely different software driven model that's productized versus a lot of discovery and build from scratch. And the secret sauce behind this is the platform where all of the data scientists have put those models in there so that they derive from each other. I would go back to, if you want to see a real proof of this, go back and look at the contest like Netflix where we came in first and second across 40,000 teams. We're doing some other contests, you may have heard of like KDD where it's all about big data models and getting these results. The one I find most interesting that proves our value is heritage health, was you have a patient who's a heart patient, they admit themselves to the hospital, Medicare pays for it, but if you discharge that patient in the next six months and they come back, they don't pay a dime. So you have to have some way of determining when to discharge, when to keep. And that's really what separates us is we do this in a software model with fixed cost, fast time to return versus the smarter planet which we like to think of as just a smart way to sell consulting. Tell us about the validation you've got. Obviously the marketplace is all gaga over big data and it is about value proposition. It's about industrializing the solution versus some academic or consultative approach. So check the box, just give us a quick update, funding. You guys have a huge amount of funding. How big is the company? Just give the quick buy the numbers, just run down the list. Well, we are private, but we did receive $84 million in funding from Silverlake KKR and so those two companies continue to drive a lot of our growth. We're having tremendous growth across private equity portfolio companies because when you buy these companies, you have a couple of big issues. When you buy a portfolio companies and bring them into your group, you want to drive, you want to get savings. So we have a solution called spend intelligent that looks at every line item and every invoice and every SKU and every contract temp labor in real time and says, while you were out, I found $8 million on these SKUs, bought off contract or you didn't get your discount or here's temp labor where you're getting billed, not according to your negotiated rates. So when these private equity companies bring us in, they use this immediately for bottom line savings and then for top line growth. So there's a symbiotic relationship there and we're seeing another round of financing come through where people are wanting to invest and we're basically trying to negotiate that now. Sales this year and next year are over 40% growth. That's being conservative but we're private so I honestly don't know how much I'm allowed to say. The growth has been exponential. Give some color on the traction. I mean, I don't want to reveal your secrets, but yeah. The biggest customers are we have about, we've announced Morgan Stanley at Sapphire but some of the other ones, if you go on the site and you read Schwann's, AmEx, American Express, all of the ones that you would suspect for fraud and risk, all of the credit card companies use us for underwriting, fraud, risk, something called bust out, which is very interesting. You can see instead of someone stealing an identity, they create an identity out of whole cloth. They get a card, they pay the card, they continue to pay the card, they get a bigger credit line, now they have $100,000 in credit, then all of a sudden it flips and they go to jewelry stores and Best Buy and start getting stuff for this identity that doesn't exist. So there's really, unless you're able to go look and see that this person really doesn't exist in the real world and that their behavior and their payment doesn't match the behavior and payment of a segmented person like them, you wouldn't be able to catch that. So in our world, we take all these data sources externally and we say, okay, based on how they pay and what they paid and the amounts they paid over time and then what they didn't buy before they got more credit, this person is getting ready to bust out and go buy a bunch of stuff. So we can cut those cards off prior to that purchase. So that's an example. I want to contrast and compare that to a traditional consulting model. You talked about, you took a shot at IBM there. I did, didn't I? And so yeah, you did. I want to explore that a little bit. So a classic consulting model is what's your problem? We can solve it. We can find whatever data you need, we can do whatever you need to do, we can develop whatever application you want. Is your ability to respond more quickly, largely a function of your focusing on specific domain areas on industries and you've got data sources built up in those industries and those models are very industry specific or is there something else? Yeah, I would say the two things. First of all, we have, from the beginning, hired data scientists and given them freedom, said, listen, we don't care what models and technologies and tools you use, but you have to prove to this community of scientists that we have here that you are bringing additional value we don't have. So we don't have level two or level three scientists. We have the MIT's, the Harvard's, the Oxford's, the people that literally are the best of the best and we have 230 of them. So all of them work in a common platform and these models are deriving. So the difference between us is the stacked ensemble and then the ability to rapidly define and extract signal from all of these sources. The other guys, they don't necessarily talk about signal because signal is something that you have to have data science to deal with. So they want to build a one-off project and in most of the cases, they want to bill you as a consultant for that project. They're not giving the customer the ability to have a signal hub so that when those signals come in, it becomes an app store, right? Just like an iPad, you know, I'm reminded of Steve Jobs. I watched an interview with him a while back and he said, someone asked him, they were making a TV show about the future and I said, what is the real future gonna be like? And he said, well, the big shift is, and this is 20 years before anything, iPad or iPhone or anything, he says, you can't hierarchically move things around to adapt to change. I can't make someone else your boss fast enough. I can't change the organization and I can't change geographically. I can't move people around. He says, but once I know the outcome I want, it's very, I can very rapidly assemble the data and then for a specific purpose and pull people and resources around that data, get the answer and then blow it up and do it every 15 minutes. And what we've created with Signal Hub is the ability to say, for this outcome, you want to put this missile on this house 3,000 miles away, what are the sensors that we need to keep that missile on the right trajectory? What are the input sources that they need to hit that goal? We go outside the technology directly to the C-suite and we say, Mr. CMO, you want to grow sales 15% or you want to grow your market share, you want to take share, and we just devoid the whole technology and consulting model and do it in a very straight way that says these raw data sources plus our science models generate Signal which informs us of an action that the customer is likely to take and then we score it, we build millions of models and simulations, we kill them all except for the top 2% and then we come back to the customer and we say there's a 98% chance if you take this action, you will get that result. So that's very different, it's machine-based. But it's very much a consultative sell, right? How much, how much, yeah, so it's repeatable. How much demand for customization do you get and how do you accommodate that? Is the machine doing that customization? And or are you sort of just picking your spots and finding that repeatable model, in like customer segments? You're asking a great question, so in the case of the signals themselves and the Signal Hub suite, we have a suite that says, okay, here's customer action, so here's a 360 degree real-time view of your competitors, here's price, dynamic, elasticity, here's individualized, personalized offers, right? And those are very repeatable solutions that 90% of the people we talk to say I want to know in real-time, all these things about my customers, I want to know how to price my products, I want to make the right offers, and I want to take market share. So I was with a company about two months ago, and to your point, they use IBM, they use consulting, they use all these different approaches, they use lots of SaaS technology. And the guy, we went through this whole process and the guy says, we have everything. And they were a very big company, big hotel chain, and we do that. I said, you don't do that. He says, why don't we do that? And I said, well, it's really simple. You have campaign software, you have CRM software, customer marketing software, data warehouse, right? And I said, but I stay 200 nights a year at a competing hotel chain. You don't have me in your systems.  But when I post on Twitter and Facebook that I'm mad that I had to pay for wifi and I didn't get a free breakfast, and I had to pay for parking, that's a signal. That's a moment you need to come to me and say, I'll honor the tier of service you're on, I'll give you free wifi, come over here and stay with us, right? So the data is not what we're talking about. And this is where I think the market misses it. The idea of signal and a hub that can bring those in and identify those moments of truth, that moment where that signal turns into an action take, that's the industrialized product we put together. So for each of these, whether it be savings and sourcing or operational spend, I love this model, we did this where a credit card issue where we looked at how they're paid and how their customers paid it. And we noticed that some of these people were effectively OCD. They just the moment they get their bill they pay it, right? So we're talking about millions and millions of dollars. So by simply segmenting out and figuring out sentiment, time history, how they paid over time, we said if you bill these customers first, you will get severe float on that money for 30 days. So take the people we've identified with that behavior, put them at the front of the line, bill them now and now you've got 25 days of float, about $400 million. So it's a business thing and what I want to bring your attention to is we did that in 30 to 40 days. So some of that is sort of more traditional but more powerful sort of analytics on real transactional data. The other piece that you mentioned is almost inferring from gesture data in real time defined as before you lose a customer or before you can't gain a customer. How do you price, how do you charge for your services? I would, the first thing you need to know is that if you're buying enterprise software, it's exactly the same. You're going to buy a license for this hub where the models are and the ability to bring in the signal. So we have something called Intelligent ETL where all your internal purpose build apps and all of the signal from sources you don't own outside the four walls, they're brought together into the hub so you license the hub and then you simply pay us a fixed fee as a subscription much as you would work day or SaaS Salesforce. And the difference between us is we're accountable. I mean, we'll take game share and I'm in three negotiations right now with customers who did that. And then- Oh, they're probably sorry. And they're very sorry. We didn't expect you to bring us 30, 40% of return. We thought you're going to give us 13 or 15, right? So- So you love those deals. I love those deals.  Let's turn, you know, let, once the machine returns. You'll G-risk that transaction. I'll be happy to do that. So let's talk about some of the realities of the marketplace. I'm just looking at our little Twitter dashboard here as a tweet up here that says, big data without defining success first is a big mistake. I love that. That brings us up to the point where, you know, we're in similar businesses where, I mean, not similar business, but similar early adopter, you know, groundbreaking models. And there's always the naysayers, right? So I want to ask you specifically, you've had success in this area. How do you explain to a prospect and a client, discretion, yeah, we want to do this, but they're skeptical. Prove it to me. So the old way was, you mentioned Steve Jobs, you know, if I know my outcome, I can configure for it. Right. The old way is, you know, I need evidence. Can you show me evidence from another client or are the proof? Kind of like pushing a rock uphill. Right. What do you say to those people? How do you sell into those environments where you know they need your solution? They kind of like not accepting it. Is there a way you talk about it? Is there a way that? I actually, that's a hell of a point. I think the reality is that, you know, to some people, what we're talking about is UFOs. This is time travel. This is teleportation, right? You walk into a company that's spent a half a billion dollars on technology and you say you've got, you're effectively asking one question, which is, is there enough milk in the refrigerator? Do we need more milk? And you're asking the wrong questions because all that technology gives you is three to five percent of the total available data, right? So it is hard for people to understand really what they can do. I mean, in the government, we've got threat assessment that can see the difference between a bad actor and someone visiting a sensitive jihadi site and make assessments based on person's past behavior if they are a threat and be proactive with those people, right? We've got solutions that are just literally, you know, they're making up TV shows about us, right?  And so my result is twofold. First of all, we productize this and we have references. So you can pick up and speak with someone that is in the CEO, CFO, chairman role at these companies because the value chain with us is such that once this happens, it's like planning a foundation in the business where they may not trust the technology, maybe they do it on Gainshare the first time and they let's put this out. Is it a POC or do you come in and engage them? Yeah, we've done some POCs and pilots. What we're trying to do now is literally we have the demos. It's a turnkey situation in eight to 12 weeks where, you know, we're bringing repeatability to this and what happens is the hub and the models and the signals, the only thing that really changes is the configuration of the data science models and the kinds of feeds that produce the signals. So once you have the hub and you've licensed the hub, what we're finding is across the suite, people say, okay, I want you to manage my liquidity. I want you to manage my spin and marketing and then I want you to look at my customer actions and then I'm also interested in you managing invoice fraud and risk. So they look across the business and they go, where else can we get exponential, not incremental, exponential returns? And because the data is unlimited, we can keep adding those sources until we find enough to make the puzzle complete and say here is a 20 or 30% return. So what we're effectively doing for someone that's skeptical is we will do a pilot with them, but we'd much rather have them speak to a customer who is in a negotiation to move from gain share to fixed fee because they're not going to pay us anymore than what they've been paying us. It's an amazing technology that really empowers an executive to bypass the noise, right? I don't care about what app to buy or what piece of software to buy. I want to sit down with you, Opera, and say here's where I want strategically to grow my top line. I want to take share, I want to make sure and drive behavior at a pet store. We drove staples, so everybody that owns a cat has to buy cat food and cat litter. Very basic thing, but they got to buy it every so often. So by frequency, we looked at that and we said, when do your competitors and how many different pet stores and how many different people that sell that, do you have to drive by between this person that you're marketing to, house, and your pet store? And then when do they do their offers and how do we preempt that and when is their frequency of buying so that we drove them to buy, and we twinned them up with someone else who does buy all those staples from you so then we could very rapidly, and that's, but with us, it's a built solution, right? It's not something we have to build. So you get time to benefit in eight weeks to 12 weeks. Sean, we're getting up on the time limit here, but I want to ask you a few final questions. One is, we've met you at SAP, you saw that whole show, you have a business relationship with SAP, now you're announcing a relationship with Oracle. This is actually what Oracle wants, they want to have a lot of applications, they want us to be the Steve Jobs of the enterprise, he needs apps to come in. So with that, I want to ask you a question. For the folks out there that aren't really following the kind of the inside the ropes, what's going on between SAP and Oracle? Share with them the perspective of both shows. SAP Sapphire, their show, and this show. You want the skinny, you want the scope. Yeah, we'll see how you handle this one. Oh my. Share with them just a perspective, just some color, not necessarily, you don't have to throw the dagger, you can just say, SAP's got this, Oracle's got this, different philosophies, just what's your perspective? Okay, so without putting words in my mouth, because that's not sanitary, the reality is that both of these companies, in my mind, have recognized that they have to be in the space of big data. The problem is that big data in our assessment, having worked with this for eight years as a company, 700 people, 230 data scientists is you, big data is essentially just data that you can't control. The moment that you have a methodology and an approach for harnessing information raw data, which 90% is noise, 95% is noise, maybe more than that. So where I think these companies are evolving to is they're moving up the value chain to say, okay, it speeds and feeds and messing with the data, and this whole notion of storage and the whole notion of being able to capture things, it's really about, I think the evolution where the two companies are, we've got to find a way to put a canoe in the stream of data and put the sensors in the water and get the results we want. And that's the result of, when they look at our partnership, it really is opera has these things, turnkey, plug and play on our box. They got performance. Yeah, they got performance, they can go faster, they're building out an algorithmic layer so our models can live in the same box as the data side by side in real time and flash memory. So now, for K-Means clustering, we can put that right in the box, we can a-dupe load through big BDA, big data appliance from Oracle. So I think what you're going to see with these guys over time is they're going to recognize that data science is how applications come together. When you have an iPad, you want what you want, you don't want to have to learn a model, you don't have to learn a feature function or a UI. You want a very specific set of data presented to you for a specific action instead of outcome. And so the ability to put that algorithmic layer with these two vendors, that's where they're both, I think, in a race to right now. And the difference in my mind is that SAP has said to get there, we have to blow up the database paradigm. We have to do this in memory in a new model called HANA. And I think Oracle has said we have a very vast customer base and we want to bring them along with our existing model. So I think what happens is they're going to meet in the middle because at some point very soon, HANA will run underneath SAP. And at the same time, HANA will be available for new application development. They'll be there, but right now, if you are a current Oracle database customer, you can plug and play big data appliance, plug and play exolytics, and those other things, and then plug signal hubs on top of it. So I think from a plug and play disruption perspective, you could be as an Oracle customer, it doesn't require as much of a re-architecture, but from an SAP customer, you will have a green field for big data in a bigger way. So it's a trade-off in my view, and it comes down to flavor. I think both philosophies are right. I think they're going to get where they want to be. What we're interested in is, are you able to stage those data science models? And are you able to give us a simple environment so that we can make those conversations with the machine flowing and not stuttering? It's a good answer. You're an arms dealer for both SAP and Oracle. I love them both. I worked in both for seven years. Seven years in SAP and six years at Oracle. You win either way. It's good for your business. It's good for the value proposition that you're offering. We love your business. Dave and I, two conversations, we think you guys are on a path that everyone should look at and say, hey, that's a path of the future. Big data, using data, connecting the applications. Sean, thanks for coming on theCUBE again. CUBE alumni. The data is the app. You heard it here. The data is the app. Certainly a tech athlete. The kind of guests we love on SiliconANGLE.com and Wikibon. We'll be right back with our next guest, CEO of DataStacks, right after this short break. Thank you.


Hi everyone, Joe for jazby's casebreaks.com with another gold and silver pack break. This is a random team break number 10 one spot gets you four and a Lot of great teams on here a lot of big hit potential here big. Thanks to this group for making it happen So let's quadruple you up. There's one already Here's two There's three and there's four All right, so there should be 84 items on each list. It's randomized each list five and a two seven times one two three four five six and seventh and final time We got Nicola plant Down to Nick nappy five and two seven times for the teams one two three four five six and seventh and final time After seven we got the m's down to my Raiders The raiders all right Not gonna read off all this, but I will alphabetize and group the teams by your first names is just a second I just want to put this on wax on tape On the recording just the list as is Now let's sort by your first names So Ben, there's your group of teams right there got my Dodgers Daniel. There's your group of teams Dayton Edward Jack we got a lot of nicks here. There's Nick a Nick L Nick n it's got a big block of teams here and there and there and There's Nick s With a nice group of teams and last but not least even came all right. There you go I'm gonna pause the video when we come back. We're gonna see if there's any trades and then we'll have the break stick around BRB Hi, everyone. Welcome back. No deals were done. So These lists right here remain the same Well a lot of nicolas's in this break one two three four five six seven eight nine ten One two the car two three four five six seven eight nine ten for twenty silver packs Good luck everybody Got the sounds of Dodgers posting the twins on in the background Dodgers up for one still a lot of game to go Top of the third inning Good luck. Lamello ball orange to 199 Hornets will be for Nick So college cards as you saw in the description will go to the team. They're currently on Jordan Lawler to 99 for the Diamondbacks That will be for Edward Desmond Ritter to 499 for the Falcons Jameson Williams to 199 Falcons that'll be for Dayton Aaron judge what a season he's been having to 199 for the Yankees and a Russell Wilson One out of ten gold vinyl that looks really sharp and that is Broncos edition and that will be where's Broncos on my Nick Nick end when there's a lot of nicolas's in this break nice. That's really cool nice Desmond Ritter as well Kate Cunningham pistons That will be for Nick end Thought that was a 101 for a second Russell Wilson again for Nick to 99 Carl Anthony Towns to 25 nice one for the Timberwolves Walter Peyton to 199 bears. It'll be for Nick Desmond Ritter for the Falcons It's another one for Dayton Juan Soto still Nationals edition to 199 for Nick Nick end Nicola Jokic to 50 for Ben and Nick and calling one of ones here's another one one of one Serge Harle's three color patch Suns edition here come the Suns do do do do That's gonna be for Nick Stober and the Suns Nick Congrats and All aboard the Big Hit Express That's nice We got rookie of the year Scotty bar and another Scotty Barnes right behind him. Here's a base Scotty Barnes For the round. Oh, no, it's actually the 499 for the Raptors That's gonna go to Nick end and Nick you get another one to you get a orange parallel a little bit lower number to 199 Paulo Bencero to 99 For Orlando, there's karate towns to 199 David Beckham to five Becks Bending it like Beckham. I don't think the England national team is on this list And there's no soccer spot. So this goes to the all other not on list spot. That's you Nicholas end With with shaved head Beckham It's a cool bex Right there and as you know out of fives and under get the train whistle All aboard the Big Hit Express And a Kate Cunningham red to 99 pistons Pistons Nick Nick end Jordan Loller to 199 John Moran to 10 nice gris That's gonna be for Nick and as well Christian Polisic Russell Wilson to 199 and a red Kate Cunningham nice little color match there 78 out of 99 for the Detroit pistons That's gonna be for Nick. Wow What a start now These gold facts could potentially be even better One two three four five six seven eight nine ten thick card one two three four five six seven eight nine and ten And then one two three four and five Five good luck. I think you got suckered into silver and gold again We're not we're not nearly done with the break. That looks like an autograph right here, too At least it was flipped around the card was flipped around so Generally indicates auto Nicholas and saying your son six years old saw LeBron's Space Jam at camp today and came home singing his phrases Then you had to sit him down through five different YouTube Versions of Jordan's 50 best plays on YouTube. There you go. You got to make sure Make sure he knows his history May as well throw in some Kobe highlights in there, too Max is Max Muncie feeling it. He takes one yard There you go. Bounder with with a double Max Muncie with a homer. Those guys have been slumping all year long alright James Weissman to 99 Justin Fields rookie KC Mice to 50 Leonel Messi for Barcelona Mack Jones KD Jalen Green to 50 and Juan Soto the Mack Jones rookie card for the Pat riots Nick and Patriots Ice last eight games, I like it. Let's hope it turns into a month two months postseason Justin Fields bears that'll be for also for Nick N and for the Warriors Nick again Nick Nappy James Weissman rookie cracked ice That these junior to 25 and a Spencer Torkelson and to 10 gold shimmer Tigers and be for Nicola plant With the former number one overall pig How are these silver and gold packs distributed at the National Sandbanks is asking? Well, we went to the Panini VIP Party and Then we had like a VIP Invite card that we had and we showed it to them and they gave it to us That's how they distributed it to tease junior Goes to the Padres Nick There's Larry Bird to 50 for the Celtics They're like here you go. I always said thanks Walter Payton to 25 behind Shaq is Devin Booker gold shimmer nice relic in there, too, but one two three four colors in there Here come the Sun's do do do do Nick Stover were the Sun's That is 10 out of 10 That Shaq cracked ice for the Lakers to 99 that'll be for Stephen Kaye Tyrese Halliburton to 50 for the King still Kings addition there Andrew Vaughn to 25 White Sox Jeff Gordon for the NASCAR spot Peyton Manning to 99 and a Nolan Ryan to 15 9 out of 15 Rangers addition that blue camo looks really sharp and that will be for Nick Nappy and the Rangers Nick and Zach Wilson rookie. Oh, what's this? Here's a one-of-one So Zach Wilson goes to the Jets That will be for Nick and look at this Nick a one-of-one jet Joe Namath Broadway Joe a little Joe mojo Snake skin parallel Joe Namath autograph and a one-of-one These golden silver packs feeling hot hot hot nice one Nick and All aboard the Big Head Express Wow, I think this has been our best break of these golden silver packs yet Joe burrowed a 15 for the Bengals That's going to go to Nick Leonel Messi to 50 for Barcelona. That's going to go to Stephen Kaye Trevor Lawrence gold shimmer eight out of ten Jaguars Stephen Kaye Nicola Nick Nick wants some Cleveland as well. Yeah, maybe we'll find some calves or Browns here Anthony Edwards rookie cracked ice Timberwolves Nick and again T Wolves Zion to 50 Julio Rodriguez for Seattle Tyrese Halliburne to 25 Mookie bets room room Jeff Gordon zebra print for the NASCAR spot Daniel Katz a rainbow warrior Wow What a break What's that Anthony Edwards number two to 99? Rookie Anthony Edwards What a break ladies and gentlemen we got more of this in the store. I'm Joe for Jaspie's case rakes calm I'll see you next time for the next one. Bye. Bye


Let's see, finally, just an update on the chips and China story that I am monitoring on a regular basis. The United States, as you know, has placed severe restrictions on the export of technology to China, and not just the export of technology, but also the export of microprocessors themselves, particularly the advanced variants of the advanced microprocessors. For a while now, it has been the case that the ASML in the Netherlands has never really sent to China the extreme ultraviolet lithography machines that cost, I think, what do they cost? 10 million a piece or something like that. Anyway, those machines have not been sent to China, and ASML is committed not to sending them to China. But again, over the weekend, further development is that both Japan, which also produces equipment for the manufacture of semiconductors and ASML have now agreed not to send to China even the one generation less advanced equipment than extreme ultraviolet lithography, so that that would cause China to be even further backwards. Japan's Nikon, which competes with ASML in supplying parts and technology processes that are deep ultraviolet lithography, which is one step less sophisticated than the extreme ultraviolet lithology. Again, both Japan and the Netherlands have agreed to deny China even that technology, which makes it very, very difficult, if not impossible for the Chinese to catch up in terms of chip manufacturing. The best the Chinese can do, and another story that broke this week, the end of last week, the best that the Chinese can do is basically steal the chips themselves. They can't produce them, but they can steal the chips. Indeed, the story that broke late last week is that chips that have been illegal to export to China for a couple of decades now, I think they were first made illegal in 1999 or something like that, it's come out that the Chinese are using those chips in various military equipment. China is finding ways to get their hands on American chips, just like China and Iran, Iran drones, Iranian drones are using American chips. Russia and China are finding ways to get around in the black market. There's a massive black market, it turns out, in advanced chips. It's still not the same. Smuggling chips in is just not going to be as effective, certainly in terms of quantity. As long as China does not have the capacity to develop the most advanced chips in-house, and as long as it is difficult for them to access the most advanced chips in the world market, they can access them in the black market, I guess, but not more than that. It does retard their progress. It does make their weapons systems at least at some level less effective than American weapons systems, potentially. And it certainly makes it impossible for them to achieve what they sought to achieve, which is technological independence from the West by 2025. That's not happening. It's been extended to 2030, but it's just not going to happen. China is going to achieve technological parity with the U.S. or with the West by 2030 if these kind of technologies are denied. Once you understand it, it's impossible for them to basically develop extreme ultraviolet lithography in 10 years. It might be impossible for them to develop in 20 years. It might be impossible for them to ever develop it, given the expertise that has to go into it and them out of subcontracting that has to go into it. In ASML's case, the company in Holland, the expertise, they get to access companies all over the West and companies that are world-class at a little, tiny little thing. That expertise is not available to the Chinese. Thank you for listening or watching The Iran Book Show. If you'd like to support the show, we make it as easy as possible for you to trade with me. You get value from listening, you get value from watching, show your appreciation. You can do that by going to iranbrookshow.com. I go to Patreon, subscribe to our locals, and just making an appropriate contribution on any one of those channels. Also, if you'd like to see The Iran Book Show grow, please consider sharing our content and, of course, subscribe. Press that little bell button right down there on YouTube so that you get an announcement when we go live. And for those of you who are already subscribers and those of you who are already supporters of the show, thank you. I very much appreciate it.


Hi everybody, Joe for jaskiescasebreaks.com with our second to last St. Nick promo break and that's 2021 flawless collegiate football pick your team at number 25 A lot of great stuff here going by the group break checklist right here The only thing that we're not going by the group break checklist is the cards in the none area right here For the players we're just going to assign them to the teams that they were last associated with on the practice squad Obviously the team ones will be randomized, Lincoln Riley will be randomized, everyone in the group will have a chance at that That's just the only slightly different thing Fresh case as well And thanks everybody who bought their spot straight up, I appreciate it Here on Wednesday December 22nd Happy holidays everybody Merry Christmas, Happy New Year, thank you everyone for all of your support throughout the year I appreciate that That allows us to do fun promos like the St. Nick promo You can find the details of that on jaskiescasebreaks.com So let's pop open the fresh case and then we'll select a die and roll the die and see which briefcase we're going to do So we'll go 1, 2, 3 for the top briefcase and 4, 5, 6 for the bottom one We'll select that die 1, 2, 3, 4, 5, 6 That's 1, we're going to do the top briefcase And this will be for pick your team number Hard to write at that angle, but that'll be for pick your team 26 Which is already loaded up, so if you want to run this back tonight we certainly can Here's one we're going to do Good luck everybody Now I'm going to use these blank cards here to cover up the encased cards Too much at what's here, that's an auto That is a patch auto over there, I don't know who that is, but I saw a patch and that's a gem on the left side Just a single gem Alright, exciting Alright, and we'll tag all these, all these hits as well Make it easier for the shipping team First one out of the gate is Braven Jordan 6 out of 10, piece of his jersey for Houston That's going to go to Mark Rao Alright, next one, there's a nice patch 14 out of 25, Devonta Smith Am I like a bowl game patch or something like that For the Fly Eagles Fly, Sean Aldinger With the Eagles Pretty dynamic playmaker We've got 8 out of 25 Chaterious, 2-2 Atwell Three-color patch and auto I believe he is a Ram Yes, he is a Los Angeles Ram That's right, Ryan Harreld, it's a Ram That's for Will and the Ramily 1 out of 10, Campus Legends Derek Carr For my Raiders, that's for Jason Next up is Michigan's Niko Collins 10 out of 25 And Niko Collins is a Houston Texan That's another Texan from Mark Nice, thank you T-Town, appreciate that And here's a nice patch, 3 out of 20 Just kidding, Dobbins Looks like a Rose Bowl, maybe? That's a nice Rose Bowl patch in there Looking good Baltimore, that is for Jason You got it, Thomas? Yes, sir Thanks, man Merry Christmas and all that Merry Christmas, happy new year You too 12 out of 25, Travis Etienne This one's like a hold on to this one Hopefully he comes back strong next year Will has the Jaguars Nice Now you're in case cards here The gem was on the left side It says that they're naturally mine Not lab grown And we have a 10 out of 25 Cadarius Tony for the New York Football Giants Nice diamond right there Shining bright like a diamond That is Jason with the New York Football Giants The G-Men Big blue I'm kind of in the middle of something right now, T-Town I'll check a little bit later But you should be getting a You should get an order confirmation via your email It goes this way A little landscape mode here Rookie And that is LSU And that is Jamar Chase 19 out of 25 Three color patch and autograph Nice one Who has the bangles? Steve Herrick With Cincinnati We're on to Cincinnati Nice Having a really solid year His first year in the NFL Alright And the final one We got some green foil We've got the University of Alabama Three out of five Emerald Auto Nagy Harris For Pittsburgh And who got the Steelers? That is Adam Koperman Who picked up the Steelers straight up Adam All aboard the Big Head Express Out of fives and under Get the world's famous train whistle That's pretty strong Alright The case is closed On Pick Your Team 25 But We got to give away those two St. Nick spots Big promo that we have going on We're down to our last break After this one So let's flip back to this screen here Let's gather everybody's names Should be some new dice right here Yes, new dice, new list And top two Let's roll it, randomize it Four and a one, five times Top two after five Good luck A lot of sad faces here But the top two will be very happy One Two Three Four And once more Fifth and final time, good luck So here's all the sad faces here Thanks everybody for giving this a shot Remember, if you win a spot You're guaranteed something Will, very close, no cigar there But hey, thanks everybody For making this happen I appreciate it Not easy to win these St. Nick spots But I appreciate everybody Digging deep and making this happen Top two Congrats You're in the promo Will, oh, you're in, Will It's all good Will's in And Ryan Harrell, look at that He was just finished saying It's not been my week But hey, you know what? He kept plugging away Kept getting his at-bats in And now has a St. Nick spot Chance at the grand prize too Thanks everybody for watching I'm Joe for Jasper's casebreaks.com And I'll see you next time For the next break Bye-bye