Everybody, I'm Jeff Kelly, Big Data Analyst here for Wikibon, we're at Wikibon World Headquarters on theCUBE. So earlier this month, IBM released a slew of Big Data products and technologies, among them was a new offering or a new member of the Pure Systems family called Pure Data System for Hadoop. Today we're joined by IBM's Tim Vincent to talk about those releases and specifically and of course also about IBM's overall approach to bringing Big Data to the enterprise. Tim is an IBM Fellow as well as Vice President and CTO of the Information Management of Information Management inside IBM's software group. Tim joins us today by phone from his office, thanks so much for joining us today Tim. Well thanks Jeff, I look forward to the conversation today. Great, so why don't you help us kind of just set the table first off. What is Pure Data System for Hadoop? And can you talk a little bit about your role in kind of developing the new system? The Pure Data System for Hadoop is just falling along in the overall philosophy of what we've got with these Pure Data Systems and the philosophy is to really try to get to the point where we can provide end users with an integrated system that really allows them to get to the point where they're spending their time on actually building out the business insights and the actual technology that they want to build on top of Hadoop that's driving value for them versus spending their time on actually engineering the system and trying to acquire all the parts, bring all the parts together engineering and designing the architecture of how to build a system, installing the software and getting to the point where they can actually do the former. We've actually had customers we've talked to who are spending weeks of time doing these activities. So you know, thinking through what they want the architecture to look like, how do they get all the software installed, which switches are they going to use, how are they going to build the systems out and we've got to the point where we have one customer we had them up and going to the point where they're actually low data ready within under two hours. So the goal here is really to get a system on the floor so the customers can get to the point where they're building the logic they want to build out versus trying to get the system stood up and running. That's really in line with what you and I discussed last fall when we were at information on demand, the idea that a lot of IT projects take a lot longer than originally projected and of course a lot of that time is spent, as you mentioned, just kind of setting up the system. So if you can kind of dive into some specifics here, how does pure data system for Hadoop actually go about accomplishing the goal of minimizing that time and minimizing those kind of setup challenges so that really you can, as an enterprise, can get to the value add that big data provides and that is kind of doing the analytics and really driving data-driven decision making? Well I think the answer is pretty simple. Think of a system that's showing up on your floor. You power it on, you hook it up to your network and at that point in time you set up security policies, et cetera, but at that point in time you're ready to load data. So don't trust that with what you would have to do if you wanted to build this from scratch. So you would have to start thinking about, okay, what is the size of my system? What servers are going to use? How am I going to lay my storage out? Am I going to use a sand model? Am I going to use a storage server model? How am I going to network the systems together? How do I, you know, you've got to order all that hardware, you've got a racket, you've got to stack it, you've got to cable it up, you've got to install Hadoop, you've got to configure that. So there's a lot of work that you have to go through, not only from a planning perspective, but from a design perspective and a physical installation perspective. All that cost is gone. It's out of the system. But I think that's just the beginning point. Then you've got to think about the ongoing management of the system. You've got to think about, okay, the reality is you are going to install software patches. You are going to install firmware patches. How do you do that? What is the process for doing that? Your data systems for Hadoop comes with a single console, which gives you that consolidated view of the system. It allows you to provide a process for upgrading software. It allows you to have a process for upgrading the firmware. But it does more than that as well. It gives you monitoring capabilities, problem determination. You'd have to think about how you would build those in and make sure that your availability characteristics are designed in. I know Hadoop's got a level of built-in availability, but you have to think through how are you going to set up your availability model, and all that is done for you. And the other aspect I think that's important here is one of Hadoop's skills. And a lot of the customers we talk to, one of the challenges they have is, even though Hadoop is exciting and a lot of people are talking about it, actually finding the people with the skills who actually can understand a managed Hadoop system is hard. So in a lot of cases, they're repurposing people they have. They are repurposing people who may have been running or are still running their warehouse system. So again, these people are trying to fill up. They don't want to have to spend all that time trying to figure out how to stand the system up. System comes pre-packaged. It's all these problems that have been addressed for you. So it's really as simple as rolling something onto the floor and plugging it in versus all these other steps you have to do. Interesting. So what is that aligned with when we think about Hadoop? We often think about the scale-up model stringing together cheap commodity boxes when you need to expand the cluster. You just kind of bring in another box. Are there scaling challenges or are there any trade-offs when you take the appliance approach as you've done with pure data system for Hadoop, either in terms of being able to scale it easily or in terms of the cost benefit analysis of really bringing Hadoop into your environment versus your traditional data warehouse, which can get very expensive when you're trying to scale to big data levels. Are there any trade-offs when you're approaching Hadoop or implementing it in the form factor of kind of the appliance model? So there's a few questions in there. So let me start with the one of, okay, how do I scale the system? So in its current, in its current incarnation, the system is a fixed-rack configuration. So it comes at a fixed size. So effectively, the decision of scaling is, in some ways, being taken away from you. You're buying a system at a price point that is very attractive that allows you to grow up to a specific size. And that size is still that significant amount of data storage in the system. So you can grow up to a size that's probably going to be sufficient for a lot of the enterprises. So that scaling problem is taken away from you to a great degree. Now, if you wanted to start with a smaller system and scale, you can still do so. But again, you have to go through all these steps. Now, the other thing you talked about is the data warehouse. Now, one of the things I think we should get into, and it's probably a separate discussion on this call, is how and where does a Hadoop system fit into your overall enterprise architecture and where and how and what are the impacts to your current warehouse systems? So I see that as sort of a second question. Well, why don't we get into that a little bit?  You've got other data management and database technologies. How does a CIO, how should a CIO start thinking about that in terms of bringing Hadoop into their environment without necessarily, obviously, they don't want to replace their existing technology. They've invested a lot of money over many years in that technology and it's often playing really mission-critical roles. So what should they be thinking about from a technology perspective about bringing in Hadoop, whether it's in the appliance form or any other form, when they're looking to bring big data into their environment? I think your question and the way you've worded the question is spot on. You're hitting the key issues. Let me start with one of the things that I've seen people do and I think it's the wrong place to start. And we saw this originally actually several years ago in China and some of the telcos in China where the researchers were starting to play with Hadoop and they were starting from the perspective of can we use this technology to replace our warehouse. So a lot of customers start with this from the perspective of a cosplay and they look at it and say, well, I've got cheap servers. I've got something that's open source. So the acquisition cost is theoretically free and can I replace my warehouse? And I think that's the wrong place to start and I think you'll find if you start looking at the cost equation, yes, maybe the acquisition cost is initially cheaper. But as you look at the ongoing cost, I'm not sure how well that costly works out and also there's going to be challenges in what will work and what won't work. So what we've been spending time on is how do you use the technology to augment what you've already got? How do you actually provide more value to the system? So the way we've been looking at rolling out big data and these Hadoop systems is really an augmentation of the systems you have today. And the warehouse is one thing you can augment. Another area that's actually interesting from an augmentation perspective is master data management systems. But let's start with the warehouse. So you've got a warehouse and that warehouse could be, warehouse is probably a highly overused term by the way. But let's say you've got a divisional warehouse and an EDW and you've got a collection of consolidated marks. They could be dependent on the warehouse. They could be independent. And you've got business users using those warehouses today. And you'll find that the performance is either strong depending on how good the architecture is or you've got some challenges. But in any case, you've got a system that's performing to a degree. You've got workload management. You've got monitoring infrastructure in place. You've got ETL into that system. You maybe've got some direct streams. We've got customers who are doing things like point-of-sale operations directly on the warehouse. So you've got a lot of infrastructure set up there. So the question is how can you use Hadoop to do more? So what we're looking at is Hadoop becomes what we're thinking of as a landing zone or an exploratory zone that you can think of almost as something that you can put into those systems. So what we're trying to use this model for is allowing people to actually expand what they've got in the warehouse. So you could actually start doing things like putting some of that warehouse data into Hadoop. So let's pick on, say, the transactional detailed data and say, okay, I'm going to actually start putting my transactional detailed data in both my warehouse and in Hadoop. But in Hadoop, I'm going to put in additional attributes, which I don't store in my warehouse today because I've not necessarily seen a valid use case for them. But I'm going to now also bring in other data. That data could be SEC filings. It could be Lexus-Nexus report. It could be social media. It could be data from other sources of data. I've got my enterprise. It could be email. It could be machine data. So you're bringing all these different forms of data into this landing or exploratory zone, and then you're letting the business users actually start doing more of an exploratory type of workload on them. Or ad hoc workload. You could be building ad hoc reports, which we're really looking across this supersetted data. You've not really been able to query before. So what we see the system then doing is this capability of doing exploration, things like you may have done with a sandbox in a warehouse before, but you're going to do it in this location instead in the landing zone. And that sandboxing activity could now be sandboxing over just, again, the transactional details from your warehouse, and you're offloading some of that work from the warehouse. Or it could be sandboxing activity where you're looking at that data plus additional data, or even just looking at additional data. So we're really trying to get to the point where people are saying this is an opportunity to do new things versus and really derive new value versus saying replacing something, which, as you pointed out, could be a very costly proposition as you start really looking at the process of actually completely ripping and replacing. So I'll stop there, Jeff, because I'm sure you've got potentially some other questions that we can scroll down. Right, well, there's some really great insights in there. And one of them that struck me was the idea of using Hadoop really is an area to do some more exploratory analytics. And you mentioned maybe even a business user doing that. But of course, one of the challenges with Hadoop is it's sometimes difficult to work with the data. You've either got to know MapReduce scientists in many cases, at least in the use cases we've been hearing about out in the market in order to use Hadoop for that kind of exploratory analysis. Now, of course, among some of the other announcements that IBM made, one was early this month, one was something called BigSQL, basically allowing a SQL type interface on top of data stored in Hadoop so that business analysts and others who are steeped in SQL can actually start accessing that data. And of course, I understand that big insights in that form, it builds off the core Hadoop patchy distribution, but it brings in some of the other elements that IBM has in their arsenal around analytics and visualizations. So maybe, can you talk a little bit about IBM's approach to actually making it possible for business users to do that type of exploratory work inside of Hadoop without having to be MapReduce experts or otherwise really sophisticated data scientists? I guess a great question, and there was many questions in there. Let me start with your big SQL comment because you hit on, I think, one of the key points. With SQL, you do have a large community of users who can build SQL, and more importantly I think you've got a large ecosystem around SQL. You've got tools such as Cognos which business users can use to build reports. So it actually starts bringing a larger ecosystem for people and the business users to use. So you are correct that the business users are not going to be the ones who are writing the MapReduce jobs. They are probably not going to be the ones who are writing hives because they don't have that skill set. But some of them do have a SQL skill and more importantly you do have tools which the business users are using today which can issue SQL. So that allows you to bring a broader set of skills and a broader set of capability into the system. And I think this is going to be a key thing going forward. And SQL is a starting point, but I think the ecosystem is going to be more important. I think what's going to happen, so if I put my crystal ball out on the table I think one of the key things going forward is going to be more that tool set that allows the business users to actually start doing things and actually doing building out analytic jobs, building reports, building insights on the system without having to become a MapReduce programmer. So the same thing that's happened in the warehouse world I think will happen on this big insight world. I'll stop there, Jeff, and see if there's any direction you want to take this on. I know I didn't answer everything, but I figured it's best to see if there's one direction or another you want to go. Sure, so I think maybe a good way to kind of wrap up the conversation would be to kind of follow on that point and maybe if you could get your crystal ball out a little bit and talk about how you see that actually developing in terms of bringing more capabilities analytic capabilities to Hadoop for business users. Do you see that obviously IBM has a play there when you mention the ecosystem out there. There are a lot of startups in this space, companies like IBM partners like DataMirror for instance that are doing some interesting visualization on top of Hadoop. How do you see that kind of playing out and maybe if we start that, if you could answer maybe from a ecosystem perspective, but also just generally speaking, how do you see Hadoop evolving in terms of being a tool for the business user? So let me take that in a slightly different way and talk about what I see big data as and I'll start off with saying I don't really like the word big data. And the reason I don't is because it means too many different things to too many different people. So for example, I've gone into customers and they said well we don't have a big data problem because we don't have petabytes of data and you look at it and say well okay but I think that what's going on in the industry is actually more than that and other people say well I don't have a big data problem because I'm not really interested in all the social media stuff I'm a financial company and all I'm doing is crunching numbers and other people say well I don't necessarily want to be into big data because I don't have the skills or I don't know how to use Hadoop and unfortunately I think they're missing the point because what we're really trying to get to is a world where you can actually look at all the data you have available and all that data is a combination of things you already have in your structure systems could be in your unstructured systems such as E.C.M. Repositories, it could be data that you're generating such as machine data, it could be email or it could be external data like again Lexus Nexus reports it could be sex problems etc. So I think what we want to get to is again where the business users, the data scientists and all the workers have access to data and it becomes more of an information supply chain management problem starting with easy tools like what we have with Big Data Explorer that allows you to understand what data you have and what that data looks like and I think that understanding of the data is going to be an area that's going to evolve, an ecosystem around that's going to evolve where it's going to be a combination of things like Big Data Explorer and enhanced metadata repositories for example. How do I then start asking questions of the data and how do you provide potentially easy simple ways to ask data, ask questions which could be natural language processing over the data as it exists and then maybe you next step is bringing it into a Hadoop system where you said okay I want a subset of that data so if I got unstructured data what I'm going to do is not bring all the unstructured data in there but I may run some annotators bringing a subset of data and I may augment that with other data that I've got and then the next thing is okay now that I've got that data into the system and coming back to the ecosystem how do I evolve it because if you look at most if you go back to a warehouse workload again and you start thinking of the flow of data scientists they're going through a level of data preparation as they're bringing the data into a model, into a format where they can run and gen their models where they can generate the scoring algorithm so you start getting into evolving data and as you evolve the data you want to make sure you're keeping track of where you've been what's the lineage of that data so when you get to the point where you say okay now I've got to the point where I've got an analytic algorithm it could be a scoring algorithm for example and I say now I want to bring that scoring algorithm and I want to bring that into my warehouse it could be a pure data free analytic system how do I actually keep track of what I've done and again come back into the lineage of the data what's the tooling that could go through that lineage of the data generate the data model I've just evolved build that out in your warehouse your pure data for analytic system generate an ETL flow and actually bring that data into the system so it really I think we have to get to the world where we're seeing this as an information supply chain management problem where you really have to have an ecosystem that allows you to look at data bring it into a Hadoop system tooling on Hadoop that allows you to build out your analytics it could be through things like SPSS model or as an example or other tools to build this out but as you're building those jobs keep track of where you've gone where you've been so as you get to that end game and you decide okay this is really important you could decide to continue to run that job in Hadoop if the characteristics of the of the workload the performance characteristics the currency of the data is sufficient or you could say okay I need a very high degree of currency on my data I have to put this into a system that can deal with very high demand because maybe this is a scoring algorithm that you're running as part of a customer care system as an example I want to move that into my an ATESA system which is going to give me a better SLA but I think we've got to get to the point where this entire ecosystem around information supply chain management becomes a reality so I think Hadoop is an important part of that and we're going to have to get to the point where we have an ecosystem that brings all these things into play you talked about visualization I think that's going to be an absolute key point in that in that view and the visualization tooling again has to plug into that overall flow of what you're trying to achieve and I know I said a lot yeah but hopefully that all makes sense. Yes some very good points information supply chain management I think is a really interesting concept and one that maybe has been left out of the conversation a little bit as we've focused on some of the more specific areas of big data like Hadoop and maybe analytics where as really it's about being able to be flexible to get the right data to the right people in the right form with the right tools and some really interesting things to think about and could certainly continue this conversation for a lot longer but I want to be sensitive to your time so thanks Tim Vincent, IBM fellow CTO and VP in the information management at IBM software. Thank you so much for joining us today we really appreciate it hopefully we can have you back on and continue the conversation it was really very interesting. I'd love to get on and talk to you about blue our blue acceleration technology that would be great. Fantastic we'll definitely do that so thanks so much and thanks everybody for watching. Okay thank you


A very merry Christmas for retailers, Americans spending $850 billion this holiday season. And that's up more than 5% from last year. And it's also the strongest number in six years, according to new report cards from MasterCard. Jonathan, Melissa are back. And I think this kind of solidifies what we've said before, that main streets and consumers, they feel confident with a 50-year low in unemployment. Jonathan? Yeah, I mean, no question. We talk about the economy being strong. Just look at the difference. This Christmas is against Christmas 2010 through 2011. Back then, consumers had closed their wallets. They were saving. They weren't spending what a difference a decade makes since the global financial crisis on employment is low, retailers, interestingly though, however, Susan, the stocks are not doing well. And this is one reason why, despite the fact that the news is good in terms of consumers spending, you know, Amazon up 8% today, but a lot of these retailers still scraping their 50 to be closed. So it's another area, I think, despite the fact the news is being good. This could be that dead cat bounce we keep talking about when it comes to this market and retailers. The retail sub-index of the S&P rally double digits this year up until this month at least. So I wouldn't say it has been a weak year for retail in 2018. Well, it's down. Most retailers are flat this year. And as you said, this last month, everything, including retailers, had just gotten completely sold off. So I mean, if you were a bull, I would have liked to see that hang in there. So I'm not a complete bear on retail, Susan, but I'd like to see the stocks doing even better to match the good news that we're getting in terms of American shopping this Christmas season. Yeah. Well, Melissa, what about the Amazon story today? They came out with stellar numbers saying that they sold millions more items this year than they did last year, signed up tens of millions of new prime subscribers, which cities as we'll get to 275 million in a decade's time. Amazon is always the one to beat. Everyone's out there trying to beat Amazon. And that's the problem. Amazon did have a big rally today, but that's taken a tumble off the highs, too, like a lot of other stocks recently. But I will say retail did have a good year to me for the most part. Some of these stocks that wouldn't normally rally to the end of the year, till Black Friday at the beginning of the year, some of them hit new highs, which was unusual, was unusual to happen so early in the year. Walmart is still in the uptrend. Lowe's is still in the uptrend, too. JCPenney, I don't know what to do with that stock anymore. It's just a mess. I feel bad for that. It's sad, actually. It's really, really sad. But I mean, as far as the other ones go, Macy still is in a downtrend, too. But some of these stocks, when you look at earlier in the year, they don't look right now compared to the last month, but the home market has fallen in the last month. So a lot of these trade off with the market. Yeah. Well, you know, retail was left for dead last year. So the fact that they gained this year has been really impressive. But just back to Amazon.  I think they really set the the negativity in motion for those report cards because they set the bar really low for this holiday shopping season, which really, really concerned the market. So I think they set expectations low, but they come out with these really strong numbers. So you know, next quarter will look pretty good for Amazon. Well, it's interesting because when you look at this quarter for earnings in total, I'm talking about everything. And then you look at the market, you say, hmm, interesting.  Now, what does that mean for the beginning of 2019? Well, we have a lot of upside potential. So if we get some good earnings and good reaction. So some of these stocks had good earnings, but bad negative reaction on the stock price. What we need is good earnings or bad earnings, but good reaction in the stock price. And because everyone had negative reactions for the most part, and I'm generalizing here, I'm talking about in general, even though some of them had strong earnings fundamentally, technically speaking, the stocks sold off, which didn't make any sense. We want to see both of those things equate together in the first quarter of 2019, where your fundamentals are good and the technicals are good and the stocks get bought. We had some good earnings fundamentally, but then the stocks sold off on the earnings didn't get bought. Yeah, that's what we call convergence. Guys, thank you so much. Jonathan, Melissa, good to see you.


If you're seeing flashes of light or hallucinations or shapes just before you go to sleep or you know at any point during the night that is called sleep hypnagogia. And it's a really powerful sleep stage which just like the sleep transition between being awake and being asleep and entering a dream. So in this video I'm going to show you how to actually follow those hallucinations into a dream directly and of course this will be a lucid dream. If you already know how to lucid dream this is a powerful technique if you don't know how to lucid dream this is going to work either way. So here's how it works. I'm just going to describe sleep hypnagogia firstly because you might not know how this feels and how it works. You're laying down your eyes are closed and what you'll experience is kind of like shapes, colors maybe like a spiral pattern appearing in your eyelids. This happens usually after about 20 minutes of you laying there trying to fall asleep so it really does depend on how tired you are of course but that's what usually is going to happen. So how do you follow that into a lucid dream? Well here's what you do. At this point you need to bear in mind if you lose your focus on this you will either fall asleep or you won't be able to sleep. There's really no in-between. You need to keep your focus on and this is the key part letting the hypnagogia happen around you without looking specifically at any one point of the hypnagogia. Say if you're experiencing this and you see like a line or a spiral pattern if you try and focus your eyes on a specific part of that hypnagogia image on that hallucination it will usually dissolve and you won't be able to see it anymore. You'll either keep yourself awake or you won't focus enough and you'll just fall asleep and then you won't have a lucid dream in the first place. So what I would suggest to do and by the way if you meditate during the day this massively helps with this because you're better able to focus on something for a period of time. You're better able to concentrate on something without falling asleep and losing interest. So meditate during the day and then when you actually come to experiencing the hypnagogia at night when you're actually seeing the shapes and colors around you and in your eyelids and all of that crazy stuff. Focus on trying to take things in without looking at a specific point. So you can practice this during the day as well just try and relax your gaze so you're not looking at anything in particular but you are taking in details around you know your peripheral vision and around you where you are sitting or standing. So if you find it hard to relax your gaze that's fine so do it like this okay let's say I'm sitting here now and I'm looking out into my garden and I can just focus on a point in the distance let's say the chair the deck chair that's there in the garden okay. So I will just focus on that point I will directly stare at the deck chair and I will allow my senses my eyes to take in all of the information around that one point that I'm looking at okay so I can notice and start to be more aware of everything around it without actually looking at the things around the point that you're looking at. I hope this makes sense. Anyway that's how you practice it during the day. Now eventually you want to get to the point where you can take in all of the stuff that's going on around you with your peripheral vision without looking or focusing on any specific one point and this takes a bit of practice at first you won't be able to do that so in that case just focus on one point and then taking the information around it after you can do that then try and focus on no specific point just try and relax your gaze you know go sort of blurry eyed I can't really explain how it feels and or looks but you'll know what I mean when you experience it and try and then take in the information all around where you're sitting what you can see that there is the exact motion or feeling that you want to try and do when you're having sleep hypnagogia and so what will happen is when you do that you will enter a lucid dream if you just do that if you relax your gaze take in the information that's going on around you so it might be like a spiral here shapes lines coming at you all of this stuff take it in without looking or focusing at a specific point let's say five minutes of doing that you will enter a lucid dream because and this is by the way how the wild technique kind of works there's a bit of change you know a few variations but that's how it works and by the way I have a tutorial of that on my channel but the way it works is that yeah you just follow the hallucinations into a dream and if you try and look at a specific point too soon too early the whole dream will collapse and you'll have to start again from the beginning however if you practice this during the day if you practice like I said the focusing on a point taking everything in around you it will be a hundred thousand times easier for you I promise you that


Hello and welcome to module 22 of Chemical Kinetics in Transition State Theory. In the last module we have derived one of the fundamentalrelations we wanted to derive which is the calculation of rate constant via transition state theory. So, we will spend this module and the next one just looking at some of its numericalimplications. So, just a quick recap the transition state rate we derived is given here. So, we wrote a formal derivation of this and this derivation remember works under 5 assumptions we made to derive this we have to make 5 assumptions. One that the transition state exists transition state again is the structure which is the maximum energy structure along the reaction path and the minimum energy structure along the coordinate perpendicular to the reaction coordinate. The second approximation we make is that we treat nuclei classically. The third we make that there is no recrossing that is we look at the positive flux only we look at sit at the transition state and we look at all the particles moving forward we do not see anything coming back. We assume that the transition state is in equilibrium with the reactants and finally, we assume that at transition state we have a separability of Hamiltonian particularly along the reaction coordinate and everything all other coordinates. So, under these 5 assumptions this relation can be derived and we did it last module. So, let us look at some of its properties of what we have derived. First let us try to look at what the unit is. So, remember we have made a special case of this relation holds only for a bimolecular reaction. This is not Hartz requirement if you have more reactants you just change the denominator. But nonetheless let us stick to bimolecular for a little while and I want to understand what is the unit of the rate constant I have got I just want to make sanity check. So, let us look kB into T is of units of energy is units of kilogram meter square per second basically energy into 10. Partition function what is the unit of this partition function? The partition function remember is dimensionless, but this Q that is written here is partition function per unit volume. So, remember we had to divide by volume to get to the rate constant and that is how equilibrium constant is also defined. So, this has the units of 1 over volume which I will just write as meter cube or liter whichever is your preferred one. So, in the numerator I have 1 over meter cube divided by Q A naught is 1 over meter cube Q B naught is 1 over meter cube and this thing is dimensionless. So, this cancels with this and kilogram cancels meter square cancels I am left with 1 over second I take meter cube to the numerator. So, I get volume divided by time as my unit we have to be just a bit careful this is correct. However, this unit is per molecule per unit molecule of reaction this is not in the language of moles. So, if I want to think in the language of moles if I want to think the reaction rate happening per mole then basically you have to rethink of your Q naught that is given by Q over V into N A we discussed this in a previous module and so this becomes unit of 1 over meter cube into moles remember Avagadro number is 1 over mole. So, this is your Avagadro number. So, now K in this unit of moles will again be K 2 over H which is kilogram meter square per second square divided by kilogram meter square per second into now I have to be use the unit of moles here moles per meter cube divided by mole per meter cube into mole per meter cube this cancels with 1 of them. So, I am left with 1 over second into meter cube per mole which is unit wise the same as liter into mole inverse second inverse that is your reaction rate constant in the language of moles without the language of moles what we had got involves liter second inverse we do not get the mole inverse this is atom per atom just to make a sanity check whether we have what we have got is right or not. Let us just look at if I have a reaction looking like A plus B going to product rate is given by minus let us say D A over D T equal to K A into B. So, K is 1 over A 1 over B minus D A over D T. So, dimension wise this is the same as you see 1 concentration will cancel 1 over concentration concentration is what moles per liter into D T is 1 over second. So, this is the same as liter mole inverse second inverse. So, you can see that what we have got from transition state for a biomolecular reaction indeed matches the correct units based on our rate theory ok. So, we have a consistency at least the formula that we have got is sensible at least dimension wise ok. Next I just want to discuss how in practice we calculate this answer. So, what we have discussed already is how we calculate partition functions and that is basically we divide the partition function into translational into rotational into vibrational into electronic we are dealing with a quantum mechanical version now. And so, we do this division for the transition state for A and for B and we get our formula as K B T over H Q translational I will use this big cross or dagger for transition state Q rotation dagger Q vibration dagger Q electronic dagger divided by Q translation A Q rotation A Q vibration A Q electronic A. And the same will go for B Q rotation B Q vibration B Q electronic B. So, the point is that we calculate each of these quantities one by one the formulas we had looked at before. And so, again do not go about memorizing these formulas for this course wherever the formulas are needed we will give you the formula. So, we calculate each component using these formulas and substitute and get the answer that is the idea that is how you calculate these kind of rate constants in transition state theory. Just remember to divide by volume. So, the volume division we basically take in the translational part. So, Q translational we take as Q over V which is 1 over H Q into 2 pi m K B T to the power of 3 half. So, the volume is absorbed in the translational part. Remember again all these Q's is the full partition function divided by volume. And what I have listed here is the full partition function. One important note about the vibrational partition function of the transition state. So, the vibrational component you can see here at the very bottom here. So, Q vibrational is given by what is the problem with transition state. So, the transition state if I zoom in it looks something like this. So, this is my reaction coordinate and again transition state is the local maxima there. While this dotted line is a direction perpendicular to. So, imagine this coordinate coming out of the screen. So, it is perpendicular to the reaction coordinate. So, we have a problem actually at the transition state what looks like a problem at least. One of the frequencies is imaginary. So, if you will put let us say a transition state structure in some electronic structure calculation and ask to do a frequency calculations it will give one frequency as imaginary. So, how do I deal with that actually it is not a problem at all and we have to be careful about it. The point is this mode should not be included in partition in vibrational partition function. Why is that? So, go back to the derivation. The point is this coordinate the motion along this coordinate is what we calculated as the flux. If you look at your derivation once more what we did was we calculated the total number of particles moving per unit time in the forward direction along the reaction coordinate multiplied by the appropriate density thermal density that is how we calculated the transition state rate. But so, this direction which has imaginary frequency has already been included in this factor here kt over h is the flux along the reaction coordinate and this is exactly what is incorporated in the q vibration of the reaction coordinate these are related. So, you can go back to the proof very carefully this partition function along the reaction coordinate we had intentionally separated. So, in the last module we had written q of the transition state is equal to q along the reaction coordinate which we called qr in the last module into q everything else. So, this thing is what enters here in my formula and this does not have the partition function of the reaction coordinate. So, the one mode which represents a reaction coordinate that frequency should not be included when calculating the partition function of the transition state. So, today I will not compute numbers but just make estimates. The next module is dedicated in doing an exact example we will actually calculate numbers. Today I just want to get a sense of it. So, let us just make one quick calculation and make estimates. So, imagine I have two biomolecular reactants biatomic diatomic sorry ab plus cd let us say the transition state looks something like this going to ac plus bd some reaction like this I actually do not care what is the product. And I ask you can you give me a ballpark estimate of what the transition state rate is going to come out. So, let us do that first we calculate what is kT over H. So, kT over H is of the order of 1.38 into 10 to the power of minus 23 kilogram meter square per second square Kelvin into let us say at room temperature that is H. So, if I just make a ballpark estimate you should start doing these kind of calculations on your own you will get something in the order of roughly maybe half roughly perhaps 0.7 into 10 to the power of that is 11, 12, 13. So, you can plug it on a calculator of course and see an answer, but it is always more fun to do these kind of ballpark estimates at least just with your head. I just took the powers 10 to the power of minus 34 goes to the numerator and I have 34 plus 2 is 36, 36 minus 23 is 13 and roughly this is 0.7 H. So, that is kT over H. What about the partition functions now? Also have broken the partition functions into translation, rotation, vibration and electronic. And you should actually keep a once you start doing practicing these problems on your own you will anyway get an order, but these are the orders and we discussed these orders sometime before as well keep like these orders with you they will help you. So, if I do this ballpark calculation for the transition state the translational function is let us say 10 to the power of roughly 31 rotation it is non-linear I do not know what I will use I will just use 1000 perhaps 100 to 1000 something in that order vibration is going to be roughly around 1 and electronic is usually around 1. So, same thing will happen here this one is linear A into 1 into 1 just rough ballpark I am not being very accurate here. So, something in that order a factor of 10 here and there is fine. So, just let me cancel this this was in the unit of 1 over meter cube 1 over meter cube and this is also 1 over meter cube. So, 1 over meter cube cancels with this and I am simply estimating the pre fact a pre exponential which is this this thing can change a lot depending on what your activation energy is depending on the problem. So, again let us try to estimate this I will take this 10 take this 10 and cancel 2 zeros there I have a 10. So, I will take this 10 and get this as 10 to the power of 30. So, I have 10 to the power of roughly 0.7 into 10 to the power of 13 into 10 to the power of minus 30 which is roughly of the order of sorry 2017 because the ballpark let me write this clearly 17 again. So, the rate constant is roughly of the order of 10 to the power of minus 17 meter cube second inverse. Let us also convert it into units that are usually reported. So, what I have got is 10 to the power of minus 17 meter cube per second inverse. So, if I want to convert it into units of liters and moles, we just discussed how to go to the units of moles per liter. So, this one will be roughly of the order of I have 23 plus 3 is 26, 26 minus 17 is 9. So, in the units of a liter mole inverse second inverse which is a much more common unit to be used we get something in the order of 10 to the power of minus 9. Ballpark, you can have a factor of 10 here and there very easily. So, this is the kind of numbers you should expect when you are dealing with bimolecular reactions, the pre-exponential factor. Another thing I just want to quickly point out is that temperature dependence we have got. So, imagine again we can discuss different examples, but imagine if I have some a plus b c going to a b c as the transition state, going to let us say a b plus c. How will the pre-exponential depend on temperature? Actually, if you go back several modules we had shown what is the pre-exponential factor for collision theory. It looks like root of t into exponential of minus a over k t. So, what will happen for transition state theory is the question. So, let us estimate this. I am just looking at temperature factors not constants. So, I have a temperature coming from here. Now, the transition state for a for transition state I am assuming to be linear. So, for a linear transition state you will have the translational partition function as this from here. The rotational is linear. So, I get multiplied by t and basically the vibrational part and the electronic part I will assume to be temperature independent because usually the frequencies are very high. So, to a good approximation I can simply assume that vibrational partition functions are not temperature dependent typically. So, I do the same thing for a, a is simply one atom. So, it has only translation. Remember that an atom cannot rotate cannot vibrate. An atom can only translate. I can also have electronic partition function, but that I am assuming has no temperature dependence approximately. And BC will have rotation and everything, but I will have this is translation, this is from rotation. So, this cancels with this, this cancels with this. So, what I am left with is 1 over root of t into e to the power of minus e over kt. So, you notice that results from transition state estimate in fact, does vary from collision theory. Even qualitatively it is not just getting numerics. You see that the temperature dependence is not exactly the same. Here I have got t to the power of minus half. Compare t to t to the power of plus half. So, it is an important thing to note. And going beyond actually the transition state estimate will depend on the kind of reaction. So, again if I go back to the previous example of AB plus CD going to some ABCD, there the dependence will be again different. So, transition state theory actually looks into the molecules more closely. It thinks about vibrations and rotations much more deeply and gets a better estimate actually. So, I just want to end today with if I give you a reaction and I want you to make estimate of the transition state theory of the rate constant. What are the quantities that you will need to find out for the reaction? So, you will need of course, the masses of the different atoms involved. You will need the activation energy. Now to calculate partition function, you need the translational partition function which requires the mass. You need the rotation partition function which requires the moment of inertia as well as knowledge whether the molecule is linear or not. So, we have different formula for linear and non-linear. So, you need to determine whether the molecule is linear or non-linear and then find the corresponding moment of inertia. Both for reactant and transition state, all reactants that I have and the transition state. And finally, you will need to find the vibrational frequencies. So, again we can do the math. The total number of coordinates is 3n plus 6 is sorry, I am 3n. For a linear molecule, I get 2 rotations and I always have 3 translations. So, for a linear molecule, I have 3n minus 5 vibrations. So, you will need these 3n minus 5 frequencies here for linear. For non-linear, you will need 3n minus 6, because there are 3 rotations and 3 vibrations giving you a total of 6 rotations plus vibrations sorry, rotations plus translation. So, the vibration left is 3n minus 6. So, these frequencies you will have to calculate, they generally come about from either an IR spectra or electronic structure calculation. All these quantities actually, they either come from spectroscopy or from electronic structure. One note again, for transition state, you need one frequency less. One frequency will come out imaginary, that frequency is discarded. So, for linear, we will have 3n minus 6 frequencies and for non-linear, you will have 3n minus 7 frequencies at the transition state. Finally, we will need the electronic degeneracy of each reactant and the transition state. One important note, no data is required for the products. Whatever is a product forming is independent, it does not determine the rate at all in transition state theory. That is of course, an approximation and that approximation has to do with the fact of no recrossing. That we will look at later. So, with that I end here. Today, we have essentially looked at a more general discussion of how to calculate this rate constant that comes about from transition state. We have looked at the units carefully. We have looked at orders of magnitude. Something that I want you to keep in your head is that translational partition function is in the order of 10 to the power of 31 meter cube inverse. Rotational is something in the order of 100. Vibrational electronic are in the order of 1. Vibrational can be a bit more, can be 10. Electronic is usually around 1. And this is the formula that is used for calculating rate constants. We will in the next module study a concrete example of calculating rate constant. Thank you.


Hey guys, Coach Mack here with this week's Beneath the Surface powered by Microsoft Surface beginning now. Today we're going to look at Darren Henry, three big runs in the game versus Jacksonville. First in Gold for plus three, nine thirty-seven in the first quarter. Titans are now in twelve personnel. They start out in eye formation with Swam as a fullback. He shifts on the line of scrimmage to make this two by two. NWI comes in motion, Jacksonville is in a five man front, they bump the motion on the back end. NWI's motion moves the linebackers a little to his left. Austin Hooper gets an excellent cutoff block on the second level player. Henry sticks his right foot in the ground and cuts back off the down block by Daly and Brewer and the cutoff by Hooper. Touchdown, Titans. Second play we're going to look at, second ten to plus twenty-five, five fifty-six, first quarter. Titans in eleven personnel start out in trip speed with all three wide receivers to the right side. Robert Woods goes in motion, creates two by two. Jacksonville rocks and rolls their safeties to adjust. Henry, in an offset out of the gun, Titans run a one-back power-old. Nate Davis pulls across the center.  Daily the offensive tackle climbs to the second level, blocks the back sidelinebacker. NWI completely obliterates the safety. Henry makes an excellent cut to get away from the players on the second level, gets a nice block by Conley and he's up the sideline, hits the jets, plus fifty, Derek Henry, classic run. Now we're at second and eight, the minus forty-two, two fifty-seven, second quarter. Titans in twelve personnel start out in a three by one, Woods goes in motion to make it two by two. Jacksonville in a five-man front, outside zone to the three technique, on the defensive right side, excellent block by Brewer. Cutting off the three technique with help from Daily, Jacksonville line backers move and try to mug up into the line they get cut off. Henry blasts through the big gap for a nice twenty-one yard game.


So my name is Neil Bertelsen and I presented at the recent MPE masterclass talking about patient involvement in HTA. So I'm just going to give you a really quick summary here. And I really started by going back to the basics and really explaining what HTA is and particularly the relationship between HTA health technology assessment and politics. So I think one of the key first points I made was that it's politicians who decide how much resources going to healthcare, and it's politicians who decide how health technology assessment works. And what rules apply in your country around health technology assessment. And so that means that you have to be aware of the political system, as well as the mechanics of health technology assessment because it's in the politics that you can actually increase healthcare spending on a particular disease area, for example. I'll talk a little bit about that but then really focused on what is it around HTA that we need patients for. So what are the key questions that occur when a health technology assessment body is looking at a new medicine and deciding whether they want to invest in that new medicine for patients in their country. And there are a lot of gaps in evidence that health technology assessment bodies really have difficulty understanding. We need to know exactly why a new treatment brings benefits to patients, specifically what is it that patients can do on this new treatment that they perhaps couldn't do on the old treatments that exist already within the healthcare system. So looking to the patient community to give them some context to help them understand what it's like to live with this condition. What are some of those key things that specific things that patients have difficulty with with this particular condition, in this case, myeloma, you know, and really understand how that affects the day to day life of people with myeloma. And from there they're trying to understand, okay, so if that's the problem with the disease, what's the problem with the current way the disease is treated. So the current treatments, the current ways that patients are managed with myeloma. So they want to understand what's good about the current treatments and the current ways they are used, but also what is bad, what still is a gap that needs to be filled from the patient perspective. So you can see already that they're looking to the patient community to give them evidence around how patients experience not just the disease, but the current treatments of the disease. And they're wanting to find out what's meaningful and important about any aspects of the new treatment that they're assessing. So not all of these questions are answered in the clinical trials. So sometimes there are big gaps in evidence that actually the patient community can help to fulfill some of those gaps and give context to them. Which is why many health technology assessment bodies now have processes for including patients in their decision making. And the way that works is different from country to country. But one common method is asking patients to write in with some evidence and the evidence can be from interviews, it could be from patient surveys, it could be patient stories, it can be quotes. But really building a package of evidence that says from the patient perspective, this is what we think you need to know when you're making a decision about this upcoming treatment. So I explained all of that in the master class, but I also raised some really important points that it's worth keeping in mind that cause some frustration between patient communities and health technology assessment bodies. Now, HTA bodies were never set up to grant the wishes of patients, they weren't. They were set up to protect healthcare systems and make them sustainable for all citizens, not for one particular group of patients. And that creates a conflict or sort of a tension within HTA, where actually it's set up to make decisions based on the health of the, or the strength of the healthcare system itself for all patients. It's wanting to protect resources within the healthcare system so that the healthcare system can meet the needs of the widest population of patients possible. And sometimes that means that they can't invest in every new medicine that comes to their door. And it's making that decision, it's really a hard decision. So when health technology assessment bodies say no to a particular medicine, it's actually really difficult decision for them to get to that no, it means that they have serious questions that have not been answered about whether this will really deliver on its promise when it's used in real life. And there's lots of mechanisms now in place to try and improve that process. And one of the important ones that I mentioned in the masterclass is things like they're called managed entry agreements. And so that's a way that a health technology assessment body can say, we're not convinced with the evidence, but you know what will give you the benefit of the doubt. We will let this medicine be available to some patients, but as long as you collect evidence to show that it's actually working. And once we start to see that in real life, it is actually working, then perhaps we can expand access to more people. So that's one way that they try to overcome some of those challenges. Another key point that I make is health technology assessment is based on input from multiple stakeholders from the industry from economists, from clinicians, and often from the patient community. And sometimes I've seen that it's patient groups can feel as though the whole decision rests on their shoulders that they need to say the right things in the committee meeting or in their submission in order to secure access to the medicine for patients. And I really want to stress that sometimes the patient involvement is not going to be able to address the questions that health technology assessment body has. So it's important as patient groups that we understand that our role is really to make sure that the patient perspective is considered within that decision making process. But it's not our role to always secure access. Sometimes there are gaps in evidence that are just too big to turn that no into a yes. And then it's up to the company to go back and collect more evidence or provide more evidence to answer some of those concerns. So I don't want anybody in the patient community to ever walk into a committee meeting, feeling that it's their responsibility and only their responsibility to secure access to that medicine. It requires all of the stakeholders to work together and provide the right evidence to do that. But I think that that we're going to a future now where we're seeing patient involvement more accepted within health technology assessment bodies. We're seeing the quality of evidence that patient groups provide getting stronger and stronger. And so we're seeing that patient groups do have an influence. They are able in some cases to say hey you're looking at the wrong thing. You need to be looking at this kind of evidence over here because that's what really matters to us. And we have examples now where we can say without patient groups being in the room that medicine probably wouldn't have been approved reimbursement. So it's a long journey. I'm not saying it's always easy, but it's really worth doing. It's really worth finding how your system works in your country and making sure that the health technology assessment body knows you exist. Knows you're there to call upon to answer questions and to be invited to take part into these processes. So do look and see how your system works and find ways of making the HTA bodies aware that you're there to help them. You're there to give them advice and support and answer their questions whenever they're looking at treatments for myeloma. Thank you very much.


Welcome folks, we have the Dow Industrial finished down 118, Nasdaq was off 20, S&Ps down 13. This is a fast, slow, razor blade down folks. That's what it is. What I mean by a fast slow is this, you drift lower and then boom, next leg comes down. You drift lower, boom, next leg comes down. That's who we're in right now. Gold. Gold contract up $5.90, trading at $17.61. We have silver up flat, $22.60 an ounce, light sweet crude, flat, $80.53 a barrel and notes and bonds. They came off the lows. We'll see whether they get any follow through out here. The 10 year up 12.6, trading $131.07, the 30 year up a full point, plus 12.6 at $158.28 and King Dollar. King Dollar's up $195.6, trading at $94.511, Euro at $115, again at $113.62 and the British pound at $135.00 to $1.00 US. Let's go over and take a look at the NQs first because you're going to see there's a lot of action in here coming into the close. The NQs popped all the way up and then just basically gave it up on spades.  The futures close at quarter past four, okay, on the first get go. That being said, you get someone that's selling into these in a big way. You're going to see the pop and this is what gets really intriguing to me. If you're just listening to the program, I was explaining to you that you had two separate pops intraday. The first one was at noon. The second one was at 2.10. Neither one of them could take out a swing point and someone is selling on the other side of this because look at this. When we came into the close, you had buy and coming into the close, but the selling overwhelmed it. That's how this baby shakes out. Now what you have, okay, if you're a day trader, which is really cool, is this. You've got an ABC structure on the way down intraday, so if these futures can stay like kind of flat overnight, it's going to be a little field day because your A point on this goes all the way up to 14,780 and your B point is 14,636, what I said, 780. So you get 150 S&P, I mean Nasdaq points, which is nothing by the way, but that gets you 720,620,580, yeah, so 14,580, so what I'd love to see, I'd like to see a shoot up all night long, when you get a price projection lower, oh yeah. Have a great night folks, have a safe night, come back and visit Tommy tomorrow morning, kicks us off at 9 o'clock, have a great one folks, have a safe one.


This time on Partners, it's the 1890 universities of America's land-grant system. From Alabama A&M's unique watershed project on the Flint River, to Prairie View A&M's 4-H efforts in creating the next generation of entrepreneurs, to North Carolina A&T's cutting-edge work with farm-fueled biosensors. These historically black colleges and universities are shaping the future of American agriculture. These are institutions that are willing to serve people, to allow people who otherwise might be denied education, accessibility to that education. Welcome to Partners! In the next half hour, we'll travel the nation and see breakthrough work in research, education and extension. That's what CSR EES is all about, helping universities generate valuable knowledge for those who need it and educating our next generation of Americans. And now it's time for Partners! In the terror and chaos of America's civil war, a surprising revolution in higher education began. In 1862, Congress passed legislation called the Morrill Act. It granted each state public lands to help establish and endow, in the words of Vermont Congressman Justin Smith Morrill, colleges for the benefit of agriculture and mechanic arts. These institutions of higher learning came to be known as land grant schools, and the Morrill Act was responsible for laying the foundation for public education through a national system of state colleges and universities. Its intent was to provide education to American children who could never hope to attend private schools. But in post-civil war America, the system did not benefit all. It doesn't initially benefit the freed slaves because the schools that are established are segregationist schools and only whites attend those schools. So while blacks might have thought they were going to benefit, it was the whites and in specific working class to lower class whites who benefit from the establishment of these schools that made higher education more accessible. There was only one school created to serve the freed slaves, the blacks, and that was Alcorn State in Mississippi. Then you really see that the majority of the schools at that time that are created the land grant schools are servicing a white population and there has to be a second Morrill Act and that comes in 1890. Since African Americans were denied admission to the 1862 land grants, Congress passed the Morrill Act of 1890. That resulted in additional land grant colleges located primarily in the south. What you see there is that 18 historically black colleges and universities are created with the passage of the second act, which is really an attempt to redress a remedy, the failings of the first Morrill Act. When they were founded with the focus on agriculture, people assumed tractors and holes and land, but nowadays agriculture has become high-tech. We are involved in robotics, we are involved in research and food science, we are involved in issues of health, we are involved in environmental science. So it's not education in a vacuum, it's education with a purpose, a national purpose in mind. In the next half hour, partners explores the efforts of these unique universities and with funding from the Cooperative State Research, Education and Extension Service, the 1890 land grant schools are making a difference in communities across the country. Today, this group is preparing for a trip on the Flint River. The waterway runs through scenic northeast Alabama and the bustling city of Huntsville. These folks are members of the Flint River Conservation Association. Their mission is to safeguard these waters and its valuable watershed, the surrounding lands that form the local drainage area. The Flint is a wonderful river. I like it because it is so close, it's a wilderness area close to an urban site. We actually get most of our drinking water from this river. Susan Weber is the Association's special projects chairman. At her day job, she's the city ecologist for Huntsville. We had a study done, NASA has said that by 2020, the county of Madison will be at 50% build out, which is going to be disastrous for water quality of all our streams in Madison County, the Flint River being the largest. While the group enjoys the journey today, their work is never really far away. Picking up trash is an expected part of the trip, a duty they practice en masse during their annual river cleanup. Our mission is to improve the water quality of the Flint River, but also in more of a large picture. We're trying to make people more and more aware of our own roles in non-point source pollution. We all have a role in that. But we are seeing some impacts from agriculture and from development, mostly in the form of sediment. So where people are not doing no-till farming and where we are getting new developments that aren't following best management practices, we do have a serious problem with sediment. And along with the sediment, of course, we get heavy metals and other things that are attached to the clay particles. It is the unseen non-point source pollution that is currently the biggest threat to the Flint's future. To get a handle on the current state of the Flint, Dr. Teferi Sagai from Alabama A&M University, the 1890 land grant school in Huntsville, is monitoring the river's water quality. He and his staff have placed remote sensing stations in aquatic environments throughout the watershed. Today, however, Teferi and his students are gathering data with a handheld device. This tube, called a sown, is carefully submerged into the Flint. Inside are sensors that measure a number of factors, including temperature, dissolved oxygen, turbidity and chlorophyll. Part of my job is to train students, especially African-American students. Besides USGS, no one is monitoring the water quality in the Flint River watershed. In general, we see a lot of pollution in this watershed. The nutrient level is low, relatively, in the last two years when we did this research, but there are some high level of lead and some other heavy metals. You see high level of free alcohol reform depending on what season. His research and what we've gotten from the USGS and from TVA and from research from our own water monitors, we have an idea of where the problem areas are and what needs to be done about them, and we've established all this information in the Flint River Watershed Management Plan. Teferi and crew return to the Alabama A&M campus. At the labs, the data gathered from the Flint is downloaded into a computer. This is the home of the Center for Hydrology, Soil Climatology and Remote Sensing. The staff here is working on a number of projects beyond the Flint River. Remote sensing will allow you to see things where you cannot see before. If there is drought, we can clearly see where the drought is originating, you know, and we can quickly take some action and we can, by estimating soil moisture qualities and how much it is in the soil, that will allow us to interrogate a field or when to drain a field. We are unique because we are the only African-American institution advancing the soil moisture work in this field. So that makes Alabama A&M unique and our center unique, and I'm getting good support from CSRES in terms of getting additional grants to continue this research. The farmers, they can benefit, you know, how to do precision farming. That means they can precisely apply chemicals, precisely apply water or irrigate a field. That means they are going to cut down their cost. One of those farmers benefitting from Alabama A&M's research is David Hodges. He runs a farm in Guntersville, some 40 miles away from campus. Environmental rules and regulations have come into play more prevalent than they were in the past, and we're trying to be good stewards here at the land. And with those challenges here, it requires a lot more day-to-day management and a lot more tweaking of operational decisions. To help David and other area farmers, Teferi and staff have installed weather stations in five counties in Alabama and three in Tennessee. This one here is actually located on Hodges farmland. The system, called Alabama Mesonet, collects data on soil moisture, air and soil temperature, rainfall and wind and speed direction. USDA's Natural Resources Conservation Service National Water and Climate Center and NASA have partnered with Alabama A&M in the project. Continuous data recorded at 15-minute intervals gives farmers valuable local environmental information. Huntsville, Alabama has some great weather stations, but they are in Huntsville, Alabama with two mountains between us. And we also have a lake. We may get an each-of-brain here and Huntsville may be high and dry. So by having a weather station right here located on this farm, we're able to get exact data at exact time. Without it, it's a hit or miss. With this data, we have our soil temperature, it's right to plant, we have our moisture, it's dry enough to get into the fields. It takes a lot of the guesswork out and without institutions like A&M, the new technologies would never be able to make it to the farm. Back on the Flint, the group is finishing the last of the day's cleanup. Alabama A&M's remote sensing technology will help them plan for proper watershed development in the future. For Teferi Sagai, his work has just begun on the Flint River and on the weather station project. This is a challenge. Every day we are facing new things. I'm ambitious and I am trying to expand this network of weather stations throughout the state. So I'm hoping with the support from USDA and other agencies, this can happen and we can reach other people in the state of Alabama. Tuskegee University, an 1890 land grant started an agricultural experiment station in 1897. Its director, Dr. George Washington Carver. Our business arcade stop has been going on for about two years already and we made some pretty good money. Samantha Pimpedli attends Horace Mann Junior High School. She and her partners have started a business called Arcade Stop. Students can choose from a number of video games and for a fee, play them on the game consoles. Our first step was thinking of like how many consoles do we need and what type of games would people be interested in and what type of age group would we want to attract. I learned partnership and how to run a business and the targeting and everything, it was great. We're trying to teach them how to work cooperatively together, but they don't know the importance of it being a team member and when they get into the business world everything now is a team. They've learned how to work out problems, problem solving, lots of problem solving. Patrice Barnes heads the 4-H Youth Development Program in San Antonio for Prairie View A&M University, Texas' 1890 land grant school. Since 1998, she has focused on T-Teams, the Texas teens exploring entrepreneurial minds project. In the past five years, T-Teams has reached over 1,000 students in inner city schools. Annually, the young entrepreneurs strut their stuff at the 4-H Youth Trade Show. Here they set up booths, sell products and services, and are awarded prizes. T-Team offers the kids a positive outlook on life from what they're accustomed to in their poverty stricter neighborhoods. It provides them with another avenue, another chance, another positive door that they can open and also shows them a way to make money in a positive way. The students that we work with, they are ethnic students, Hispanic and black students. They come from economically challenged neighborhoods and some of them live in what we as known as the projects and some live with their aunts or uncles. Neighborhoods are really poor. You may see graffiti on the walls or you may see kids in gangs and those kinds of things. T-Teams businesses are as diverse as the project's participants. They range from hair braiding to lawn care, from t-shirt shops to candle making. In the process, the kids learn fundamental business skills of organization, marketing and economics. One recent project was started by two students from Sam Houston High School. The name of our business is Just Westlow and it's a car business and we just like trading cars with our friends and stuff so we just decided to make a business out of it. It's our business plan right here. We have our company name. Our benefits is just having fun and our products and our target is young people and old sports fans. We just had a lot of fun. Just us being together, it's always a lot of fun so sometimes you get on my nerve but it's alright though. I see them walking away with self-esteem, a knowledge about a career option they had no clue of. I see them gaining reading skills, organizational skills, management skills and these are skills that they've learned in managing their own businesses but these are also skills that they can take with them into their future careers in their lives. There's one student in particular and his name is Clifford Jefferson. He was a very shy student, had a monotone voice, always had his head down every time I saw him. It was about three years into the program that I got a chance to observe him being interviewed by Channel 9 News here in San Antonio at one of the trade show events and if you could see him, he had a nice suit on. He was talking to the newscaster with so much confidence and to see a child go from one area and develop into something that you could not possibly imagine is really worthwhile singing in a time of career. Since CSR EES began in 1994, the agency has granted nearly $1 billion to the 1890 land grant universities. The Delta country is a beautiful land in a natural state. We have a lot of water, it's a flat land. We don't see a lot of mountains, but we have a lot of river and lakes where we can do a lot of hunting and fishing. Fishing is really part of the culture here in Arkansas. We have also a lot of agriculture, we have cotton, we have rice, soybean, corn. Steve Palmer-Lew is a research assistant with the University of Arkansas Pine Bluff, or UAPB. Most of his time is spent in the field working on the 1890 land grants Date Fish Verification Program. The project's goal is to verify the recommendations for effective aquaculture based on the university's research findings. Most of the research conducted at UAPB is done in small research funds of about 10th or quarter acres as opposed to commercial ponds that are over 10 acres. So we want to apply the results of the research conducted at the university into commercial scale ponds. One of the producers that Steve is working with is Harold Saul, a second-generation fish farmer. Together, they've tackled some difficult problems, helping Harold stay profitable in the competitive world of baitfish production. I love the personal interaction with the farmers and really being able to help farmers and keeping them in business, and this is all what it's all about. What they've done for us is took out a lot of guesswork. With the verification program, what they've done, they've got us more accuracy. Coming up with better feeds, better formulas for feeds for us to feed our fish and grow our fish more economically. Naturally, if we can grow, the production out of less feed, the better. They are the only research center in Arkansas that we can get a verification on our fish to be safe to ship out of state. And if we can't ship out of state, we're out of business. It's that simple. The baitfish verification program that Harold and others are involved with is based on the work of Nathan Stone and others at UAPB. The team has done extensive research with a fish called the Golden Shiner. Nathan is now turning his attention to the rosy red minnow. The rosy reds are an orange color with a little pinky tones to them. They have a white to a silvery underbelly. We'd like to have nutritionally sound brooders. And then that would translate into larger eggs, higher quality eggs, and better fry. Fry is the little baby fish, the one that just hatches from the egg. We're hoping that it'll reduce their costs and make them more efficient. I mean, really, the spawning rearing pond method, it's a mix of sizes and it's fairly inefficient. With jar hatching, we could turn out even-aged fish and then go into the feeder market or it'd be a lot more predictable for producers. Hi, John. How are ya? For some, this exceptional effort may appear disproportionate for simply meeting the leisurely needs of Arkansas's anglers. But the economic impact goes way beyond the fish farms and river banks of the state. Arkansas is the leading producer of this baitfish in the United States. About 23 million by the census of aquaculture. But there's a multiplier effect of six to seven fold here in the local community. It creates a lot of jobs. The feeds that go into baitfish, things like soybean meal and cottonseed meal are products raised here in Arkansas. And of course, there's a 10 to 15 fold economic multiplier when you go to the retail markets. So it generates a lot of income. There's been very fast adoption of the practices from our research recommendations and also the farms are very progressive. And so I think some of the yields and survivals have been excellent. We really depend on USDA and CSREES for the funding and the research support. It's matched by state dollars here in Arkansas. And that's really enabled us to support our stakeholders, the fish farmers here in Arkansas. It's really what the purpose of a land grant is and that's what we believe we're doing. In addition to the University of Arkansas, Pine Bluff, these 1890 land grants have active aquaculture research centers. Salmonella was our focus along with another bacteria. And those two are foodborne bacteria commonly found in poultry. And North Carolina is a major poultry producer in the nation. Mohammed Ahmedna is a food scientist at North Carolina A&T State University. He and his research team are working on ways to detect contamination early so that the public can avoid foodborne illness. And the stakes are high. The Centers for Disease Control and Prevention has estimated that salmonella infections are responsible for nearly 1.5 million illnesses annually. This has resulted in more than 16,000 hospitalizations and nearly 600 deaths. The economic repercussions are in the billions of dollars due to lost productivity. North Carolina A&T was well positioned to work with the poultry industry to solve this problem, considering the University's long history helping local farmers. Speed through technology might just prove to be the answer. In short, a biosensor is basically a device that detects biological organisms such as salmonella or any other bacteria, living things. The biosensor is basically enabling you to have the speed that you don't have with traditional methods. So speed is an issue and also portability and costs. This instrumentation may look simple, but tests to date have shown that it can rapidly detect salmonella. Currently, industry methods for foodborne bacteria detection can take several days. This biosensor unit, once refined, may deliver results in just a few hours. The payoff for consumers is a safer food supply because of early detection and CSRES has been instrumental in getting this line of research going. We have gotten a grant that started this research and our ultimate aim is to have it licensed to a company. The university has filed for a patent for the technology and we have had some interest from a couple of companies. In the meantime, Mohammed and his staff are looking at other aspects of biosensor technology that may have impact far beyond the poultry farm. The work that Mohammed is doing is looking at pathogens and other kinds of disease organisms that may affect our food supply and other kinds of projects related to primarily minority farm workers and their health concerns. It's geared towards populations that have been underserved and their health and safety concerns have often not been considered enough. So in that sense, it's extremely important. It is here at a field station of the North Carolina Agro Medicine Institute that Mohammed plans to test remote biosensors in actual farm field conditions. This instrument platform, now being tested by the Institute, will allow for real-time transmission of data through GPS satellite communications. It is self-sufficient with all instrumentation being powered by batteries charged by the sun and it has potential for agro-terrorism applications. They're working a lot on making sure that our food supply is safe, non-contaminated, either from an induced terrorist activity or from natural pathogens that can occur. Ten miles away at the Institute's office center, Mohammed is received real-time. Mostly weather conditions are now being transmitted. But when North Carolina A&T's biosensors are added to the platform, critical biological detection will also be available. What the machine now is ready for is real-life applications and for either researchers like Mohammed to utilize in their research or for commercial or governmental or what-a-have-you applications in the field. Your happy day is the day where you feel like, yes, I think I'm on to something and hopefully that's what I am right now. I feel that our job as scientists is to give back to the community and then play a role in making the lives of our citizens better. From remote sensing research at Alabama A&M to youth entrepreneur programs at Prairie View A&M from the aquaculture endeavors at the University of Arkansas Pine Bluff to food safety experiments at North Carolina A&T, 1890 institutions are making their mark across the country and CSREES is determined to continue support of these historically black colleges and universities. On the next partners, it's the National Research Initiative. In California, UC Davis scientists are unlocking the secrets of wheat by mapping its complex genomes. It's vital information that promises benefits for both farmers and consumers. And in Mississippi, researchers are exploring the medicinal wonders of the May Apple, a wild plant that produces critical chemicals used in today's anti-cancer medicines. That's all next time on Partners. Information on 1890 land grants and other partners' episodes, log on to this website.


Here are 5 shiny hunting tips you must use in Pokemon, Scarlet and Violet. Open up settings, go to system, scroll down and when you see zoom, select it. When you double press the home button, you can now zoom in on Pokemon and see the slight variations in their pixels and you won't miss out on a shiny ever again. Sandwiches that boost shiny Pokemon are in this game. You can see how long you have left on your sandwich by pressing D pen right. You can also hit the home button to pause the sandwich. You can reset Pokemon spawns by walking in and out of towns. When you combo this with encounter sandwiches, you'll get easy shiny Pokemon. Do not go fast in this game if you are hunting a Pokemon that has a solo spawn. Going fast allows you to render in group Pokemon quickly, but you won't be able to see solo spawns of Pokemon as they take longer to render in. You can despawn groups of Pokemon by either walking away until they despawn from your render distance or by doing a picnic and immediately closing it. This works really nice in mass outbreaks and if you hit that subscribe button you'll increase your shiny luck by 100 times.