welcome everyone to fraud detection use case demo walk along or follow through or follow along however you call it this is the version 2 of this use case I had a previous video recording for this for this too but this is an updated version we have made some changes to the use case like the executed federated query and materialized views we have added those things and I've fixed the resolution in OPS this is more this is going to be a lengthy video and probably 30 minutes or 40 30 40 minutes so double cup of coffee or something if you but at the end of this you would know about roads not everything about roads it's a it's a bigger project product but parts of it and starburst and what is serving and open why no all of these things so if you're absolutely new you can you will pick up some some things here if you are already in this field of AI and ML probably will have a nice use case to demo to your customer or a potential customer or just play by yourself I'll stop talking and and I will bring the blog which my manager and I wrote so this is the blog which is associated to this to this video and this use case and you can read more over here it will explain what we have done I'm gonna replace this video with the one which I'm recording right now this is the older video but the last link over here will take you to the GitHub repository so this repository is the base repository for this workshop our use case and if you can if you don't have access to demo dot reddit.com or RHDP then you will be bringing your own cluster in that case if you are you will have to install certain things in it there are some prerequisites the config folder tells you about all of that what you have to install make sure that you you also need to bring the starburst license if you have your own if you bring your own OCP cluster so you probably have to get a trial license or something or contact starburst for that your postgres credentials which you have to deploy and then get credentials and your Amazon S3 bucket or Amazon credentials this will be used for you we will be creating an S3 bucket and access read our data source or sorry our data set from there so you're getting used to the terminologies yeah but the best part is if you have access to RHDP I would highly recommend you to deploy the workshop which most of these overhead which is associated with this workshop I have automated using an ansible scripts the workshop link is over here if I click on it it will take me to the workshop and basically I click on order I'm using my activity as practice and you can use your own but remember some of them you need to provide the Salesforce ID otherwise this will remain in grayed out most important thing is to make this an open environment US East to region I confirm and now I'm gonna order this and this will take about I don't know it will take about 30 to 40 or maybe an hour to deploy an OCP cluster for me and then it will run my ansible script to do all the overheads the pre-requisites like roads starburst conflict deploy them install them install them configure them according to my the needs of this workshop once all of that is done once all of that is done I will come and I'm gonna pause this video now but once all of that is done I'm gonna come back and then we will continue from there thank you come back everyone so our cluster is the RHDP cluster our workshop is now deployed and we can continue with our workshop let me just get myself out of the screen and put this on great so still recording yes good so this is our base repository and this is where we had schedule it was provisioning now it is status is running and so here because we enable open environment we are getting our AWS access key ID AWS secret key and all of these all of these information is also email to you the email which using which you did in the deploy this workshop some key things which you will see which I have added over here is the starburst query editor link which is this is the link for that and the AWS bucket which was created by the workshop yeah so these are the two important things which are just three actually these two this and this keep an out of it so have this page open and one some side in one tab and then then head over to the open shift console and also for AWS key just keep an out that these these keys are only valid as long as your cluster is there and it's a pretty formal cluster so it's gonna delete itself there's some auto destroy auto stop so so these keys are no good after a certain point in time and but just keep in mind not to expose these keys so let's log in is the login credentials so we log in as cluster admin here so we are in the next thing we are going to do is just just hover over and take a look at the see this is this is what the operators are installed on so the road is installed starburst enterprise helm operator is installed to there wouldn't be anything here I'm not but in the starburst section I do expect some I do expect some some instances yes and there is the starburst type instance in starburst enterprise instance which are initialized and deployed that's check the resources yes all of them deployed and running that's a one thing to note here though that because the how red demo platform is is is set up which is it has a auto stop feature and how starburst licensing is concerned the license is deleted from the cluster after deploying the workshop so if your OCP cluster or this workshop stops then when you restarted or stops or pauses or whatever and then when you restarted it you will have these pods in pending or config error or something like that because they will be looking for this secret which is not there so that's why make sure that you kind of do maybe non stop it's not advisable to do non-stop but you can take responsibility of it but make sure you don't you know spend too much on the cloud fees here and if that that is something which is concerning then what you can do is you can contact the starburst partner from within Red Hat and there are a few people who represent starburst and ask for the license from them and once you get the license from them you basically need to create a secret which this this deploy or where is it this deployment is kind of referring to the deployment is referring to let's look at the yaml file yeah so this is the this is the license which which sorry not that this is the guy stand the secret name is starburst data so you can create a secret named starburst data and and just insert your basically this this guy over here if you go to config if you go to config then starburst license this is the this is the thing which you need to create in your cluster all of it is there you just need to install insert the license which you receive from our partner starburst or anyone you know who has the license or demo license or anything or if you receive or if you sign up by your I believe they have one for the for people who visits their website but I'm not too sure that's that's a little information there but let's go back and so all of this is good over here so we'll head over to the red the roads dashboard or ODH or roads dashboard whatever however you call it it's the same username password which you see over here it's the authentication yeah good so we are over here now let's do one thing let me put this as a side like this and let's see if that works surprisingly it doesn't work how can I and another window share can I okay let's let's skip that part but basically what I was trying to do over here is have to is wanting to have both of the dashboard and and they get a repository side by side but I am a little bit technically challenged here not a video expert or how to do all of this alright yeah so but but point which I'm trying to do is all these steps which I would be doing now are mentioned over here as step one and what you need to do step 2 step 3 all the way up to here so I'll refer back back and forth to this repository as I move forward alright okay so the first step is is to create a data science project so credit card fraud create this guy then the next step is to create a workbench which I do the instant image which I would be silly is selecting notebook images tensorflow everything whatever is recommended small size use the data connection this is something new I haven't used it you connecting data connection through my workbench but yeah something I will explore it so so right now I'm just leaving it unchecked I'll create the workbench so we have come up to from step one to yes so we are over here at step 2 okay so we'll wait for this open to be this guy to be let's look at the event log okay I don't know how long it's gonna take I would assume it takes a minute or two but since I don't know so I will just pause the video and come back when this is deployed okay so our workbench is ready now and we can start working on it so like you can see it's you instead of starting you know it says open so we click on it and again it's gonna ask you for the username and password which we received over here and this E this is also in your email just in case if you lost it there is some authorization allows selected permissions great so we have our notebook now and let's go back to this we have all of this we did allow all the next thing is to clone the repository so we'll just go ahead click on this which will copy the link clone repository paste over here clone takes a second great now we have that all of that and our pause and our notebook so now we don't necessarily have to go back and forth with the read me that much because most of the instructions are over here and all the rest of the read me files are over here too so I'll just start by executing the requirements file yeah there are a few if you are curious what it installs it installs these stuff here and those are the on-axis needed for for us to get the things for one of the I forget for something which we are doing later this is for the imbalance algorithm which we are which we are going to use which is called the smote the smote the that that comes from there and yeah anyway so next we'll just run the imports here TensorFlow is usually very chatty and give you a lot of errors sorry not errors but warnings and stuff so this is going to reduce the number of warnings which we are going to receive the next is this particular cell is actually gonna make the look the graphs and everything look pretty but let's look into the data set which we have so this is the data set this is where I got the data set from in from one of these links I think and some of the work we a lot of it is from here some of the only differences I think the way they are doing the balancing is different from what I'm doing here I rather followed this other blog which is yes this blog by Sarah best share this blog I have followed oh no I can't see it I need to sign in but but this blog is a little or is older and a lot of functions and things are are no more have been changed with the newer versions so yeah it's quite it's three years old but it uses the same data set which we are using yeah anyway if you want to read more about what we are doing in in this workshop then you can you can head over to the blog which I had it open earlier yeah here we are talking about what's going on in the in this whole use case so if you need more but I will carry on with just the not much explanation of the data set or anything I'll just explain the little bit which is needed for for our for this follow-along so this data said content it's a credit card data set which has these columns so it took me a while to don't know how to scroll there is more actually on that side there's one more called class and each columns represent each row represent a transaction and this V1 V2 all this time these are all my variables and my output or my output is the class that's that's my feature if I see yes so basically based on these columns what we are trying to do is we would be trying to our models who try to predict the class in future in future when we provide a transaction which has all these variables and it will predict the class for us that said so now this data set is a bit there are few the original data set was clean but we have added some some just empty value just to mimic that we are doing some pre-processing to do that I have we have this starburst readme file and this is where the starburst comes into play so note that all of these steps in the beginning are optional if you are using the demo platform all of this is optional the only thing which we will we will straight ahead and jump to these queries which we are going to execute in in starburst so we head over to the starburst query editor link just give it a name then click over here we should already see this postgres and S3 thing to note here is before in version one of this day of this demo all of the data set was in S3 but now we have split that data set and and we have kept part of it in S3 and part of it in postgres the reason why we have done it in this way so that we can show you a federated query and then create a materialized view of bringing both of it together so let's view how all of this would work so let's go over to I'm gonna close this guy I'm gonna also close this guy just so I don't know so the first thing is to create a schema well for the schema for we need to create a schema for S3 here so that we are able to read a good thing to note here that if you want to check the data set in S3 basically you head over to either you can use the AWS AWS CLI or you can head over to the web console and in this web console this is your username which you can use and this is the password which you can use and once you sign in you will be able to see that the workshop has already put into where is it S3 has already submitted the data set or uploaded not submitted uploaded here's the bucket name which matches with what we see here and if you go inside that you'll see data folder and data folder has feature dot CSV file and that's part of the that's one part of the original data set yeah anyway so to access this guy in starburst or to read we will have to create a schema replace that press and enter control and enter and then we'll go back so that the schema creation the next is we'll have to create the table within that schema so that so that we can do a select statement and visualize the data so control enter yeah so the next task is now we are going to verify that I can read on that I'm gonna close this guy too I don't need it I will need this guy later so I'm gonna keep that enter enter paste control enter yeah so you can see so one thing which we also have done is we have added an ID field which is why we have that's how we have modified the data set and I know this in the in machine learning you shouldn't use ID in the in data set the only reason we have this ID is so that we can run our joint command which you will see later and once the joint command is run you will see that over here we are dropping off the ID from our data frame or yeah yes we are we are dropping the ID column from our data from but so need don't worry that we are using the ID field to train a machine learning one and more over this this this workshop or use cases not to make is not for to make the perfect model it's more to show what we can what we can do with the help of our interactive engagement with the user or let's say power user who know a little bit about commands and this and that okay so we can see we can read from s3 so this is the section of where we have we want we want to all the way to v28 what we do not have in this part of the data set is is time and class you will see that is there in that is there oh actually that's not so we don't have a command for that over here but let me do something select star from postgres yes public dot transactions okay so we'll do postgres so postgres is our data source this is our schema and this is our table transactions is you see here transactions is a table to us be one like you can see that we have our our the second portion of the data set which is which has time amount and class class we later see in this demo that this is the one which we would be basically will rename this zero means not fraud one means fraud anyway so we can we saw both of our the two section of the same data set next let's do now is this is the most important command in this whole workshop which is going to demonstrate to you two things one it is going to do a federated query which is a query which has two different sources one in the in our case one is s3 and the other one is postgres and and then it is creating a materialized view of that of those two data sources and you will see how that when this is that now when we query this the query this materialized view we will be able to see will be able to see the data the full after the join how the total data set look like so to query that we'll do it something like this yes features was our original data and s3 is the one which we created that's what we have it here go control and enter now let's see you have dv1 to v2 all of that all the way up to v28 and then you have time amount and class great so we have successfully executed the federated query and a materialized view I think our task over here is done yes so that's all the commands which we would be running here now we are heading over to our notebook and and we will continue so we did this part the next is we would be using we will be connecting using we will be connecting from our notebook to the starburst host so let's learn that and now the next thing is basically here we are reading it in this way and it's not only reading that the raw data frame we are using pandas to read sql we are then performing this drop because we don't want the id column when we are training our model you would I think this yeah it's done the next thing is we will be changing some of the data types because of whatever reason pandas was not able to understand so we'll have to convert that actually okay I remember because when we did this it got imported as a object data type which was giving us a hard time in this command and subsequent commands anyway let's go ahead and run this shell or cell and then run our describe command we can see the count mean this is the this is the description of the whole data set which we had and if I go back oh actually even in that classification you should be I just don't know why I'm not able to scroll horizontally over here it is supposed to scroll horizontally I know there's more stuff but sometimes chromax really anyway yeah basically the reason I wanted to show you this is because what you see over here would be same as what you see over here next what we are doing over here is we are trying to get we are renaming class column to is fraud like I was saying I give you a little sneak peek over here that we would rename that as is fraud if it is zero means it's not fraud if it is fraud then it's one after renaming what we did we were calculating the percentage of fraud in this data set and it appears to be 0.17 percent which of the whole data set is fraud this is why this data set is called an imbalanced data set set and our subsequently some of the next tasks which we are going to do would be to to balance the number of fraud in the data set and the number of non-fraud in the data set so that otherwise our model will be trained in it will be a bias model it will not have enough learning about fraud and it won't be able to predict nicely and there's different ways to do it I have used the smote SMOTE technique yeah anyway so let's look for missing data we don't have any missing data and the reason we don't have any missing data and what what I'm calling as missing data is basically some rows which has empty values some in some of the rows there are few columns which have empty values that's what I'm calling as a missing data and the reason it is zero over here because we earlier performed all of this uh they these over here the spec specifically this command which excludes which basically removes all the empty rows because those I am considering in this in this model training as as dirty data or yeah basically this is what I'm calling it as preprocessing next is we will check a correlation power up graph plot which will show us how different columns are connected to each other I'm no data scientist so I understand little but data scientists who are who are exposed exposed to data science and machine learning probably understands better the next part is we're defining the x and y variables like I was saying and I'm trying to remember I'm using the terms feature set and variable as the right thing here okay yes so like a y is what we are trying to predict and x is is our feature set which is the values which we know and using those we would try to predict y next we are doing some standardization um extreme extreme x test y train white test uh this is where we are applying this mode uh technique this graph shows us uh that now we have equal number of fraud and equal number sorry this is fraud uh an equal number of non-fraud uh in our data set after applying this more technique earlier it was only 0.17 percent of once here right next we are using deep neural network dnn and uh these are the layers uh these layers uh I have taken them from uh the blog which I showed earlier uh they have done some rnd and the these number of layers and dropouts uh give a good result that is why there is this many number of layers so let's run that so this is our model summary the next part is the matrices and the training part uh the most important and they compile and fit uh so epoch the higher the number is the better or the more accurate it is but 100 will take a long time to train uh with the amount of resources I've given to this cluster or this cluster has so I will just put put it down to five and uh so there will be only five uh epoch instances um yeah so we'll give this uh like it takes 100 will take usually I don't know it has stayed in 30 minutes or 40 minutes for me sometimes five will take two to three minutes so pause the video here and come back when it's done okay wonderful now that uh the five uh five epochs are done we can see the summary over here that test loss is this much to uh 1.2 percent and uh test accuracy is 26 percent persuasion or precision is 69 percent and recall is 85 percent which is pretty expected as you let it continue more and towards 100 you will see that the precision increases recall decreases accuracy improves um yeah but uh for the sake of time we are doing less but like I want to remind that this is not uh this this use case is not to make the perfect model it's more to just demo the features here okay so now that we have trained our model so we have a model and let's save that model we use TensorFlow framework and let's save that model and after saving it we'll save it in the folder called TensorFlow PB models uh we are going to use model optimizer which is a binary provided by open wino to convert it into open wino IR format which is which basically makes the model remove some dead neurons and uh it's a it's a performance optimization and also uh it will uh later when we are serving the model it will help us to use that um that format open wino IR format is what is supported I think now maybe they do support other uh formats to serve in roads or the work is going on but but we will convert it on to into excuse me open wino IR here so let's go ahead and do that okay so TensorFlow assets were written over here like we see that there are two uh folders were created this has the all the pv models a and then we used a mo to create uh uh open wino convert that model to open wino IR and these are the three files which were created now the next step is this step that is because when we would be serving we would be what we would be doing is we would create a data connection here which is fill in all the details and connect to s3 and our model will reside in s3 and then we will be serving that model reading from s3 we will be serving it I do think that it creates a local instance a local copy of it for serving it but yes that's that's the way so we we have followed again all of this information what we are doing here will be there in the blog so that you know what's going on okay did my okay my chrome froze for a second there so this particular guide is optional and the reason it's optional is because all of this uploading we will do using this guy uh and um however this will will be needed to be done by uh by people who are not using a demo dot red dot com okay so so let's go ahead and the reason the other people would be needing to do this because for them I do not know the bucket name and secret key and all of that so they do they can do it manually or they can use the script if they put in this in these information so for us what applies is we I'm going to copy this access key and paste it here copy this to paste the secret here and the next thing is I need the pocket name which is this guy two community tabs and then we should hear now when we run this shell what will happen is this will in our s3 bucket it will create a folder called model default one and it's gonna upload this guy these three files in there so let's go ahead and run that it's done let's go ahead and check that if it was done so when we come over here let's refresh this guy we have a models folder now it has default and it has the version one and these are the three files which we had it is up in in the s3 bucket which is this guy so so the model is now in s3 the next we will have to follow some steps here in this guide to create the data connection and then do deploy configure a more server model server and deploy a model so let's do those things let's add a data connection let's give it a name something cc access key again let's copy these guys all to here the end point end point do we have an end point yes we use the us s2 http m so let's do http colon forward slash forward slash s3 dot us east two dot this is the new format the way um end points are done back back in days the legacy format is s3.8 amazon form was just s3.amazonaws.com the new one is you need to the format is you need to put punch in your region in the url itself amazonaws.com do i don't know if that matters but i'll just put it i don't need it's not required feel a bucket that's where i will see this guy so hopefully this works i'm not connecting it to any workbench because i don't need to so the object storage is now there now let's add a server let's name it this runtime we will use open vinyl model server and and oh things might have changed a little bit here before there was nothing called runtime so this screenshot is a or this gif it's a little bit old um click make sure you make it externally routable this click on that link and then and here so that we configured our model server the last part is to deploy a model in our server let's name it this model framework oh see look they are now using tensorflow as tensorflow 2 or which is api version 2 i think as uh uh as one of the model framework so that's good so this conversion which we did over here is not necessarily needed uh you can directly use uh just upload the pb file um but it's good to do this conversion because i i don't know a lot but i what i've heard is this is just a performance pools and it's good um to do that open my noir format is good so we'll since we have already done it we'll come over here existing data connection yes at cc model part this is where we're going to say models let's actually copy that uh models data copy and paste looks good let's click on deploy everything goes well here up to this point we should see this part being deployed so it's just a waiting game again i will pause the video and once it's deployed i'll uh come back and continue from there thank you okay so i have an error over here which is it says fail to pull model from storage due to error unable to list objects in bucket fraud detection that's missing region so uh now that i have missed uh i see that error i will actually go back and edit this and i will put this region i'm guessing that's the problem and update the data connection after updating that i'm going to go back here delete this guy we'll try to redeploy the model i have to delete the whole model server for doing that i think so probably i'm gonna delete this whole thing the model so actually that would probably make more sense because the model server is what is attached to uh and shift side of things just to check if things are working the way to do that would be come to over here and every data science project you know in our case eric got far the name that is the guy who with that name there is a namespace and um in that namespace there will be workloads and for uh my model server i will have this deployment and uh so apparently it is running it just says it's just started right now all of that um let's check for some logs okay look at that so uh status is loaded however we are missing the inference point i don't know why let's refresh this page maybe it will appear yeah there it is so our model is deployed now and we have also got the inference point we'll just click on copy and um we don't need these guys anymore and um we'll come over here so we followed this guide and to how to do the model serving or and deployment we'll copy paste our inference link here and i have a sample request uh with all the value with the data over here and i will see if it can pray what does and we'll see what it predicts when we run this uh server so it predicts that it's it's a not a fraud so uh that's what our model predicts but i think if we go and train our model more with more epochs uh like probably close to 90 or 80 there is more accuracy and that's when i think this particular data said if i remember correctly is actually should predict as as as one which is it's a fraud uh so that's the whole uh no workload oh sorry no workshop not workload workshop and uh the use case and i hope you guys enjoyed this is a bit of an extra stuff here you can um we'll do some fun stuff here which is we'll get an accuracy score says the accuracy score is 99 which is not right uh but if we look at the f1 score that's when we see that it's 76 percent so which is out of 10 times seven times you will be right three times you will be wrong i guess um and last bit is this is a confusion matrix um yeah people who work in the data science they know all of this i think what i understand from this confusion matrix matrix is um fraud and predicted so these 12 are the problematic one which is it in real in in reality 12 of the transactions were fraud but they were predicted as non-fraud so that is problematic uh and the number of true label which were fraud and were predicted as fraud was 69 of them number of non-fraud which were predicted as non-fraud is this that's fine number of non-fraud which were predicted as fraud is 30 so this is fine non-fraud if they are predicted as fraud that's still fine it's a it's a false positive kind of thing uh but but it's still okay you as a bank or as a financial institution you're not going to lose money on that but um or or as an individual you're not going to lose on that what you need to consider is this guy but these are all bonus stuff this is our model it's not perfect uh like we like i mentioned earlier this is not about the model it's more about the whole usability and looking at the futures and all of that anyway thank you thank you so much everyone for being with me here and um and um and doing this uh follow along with me hopefully you made it till the end and uh thank you take care


Senator Mitch McConnell returned to the campaign trail Thursday for the first time since the coronavirus pandemic began. McConnell made one stop in eastern Kentucky speaking with voters in Leslie County. Heading into the final weekend of the campaign, the Senate Majority Leader is confident that voters will send him back to Washington for a seventh term. The enthusiastic crowds that are coming out even the midst of rain and coronavirus certainly further convinced me that we're gonna have a happy day on Tuesday. But he is not as certain that he will return as Senate Majority Leader. He likens his party's chances of retaining control of the Senate and the White House to a coin flip. I think the president's in a close race and I think the majority in the Senate is going to be close as well. We've got hard fought contests all over America and I'd say it's 50-50. It's going to be a nail-biter all the way there. McConnell is continuing his campaign swing through western Kentucky on Friday.


Hi everyone, Sean with jaspyscasebreaks.com here doing a 2020 Maculet baseball pick your team for random number block randomizer for the Dodgers, Blue Jays, Red Sox, Phillies and what was intended to be the Washington Nationals but they were picked up right as we moved it over to a random number block. So in place of them we're also going to give away $80 in between the customers involved in this. So I'm going to randomize the list of customers names from Tyler down to Rob Runkle and the two-spot, last-spot mojo there as well as the numbers zero through nine. If anybody hasn't been a part of a random number block before basically what we do is we randomize zero through nine and with the customers in there for example spot three you would get any and all cards with the three to the left of the slash so thirteen of ninety nine three of twenty five forty three of forty nine ninety three of ninety nine and etc where the zero spot gets any and all redemption cards even known one of one redemptions in person one of ones will obviously go to the one spot. So let's do the randomizer and then after this we're going to go ahead and randomize that same list of names and give away $80 in credit. So here's the list of customers list of numbers and dice roll to see how many times we're going to randomize each list and it's a three and a five eight times one two three four five six seven and eighth and final time from Rhea down to Sean. Now we're going to do eight times on the numbers one two three four five six seven and eight final time from eight down to three it's a little bit easier to see so Rhea Mitchell with the eight Rob Brunkel with the six Tyler Brenner with the nine Rob Brunkel with the seven Oliver Soria with the two Robert Brunkel with the one and the five Kip Johnson with the zero Robert Brunkel with the four and Sean Maddock with the three so let's sort this by number. So Kip Johnson gets any and all redemptions even known one of one redemptions while Rob Brunkel will get any and all in person one of ones quick trade window not very often does this happen but I will allow it give it about ten seconds if there's any chat while I'm doing that I'm gonna go ahead and set up the randomizer for the eighty dollars in credit so it looks like everybody's okay with that so that's locked in now so let's go ahead and give away eighty dollars in credit same list from Tyler down to Rob Brunkel last spot new dice roll on this now we're going to give away eighty dollars in credit four and a three seven times whoever ends up in spot one at the end of seven times gets the eighty dollars in credit one two three four five six and seventh final time congrats Rob Brunkel you had the odds so you're gonna get another eighty dollars in credit back to you to the email that you used to purchase so Sean with jaspyscasebreaks.com pick your team for immaculate full case break coming up next we'll see


The CBS radio workshop dedicated to man's imagination the theater of the mind Tonight the eternal Joan The story of Jean Dark as provided by historians novelists and playwrights of different nationalities and points of view Narrated on tape by Lewis Cronenberger author playwright and critic and dramatized by Henry E. Fritch with Elspeth Eric as Joan Oh A saint this was the old market square in the French city of Rouen on the morning of the 30th of May 1431 The occasion was the burning of a young peasant girl not much past her 19th birthday Her name was Jeanette her family name was dark First without an apostate then with one after Joan had got her family a noble We now know her as John dark or in the English equivalent Joan of Arc Joan died a fearful death on that pleasant May morning more than five centuries ago She was tied to a wooden stake standing on a pile of kindling and firewood and slowly charred She had first been excommunicated by her church We having seen and weighed all there is to see and weigh Have said and decreed that in the simulation of your revelations and apparitions you have been pernicious seductive presumptuous of light belief rush superstitious a witch a Blast femur of God a despiser of him in his sacraments a Provaricator these are actual quotations from the official record of the trial of Joan of Arc before an ecclesiastical court at rule Very gravely at our faith and by this means having rashly trespassed against God in the holy church Therefore we denounced she was a rotten member which must be cast out and given over to the secular power The secular power to which Joan was given over by her own countrymen That is the English army of occupation did not even take the trouble to hold a trial or to make formal charges Her sentence took less than a minute What now priest are you going to keep us here to dinner? English soldier broke a stick in half and tied it together in the form of a cross Joan kissed the makeshift emblem and clutched it tightly to her body a Short time later. She was dead Yet only 25 years later Joan was cleared of all the accusations that have been brought against her and she was officially declared a saint in 1920 More than 40,000 statues have been erected to her in France alone plus many others in other lands including our own United States Why? Thousands of books and plays have been written about her in various languages Why? Here is our own Samuel Clemens Caesar carried conquest far But he did it with the trained and confident veterans of Rome and he was a trained soldier himself Napoleon swept away the disciplined armies of Europe But he also was a trained soldier and he began his work with Patriot battalions inspired by the miracle-working new breath of liberty Joan a mere child in years ignorant Unlettered a poor village girl Unknown and without influence Found a great nation lying in chains Helpless and hopeless under an alien domination Its king was cowed resigned and preparing to flee the country and Joan laid hands upon this nation this corpse and it rose and Followed her But this point alone is not enough to explain the universal appeal which Joan the maid has had for all the world Yes, even for the Marquesas Islanders in the Pacific Who to this day are convinced that the English ate Joan or else? Why would they have wanted to roast her there's little doubt about the bear facts since? Fortunately complete records of Joan's long trials have come down to us But for the touchstones of human understanding we have to turn to those who have tried to interpret Joan on the basis of individual insight Her historians novelists and playwrights of different nationalities and points of view I'll start at the beginning It's always nicer at the beginning I'll begin with my father's house when I was still very small I'm in the meadow now watching my sheep It's the first time I hear the voices after the evening Angelus That was the voice of Joan herself describing her first experience with the mystic powers that controlled the rest of her short life The scene is taken from the clay the lark written by Jean Annoui of France and adapted by Lillian Hellman. I Still wear my hair in a thick braid. I'm not thinking of anything.  I live here happy with my father my mother and my brothers and suddenly Someone behind me touched my shoulder. I know very well. There is no one behind me. I Turn There is a great Blinding light the voice is grave and sweet. I never heard that kind of voice before That day the voice only said Be a good girl Joan and go off into church Well, I was good and I did go off into church So I didn't understand why the voice spoke that way and I was frightened But I didn't tell anybody I don't know why the second time it was the noon Angeles Came over the Sun and was stronger than the Sun There he was I saw him an Angel in a beautiful white robe that must have been ironed by someone very careful He didn't tell me his name that day, but later I found out he was more senior the Blessed Saint Michael Joan Go to the aid of the King of France and give him back his kingdom You will go first to Monsieur de Baudrecourt. He will give you men's clothes and have you taken to the dofa St. Catherine St. Margaret will go along to help you The dofan of course was Charles son and presumably legal heir of the last French King Charles VI Charles the father was generally regarded as mad and had signed a treaty with England This avowing his son and recognizing Henry the fifth of England as his heir He had also seated all of France north of the River Roire, which gave the English justification for their French campaigns The dofan was weak Yet this was demand for whose coronation at Rance as Charles the seventh of France Joan was to give her life Her voices had told her that so Joan received her mission. She didn't leave her native. Don't rain me right away She was 17 before she felt ready to set out into the unknown world to help rescue France For a poetic view of Joan's farewell to her beloved countryside There is nobody better to turn to than that German romanticist to end all romanticists Friedrich Schiller and his play the maid of Orleans Well, you mountains your beloved glades alone in peaceful valleys Very well Through you Johanna never more may stray Johanna goes and there returns again Such is to me the spirits high behest To galls heroic sons deliverance brings Relieve beleaguered worlds and crown thy King And so instructed by a power higher than any earthly authority Jeanette reluctantly left her childhood home in Don Ray me to become the maid a few months later in Rance Joan saw her great dream come true The dofan her dofan was anointed with the heaven sent oil and formally crowned King Charles the seventh of France When Joan left home Papa Jacques and Mama Isabel knew only that their daughter was going to visit her favorite uncle in the village of Bury But Bury was near vocular the residents of Messiah Robert de Bodricure when the archangel had directed Joan to see as the first step in her mission The actual meeting when it took place may well have been one of the most hilarious interviews of all time Here is the version of Bernard Shaw whose place ain't Joan is generally regarded as the classic Interpretation of Joan's character and behavior the loud voice is that of the captain sire Robert de Bodricure But the first voice is that of his unhappy steward fresh out of eggs Sir, I tell you there are no eggs There will be none not if you kill me for it as long as the maid is at the door the maid What maid what are you talking about the girl from Lorraine sir from Don Ray me 30,000 thunders. I told you to throw her out You have 50 men at arms and a dozen lumps of able-bodied servants to carry out my orders. Are they afraid of her? She is so positive sir positive now see here. I'm going to throw you downstairs. No, sir, please Well, stop me by being positive. It's quite easy any sloth of a girl can do it sir Sir, you cannot get rid of her by throwing me out you see sir.  She really doesn't seem afraid of anything Perhaps you could frighten her sir perhaps Where is she now down in the courtyard sir talking to the soldiers as usual She shall talk to me a bit And up you there come up here you soldiers show other way and Shove her along quick. She wants to go and be a soldier herself.  Go back to him and tell him that I am neither duke nor peer at his orders I am Squire of Vodricor and I take no orders except from the king. Yes, that's all right My lord is the king of heaven. Why the girls mad. They all say I'm mad till I talk to them squire But you will see that it is the will of God that you were to do what he has put in my mind It is the will of God that I shall send you back to your father with orders to put you under lock and key and fresh the madness out of you Thank you will squire, but you'll find it all coming quite different You said you would not see me, but here I am now listen to me. I am going to a suit myself. Please do squire The horse will cost 16 francs. It's a good deal of money, but I can save it on the armor I shall not want many soldiers the dofile will give me all I need to raise the siege of all y'all to raise the siege of all Yes, that is what God is sending me to do. Well, I am damned no squire God is very merciful You will go to paradise and your name will be remembered as my first helper Joan got her horse her equipment and her escort But when her small party arrived at she know after a hard ride through 350 miles of winter landscape She's still faced her main problem How was she a poor illiterate country girl going to get an audience with the Dauphin himself? News of her arrival at she know finally reached even the ears of the Dauphin and Joan was in the end admitted to the court But to make sure that she was really divinely inspired Charles and his courtiers decided to play a trick on her Someone else would sit on the throne and pretend to be the Dauphin While Charles himself would hide in the crowd of attendants making himself inconspicuous Here are two versions of what happened when Joan entered the royal hall the chateau at she know The first is that of sure as Joan enters She surveys the resplendent hall and looks up at the army commander dune Wah who in this version is impersonating the Dauphin on his throne Dune Wah welcomes her now the wondrous me Dune Wah more Leon thou will tempt thy God This place abandoned which becomes the knot To this more mighty one the maid is sent Joan walks firmly toward the Dauphin who is peering from behind the back of a knight She kneels before him Charles is understandably surprised maiden Donner has seen my face before Whence hast thou this knowledge the I saw when none besides save God in heaven saw thee Be think the doffer in the bygone night when all around lay buried in deep sleep Thou from thy couch didst rise and offer up an earnest prayer to God disclosed to me my prayer and I shall doubt no more that God inspired thee God is pray that if there were appended in this crown unjust possession Or if heavy guilt occasioned this most lamentable war God would accept thee as a sacrifice Have mercy on thy people and pour forth upon thy head the chalice of his wrath Who art thou mighty one? Whence comes thou? Shall I indeed withstand mine enemies friends? I will lay submissive at thy feet and all he answers Thou will not be surrendered the law shall sooner run its waters back shall I in triumph enter Rass I through 10,000 foes will lead thee In the modern a new a helman play the doffer is no storybook king as the scene opens Charles is lolling indolently on his throne playing with one of those cup and ball toys that still delight children today Enter the Archbishop of Rass watch Bishop You have arrived just in time. I am on the point of governing. There is not time for gesture majesty We are faced with a dangerous problem of this peasant girl The people are in love with her they're convinced that God has sent her to you and that she alone can save France They think she works miracles. I have sympathy for them. They are as desperate as I am As for this girl. I have no curiosity about her. I Know too many people as it is and a messenger from God doesn't sound very amusing But I want to be a good king and please my people therefore I shall see this girl I think I might like to play a trick on her. Let's put a page upon the throne Let's clothe him in the royal doublet with the fewest patches He'll look better than I do and let us enjoy the sight of God's envoy pleading her cause to a page boy And so when Joan timidly enters the throne room, it's a young court attendant who is impersonating the doffer She sees through the deception. However, just as quickly she did in the case of Dunois in Schiller's play She surveys the crowd and will walk straight to Charles who tries to run from her. What do you want? No, but I am Joan the maid The king of heaven has sent me to tell you that you must be anointed and crowned in the city of France Well Well, that is splendid mademoiselle, but Rance is in the hands of the English as far as I know How shall we get there?  Tell me that I ought to be a great king Yes, Charles in the end Joan gets what she wants her army her appointment And a dazzling white suit of armor especially made for her Joan's effect upon the English troops who had been just about invincible until she appeared is described by Schiller The voices are those of English soldiers except for the rather heart-rending lament of their commander to John Talbot Impossible it cannot be how come she in the tank I threw the air the devil aided her why They heed me not They stain out at my call the sacred bonds of discipline are loosed Cannot rally in the smallest troop Wish she then be irresistible the dread inspiring goddess who does turn it once the tide of battle and Transform to lion's boulder heard of timid deer a woman snatched from me all Marshal fame Well as we know The army with which Joan took the field did liberate Orleans and within a few months had cleared the road to Rance So that the Dauphin could be properly consecrated King of France Joan's army then turned to liberate Paris, but the siege of Paris was called off Joan wounded for the third time in a year was captured some months later by Burgundian soldiers Who were then allied with the English the Burgundian soldier to the English command for 10,000 French pounds a Some normally paid only for the highest nobles The English in turn handed Joan over to a French ecclesiastical court convened in English occupied Rouen For trial on charges of sorcery witchcraft or anything else that would lead to excommunication The idea was that once Joan had been convicted on any such charge by her own countrymen The English would be free to condemn an executor for the damage. She had caused them and That's the way it worked out Joan Commonly called the maid Having been captured within our diocese of Beauvais and having been surrendered Dispatched given and delivered to us as a person vehemently suspected of heresy after weeks of badgering Joan exhausted and terrified of death by fire Finally put her mark to a paper which she was unable to read Admitting all the sins charged against her and for swearing them for the future She even resumed female clothing for a short time But when she found out that her promised acquittal would mean only life imprisonment on a diet of bread and water The bread of sorrow and the water of affliction as the judges put it She decided that she preferred a swifter death.  It is not the bread and water I fear Bread has no sorrow for me and water no affliction But to shut me from the light of the sky and the sight of the fields and flowers To chain my feet to make me breathe foul damp darkness and Keep me from everything that brings me back to the love of God. I could do without my war horse I could drag about in a skirt I could let the banners and the trumpets and the nights and soldiers pass me and leave me behind as they leave the other women If only I could still hear the wind and the trees Locks and the sunshine The young lambs crying through the healthy frost And the blessed church bells But without these things I cannot live What I am I will not denounce What I have done I will not deny and We still have from Joan what is perhaps the most touching answer ever given to a life-and-death question in the history of Inquisition Joan do you now believe yourself to be in a state of grace? If I am not May God put me there if I am may God so keep me that was Joan of Arc Did anyone ever understand her? Do we understand her today? Perhaps Sean on newy said it best you cannot explain Joan Anymore than you can explain the tiniest flower growing by the wayside There's just a little living flower That has always known ever since it was a microscopic seed How many petals it would have and how big they would grow? Exactly how blue its blue would be and how its delicate scent would be compounded There is just the phenomenon of Joan as there is the phenomenon of a daisy or the sky Or a bird What pretentious creatures men are if that is not enough for them You have been listening to the CBS radio workshop and the eternal Joan a treatment of the Joan of Arc story Written by Henry E. Fritch with Elspeth Eric as Joan and Louis Cronenberger drama critic for Time magazine as tape narrator The eternal Joan was produced and directed by Paul Roberts Music composed and conducted by Alexander Steinert Included in tonight's cast were Alan Hewitt as Baudrecourt and Jack Manning as the dofan Also heard were John Gibson Daniel Acoe Bob Dryden Louis Van Rooton Roger Decoven Ed Prentice Guy Rep Ellen Muir Gladys Holland and Ruth Tobin This is Bob Height inviting you to join us next week when from Hollywood We'll present a portrait of Paris a Word picture of the French capital recorded by David Shonebrunn chief of the Paris Bureau of CBS News America listens most to the CBS radio network


Madrid, by its position, is a natural fortress, and each day the people make its defenses more impregnable. Food for supper. Sometimes the food runs out before you reach the door. Sometimes a shell falls near the line, and at home they wait and wait, and no one brings back anything for supper. Before they enter the town, the enemy tries to destroy it. This is a man who had nothing to do with the war, a bookkeeper on his way to his office at eight o'clock in the morning. So now they take the bookkeeper away, but neither to his home nor to his office. The government urges all civilians to evacuate Madrid. Where can we live? What can we do for a living? I won't go, I'm too old, but we must keep the children off the streets, except when there is need to stand in line. Recruiting is speeded up by the bombardment. Every useless killing angers the people. Men from all businesses, professions and trades enlist in the republican army. In Valencia, the president. They attacked us with the people, ignoring the long struggle that the Spanish masses have followed against the tyranny, surprising their opposition to fascism as much as their great help to the capital, even in the smaller towns where, at this very moment, this work takes time for the new defense of Madrid. We already have the device that was bought with the money that we had left last year, and, of course, the disposition to work. We only need the cement, which will be here in a short time. Julian catches a ride on an empty truck and comes home sooner than he expected.


I will continue with the examples which some studies first what has been done in Dhruva but I'll also use examples from other sources. In these examples I tried to mix length scales as well as materials because both of them are of interest to various people. One is that from started from microscopic structure we are now moving on to mesoscopic structure and then mesoscopic structure in various materials are of interest to various group of researchers and also various industries. Last lecture I discussed with you some examples from micelles forming using surfactant molecules. I'll continue a little bit with that. I'll also talk about polymers and I'll also talk about and very interesting subject known as vortex plus lattice. So in these lectures I will mix up length scales, length scales and materials because sun's technique is of interest interest for materials and this lecture I'll try to cover a range of materials so that you will get a flavor of what can be done using sun's techniques. I will repeat with this figure small angle neutron scattering as I told you earlier with a little derivation that in the low Q range you have guinea f log p to the power minus q square rg square by 3 i0 that is intensity so log of intensity versus q square should be a straight line of course this depends if it is an rg square if it is not they can deviate from straight line but such low Q plots can give me the values of rg. This is in the guinea region and in the porous region when you go to slightly larger Q then the intensity is given by some constant I will not know divided by q to the power n depending on the fractal dimension of the material that you are using and it is surface structure and here it is q to the power 4 for perfectly perfectly spherical smooth surfaces but it can be other parts of q if you are using fractal materials and I will show you as an example what fractal material that we get and I've also talked to about fractals earlier that fractals are materials which have non integral dimensions instead of one two three like one dimensional two dimensional and three dimensional object fractal objects can have dimensions between one and two or two and three and why because the way they're embedded in space as I showed you as you reduce the length scales the number of scales required don't go as a power node and I took the example as one dimensional fractals and the coastline of India and showed you that as you go to shorter and shorter length scale your lengths become longer and longer faster than a power an integral power also small angle scattering in higher angles of the small angle scattering you can get structure from structure of the nano nano size in inhomogeneity that you are studying so interatomic structure will give similar to Brad Pitt or more precisely similar to what we saw and liquid and amorphous system you will get p that will tell you an average inter inhomogeneity distance and of course in all of these the x-axis is q vector all our structural works are q vectors and I must mention at this point before I miss it that in all this structural work we are considering coherent scattering cross-section and not incoherent scattering because incoherent scattering cross-section does not help me to get g of r or inter particle correlations but incoherent scattering cross-section can help in dynamics I'll come to that later but when I'm talking about all this structure was I will be talking about coherent coherent scattering length please remember this and I also showed you that in case of small angle neutron scattering you can make mixtures of d2o which has got six point something 10 to the power 10 per centimeter square which is the scattering length density and h2o has minus minus 0.57 into 10 to the power 10 per centimeter square and mixing the two d2o and h2o we can make a contrast with respect to the particle that was studied to our desirable values and we can sort of lighten up different parts of a particle so this gives the broad range of q values and the broad stuff kind of information that we get from this values now I have chosen a few examples I showed you micella structures earlier now I'll be talking about first about something called gemini surfactants very interesting as this simple sketch shows that micelles where as I talked to earlier micelles were having a head group which can be a cation anion or a neutral head group and a large tail group hydrocarbon hydrocarbon carbons so this large head group is generally positive or negative if it is then they will like to be actually like to face the water whereas the tail group which is making a made of hydrocarbon chains they will be hydrophobic and then I discussed with you then the simplest case they'll form a sphere with the heads looking outward and the tails looking inward and micelles are very important component in various chemical industries so made forming of science surfactants now I'm talking about one step ahead from this kind of single head group surfactants so these were designed by a specialist group at ISC Bangalore the experiments were done at Tuba so these are called gemini surfactants which has got two head groups two head groups connected by a chain so this is a my myself with multiple head groups and multiple tails so in this case there's a spacer layer and there are two head groups now question is the question was asked that how do they combine to form a material in the in an electric in a solvent something like a micelle so and there is a competition and that the studies used either a flexible chain so here the flexible chain is a chain of CH2 CH2 CH2 depending on the name is the number of chain length these flexible because you can bend it this chain can bend and here there's a benzoic ring which is stiff one you cannot bend it so basically these are flexible these are flexible head group sorry flexible spacer group is a rigid spacer group we form micelles using surfactants with two kinds of spaces and see the difference in the small-angle data here you can see that when we have d sigma by d omega these by 1 by q square fitting for when the space group is free when the spacer group has four chains then it is 1 by q whenever five chains it is zero slope at the low q this is because the confirmation of the surfactant when it forms micelles is changing because it can bend so this gives us a lot of flexibility in forming structures by playing with the length of the head group and that is evident from the low q data at the same time whenever rigid spacer group we do have a structure which is vesicular a vesicle is basically a micelle but forming the bilayers so there can be bilayers or there can be more layer number of layers so in a case of bilayers actually it can be like this the head groups that micelle comes here head group in the present case because there are multiple head groups so two of them will be there let me just be a little more clear about it so you have bilayers means this is one layer this is the inner layer so these will have the micelles stacked up and this is called a vesicle and here in this vesicle you have two head groups coming here also you have two head groups coming and in between you have got back gap where the solvent goes in this called a vesicle and in case of rigid spacer layer we find that it's a vesicular structure this is the structure which is preferred unlike a flexible chain connecting the two and this vesicle structure is more clear if you see this what I was talking to you about they shown as the part over here at the head groups these are the head groups from the surface layers here and here and in between the tails are in this vesicle and there is an inter particle distance because this the circle inside circle inside circle you have got a first layer second layer third layer and you have got a coherent small peak so we have multi laminar vesicle where it is one sphere inside another inside another and you can make out the shell thickness from this small peak that we see around 0.1 to 0.5 angstrom inverse in this sounds data so this is one example this is a very fine work where the gemini surfactants are made and their structure in the micelles were studied using small angle neutron scattering the similar studies are possible if you are interested in the structure of this kind of in homogenities in a solution then I can talk about multi-headed surfactants the previous examples were two surfactants connected by a chain but here a single tail and there are multiple chains multiple head groups the tail is like here it is C14 H29 so it's a long tail but you have got now two head groups so here there are three head groups so we can make such surfactant molecules where the hydrophobic tail is one hydrophobic tail is one but connect to multiple head groups I'm simplifying it for your understanding so there are multiple head groups I can have one head group I can have two head groups or I can even have three head groups and let us see what we get when he tried to form micella structure with this head groups so this is a schematic of the structure that it will it might form of course it is shown in two dimension in reality it will be in three dimensions so when you have h equal to one the tails are stretched and you can see depending on the tail length that means approximately twice the tail length you have got a circular structure actually in case of three dimension it will be a spherical structure but when you have h equal to two I am showing myself with two head groups you can see the two head groups they're repelling each other because the head groups have same charge and they're repelling each other so when they repel each other then this association is difficult to be made because they'll try to be away from each other and the tails will have a hydrophobic interaction which will tell them to be close to each other so the head groups try to go away and the tail groups try to come in together and same thing so they start bending the tail start bending so if these two head groups have to be away but the tail is there and there is one more one one head group and two head groups they have one tail there is one more has got tails now these tails they try to come to close to each other so they like to bend and get entangled in the in this place inside the inside the myself inside the myself and this entanglement increases as you increase the number of head groups so the coordination number decreases and the entanglement increases because they have to fill up this space not allow any water to come in at the same time the repulsion between the head groups have to be respected and then the data I show you here you see so this is the small-angle neutroscating data so it has got two parts one is the form factor per one is the structure factor per the structure factor is that gives which that which gives us the interparticle distance and the peak comes from there now you can see as a peak is moving out that means the size is becoming smaller so that means with increasing number of head groups the myself becomes smaller that is one indication because the peak is moving outside outward also by fitting this slope we can find out that number of coordination of such a suffix turns in a myself excuse me under the assumption this is a proleth spheroid we find that for one the coordination number is 244 for two the coordination number is 48 as I mentioned to you earlier and for three the coordination number is 20 and also you can see the A and B values actually this is a semi major axis and B equal to say the semi minor axis so it's a proleth spheroid B equal to C A so you can see under the assumption that the myself it takes a proleth spheroid shape we find the parameters for the proleth spheroid these are all in angstroms so I want to point it out to you that you see I'm dealing with length scales which are much larger than crystallographic length scales but I'm able to predict interparticle distance like I did in case of atomic liquids and molecular liquids for liquid and amorphous diffraction I'm able to find out the interparticle distances from there I can find out the particle size by finding a fitting the models for a spheroid so this is a finding in sans where I can find out the shape of the particles and also they are interparticle distance in a sans experiment this was also an example from what we did at Dhruva now I will go ahead with the example of sorry surfactant and protein interaction now again I just very quickly mentioned that pq is the structure form factor and sq is the structure factor why you have the dimension D of the fractal dimension so you cannot don't pay too much attention in this expression except that you have a form factor and a structure factor part here the form factor I have derived it earlier for you for a sphere and our P is the radius of variation for a particle and this expression it gives me the structure factor as a power of q d where D is the dimension it can be a factor dimension for the object so these are the two things we must pay attention to in this expression so I'm talking about surfactant induced protein unfolding now proteins proteins are important for all the fun for most of the functions in our body and proteins are actually they made of amino acids the list is limited amino acids amino acids but with various combination of amino acids the proteins they form helixes and beta sheets beta sheets and helixes and they fold up the protein folding is an important aspect of proteins activity so the protein are made of alpha helixes and beta sheets so you have parts of the protein which will be like this possibly like this and also there are helixes and sheets that they fold up in its environment in whichever environment you are putting them so depending on the environment protein folding changes and proteins activity also changes because it has lots of charge size now in this example the interaction between protein and surfactants have been studied in a solution so I just give an example this is bovine serum albumin is a protein and SDS is a micelle and the interaction between the two and you can see that when we add this SDS to the solution the protein this this background chain is protein this background chain is protein and there are these small spots where inside which because of the charge and the protein we have this kind of micelles for me so very interesting system interesting biologically and also chemically that the proteins are interacting with the micelle that has been formed using SDS surfactants and with the percentage of BSA and SDS we can see that the this from this results that the structure of the micelle changes and again in this experiment the protein we call it a necklace and the bead arrangement so as if the protein falls the background the chain of the necklace and this micelles they're forming beads on them on a very very simplistic model and you can see that these experiments with and their contrast match so you can see the protein or you can see the micelle found with the SDS SDS surfactants and by contrast matching the background which is an H2O and D2O mixture and also we can use hydrogenated SDS and D2O hydrogenated SDS so what we find here that with the SDS concentration it's in millimolar solution millimole you can see that the structure of the micelle changes not too large but they're almost similar but marginally different but we can find out and when this unfolds that means the protein can unfolds along with the micelles then there's a drastic drop in the radius of gyration but with unfolding the radius of gyration increases so this is one interesting aspect of protein and surfactant interaction in a solution and it's a study using small angle neutroscate so small angle neutroscating I have used examples from organic chemistry I have used example from biology so it can really integrate studies in this whole range when you are okay with the length scale that you can study this is 70 angstrom 70 angstrom 70 angstrom you can see these are 70 to 80 to 90 angstrom size semi-major axis semi-minor axis around 20 angstrom then unfolding unfolded structure this is folded structure when it is folded then what is it and when it is unfolding you get a radius of gyration that means the micellar arrangement is becoming larger that's what this part indicates that the radius of gyration for the micelle is increasing when you unfold the protein structure and unfolding depends on the strength of the SDS surfactant so up to 20 millimolar concentration the structure remains folded the protein remains folded and beyond that the protein starts unfolding from this point onwards and we have found out the radius of gyration of the micelles in this unfolded protein structure and we have done the experiments with 1 weight percent of BSA protein which is bromine serum albumin and with the hydrogenated and deuterated SDS micelle this is also a struggle done on the slit and velocity selector based small angle neutroscating machine in Dhruva and this is a statement of the beat necklace structure protein and the surfactant and the fractal dimensions now this SDS micelles as they go to higher and higher concentration as the chain becomes more and more linear you can see the fractal dimension actually the fractal dimension should have been three if it is a three-dimensional structure three-dimensional but it is never so because you can see the starting from 20 millimolar the fractal dimensions 2.27 so this structure it as it becomes more and more linear you can see the fractal dimension goes towards one but it is not one it is 1.71 so fractal dimension is 2.27 it is just a from three-dimensional structure to it goes to a linear value and of course the micelle radius remains almost fixed for the this is the radii this is the radii of micelle radii which is sitting in pockets in the unfolded chain but the unfolding is like the coastline of India that I discussed to you discussed with you so this protein can be unfolding like this like this and then from there it might go to more and more linear chains and when it goes to more and more linear values then you can the fractal dimension because now the how the surfactants attach to the background protein structure dictates how the fractal dimension will be coming out and also the number of micelles and the aggregation number that you can see so here I complete my discussion on what we did on surfactants to protein unfolding now question is that we can expand the length scale using m-sans which is medium resolution m-sans is medium resolution sans that is m-sans and we can also add up sans with small angle x-ray scattering or sacks machine this is the very interesting observation that you can have data from sans you can also data from sacks if your sample allows not that all the samples will allow you to get small angle x-ray scattering data for example the example that I use so far are deteriorated or hydrogenated samples and they cannot use cannot be used for small angle x-ray scattering so I will be using some examples where m-sans and small angle x-ray scattering can be added and we can get data over larger curing in the next module or next part of the talk


Hi everyone shot with Jasper's case breaks calm here doing a random number block randomizer for the Eagles Packers Cowboys Washington football team Raiders Colts and the Rams for 2020 flawless two box case pick your team number 21 Coming up next So every five gets a random number for the break zero through nine For example, if you get the number three you'll get all cards with three being the first number next to the slash on the left side of The slash so if the card is numbered to 43 out of 299 1399 3 out of 25 43 out of 49 93 out of 99 And so on the number three with the card zero will get any and all redemptions for the teams including one-of-one redemptions One will still get any live one-of-one pulse So here's a list of customers from Andrew Rock down the PJ McDeley and the numbers from one through zero Here is our spreadsheet that will keep track of everything on there and the dice roll for both lists Two and a three five times. So let's randomize it and match them up Fifth and final time from Andrew Roth down to Matthew sure and five times on the numbers Fifth and final time from four down to six Five times on the numbers five times on the names and five times on the dice So Andrew Roth Roth with four PJ with zero Matthew with nine Chad with five Matthew with seven PJ with eight Matthew with two three one and six So let's sort that by number number not name There you go PJ with zero would get any and all redemptions for the Eagles Packers Cowboys Washington football team Raiders Colts and Rams Even known one-of-one redemptions while Matthew with the one-spot would get any and all in-person one-of-ones And there you go guys. That's the random number block randomizer for the Eagles Packers Cowboys Washington Raiders Colts Rams and For 2020 flawless to box case picker team number 21 coming up next. I'm Sean Josh these case breaks calm. Thanks for hanging out See you next time


אוקיי, תודה רבה, מיידי. עכשיו אנחנו נתתתלנו על הלב-אי-קל-אופורטיינס, של פרקות נטב-אי-קל-אופורטיינס, שערעת על מולטי-ריג'ן ומולטי-קלאב-אי-אנווירמנט. אני נוי, אני אצל ה-רנדי-דרקטורים של קלאבנט, בבסינסי-אי-אנו-אי-קטל-אוסנט. כאן, אני נוא, לימור ומחל, סינירוספטורים של תאמן. ת uda villagers��� nosotrosרמי כבשטח mounting אני נתתלמיד על הפבה에서도 של הלב-אי-קלאב-אי-קלאב-אieten שב pine-oni разные פרקות, opportunity counlaş, seperti אי-קלאב-אי-קלאב, אם עבלaaa appeared,鍵ו, אז תודה רבה 12OO14 תודה רבה 12OO12 ävenounterate pages. וכך, משוסעיPLYCHVR 2002 בלמょfalSurfeld SuperGRB extensive mature PT ponieważ, אין איך Township זה עקבות נתנו כמה finely אוכן, מffe תקדימה על מה אנחנו צריכים להקראת כל הדברים. אז קלודבנט הוא סוגי-אונטר בן-אלכתלוסנט. אלכתלוסנט תקדימה סולטיין לנפבי נציגי. אנחנו תקדימה נפבי האנפסטרקטי, נפבי אוכסטרשן, סדן סולטיין, ולבדות פרקות אונטרו-לטרוק שיכולה להתקדימה על כל כלבים. קלאדבן הוא איזה סיימצג, שבאמצג 4 שנה, והוא פוקוס על קלאדבן פלטפורמי, שבאדרס את ה-NFV נדס. We have a strategic partnership with infrastructure vendors like HP, Intel and more. We have an ecosystem with more than 50 vendors that onboard virtual network functions, testing them, making sure they are working properly. We are active players in the open source area. We are contributing in OpenStack, in Oasis and other area, and it keeps us open as we can, and we stay with it up to date with the latest features we have. We have in our business unit around 150 engineers, and we are located in Israel. So that's it with the marketing staff to give you context. Now let's dive into the challenges and the solutions that we provide. Okay, so every one of us relies on telco applications to make him constantly reachable and accessible. We've grown used to the fact that we can call whenever we want, whomever we want, wherever it may be. But think about what this entails. Think about the order of a million of human work hours that was dedicated to make these applications as reliable, as dependable as they are today. But all of this work was dedicated to make this application in a physical boxed world. As we can see here, every telco application in its physical form was made in a special box which contained both the software and hardware. The software was written and ran on very closely integrated with the dedicated and expensive hardware. This fact made the service providers have to think a lot of times when, for example, for a service provider, when he has to scale an application, he has to anticipate ahead of time how many of these boxes does he need to make this scale cost-effective. Since every one of these boxes, of course, had a financial cost for the hardware itself, and on top of that, you need a person, a technician who specializes specifically in this box to come install it and later on maintain it through the application lifetime. So this thing is what's causing now this NFV transition we're seeing. So every one of the, all the service providers are trying to move forward and make the VNFs they run, sorry, to make them virtual VNFs, and all this while keeping them at the quality they have today. So they're trying to redeploy all of these services on a cloud architecture which will enable them to run on a common IS layer with shared compute network and storage resources. On top of that, we're going to also have a management layer to enable all of these VNFs to have lifecycle steps run automatically and generically. So when we're talking about application lifecycle management, there are a few steps that we're familiar with, of course. The first step is onboarding the application and testing it. After that, we go to deploying the application which should be on a cloud application as automatic as possible. Later on, once the application is running, we need to scale it, maintain it, and heal it all the time. This is done while we constantly monitor the application in all the layers we can to make us know constantly what its real state. And of course, later on, we terminate it. So today we're going to focus on four main steps here. The first one is deploy, scale, monitor, and heal. And I'm going to start with the deploy phase. So when we look at deploying a VNF, the main problem we have, the main requirement we have, is the distribution. Since when you talk about VNFs to keep their service level agreements, they have to be very massively distributed. For comparison, if you look at the Amazon IT Cloud, they have nine data centers all over the world which shows all of their applications. But the telco applications in their physical form, some of them had to be deployed with an instance on every city they operated in. So to get this in OpenStack, we have three options we can look at. We have the single-region option which is a local data center, so not a lot of distribution there. We have the multi-region option which has multiple regions all managed by the same keystone for authentication and have the same horizon. This wringles a lot closer to the deployment requirements we want. But there are still some limitations here. For example, the first one is that keystone, when we want to run it in HA on different nodes, might become a problem. The second one is that if we want to actually have a very massively distributed application, this means that we usually would like a policy placement engine that will tell us how can we optimally deploy it, on which node should we put each resource and not decide it on our own. So what we're doing in CloudBend is that we have something a little different here. We have on every data center a separate instance of OpenStack and we've developed a top-level management layer which can look at it from the top level and see all of the nodes and all of the resources and once the person wants to deploy the application, he can tell it how will he want to divide it and then it will tell him where is the best data centers to deploy the resources. So we have the management layer on the other options as well but this is where it comes really important.  So I'm just going to log in to the screen here. This is the CloudBend GUI. So I'm going to, this application was already on boarders. So I'm going to walk into the catalog here and we're going to see it in a second. So this is an application of VNF demo. That's what I call it. It has free servers and each server has two storage. We're going to see all of this soon. I'm choosing here the networks and the images. You can see here in Keeper's, they're all distributed through all the data centers and I'm going down to the segments section where I'm going to have it distributed. So I'm going to add a segment here. The first segment is going to be on CloudBend in Vancouver right here and I'm going to add another one and it's going to be in Tel Aviv and we're going to divide here the tiers of the application between the segments. So we're going to choose 100% because we have one server per tier here so it doesn't really matter but you can do it in whatever percentage you want and this enables you to have an application run in HA on different nodes. So it's useful. So I'm going to deploy it now. So it's going to deploy. We're creating an open stack while we speak and we're going to see it later when it finishes. I just want to show you here this shows the topology of the application. I'm going to expand it. So you see here that we have three tiers. Each of them has one server and two volumes attached to it and you can see down below on the left side that we have two of them on Tel Aviv and one of them in Vancouver that's the distribution view. So let's go forward. We'll come back to that later. Sorry. Let's talk about scanning. Well, I already mentioned the virtual network functions or internalization space between the physical world and the virtual one. As a result of this phase can you hear me? As a result from this phase we still have some legacy code that was running on a dedicated hardware that now is imported to the cloud. Sorry. This legacy code relies on a specified physical configuration and as a result both the scale in and scale out are relying on restricted rules. For example the next VM in a scanning group can be different from the previous one. It can acquire different image. It can acquire different configuration. Furthermore another critical requirement is controlling the scaling order or sequence. Achieving those capabilities and OpenStack today is very challenging because he do not support distinguishing between different resources in a scanning group or controlling the the sequence scaling. To solve this problem in Cloudman we are today using stack update and resource scope. It allows to control both the parameters of instance in a scanning group but it also means we are not using scaling group and we are not enjoying all that it gives. We cannot enjoy, for example, the auto-scaling on a silometer alert and sometimes update stack can take a long time and it will affect the time of a simple scaling event. Moving forward we are now presenting a set of blueprints to give these capabilities to hit. These blueprints include features like adding index and parameters to adding index and parameters to the scaling group. Who wants to talk about monitoring? Yay, monitoring! We are now going to talk about monitoring especially finding the fault monitoring. When talking about monitoring there are two fundamental issues. One, time is money. I want to find and I want to fix the problem as fast as I can. The second one at the end of the day every cloud application is sitting on a bare metal note. That means that we have actually three layers of monitoring. The physical layer, the virtual layer and the application layer. Each layer can generate tons of alert and one system event can generate a lot of events on different layers of monitoring. I know that knowledge is power and sometimes I need every bit of information to solve the problem but too many data can create a chaos and hide the real problem. For example, if I have a bunch of alerts on VMs being done I can miss the one alert about the host being done and not understanding the relationship between the host and the VMs will affect the way of finding the problem. For monitoring the different layer we have a different tool. We have Nagios and Gandlia for the physical layer. We have Sinometer for the virtual one and we have Monaska and different built-in tools for the application. Cloudmen collect all the alerts and analyze and virtualize the relationships between the different layer and the correlations between the alert. You can see over there on the top the assemblers view of the relationship between the different layers. Every data center is actually a slide on top of that you have the host, availability zone and the VMs. Here you can see the connection between the different alerts. We can see here that we have here, it starts from here, you have memory load that leads you to a host fall down which creates a lot of VM alerts which will affect my tier and eventually will make a failure in my application. So in continuous to root cost analysis on monitoring that we more just discussed once a problem has been detected it has to be healed by the management layer. In our case we are talking about cloudband. It doesn't matter if the problem already affected the applicative layer or it will affect it soon. Let's take one example of a problem that is about to affect the applicative layer but hasn't yet. Imagine a fan is malfunctioning on a physical host. What is likely to happen is that the host will overheat and shut down killing all the VM hosted on that host. A healing solution that is favorable for such a problem is migrating all the VM to a different host. The placement of such a host cannot be chosen arbitrarily. It has to comply with certain SLAs agreed upon between the stale co-vender and his customer the service provider. The SLAs is service level agreement. Let me show you now. Such a SLA can include as specified here zero downtime or data backups to make sure as smooth as possible and continuous of service to the customer. For example, let's take a service where it's very important. Imagine you are making an emergency 911 call and your call doesn't get through because the service is undergoing migration. Clearly such a behavior is unacceptable for this kind of service. Healing challenges. So today OpenStack lacks sufficient support for automatic healing. One of the reasons for this is that sometimes healing flows are very complex. If we take the migration example we can make it even more complicated by adding a demand for all applicative processes on the hosted VM as much as possible being gracefully shut down. Another requirement can involve reservation of IPs or reattachment of block storage. In a distributed environment there are even more challenges. The distances between the original placement of the host and the target location can be very big and that can be problematic if you want that process of their healing to happen fast enough. So the way you can help the process to be fast is doing some preparation steps in advance. Examples that we are doing specifically in Cloudband as you might have noticed from what Noah said we distribute all images in advance and even we do it very useful for the user, it can deploy the image in either OpenStack node and we distribute it from there all over the system. It doesn't have to go through our management layer. Another example is the creation of virtual networks. So a deeper understanding of what the problem is will always give you a more optimal solution. Clearly, migration isn't always a solution and another favorable approach is redeploying the resources somewhere else. To make it more optimal you can use tools such as LIMOR describe for root cause analysis because always when you know what the problem is you're more likely to find a better quicker, more optimal adapted solution that will fix your problem better. If you are lacking right now the option to find the root cause of your problem there is another approach that is based on gradual treatment process that tries different healing solution in escalating severity. This is an example here. Once a problem had been detected you can go to your lightest solution possible that is quicker. So this is an example for such a flow for an application. If you find a problem in your applicative layer or somewhere you don't know, you can start with applicative layer and simply restart the process. That will solve your problem if it was a software problem like a deadlock that was pushed in by some careless developer. If that worked wonderful, if it didn't you can go on to the next solution in a higher severity. You can try to reboot the VM. If that worked excellent, if it didn't you can try redeploying the VM somewhere else and if all else failed you can go to the old world approach and just call a human. So what we are going to show in our demo very soon is a solution based on two open stack tools Convergence and Mistral. Convergence is a set of blueprints in heat project that is aimed towards fixing a stack whenever it is different from its template for some reason usually a failure like if a service is in error and it should be up by its template because obviously you don't want to deploy a server in error then it should fix it in some way and get it back up. In Kilo the first phase was already pushed and today it doesn't include any fixing capabilities it only has the option to do an API call of check stack and if the stack is different from its template change the stack state. Mistral is the workflow engine of open stack it allows you to write a complex healing flows that fits your application as best and possible. So let's see them Mistral workflow this is our Mistral workflow we written specifically for this demo what we are doing is simply check in every minute if the stack is okay and if it's not okay we will update the stack with update stack which is an API in heat we check if it's okay with the convergence tool that was described so the demo will shortly start and I will explain you the rules and you can scan around. So what we are going to do now is demonstrate how we automatically heal the application so I need the audience assistance here I need everyone to take out their cell phones and go open the browser and go to this address you will be asked to enter your name and company name and then you will get the game view where you will have to hit on those small linux pingwins every time a blue one comes out you need to hit him on the head every pingwin represents a VM or a volume in our system you will see it live happening we have a scoreboard and the winner will get north face fleas with the cloud bin log on it so I will give you 20 minutes to go inside and I am switching to the scoreboard view with the runtime view 20 seconds sorry I will show you again you can start play by the way I think you can already ok now we can see the players I will maximize the screen ok so ok you can see on the scoreboard you can see David from cloud band ok we are not giving prices to cloud bands so make sure you are not winning we have second place so the minute one of the VMs or the volumes will get 500 hits sorry 200 hits in total it will go down and then we will see the mistral workflow automatically heal the VM or the volume so I think it will take like another half a minute one of the volumes has already 150 hits ok game over so we have a winner Dawn from Sandvim who is it? come to the stage we will give you the freeze code you can see in the meanwhile that the volume number 3 over here was hit you can see it turned yellow now the workflow engine runs on the background now it became red it detached and destroyed the volume and in a minute the workflow will heal it create a new volume attach it to the VM so it takes like between one minute to one and a half minutes you can come to the stage I will give you the freeze in the meanwhile so as we said earlier there is a coin job of Mistral that what he is actually doing is checking the stack in the background and if it recognizes a failure it runs an update stack the update stack actually fix the problem in the topology and create a new volume so sometimes it takes more than a minute on the way try to refresh maybe ok and the new volume became live you can see a new volume was added to the system and was connected to the server and the system can continue to run so thank you very much so just to conclude we walked through the operations of virtual network function we talked about the requirements and the challenges that exist today in OpenStack and how we address them around deployment scaling monitoring and healing we have a follow up session 10 minutes from now about virtual EPC the challenges around there and how we handle root cause analysis and monitoring so you are welcome to stay yeah and for questions if anyone has a question feel free to ask so you want to answer that the problem wasn't the different component the problem was that we need the different parameters or configuration in each one it can be like two VMs that have the same image or something but they need different configuration so virtual network function usually a scaling group doesn't really represent the virtual scaling group because the fifth VM is not necessarily similar to the fourth VM it might include different image different configuration that's why the use of today's scaling group is not address this need so we as limo mentioned solving that by working with a resource group or even with workarounds like update stack preparing in advance the scaling options and doing update stacks and we have two blueprints that are now in progress for adding those capabilities to the scaling group okay thank you very much