All right, so welcome back everyone. This is session room number four and We have Yakub here with us Yakub Schultz is a principal software engineer in Red Hat and the topic will be build your own social media analytics with Apache Okay, Yakub, the floor is yours Thanks for the introduction and yeah, welcome everyone to my talk about building social media analytics with Apache Kafka You already heard most of the introduction what I really do as my main work is I work with Kafka and with the project called Strimsy, which is about running Apache Kafka on On Kubernetes and that's really how I came up with this talk because these are the things I spent a lot of time with What I would really Love you to take away from this talk is that Yeah, you can do quite a lot of nice and cool stuff with Apache Kafka and Something like Twitter, but I also want you to take away that Apache Kafka is more than just the messaging broker which probably everyone heard about and everyone knows and that It makes it really easy to build fairly complex Applications without too much code and without too much effort, but don't worry. There will be some code as well and What's what's really important to understand is that? Apache Kafka, it's fairly big ecosystem over different tools and and parts and some of these are directly in the Apache Kafka project itself That's of course the the Kafka brokers But it's also the connect API for integration with other systems There is a tool called mirror maker for mirroring data between the Kafka clusters There's also the Java clients for consuming and producing messages From Java, there's the streams API for streams processing and there are some other and smaller components as well, but then what really Then shines is that there's a huge amount of different tools and components which is outside Apache Kafka project itself and that's for example all the different connectors for for the Kafka connect Framework, but it's also things like schema registries, of course clients for for other programming languages There are operators for running it on on Kubernetes such as the stream Z project I work on there are all kind of tools for stream processing ETL processing for for artificial intelligence or machine learning which integrate very well with Kafka There are different UIs for monitoring or managing the different Kafka components, so there's really a huge amount of things you can choose a bit and just by kind of Reusing them and building your applications together from them. You can do quite a lot there's also this this idea that Kafka isn't really messaging, but it's something called even streaming platform and Yeah, to be honest even streaming platform that sounds like a bit random buzzwords put together And actually if you would Google it there will be probably many different definitions But one of the definitions which I like quite a lot is that something would cause itself even streaming platform should Handle several different capabilities. It should be able to import the events from some other systems from some other platforms It should be able to store these events and then Distribute them to the applications which are interested in them It should be able to process these events that can mean all kind of different things from some transformations and Richmond and Doing some actions based on these events and so on and then at the end quite often you would want again to export the events or the results of these events to some other systems and Kafka does this really well the import and export part. That's what the connect API handles The Kafka brokers They are what handles the storage and The distribution of the events or of the messages and then the streams API and that's what you can use for processing The events together with all kind of different clients, of course so yeah, Kafka definitely matched this definition of events streaming platform and What we will really do in this talk and in the demos I will show later is We will start with with Twitter and We will be basically reading the events from Twitter and in this case the events They will be basically the tweets published by different people and we will use the Kafka connect framework together with something called Apache Camo connectors to get the tweets from from the Twitter itself and Get them into our Kafka brokers as a message into some topic and then we will use the streams API to do different kinds of analytics and processing on them and Just for fun, but also because yeah, that's really what I spent Most of the time on we will do all of this on top of Kubernetes So let's have a look in a bit more detail into the Into the different parts of this. So let's start with the Twitter Twitter, of course If you use Twitter then in most cases you probably use it from some browser application or on your smartphone or iPad But of course, there is some API which is in the background which allows you to kind of talk with the Twitter servers and ask for the tweets on your timeline or the retweets and likes or direct messages or search for some keywords and There is a version of this API which is available for free it has all kind of Rate limits of how often you can use the different APIs and how often you can do something But the free version is what I use in this In this demo and at the end I will share with you also the link to the GitHub repository with all the source code So if you want to if you want to try this what I'm doing here, you can really just use the free account free developer account for the Twitter APIs and Repeat everything you don't need any special kind of kind of access to to Twitter to to repeat this demos at home and Then the Kafka connect that's part of the Apache Kafka project But it's a standalone component People sometimes think that the Kafka connect and the connectors they are running as part of the Kafka brokers But it's not the case. It's really a separate process separate Java virtual machine which runs the Kafka connect application and The Kafka connect or the connect API That's what's really used to get data from other systems into Kafka or the other way around from Kafka into other systems and the Apache Kafka project itself really contains just some very simple more less example connectors and really what it works with is Connector plugins basically developed by different third-party developers and companies which provide integrations With the different systems platforms and applications So if you want to integrate with something what's fairly common and what others are using it's really quite likely there is already a connector plugin which exists and You have of course also the option to write a custom connectors either for your own systems or For something what nobody used it and the connectors They are always distinguished into source and sync connectors the source connectors They are always used to get data from outside into Kafka and the sync connectors They get the data from Kafka to the to the outside so to some other system and Then in my particular example and demos I will use the connectors which are part of the Apache Camel project I guess everyone probably heard about Apache Camel It's one of the biggest Apache software foundation projects, which has several hundred of different integrations So kind of ways how to connect with some system or platform and exchange data with it And you can use it in many different ways, I think there were some Other talks here at Defconn about something called Camel K for example Which is more about serverless and using it directly, but I will use it in the form of the connector for for Kafka connect and in particular I will use three different connectors which Then leverage the Twitter API and it's a timeline search and direct messages connectors Then Kafka brokers They will be there in the demo as well They are kind of the central part of every Kafka architecture And they are responsible for Distributing the messages from producers to the consumers, but They basically decouple them and the messages are also stored in the Kafka brokers And if you want then you can really store them there for a very long time It's not like they are stored for a few seconds and then pass to the consumer They can be stored for years if you want And the brokers it's also what allows Architectures based on Apache Kafka to provide high availability and And scalability as well. What is what is good to understand is that I Talk here or I already talked about the connect Parts I will talk a bit more about the streams API While this seemed like a separate component inside of them, there's always Kafka consumer and producers So when they connect to the Kafka brokers The broker doesn't really they don't have some special connections some special protocol They are really just the same as any other clients and they Use the same same protocol and the same technology and then Once we have the the tweets the events in the Kafka brokers We can do the processing and what I will use for that will be the streams API Which is really just a library which you can include into your own Java applications. It's not some complicated framework with some workers and some jobs and some Scheduling of the jobs to the workers through the processing. It's really just a jar which you add to your application And you can just use it from there So it doesn't matter what kind of framework you are using or whether you just write your own main functions directly You can always use the streams API and despite it being this kind of simple It has really quite a lot of functionality. It can do all kind of stateless and stateful operations It can do transformations. It can do aggregations. It can do joins. It can do windowing where you kind of process only some windows of the data and the whole thing is also scalable In the in the demos here I will be mostly working with just my Twitter Timeline which isn't that big and it's not really million messages per second But everything I will be showing it really is scalable and if you would want if you would have the use case for it And of course the hardware then yeah, you can really scale it You can run it in many instances in parallel in many replicas and you can get quite a big performance Out of it if you want and then so I said that the streams API you can basically use it with any kind of framework or However, we want in Java I in particular will use it with something called quarkus Which is a framework or Java stack if you want Which is designed for cloud native deployment. So during the design there was a lot of fold put on how to make it start up very quickly and to have small memory footprint and It supports things like native compilation so that you get just one native executable instead of kind of of these Different lib directories with 10s or hundreds of different jars. So that's all Super nice and it makes it quite easy to run these things on on Kubernetes So that's why I'm using it and it also has a has great support for the Kafka consumers Producers and for the streams API. So I of course leverage it as well But everything what I will be showing later in the source code That can be really reproduced just with the streams API itself You do not really need the quarkus for that. So if you don't use that and use something else you can still do the same and Then in one of the demos I will also do a bit of a machine learning and for that I will use this deep Java Library, which is a Java project which builds on top of some other projects such as PyTorch Apache mx.net or TensorFlow and it can be used for for machine learning and deep learning So you can really use it for things like image classification object detection sentiment analysis And to be honest, I'm not the biggest expert on the things like machine learning and artificial intelligence and so on But if you want to start with it, if you want to at least play with it a bit this is a great project which has nice examples and it has kind of this library of pre-trained models which you can easily use and Yeah, that's why I'm using it in this talk as well And then of course to run the whole thing on Kubernetes I will use the Strimsy project which provides the operators for running Apache Kafka on Kubernetes and it tries to make it super easy to deploy Kafka on on cube and it supports all kind of components including Kafka connect and connectors and And all the things which we will be doing today Okay, so That's more or less the theoretical introduction Into the different things I will be doing and different projects I will be using now before we actually do something we will need to deploy kind of the backbone We will need to deploy the Kafka cluster with the brokers and and the Kafka connect so let's have a quick look at how you can do that and To be honest, I have it already deployed here and running and that's not necessarily because I Will be afraid that it would not work in a live demo or it would take long time It I'm quite confident that it would work and that it would be fairly quick But I have it already deployed for around a month I think since late Christmas or beginning of January so that it can collect all the different tweets from my timeline During all this time and we have a bit more data to analyze because if I would just deploy it right now Then we would need to rely on my timeline actually getting some tweets Now in the few minutes during the demos So that's why it's already running and you can see that I have here three different pots on my Kubernetes cluster with the with the Kafka brokers. I have a zookeeper here. I have The connect cluster deployed here as well, and I have some other tools to help me manage and monitor the whole thing, but also I have it already deployed. I will just quickly show you how you can Deploy it. If you already use some operators, it's probably not completely new to you But you use this kind Kafka custom resource which works kind of as the blueprint for your Kafka deployment and you describe there exactly how the Kafka cluster should look like so I can for example say here Okay, I want to have three replicas of my Kafka brokers you can specify the resources which You want to have in your cluster as you can see in my case it's really running on my home cluster and it's not not too big deployment, but Yeah, you can use the same Projects and tools to run something on dedicated hardware with Hundreds gigabytes of memory and so on if you want. I also configure some some JVM options What's really great is that I can also configure the listeners so the ports where the clients will connect including things such as authentication and I have your authorization as well and What else do we have here? We have the storage configuration. We have the metrics configuration There's built-in support for Prometheus metrics for monitoring and then pretty much we do the same for zookeeper as well And basically we create a resource like this and then you just do cube cuddle apply on this and the streams the operator will take care of the rest and deploy all the clusters and configure everything and Very similarly, we also deploy the the Kafka connect Cluster as I said inside the Kafka connect cluster. It's really just a consumer and producer. So I create this this Kafka user Which it really used to authenticate and I give it some ACL some access rights, which it needs to to use the right topics for storing the configuration, but also to Publish the tweets to these topics and read from them and so on and then this is a part which I have commented out but As I said the Apache Kafka project itself. It doesn't have that many connectors So you need to add the connectors which you want to use Into the deployment and for that what the streams they do for me It will build a brand new container image and it will add there the connectors which I tell it that I want to use so that's why for the actual deployment you would need to specify some Docker credentials to the repository where you want to have the Docker container or the container image deployed and then so that's commented out so that I just don't share the credentials and Similarly, we will also need to have a secret with the credentials for the Twitter API Because that requires some authentication. So the connectors will need to use this to talk with the Twitter API and then we have just the The Kafka connect deployment, which again kind of follows the same principle it How many replicas it have what are the resources the configuration here we configure how to connect to the Kafka cluster and Authentication and so on and again, you would do kubectl Apply on this to deploy it and then when it's running you can also do something like kubectl get Kafka connect OYAMO and you can see that it's already deployed and that it has the connectors which I want to use Edit there. So all these Twitter direct message connector Twitter search connector Twitter timeline. It's actually I skip that In in the YAMO, but in the Kafka connect resource, there's this build part Where I specify all the different Connectors which I want to have edit into redeployment So Twitter search Twitter timeline Twitter direct messages and they will be all automatically edit there so that's the that's the Kafka cluster and the connect cluster and We have it already up and running so now we can move to the actual demos which will be a bit more about code and it will show Something a bit more interesting and The first one will be what I call timeline world cloud demo. So what we will do is we will deploy Connector into Kafka connect which will read the tweets from my from my timeline So these are the tweets or retweets from the accounts, which I'm following If you don't use Twitter and then we will use the Kafka streams API to analyze the tweets and try to find out What are the topics? I am most interested in what are the hashtags or the Twitter handles mentioned most commonly in my timeline, so let's First check, how would one deploy the connector? so Again, I have it already deployed and running but this is kind of the YAMO which would configure the the connector first we have to create a Kafka topic where the connector will send the messages and Then we create the Kafka connector resource which will actually configure the connector itself And you can see we say okay the class which we use for this connector It should be this comma Twitter timeline source connector if you remember its source connector So it gets the data from Twitter into our Kafka broker and then we Also have to specify where does it find the credentials for the Twitter API Which will be in the environment variables met from the secret and then we also tell it to send the messages in as As Jason and when I do kubectl get Kafka connector with their timeline You can see that it's ready. It's already running and when I quickly switch Into the Browser and check my Grafana and check the Kafka exporter and let's find the Timeline topic then you can see that it has already almost 5,000 or that's the offset here that it has around 4,843 messages right now. So that's the number of tweets which it basically collected since since I deployed it for the first time and Then when I switch to my Intel EJ We can have a look at the streams API application which we'll use to process these So let me zoom this up a bit into the presentation mode So this is the POM file for my For my application which is Doing the data processing as I said, it's using this this park was framework and It also when I scroll down it also loads the the streams API dependency but as I said if you would use some some other Framework or just pure Java without any framework then the code will look pretty much Pretty much the same. It's really just Kafka streams what I'm using here and The most of the code is actually in this In this build topology method This is the method where I create the Kafka streams topology where I basically take the Kafka tell the Kafka streams API how it should read and process the data and First I need to define something what's called Saturday So Saturday stands for serialization deserialization and that's kind of a class or object Which the Kafka streams API is using when it's reading the messages from Kafka It's using it to deserialize them and when it's sending them back to Kafka It's using to serialize the messages. So with this this tweet Saturday, which I have prepared here That's basically what helps Kafka streams API to take the Jason's which it's receiving and decoded Directly into some Java objects so that I can easily use it in the application and Then in this case what I will be doing is I will be counting some data And I want to store these data and want to query these data later So I also prepare these store suppliers which kind of create Kafka stream stores They will have some local caching done locally on the in the pot where I will be running this application But like all the data which I will use there and the aggregation results They will be also stored inside Kafka. So when the application restore restarts Because I don't know some upgrades or for whatever reason it is basically able to reload the previous state from the from the Kafka cluster from the special topic and then continue in the processing and Then I use the actual stream builder to build the the stream processing pipeline So I always start with a stream which basically says okay now start reading this this the data from this topic and use these service to Encode or decode them. So this is the first one. That's kind of further for the message key Which we don't really care about. This is for the message payload. That's where the actual tweet will be and then We will get basically a stream of the tweets coming from the topics and we can do the processing using this streams API DSL language so what we will for example do here is in this flat map values We will take the tweets we receive and we will extract the The text from them because it's really the text we are interested in here It's not about all the all the metadata about what application was used to send it What was the place where it was sent from and so on so we extract the text here And then what I do to find out what the tweets are really about is I will basically split it into words So I use a regular expression to to split it into the individual words and then I do some filtering. So I'm not interested in words, which are too short. I'm not interested in any URLs and I have a kind of I don't know 23rd the list which I have on the ignore list Which are kind of these common words in English like this that then and so on which are in many tweets But which actually don't mean anything on their own. So I'm ignoring this and then I basically go through this and group all the words Which are in the tweets and then I'm going to count them So as a result, this will basically tell me that in the tweets in my timeline The word Kubernetes has been out of the almost 5,000 tweets has been I don't know 200 times for example And I do this counting twice. I Once I'm interested for the whole time. It's running. How many times the words were there and And what's there really the topics there, but I'm also using this windowing I mentioned before To basically tell me, okay, let's not care about what was month ago Let's look at what was happening in the last one hour So this will look into one hour windows and we always move to window every minute basically So this will basically do the counting not since I started this application But only for the last hour and that will kind of tell me what were the main events happening during the last hour and Then I really just build the topology it will start reading the messages and it will start doing all the all the processing form and What I have here so that I can actually see these data and look into what are the results. I Have here the simple rest endpoint which Can give me two basic things so I can ask it for the all-time top 20 30 50 words and it will basically give me the the number of words from the top of the table which are most common in in my timeline and Similarly, I have another one for the latest word. So that's for the window counting So that's for the most common words in my Twitter timeline in the last hour and if you check what this interactive queries is doing then That's actually the store if you remember the store suppliers we created on the beginning It's using these stores and it's basically querying the calculation results the counts results And it tells me what the current results are and what are the most common? common results and When I get out of the IntelliJ and switch to my to my browser Then this is actually the result and if you know me a bit, you know that I'm a football fan So this AVFC and villa. That's the Aston Villa football team, which I support So there's a lot of tweet about that obviously in my timeline But you can see here also Kubernetes for example, there's there's cloud Well the new year 2022 that was probably quite big on the beginning of January So that's there as well and I can also check just the just the latest tweets Which kind of show what was in in the last Hour so that you can see gives us different results And what I didn't have here as well is this is this tech cloud So the the boards maybe they show quite a lot of about what I'm interested in But maybe the things like the hashtags or the mentions maybe they will show a bit more so we can see again the Aston Villa football club is the top place here, but there's Kubernetes I guess somewhere there might be Defcon as Well, I don't see it right now But it was there Yesterday when I was doing the rehearsal. So yeah, you can see that this way you can easily take and visualize your Twitter timeline and you can easily see on what's going on in the timeline and Yeah, I kind of know what I'm following of course, but I I can't really say that Yeah, the cloud with the words Does really well job to represent What are the tweets I'm following and what I'm interested in so let's Move to the next demo and let's Bring it to another level so now we were just kind of getting the tweets and and analyzing it and Yeah, it might be interesting to see that but at the end I'm probably the only one interested in what's in my timeline and I already know that so let's see if we can do something a bit more practical and So imagine the situation that we have some open source project or maybe a company or your personal account and Yeah, people might be tweeting about you and you might be interested Maybe if someone tweets something nice about your project Then you can maybe come back and retweet it or say thanks for the kind words And maybe if someone tweets something bad about your project that they were unhappy or about your company Maybe you can get back to them and try to find out what they didn't like what should be improved and so on Then that's what we will do here in this second demo. So what we will do is we will use a Connector which will search for some keyboards on the on the whole Twitter and We will use again the Kafka streams API to analyze these tweets, but we will now employ the machine learning Technology with this deep Java learning library and we will use the sentiment analysis Which will tell us whether these tweets are positive or negative and kind of how much positive or how much negative They are and when it identifies some tweets, which are really Strongly positive or strongly negative it will Send us a direct message on my Twitter and tell me oh, hey look there is this tweet You know, that's quite negative about your project. Maybe you want to have a look into it and If you want to if you want to take part in this then you can send some tweets with this hashtag B y o s ma as in build your own social media analytics And if you use this hashtag then it should reach Read it should match that the search and reach into my application running here and we might see it in the in the demo pop up if it's sufficiently positive or negative, so if you want to if you want to try this then you can give it a try and I'm going to switch again to the command line and again, I have it all Deployed but just to quickly show it so I have this this search connector, which is searching for this for this hashtag which I mentioned but I have also the Sync connector this time and the sync connector is The Twitter direct messages sync connector So that's what we'll be reading the data from a Kafka topic And it will be sending them as a Twitter direct messages to my account So don't get confused this weird number is actually the user handle for my for the Twitter account I'm using for development and for demos so that's the actual Twitter account and then we have again the credentials and we have the topic which it is consuming and it will read from this topic and and send me the direct messages as alerts and We can also have a look This is the actual deployment I didn't show that for the previous demo but the actual application is always running as a pot inside the Kubernetes cluster as well so I again create the Kafka user and then a Regular deployment where I just point it to the container image with the park whose application and I configure it very to connect I again configure the users authentication encryption and so on and that's really just running as a deployment In my in my Kubernetes cluster So this is the sentiment analysis one these were the tuck and word clouds We can also check the source codes of this Of the sentiment analysis application So if I go to the palm file what you can see here, it's It's again, I have all the quarkus libraries, but I also include these These AI dot the DJ L. So that's the deep Java learning library And I include the model zoo which contains the pre-trained model So I didn't really train the sentiment analysis on my own. I'm just using one of the examples and then When I go to the to the topology It should be really familiar to you. I Again create the tweets are there for the decoding and then what's new here is This is the part where I load the The deep learning model which does the sentiment analysis, which does the prediction And then I really build the stream application. So I again read from the Kafka topic And then I filter out the tweets which I'm interested in I take the text of the tweet and then I run this predict method of the predictor and That's what does the sentiment analysis and it returns me this classification object which basically says How it was classified whether it was positive or whether it was negative and it tells me the probability So kind of how much positive or how much negative it is And what I'm really doing here if the probability is more than 90 percent Then I will basically take the URL of the of the tweet which was analyzed and I will prepare some additional message which says the following tweet was classified as So this will be replaced with positive or negative and I give you the percentage as well and I will send it to Another Kafka topic and this is the alerts topic which the the sync connector is reading and which will be sent to the To direct messages. So then I would again just need to compile this build a container image and deploy it and Here is my Twitter account and we can try if it works So normally I would like now type some message how the better was nice and sunny and so on But to be honest today here in Prague outside my window. It's really cold and raining So it's not that great. So let's try some negative message today. So the weather is variable today It is freezing cold and raining and I have to add the hashtag. So build your own social media analytics and now I can just tweet it and now the the search connector should find this from the Twitter API the Streams application should analyze it and if everything works. Well, I should see you here in my in my messages the the information and you can see this is 30 seconds old and it tells me okay the following tweet was classified as negative with 97.60% probability and it gives me a link to the to the tweet Which is the tweet I just sent the weather is terrible today.  So we can see that it that it worked Hopefully if you use this for your project or for your company you will be getting only the positive notifications and not the negative ones but Yeah, we can see that the sentiment analysis works quite nicely and it identified the tweet and send me the notification and I can now Yeah, talk to this guy to stop complaining about bad weather so now The last demo I have is a little bit different. So now we were for for in all these previous demos we have something what we deployed and what is running and running and running and Now I will try to do something different I will more try to do some ad hoc analysis and just play with a stream of the tweets and What I will do is I will try to confirm some idea or some some Hypothesis whether it's true or false now Gunnar more link who is one of the authors of the Debezium Kafka connector for change data capture, which you should check out if you are interested in databases and Kafka He always says that when you tweet something you should always attach some image or a video because such tweets They kind of get more attention more retweets and so on. So let's see if we can if we can confirm this if this is true and And So I have here this this ad hoc application Which what it really does is? it's reading the feeds from my from my timeline and It's doing this aggregation. So in the previous in the first demo we use this counting Which is aggregation as well, but it just really does the counting. This is a custom aggregation where we basically Get the tweet we check whether it has some image attached or not and We count how many retweets they have if they have some image attached and how many retweets they have be doubted and then we count the the average and In this case, I'm not going to send it to Twitter as direct message I'm not going to send it to another Kafka topic I'm just using this peak method to really log it into the command line and see what the result is So let's switch to the command line And So because I'm still using Clarcus. It has this nice command Clarcus Def which you can just use to kind of run the application in this demo where it's automatically recompiled and You can easily kind of run it and debug it. So I'm using this to run it now It will take a while to start it will connect to the Kafka broker It will create some helper topics for the aggregations and you can see it's now running and it's doing the calculations And I guess now it's probably finished. So what we can see is the last last result is that out of the almost 5000 tweets in my timeline it found 817 tweets which had some retweets which had some media and Average number of retweets was 9.9 so almost 10 retweets if you had some picture or video attached and then it found almost 1500 tweets without any media and the average retweets there were only two retweets. So Yeah, it looks like when Gunnar says you should attach picture or something to your tweet to get more attention It's definitely true. It's The Example here 5000 feeds it might be too small, but yeah, it kind of suggests that he's right and you should Always always do it. So yeah, that's kind of One of the experiments you can do and you can of course if you want you can play with these ideas a bit more you can Try to find out what's the right time to publish a tweet Where the people tweeting about you are living or what apps they are using and so on and you can of course also use the other social media networks as well and That's for the end of the talk here are the links of the different projects Which I used during the demos and more importantly here's the link for the github repository Which has all the source codes and the YAML files and so on so if you are interested in this to Try it out and see how it works That's where we can find it and I hope You learned how to do some some cool things with with Twitter But you also learned that Kafka makes it really easy to just take the different parts of the ecosystem and Build them together into fairly powerful applications, so That's it and thanks for watching and listening Thank you, I could it was a great presentation We still have some time, but I don't see any questions So I think maybe if someone is interested to connect with the ACOOP you can try in the work adventure And otherwise, thank you and that's it You


As Nigeria joins the rest of the world to mark World Labor Day, the breakfast this morning takes critical look at the state of the Nigerian labor market. And as we grapple with internally displaced persons within the country, occasioned by communal farmer-header clashes, we also are faced with having to ferry home our citizens from war-torn countries around the world, like Ukraine, Russia and Sudan. Today, we will look at a critical issue, the mental health consequences of fleeing conflict zones. We'll also be taking a look at the headlines of the newspapers, national newspapers today. And of the press, we'll have someone join us to analyze the headlines on the breakfast this morning. You're welcome to the breakfast on the Plus TV Africa. I am Maureen. My name is Nyam Gul. Welcome to Mindset Monday, as it is. We're hoping that you're going to have a very positive mindset this morning. And it is workers' day. Congratulations to all the workers. And we're hoping that your conditions of service will keep improving. Congratulations indeed. And yes, we hope that your conditions of service will improve. Of course, today, everyone is talking about the state of the Nigerian Walker. What is the state of the Nigerian Walker? And in analyzing that, you cannot take away the fact that people begin to assess how this outgoing president, administration of, headed by President Muhammad Al-Buhari, has treated workers. Well, what the federal government answers the people who are complaining, they will say it is unprecedented, the kind of welfare packages that this administration has given. And in fact, it is this administration that conceived a raise in their salaries, which will not be implemented anyway. But a lot of things will be said that is so good, you know, this administration has done well. Buhari himself said he employed 60,000 armymen in his eight years in office. Well, you have to give it to this government in some areas. I mean, when they came in, the minimum wage was 18,000 Naira. And then they moved it to 30,000 Naira, which has not been implemented in any way in some states. In some states, it's been implemented. There are a few states where it's not been implemented. And they set up a committee, a monitoring committee to monitor the level of compliance across the states. But as you said, some states have not complied up to today. Well, so what did the monitoring committee do, like Europe, if you will say? If you ask me, who are go ask? What do you mean by that? Who are go ask? Okay. Well, but like we said, we're hoping for the best all the time for the people who who states have not implemented the 30,000 Naira minimum wage, at least God is implementing a program that is making you survive as it were. Yes. Well, don't forget that just last week, the federal government implemented 40% raise for workers, federal workers, although we also had the protest. Implemented or approved. Approved, approved by the federal government last week. And then we did have some section of the federal workers protesting their exclusion, especially in the health sector, protesting the exclusion from that 40% that was given to federal workers. But then if you look at all of these that have been given to the 30% minimum wage, the 40% that was given to them last week, and vis-à-vis the fact that market to women and men are also looking out for whenever the Nigerian worker gets any kind of raise to increase the prices of food, you begin to wonder. Mention landlords as well anyway. Landlord, transportation, hype, everything. And also you would also consider the fact that the inflation rate increased from 2015, when it was in 2015, it was at 9.01%. The inflation in Nigeria. Now it's double digits. Yeah, now it's 22.04%. Which is terrible. Which is terrible. So the state of the Nigerian worker is one that is not to be envied, especially, you know, we're talking about the removal or not of the fuel subsidy. And so when you compare the life, the living standard of a Nigerian who is a member of OPEC. It's annoying. With that of our counterparts living in other OPEC nations. It's annoying. Sometimes when you talk about these things, you complain about these things. They tell you that even in other countries, they pay XYZ for tax, they pay XYZ for fuel, they pay XYZ for that. And then you ask them, in countries that you're comparing us to, what is their minimum wage? The minimum wage in some countries is more than the salary of a director here in Nigeria. And you're talking the kind of things that you're talking. And to talk about minimum wage, you know, you are approving it and all that. What is left for the next administration to do? Especially in a country that is neck deep in debt. Why not? I don't know if the incoming administration had any input into what should be implemented or should be approved. Why not consult with them to see if it is 20% that is feasible? So that when they come, because they are the ones that will implement it. If you can postpone sensors because you want the next administration to be the one to handle it, you postpone a fuel subsidy removal because you say the timing is bad. That is because you're leaving and so many other things. Why not postpone this one? You're just raising the hopes of people and when the next administration comes, we know that Nigeria, even though they say that government is a continuum, it doesn't always work that way. Yeah. Well, maybe in this case where it is the same party, the same ruling party, the APC, that is being handed over to perhaps that continuum, that continuity, perhaps we'll see that. And then when you're comparing what Nigerians are earning and what counterparts in other parts of the country, the world are earning, what brings to mind is the exchange rate. What is even the worth of what we're earning? What is the worth of a Naira? It's just terrible. Today, the official rate of the Naira stands at 460 Naira per dollar. In 2015, when it was just 18,000, it was 219. So the purchasing power of this salary has been increased. How many people can even access this official rate, this dollar at official rate? When you're talking dollar, you talk black market, whether we like it or not. Even though the federal government at one point shot down someone who was giving us details on the black market and all that, I don't want to mention his name. But they said that he is the reason the black market is flourishing and he's misleading a lot of people. But since he was shot down till now, nothing has changed. So what did you do? What improvement did you get by just shutting down someone who was making a living and also following people, because a lot of people were going there to find out what the market value for a dollar is every day? And they're shutting down. It's out there. It really is out there. There's no secrecy about it. You want to sell a dollar, you want to buy a dollar, you know where to go. We know where to go. We know where to go. Anyway, it's still Mindset Monday, that's how we call it. And it is the Breakfast on Plus TV Africa. And we're hoping we're going to stay on. Like we said, we're going to be talking about mental scars of the conflict and wars that we experience all around the world. And what it means to the people who are being ferried out of these countries. Sudan is at war. Ukraine and Russia are at war. And so many other countries could be at war. And Nigerians must be in all these countries. Like they say, if you go to a country and you don't find a Nigerian, do not stay. It's not safe. It's not safe for you. You must find a Nigerian somewhere. So we'll be looking at all these ones. But I think we should just immediately go to the press. Yes, we should. About 7.30 will be joined by Opunabor in Quartaria, who will be looking at these headlines individually and give us insight to what he feels about the headlines. In the meantime, we'll go back to the trending issues as a follow-up of what we have just read from the newspapers. Yes. So to the trending issues now, I'd like to start with NAF departs to evacuate Nigerians from Egypt. That is the Nigerian Air Force plane has departed. Actually on Saturday night for the evacuation of Nigerians stranded in Sudan from Egypt. I was hoping that at this time all of them had reached Nigeria. And now that we're hearing that there are some who are even undocumented, like we talked about, that there may be some people who are undocumented. So did the federal government make plans for emergencies like this? Let me call them emergencies, because they're still our citizens. And I don't know what they're going to do about that. Well, that's a valid question. But what we don't know, what we know for sure, I do not have the answer to that because I do not work with Nima. But what we do know is that the first batch have arrived Egypt. They arrived on Friday. 137 of them have arrived. And the second batch, where they left on Saturday. The second batch left on Saturday. 29 buses left on Saturday to Egypt from Sudan. So that's the much we know. Would they arrive today? We don't know. We can only speculate and guess. But as you have said, we had expected that by today, we would have heard, or over the weekend, would have heard that the first batch, if not all of them, have come into the country. But the last we know is that the first batch have arrived Egypt, waiting to be lifted. And the second batch of buses have left Sudan for Egypt. Okay. Well, let's hope that they are going to arrive. Because if a hundred and something is the number we have that has reached Egypt out of 5,500 that we were expecting, it's really a very small number. And now that we're hearing that the undocumented ones, which I'm sure we're not part of the 5,500, are still there. They're the undocumented ones. I don't know. The federal government has to do something about it. The trauma of being in a world zone is enough already. And then having to think that maybe you are abandoned because that's what will come into the mind of the people who have not been evacuated as at this moment that we're talking about. Well, God help us. Yeah, the federal government also, that's from Nima. They've also advised Nigerians to stop knocking them with regards to the money, the $1.5 million that has been dollars earmarked for this exercise because this is a war situation according to them. And things, negotiations are difficult at this time. So the buses that you would have rented for a certain sum on a normal day would not cost you the same. It will cost you during a war situation. But that's why governments must at all times try to win the confidence of the people. People don't trust our government anymore. For instance, the time of COVID was a time of emergency and there were palliatives. Palliatives were hoarded by the same government. Hoarded and allowed to waste seats. Yeah, so how do you now explain to the people that, okay, in terms of emergency, there are problems that we are facing and all that and all that. When there were no problems, they couldn't do what should be done. How will the people trust them and stop knocking them from the past? We know how they have behaved with the things that they should have given us without any problem. So government should seek at all times to win the trust of the people so that whatever they do, people will get to believe them because if the people don't believe, no policy will work. No policy will work in a country where they don't believe because they're always suspecting you. If government is giving me this, what are they taking from me? Even I don't see it. And that is why this debate about removal of failed subsidy is so controversial and so divided. Because those who are saying remove it, they have the reasons. They see this lack of trust you're talking about. They are the ones that say it's an organized crime against the people because, I mean, NNPC, our four major refineries are just not working. NNPC accounts for just 8%, 8%. And that's according to an OPEC report. It accounts for 8% of the four used in the country. And so we import how many percent, 92%. And that's what give birth to this subsidy in the first place because government then has to make up for the cost of importing this quantity into the country. So those who are saying remove this subsidy are saying let us fix the refineries. Stop importing. Let us give ourselves this oil that we need. And those who are benefiting from this subsidy, we stop having the benefits of, because the controversy surrounding the quantity of oil that we import, the quantity of oil that we use on the daily, and everything, the secrecy surrounding that sector. So those who are saying it is an organized crime are saying let's stop this so that we can stop the leakages. We can stop the oil theft. And then those who are saying don't remove it, like labor. It's one of the questions we're going to ask our guests who's going to represent labor today. Labor does not want it removed. And you can understand that. Because imagine what will happen when they remove subsidy from fuel importation. The cost of leaving will just go up the roof. And Nigeria is ready for that. Look at how subsidy has been removed from, okay, when they talk about oil removing subsidy from oil, they're really talking about petrol. Okay, because diesel has no subsidy anymore. How much are we buying diesel today? It's almost a thousand. Almost a thousand. We're buying diesel. Now, government will say if we remove subsidy, there will be money left to do other things. But do we trust them to do those things? With the money. School feeding came. Who won this amount was spent? I didn't see where students were fed. I saw on social media one miserable-looking portion from Aga Gay Bread. And one miserable-looking portion of stew made with, you could just see palm oil floating on top of uncooked tomatoes with nothing inside of it. Given to a child in one of the schools in one of the eastern, western states, I worked for Nigeria when I saw that. It's a terrible thing. We don't trust government. Government should know this. We don't trust government. We the people, I'm talking like the people, don't trust government. That doesn't mean that government is not working, but government should do their best to make sure that people trust them so that when policies come, people can contribute. Dividends of democracy is not building roads. It's not building skyscrapers or anything. It's not doing what you normally should do because even the military administration do the same things. Dividends of democracy is representation. The ability for everybody to have a voice in that government to choose what they want and how they want to be governed. That is dividends of democracy. You can't build a bridge and tell me dividends of democracy. IBB built that mainland bridge. That was not a civilian government. So you don't tell me that what you should ordinarily do as a governor or as a head of state or as a president is dividends of democracy when I'm being gagged. When I don't have information to what you're doing. All right. So as we wrap up top trending, resident doctors is another top trending. They've given the federal governments the two or three weeks ultimatum for salary structure review. The issue of con mess has been going on for a long time. Courageous. Yeah. And I wonder why the health sector appears to be neglected. Of all sectors. Of all sectors. And I just saw what the minister of information said this morning that the Buhari administration's intervention in the health sector is unprecedented. So everything is unprecedented. Unprecedented. So if you are neglecting the doctors or nurses and all that, then who else are you putting in your review of the salary structure of workers? Well, I think we'll just take a short break and return with our guests that we'll be talking on the newspaper headlines. Stay with us.


Good afternoon, everyone, and welcome. Welcome to one of the Stockswish Show plays today here. This is Cree. I call this trade late, actually. And I got a little bit of a late entry in it right before I was closing out the room. I just decided to do it because I really read the market to be lower today. So I felt that a lot of things were going to follow through to the downside. And Cree is one of them. And this is very late for me to be trading stuff. It's almost 3 o'clock. But every once in a while, you do it. That's a bit of tension here, though. I just want to see if this ends up continuing to break 32. And let's take a look at it. Here, let's go back on the one. Now, I didn't do this early. I was looking for stuff then. I was looking for stuff to short because I saw the market. But you could have been very aggressive with Cree this morning. First drop, you could have been taking this and been all out. This was a nice trade in here, boom, all out. I saw this break, then went back and looked at it later. I don't know if anyone in the room did this or not. Perfect entry was right in here, though. With a decent stop and a good risk to award, if it went to 32, which it basically has. I just want to see if it breaks it. I think there's a chance it can happen. There's still an hour left on the day here. This is going nowhere but down. I mean, this is just nowhere but down. So my original move on Cree, dream target, dream target the day of the gap, was 30, 31, 31, and 30 wasn't even out of the question. Why? Because Cree can really rock and roll what it wants to. Short of that, the day of the gap worked, was a nice trade. Has followed through every day since. Again, a wonderful, amazing golden gap and look at the follow-through. Could have shorted here this day of the gap. Could have done it as a swing trade, as a court trade, whatever. This isn't getting to 31 today. I just want to try to get as much chance as possible here, though. But it's really kind of close for the timing here. So I have to watch what I'm doing. But this is going to break 32. I don't know if it does today before four o'clock. I don't know if it does by tomorrow. This is not at the, well, this is at one of the swing trade targets. If we're swing trading this 32, really, it's gotten there. For a longer term target, though, again, 31, 30, it's not there yet. This is really, really nice. So it's so rare that I would take a trade late, but it was there. It was there. And it was a 15-minute setup and it's valid. I'm allowed to do it. It was perfect. And it worked. Okay. And actually ever since the trade set up, you were never down in it. I mean, you just let it bleed and bleed and bleed and bleed and bleed. And there was even an ad here around 115 into 130 reversal time on the 15 minute. Again, when you choose to trade late, you have to really focus on what you're doing, which is what? Make sure you're watching where your stop is. Make sure you're not missing your exit. Make sure you lower your stop if you can because as the day goes on, things take longer to get places. And you don't want to miss your exit because something could go down, be hit right at it and be done like in a millisecond it could be done. Well, it's like that in the morning too. And you also have to be attentive to the market. And I suggest if you don't know how to read the market, well, which I actually do know how to read the market well that you don't really trade it the afternoon. You don't really trade it after 10 o'clock, 10, 15, if you don't know how to read the market. But I'm lucky that I do and I do because I read gaps. I was just talking to someone today, you know, it's one of these things where if you have a sound methodology that you can use to trade on a regular basis, you can apply it for many, many different things. I mean, the applications in something where you understand how to read the directional bias of something which I do in gaps is like a plethora of things you can do with it. And there's been so many examples of this lately. I just can't even name them all. I just can't even name them all. A Korean is one of them though, all right? Just a hundred percent conviction here. And I guess that's why I did the trade and I was already in the soda anyways. So I figured I was gonna be sitting here anyways. I don't know if I would have done this today if I wasn't already involved in a trade that was working. Probably not, but I was so I decided to do this too. And I knew if I did not regret it. Beautiful dollar move here in the Cree. Nice, nice follow through. And we'll see where it goes here. It got an hour left of the day, gotta watch my timing. Very nice move in the Cree, following through. It's gonna continue, it's gonna get to 31. It's gonna get to 31, not necessarily today, but it's going to get there and possibly 30 before it does anything else again. So another stocks which showed play in here today. Nice read I did in the market. Tough, tough read in the market, but I read it extremely well today. And that's what gave me the conviction to do some of these late setups, which you can do. You can do, you can do if you see them. And this isn't really a number here. So I really think this continues. Yep, this is gonna, ay, ay, ay, ay, ay. This is a 60 minute on this. Wow, what a nice start to lock to work. But it's funny how a couple of really solid trades can really just make your day and your week. The golden gap course is this weekend, Saturday and Sunday, Saturday and Sunday. If you wanna train and learn how to play gaps and learn how to do it all here with the gap system, with the 26 point rating system, rate the gap, take the entries, figure out the targets, the resistance to support all of the above. Email me at melissa at thestockswitch.com. Have a great day, everyone. Have a fantastic evening. I know that I will. Have a good one.


Case dismissed. I'll lighten the click, eh? They're most likely the whole weekend, too. It's a real great deal. And you know why? Because all the laws are made for women. Hey, tell me, Mr. Smart Lawyer, when they're going to start making laws for men? Some people underestimate their legal rights, others overestimate. Now, this confused young man is deeply involved. Has he gone too far to have his... Case dismissed. WMAQ, in cooperation with the Chicago Bar Association, presents Case dismissed. This is the story of your legal rights. How vital to preserve and protect them, how easily they may be lost. Our story begins in a loop restaurant where attorney Elliot Ruth is illustrating a point to his young law clerk. Son, you're wondering if the average dependent involved in a child support case is really a fairly bad guy, as they say. Well, I'd say in many cases he's just a mixed-up guy, a fairly confused person. I remember one case in which a young woman thought she had no rights at all, and a young man completely misinterpreted his rights. Oddly enough, it all began quite literally on my own doorstep. My secretary reported an early car in my outer office. Yes, Ms. King? There's a Miss Harris. See you, Mr. Ruth. She's here about that ad, you and, for part-time timers. Oh, yes, yes. Send her right in, please. Yes, sir. Yes. Are you a Miss Harris? Alice. Alice Harris. Elliot Ruth. Oh, it is you. Don't make it sound so distasteful, Alice. Well, I haven't seen you in, well, at least ten years. At least. It was a blind ad, Elliot. I didn't know until I saw your name on the door, and then I couldn't be sure just from the initials. But I hoped it was someone else. Oh, now wait a minute, Alice. What is it? Two old high school friends meet, and one gives the other the cold shoulder? I don't get it. Oh, I'm sorry, Elliot. Really, I am. It's only that... Well, the last time we saw each other, things were so different. I didn't want you to say... The last time I saw you, things were wonderful. You were marrying some young fellow just out of the service. Jack Brooks. Oh, of course. I remember meeting him. Jack Brooks. How is he? I really wouldn't know. You're separated? Divorced. No. But I heard you had a youngster. Too well. But it wasn't enough. Nothing would have been enough. Divorced? I can't believe it, Alice. How long ago? Nearly three years. Oh, it dragged on and on. Jack made it pretty rough. Finally, I decided to waive alimony. Well, the court, of course, awarded child support. Yes, $40 a week. I went to work in a neighborhood department store. We made a go of it, the children and I. But now you're applying for this part-time work I have, Alice? Yes, that's right. I have to have it, Elliot. Well, now that you know all about what's happened to me, I'm not sorry at you after all. Am I a successful candidate for the job, Elliot? No, of course. But I thought you said you were making out all right. Look, I hope you won't think I'm getting nosy, Alice. But what's happened? I just need the money. But working day and night, you'll break down, girl. Think of your children. I am. They have to be fed and closed. Can't you do it on $40 a week, Alice? I could when I was getting it. But Jack hasn't sent anything for... For how long? This makes ten weeks. $400 behind in his child support and you're letting him get away with it? What can I do? Well, where's the lawyer who handled your divorce suit? He moved to California a year ago. But there are lots more of us, Alice. Oh, it's not that, Elliot. A hundred lawyers couldn't get money from Jack if he doesn't have it. And he told me he doesn't have it. And did he tell you what he was doing with it? He had the money once. Why not now? That's easy. Jack remarried a year ago. They have a baby now. But that shouldn't affect you, Alice.  All I understand is that's what's happening to our $40 a week, Elliot. There's nothing I can do about that. Oh, I don't know. It seems to me since the court ordered this money the court can enforce it. Really? Well, could you help me, Elliot, would you? I believe I could, if you want me to. Somebody's led you astray on the subject of your legal rights, girl. Don't you want to fight for what your children have coming? Well, yes. Yes, of course I do. But I just didn't think I could do it. Oh, excuse me, Alice.  Yes, sir. Now, Alice, first we're going to make out a statement of arrears showing how far behind your ex-husband is in support for the children. We've waited far too long, as it is, so we'll go to court immediately. I'm sure the judge will help us get some swift action. Court has examined statements of arrears and find sufficient justification for issuance of a rule-to-show cause against Jack Brooks. Elliot, what is this rule-to-show cause? Well, as you know, until your children reach legal age, their wards to the court. Oh, yes. I remember that was brought out in our divorce. That's why the court ordered $40 weekly support based on your ex-husband's weekly earnings of $125. Now, when it appears that support payments are not being made, the court calls upon Jack to show why he shouldn't be charged with violating the court's order. In other words, contempt of court. That challenge to Jack is called a rule-to-show cause. I see. And what if Jack doesn't show any cause? Then he's held in contempt and bingo into jail he goes. Well, I hope Jack can explain this and then pay up. He'd better. He has 10 days to get his story and his money together and show up here in court. Well, as I told you before, this boy Jack wasn't like his ex-wife, Alice. He knew he had legal rights. In fact, what he didn't have, he invented. Oh, but that's getting ahead of the story. When the rule-to-show cause was served on Jack Brooks, he at least had the sense to phone his lawyer, Attorney James Waterbury, who informed him his future looked pretty grim. You see, Mr. Brooks, by falling behind in your support payments, you're in direct defiance of a court order. Who? I should have my head examined the day I married that James. None of them are trouble ever since. I'll make it fun. How do I do it at this point? Well, the court allows you 10 days to prepare your case, appear in court, and attempt to purge yourself. Purge, merge. I don't get it. You have to show some legitimate reason for not paying the support. What can you say? I could plead insanity. Mr. Brooks, I don't believe you realize the seriousness of this action. Contempt of court can mean commitment to county jail for up to six months in this case. Oh, brother, that's real great. Six months in the pokey, and me with a wife and new kids. A real cozy situation. Uh, tell me, are you able to make a sizable payment on the support money you owe the $400? No, I'm not able. And I'm not ready or willing, either. I wouldn't pay that dollar dime if I didn't have to. I got a family of my own to take care of. That's neither here nor there, Mr. Brooks. I'm afraid you'll either have to purge yourself to show you are not in willful contempt or go to jail. If your earnings have been adversely affected in some way since your divorce, well, that's about all the court would consider. What do you mean? Well, the court ordered $40 a week support based on your salary at that time. Why haven't you paid it? Yeah. Yeah, I think I've got it. Yeah, that's it, sure. Don't worry about it today, Mr. Waterbury. I'll have it all set for you when I come to your office tomorrow. All the facts you'll need. Well, I hope you know what you're doing. Sure I do. Exactly what I'm doing. Just have to check my salary and work record with my boss Dick Garth. See you tomorrow, Mr. Waterbury. Hello, Dick Garth. Hey, boss, this is Jack. Yes. Look, you know about this trouble I'm in with the XY. Yeah, that's it. Well, listen, Dick, I've worked with you ever since I got out of service, right? Practically ten years. Okay, okay, eight, man, eight. Anyway, I've worked hard for you, haven't I? Very funny. Okay, Skip, the point is you've got a big construction job coming up and you can't afford to have me sitting in jail. Good carpenters are too hard to find, right? You'd be sunk if I wasn't on the job, huh? Uh-huh. You catch on quick. So this is what I want you to do. Tomorrow in court, you stand up and tell the judge that as my boss, you, Dick Garth, personally know that for the past year... Court has heard Mr. Brooks' employer, Mr. Richard Garth, testified that because of continuing illness, Mr. Brooks' weekly income is not equal to $125 he was earning at the time Court ordered support payments of $40 a week. Mr. Garth has further testified that he would estimate Mr. Brooks' average earnings during the past 12 months to have been not more than $75 a week. Does counsel for the defendant, Mr. Brooks, wish to enter any further testimony? No, Your Honor. Does counsel for the plaintiff, wish to subvene Duke Tickham and Mr. Garth's books in order to ascertain whether this is an accurate estimate of his employees' earnings? No, Your Honor. We've been in conference with counsel for the defendant, Mr. Brooks. This is a statement of protracted illness in reduced earnings. It is our belief that Mr. Brooks can purge himself of contempt of court charges by mutual agreement on a fair and equitable settlement for the arrearizing support payments. The Court is ever-mindful of the need for quick settlement and continued payment of child support. Never the rest of the defendant's willful neglect and failure to make the payments ordered by this Court is inexcusable. Your Honor, my client, Mr. Brooks, will take the gravity of his error and give assurances of prompt payments in the future. Counsel is certain his client understands the penalty provided by law? Yes, Your Honor. But what good will it do to incarcerate this young man and thus prevent him from working and earning for future support payments? Court agrees. What manner of mutual agreement has stated and defendant decided upon? Your Honor, the defendant, Mr. Brooks, has informed us that he can obtain $250 within one week's time. Now, my client, Ms. Harris, is willing to accept this lump sum in lieu of the $400 in arrearizing. If this enables him to make prompt future payments, he is willing to call it square. Defendant agrees to this settlement? Your Honor, my client is most eager to comply with the arrangement. Court enters an agreed order. Both parties will please side. Case dismissed. In less time than any of us could have imagined, Jack Brooks proved he hadn't learned a thing. It started in the evening at his home where Jack and his new wife, Noreen, were going over their sorry situation for perhaps a hundred... They should have seen Alice's face. It lit up the minute I admitted I could maybe rate two or $250. That gold diggin' doll. Honey, I've got only one question. Where are you going to get the money? Did I say anything about getting it? Well, I just wanted because unless you're holding out on me, we've only got $150 counting the check Yeah. But what happens when you don't pay up? We've got to live too, you know. I know. I'll be living on the county, I guess, for maybe as long as six months. You wouldn't really go to jail, would you? I'm mad enough to. That woman, I... No, I think the boss will come through with the extra dough, keep part of it back every... Honest, it looks pretty miserable, Jack. Sure it does. And who's the cause of it all? Can't stand seeing me with a wife and a family. What's her trouble? Maybe we ought to move away. We ought to leave this part of the country, Jack. How many times have my brother asked you to go in with him down there in St. Louis? I don't like it around here, Norine. Oh, sure. Everything's so nice around here. I don't care. I still like living around here. But you don't have to shout at me. I'm going out. What for? To do some thinking. Got any objections? No. No, not at all.  They tried to throw the old lob at your old friend Jack today. Yes, sir. But I outwitted him. Fucked the old Jack Brooks. He really tricked the old judge and the old ex-wife and her old lawyer. Matter of fact, they even fooled my own old lawyer. You know what I'll tell him, Claude? I tell him I couldn't pay that old battle actor money on the counter. On the counter, I've been sick. Yeah.  No, sir. Not even 250 cents, Claude. Yeah, just decide it. No paid old Alice, Claude. Hey, Claude, where'd you go? Hey, come back and listen. Hey, and George. Where are you, George? Take some attention. Old Jack Brooks is here. Yeah. What are the usual, George? Hey, you look more like old Claude. Hey, say you'll have to be quieter, sir. Okay, okay. I'm leaving. You know where I'm really leaving, pal? St. Louis. Sure. I'm taking my wife and my little kid and I'm shoving off for St. Louis tomorrow. Yes, sir. Just trying to get my 250 dollars that old judge. Try getting me back from another fatal mean and spiteful ex-wife. Can't touch me and St. Louis. I'm from Missouri. Show me, baby. No more George. I want you, sir. I want you. Don't push me. Don't push me. Come on. Get going. Just make me in St. Louis. George, he means me. Come on. Come on. Get going. I'm sorry. I'm sorry. Imagine meeting you on the street. Yeah. Well, well, well, well. If it isn't my mean and hateful old ex-wife in person, what you're doing out this time of the evening is our little boy. What's the matter, Johnny? Don't you recognize your old man? Don't you ever want to... Jack, for heaven's sake, get control of yourself. You just get control of yourself, old Alice. Got a couple of things you should know. Oh, stop it, Jack. Let Johnny and me go on home. First, you got to listen to just a couple of things. First, I've never been sick at all. But Boston, I just pulled wool over your eyes, kid. No, you wouldn't. No, wouldn't I? You know what else I wouldn't like? I wouldn't pay you that $250 ever. You're going to get nothing. Big round zero, you get. You know why? Because me and my wife and my kitty were taking the train to St. Louis tomorrow. No. Oh, yes, we are. And once we're in another state, we can't touch it with a ten-foot pole. No more money, old Alice Blue Girl. Don't meet me in St. Louis. Alice, meet me in... He couldn't even see his own son.  He'd be only knew. He'd be only knew who he's really hurting. Alice Harris should have fooled me that very evening, but she was too shaken by her encounter with her ex-husband Jack Brooks. She called my office the following morning, sounding beaten and dejected. Oh, he's on my way home. Oh, I'm terribly sorry to hear that, Alice. Yet you're fortunate it can be remedied. But I'll can it, Elliot, without money. Jack may have been terribly drunk, but I know him. I know he meant what he said about taking his family and skipping to Missouri today. The children will never receive support again. Alice, you're forgetting your rights again. You give up too easily. Now, you've never heard of a rid of Neaxia. And neither has Jack, I'm sure. But he's going to know all about this rid in a matter of hours, much to his regret. I'm going to leave the court as soon as you can. Let the rid of Neaxia to issue to prevent Jack Brooks from leaving the jurisdiction of this court. Bond for defendant is set at $2,000. Bond for plaintiff, Ms. Harris, is set at $1,000. You'd better see that the deputy sheriff serves this rid immediately before the defendant attempts to leave the city. I'm going to leave you here with the bondsman. Because I'd like to accompany the deputy sheriff when he slaps this rid of Neaxia on your ex-husband. All right. But Elliot, I don't completely understand why I have to have a bond. That's to prevent unwarranted interference with completely innocent people, Alice. In your case, of course, there's no doubt that Jack really made all the rash statements you say he did. Oh, I see the point. But will I be able to get a bond? No problem at all. Most bondsmen will take a plaintiff bond. You won't have any difficulties. Now, if you'll excuse me, I'll keep this date with that show-me character who thinks he's skipping to Missouri. I don't think there's anyone here, sheriff. Looks as if the Brooks family jumped out of us, Mr. Roos. Let's check at that tavern on the corner. Apparently, that's one of our boys' pet hang-ups. Jack, sure, Brooks was in here this morning blowing about how he was heading for St. Louis, going into business down there, he claimed. Any idea how he traveled? Sure, I think he said the two quiddy out of the Union station. Jack, let's call Mr. Roos. We don't have much time, sheriff. We're good enough. I've phoned the railroad authorities. Good work. I'm swinging in here by the loading platform. Crack 18. That's over here. Well, it's the last day, coach. Wouldn't you know? They have to be in here. I thought I'm for sure. I can't. Wait. Huh? There you are, sheriff. Good. Hey, uh, are you a rough boy? I don't think so. I will see. You a Jack Brooks? Yeah, sure. What? Got a rid on the act for you, Mr. Brooks. A what? You have to come with me. Hey, what's a big idea? So we're just about to leave for St. Louis. Maybe you are, ma'am. This fella's going nowhere. Okay, Mr. Brooks. You've called every bondsman in the book and every friend you've ever had. You ready to give up? Keep your shirt on. This is a Friday night. Why do you expect me to get a $2,000 bond on a Friday night? I don't think it's the Friday night so much, Mr. Brooks. You tried to skip town. Who wants to go your bond? You guys are sure giving me all the breaks. I suppose you were giving your former wife all the breaks, running out on your obligations. Obligations? Very funny, Mr. Lawyer.  Why should I go on paying her? I've got my own wife and kids. The judge reminded you once before that the court can't be responsible for these new obligations you've decided to take on. And that is for everything, don't you? Well, I want my rights.  Who wants my lawyer? That guy Waterbury's no help to me. I think what you really mean is you and your boss lied to your attorney. Let him astray. Now you haven't the nerve to face him. Haven't you, Mr. Bright Lawyer? What comes now? I can't get $2,000 bond tonight. Do I go home now or what? You spend the night here with us, Mr. Brooks. All night in the clique, huh? Most likely the whole weekend, too. A real great deal. And you know why? Because all the laws are made for women. Hey, tell me, Mr. Smart Lawyer, when are they going to start making laws for men? Mr. Brooks, the laws you happen to be screaming about aren't for men or women. They're laws for the protection of little children. Laws that tell irresponsible fathers their duties must go on for food, for housing, for education, for medical care. Like the special treatment your little boy Johnny's going to need. Well, Mr. Brooks, I can see I'm making a great impression on you. No, wait a minute. You said something about little Johnny. I said absolutely all I'm going to say tonight. Maybe a quiet weekend by yourself is just what's needed here, Mr. Brooks. Good night. Well, I didn't have much hope to tell you the truth. Jack Brooks had led a self-centered life for many years, and you don't change overnight. I've been in the thoughtful atmosphere of a cell in the county jail. So even I was hardly prepared for the near miracle that came to light on Monday morning when Jack Brooks stumbled me through his... Look, Mr. Brooks, I realize you're my ex-wife's lawyer and I probably shouldn't even be talking to you, but I've had a chance to do a lot of thinking in here. Good. I'm delighted to hear it, Mr. Brooks. Did you reach any conclusions? Any conclusions I reached about myself are for polite society, Mr. Brooks.  All this time I've had some kind of a crazy idea. This dough I had to come up with every week was strictly for my ex-wife Alice. All I could see is her spending it, living it up. Well, I kept my nose to the grindstone. Sure, I knew it was called support, but I never even gave a thought to the kid what it cost to bring him up and how my money was really going for that all along. Well, now I can see. Quite an awakening, Mr. Brooks. I don't blame you for not believing me, Mr. Roos. But this weekend I followed up on what you let slip about my little boy, Johnny. The sheriff here helped me find out. Johnny's got to have leg braces, Mr. Roos, and a lot of medical treatments. It'll take quite a bit of zone. Yes, I know. Alice will have to continue working evenings and Saturdays in addition to her regular work. Well, not if I can help it, Mr. Roos. I've got no love for her, but I sure can do extra work a lot easier than she can. You must have plenty of overtime for me. I've just been avoiding it. Kept thinking about just having to turn it all over to Alice anyway. Now things are different. If I can just get out of here, I'd like to share in the book. Your Honor, I've talked at length with the defendant Jack Brooks. I've come away completely convinced that the man deserves a second chance. Counsel for a plaintiff recalls what this man did with his first chance. Yes, Your Honor. I at first found it difficult to believe that he's had a change of heart. But I've discovered that he has his confused picture back in proper focus now. He's seeing clearly. Counsel does not feel a few more weeks of incarceration would help the defendant see even more clearly. No, Your Honor. He appears ready, willing, and able to square himself with his children. He has even indicated a desire to earn more salary and provide greater support to cover additional medical expenses for his son. I believe him. And I believe the court should give him the benefit of any doubt the court may entertain. He still has to face perjury charges for false testimony about his illness. And the best he can hope for is probation. Court has viewed many irresponsible parents in the past some worse, some better than this defendant. On the basis of that experience and his old investigation of him, Court has decided to dismiss the writ and release this man. Case dismissed. Here to summarize today's case dismissed is your Counselor, Dean John C. Fitzgerald of the Loyola University Law School, Dean Fitzgerald. In many civil cases, and especially in matters of domestic relations, participants often are victims of confusion. Alice didn't understand her legal right at all. And Jack completely misinterpreted his and went on to commit perjury, a criminal offense. Failure to seek and to follow competent legal guidance can lead to serious trouble and a litigation. In domestic relations cases of serious need, mothers can get assistance from the Cook County Bureau of Public Welfare, which maintains a court service division to help out in enforcement of support orders. In addition, the Legal Aid Bureau may help in certain cases. Before we leave tonight's story, I want to remind you that the legal points in the story are based on Illinois law and may not apply in your state. May I point out too that the facts in your situation will probably differ from the facts presented in this story. This difference in the facts may change the application of the law. So if you are in need of legal counsel and do not know a lawyer, get in touch with the Chicago Bar Association. The Association as a public service maintains a lawyer reference plan which will refer you to an attorney. Next week, WMAQ and the Chicago Bar Association take up some legal problems involved in installment buying on case dismiss. Until then, this is your counselor, Dean John C. Fitzgerald, wishing for each of you a good night, good luck, and good law. Case dismissed. Case dismissed is written by Robert Carmen and is based on information supplied by the Chicago Bar Association and its lawyer members. All characters were fictitious and any resemblance to any person living or dead is purely coincidental. Members of the cast, Rosemary Kelly, Muriel Montel, Jack Lester, Phillip Lord, Harry Elders, and Claire Baum. Case dismissed is produced by Betty Ross, directed by Herbert LaTolle. Musical effects transcribed down by Tom Evans, engineering Harold Witteberry. Lee Bennett inviting you to return next Saturday at the same time when we'll bring you a story about your legal rights in installment buying on case dismissed. This is the NBC Radio Network.


We have seen it all – more and more extreme weather events, endangered species fighting for their existence, people losing their traditional way of life, and our forests and peatlands disappearing. The UN estimates that since 1990, forests globally have decreased by 300 million hectares, an area larger than Argentina. And this destruction has led to a major calamity – the release of huge amounts of greenhouse gas emissions that contribute to climate change. With a third-largest forested area in the world, Indonesia's forests and agriculture are believed to account for the majority of its greenhouse gas emissions. We are very vulnerable to climate change, given our geography. We have one of the longest coastlines in the world, and we also have around 14,000 islands, and we have a vast tropical forest that we have not managed too well in the past. And also the number of people still below the poverty level is also still very high. Most of them live in these vulnerable areas. Managing economic development while preserving the environment presents a major challenge in a country where millions of people rely on the land for their livelihood. Despite this, Indonesia has pledged to cut emissions by 29 percent below business's usual levels by 2030, and if international assistance is provided by up to 41 percent. However, greenhouse gas emissions have traditionally been difficult to quantify, especially those released from agriculture, forestry, and other land uses in a country as large and diverse as Indonesia. So developing new technologies to measure emissions is going to be vital in the fight against climate change. The INCAS is being developed as a national platform for greenhouse gas accounting for the land sector. It will help Indonesia monitor its emissions so it can design, implement, and assess the effectiveness of policies and programs to reduce greenhouse gases. It is also designed to support Indonesia's future measurement, reporting, and verification requirements under a new global agreement on climate change. The system currently covers all of Indonesia's forest and peatlands, measuring net greenhouse gas emissions from red plus activities, including deforestation and forest degradation. Sustainable management of forests and enhancement of forest carbon stocks. Emissions from peat fires and biological oxidation are also included. In future, it will be expanded to cover all land sector activities. The need for INCAS arose after the UN climate change conference in Bali. This is where red plus rose to prominence and the idea for reducing emissions from deforestation and forest degradation first arrived on the international stage. Following this, Indonesia and Australia entered into a partnership on red plus, known as the Indonesia-Australia Forest Carbon Partnership, and the collaboration to develop the system began. The INCAS is a big data platform for monitoring net greenhouse gas emissions in the land sector. It uses a combination of the best-available satellite and ground-based forestry data taken from across the entire country. The data is analyzed by an emissions modeling team to determine annual changes in carbon stocks in the landscape. From these changes, greenhouse gas emissions released into the atmosphere and carbon removed from the atmosphere due to new forest growth are measured. The difference between the two shows the total amount of greenhouse gas emitted by the land-based sectors in Indonesia. These results are then sent to emissions reporting authorities. The information can then be used to meet international reporting requirements and support informed decision-making on how best to reduce greenhouse gas emissions and more sustainably manage Indonesia's landscapes. Managed by the Ministry of the Environment and Forestry, the development phase is being led by the Research Development and Innovation Agency, FORDA. We hope that with this kind of consolidated methodology and the modeling, we could improve the data and INCAS will contribute to what do we expect in the climate change policy in Indonesia, so it is very important. Core technical expertise has been established within the Ministry, allowing for the system to be developed and continuously operated according to Indonesia's national needs. We have been working on developing the INCAS for several years now and at the beginning we just working on the very limited information and data we have but we realize that actually our country Indonesia already has very rich of data available in the country but the data actually available in different various agencies which make us to work more on how this data can be gathered, can be compiled, can be worked together to produce greenhouse gas emissions estimate. In addition to data preparations, we also work on the development of the methodology to develop the framework how the system can work using the best available data that we have in our country and how it can be put as an input in the systems. Indonesia's official forest data sets are the responsibility of the Directorate General of Forest Planning, Planology. INCAS relies heavily upon these data inputs including national forestry inventory data. The INCAS also uses satellite data from Lapan, Indonesia's National Institute of Aeronautics and Space, which is used to assess annual changes in forest cover. We receive, we process and we manage the data and then we distribute both the data and the added value, the information derived from remote sensing satellite data. So we collect the data from various sources mainly from Lapan's ground station. For the national coverage, we use Landsat data. This is the US satellite. Lapan monitors forest cover loss and gain on a 25 by 25 meter grid across the entire country. So far it has generated annual data for a 12-year period starting in 2000. The INCAS remote sensing component will continue to be updated with each new year added to the time series. The INCAS uses the best available data to monitor emissions with the highest possible accuracy. This includes data showing annual changes of land use, management history, forest type, soil type and various other biophysical data sets. When all this information is run through the INCAS framework, the loss or gain of carbon stocks can be tracked and from here emissions and removals can be quantified. The system is being developed according to the guidelines set out by the IPCC, the Intergovernmental Panel on Climate Change. The Center for International Forestry Research, C4, which is based in Indonesia, is supporting the development phase of the INCAS in partnership with the Australian government. It provides policy and technical guidance, training opportunities and research support. INCAS is a very complex system, so C4's role is simply to provide support into the Indonesian government for their development and operationalisation of the system. Even in annex one countries, there are very few systems that have been developed for the land sector that are what we call tier three level, which is sort of the highest level of greenhouse gas accounting as recognised by the IPCC. So this is one of the very first to be developed in a non-annex one country to the UNFCCC, so it really is a fantastic effort by Indonesia. Australia is providing funding and technical support for the development of the system and Indonesia's efforts to monitor greenhouse gas emissions. Australia and Indonesia face a common challenge, the challenge of climate change. One of the great complexities of climate change is the measurement of greenhouse gases. In Indonesia, with the Indonesian National Carbon Accounting System, we see something at the very forefront of the way countries address this challenge. We need to know how big the problem is before we can find ways to address it, and I'm very pleased that Australia and Indonesia are able to be working together on this. I thank Indonesia for its cooperation and I think we'll see into the future a lesson for other countries in this as well. The first national results produced by the INCAS in 2015 include an estimate of greenhouse gas emissions and removals from red plus activities on all of Indonesia's forests and peatlands annually.  In future years the system will be expanded to include all agriculture, forestry and other land use activities. The team developing the system will also be expanded so that the ministry can continue to develop and operationalize the INCAS that can support future government strategies. Before we launch the policy we have a process that we could scrutinize in very detail and carefully before that policy. So we hope with this INCAS we'll give more confidence to the decision-makers and we will support with that. And as INCAS develops and grows so will the vital greenhouse gas data needed by Indonesia to implement policies that will preserve its forests and meet its goals to reduce greenhouse gas emissions. In the beginning we just only how to develop a robust method to measure the emissions and also the change of emissions after we do something. But after I read the results for this now, it can be used for land use planning. It can be used for long-term forestry or landscape planning. It can be used for other decision-making process. It's amazing. I never expect before. Measuring greenhouse gas emissions will be key to all efforts to manage our environment.


A marine injured in Afghanistan completes the Boston Marathon and a clever double dog finds his way home. All this and more, this week on The Core Report. A marine's clever canine escaped from the vet and went on the lam in Virginia April 10th, 2017. General, a great Pyrenees, was caught on camera opening several doors with his nose and busting out of Akia Garrison Animal Hospital in Stafford, Virginia. He has since been reunited with his owner, Lieutenant Colonel Stephen T. Campbell. According to Campbell, General was rescued in 2012 by the family and within two days of coming home was opening locked doors by himself and has been doing so ever since. A Japanese soldier is about to make history at Marine Corps Air Station New River. Sergeant First Class Mizuru Miyazaki is slated to be Japan's first soldier to be a crew chief of an MV-22 Osprey at Marine Medium Tilt Rotor Squadron 204. So far, Miyazaki has been learning the ground service and basic knowledge of the Osprey in order to learn about the crew chief's mindset. Prior to training with Squadron 204, Miyazaki was a crew chief for the CH-47 Chinook in the Japanese Ground Self Defense Force. Staff Sergeant Jose Luis Sanchez, a marine who lost the lower part of his left leg in Afghanistan in 2011, completed the Boston Marathon April 17th. Sanchez completed the race in almost six hours, carrying an American flag gifted to him by his patrol unit as he recovered in the hospital. Well, I wanted to not only recognize better at the stuff, but everyone that thinks that they're not able to do something. He previously ran the Boston Marathon and Marine Corps Marathon in Washington D.C. last year, carrying the same flag. Semper Fi, Staff Sergeant. That's all for this week on The Crew Report.


Hello and welcome to the session. I am Deepika here. Let's discuss a question which says if A and B are any two events such that probability of A plus probability of B minus probability of A and B is equal to probability of A then probability of B upon A is equal to 1 probability of A upon B is equal to 1 probability of B upon A is equal to 0 probability of A upon B is equal to 0. So let's start the solution. Now we are given probability of A plus probability of B minus probability of A and B is equal to probability of A since probability of A plus probability of B minus probability of A and B is equal to probability of A this implies probability of A plus probability of B minus probability of A intersection B is equal to probability of A again this implies probability of A plus probability of B minus probability of A intersection B minus probability of A is equal to 0. So this implies probability of A intersection B is equal to probability of B. So this implies probability of A intersection B upon probability of B is equal to 1. So this implies probability of A upon B is equal to 1. Now this is our option B. So B is the correct answer. I hope the solution is clear to you. Bye and have a nice day.