And business leaves now. The Ministry of the past has reached no plan to sell the transmission company of Nigeria, TCN. In a statement in one statement, the Ministry urged the public to dismiss the statement regarding a non-existent plan to privatise the TCN. Now these are respirants, the media reporting statements, claiming that there is a plan to privatise TCN. Now some of the reports claim that the said privatisation is going to take place in common months. And that's the news update for most tourist police followers on our Facebook, Twitter and Instagram. And do subscribe to our YouTube channel at Plus TV Africa and Plus TV Africa Lifestyle. You can also view us from Limex and WWW, the Limex.TV and low TV apps anywhere in the world. I am Justin at Padunya Menefens for watching. We urge you to join us again at half past nine for our flagship bulletin. Good evening.


Introductions. I am Marceni Chernoff. You've probably seen that I started as a co-organizer to this meetup. So together with Han Su, those of you that know Han, he unfortunately had to leave to get small kids. So Han and I, we're all looking for speakers for the next meetup. We also look for the next venue sponsor. So if your company wishes to invite this very honorable crowd and talk to us in a short manner for a few minutes, we're very happy to talk to you. I'm from Databricks. I started three months ago. We are actively hiring. So also if you're looking for additional challenges in AI and big data space, let me know. So I will introduce it to people and we can start talking about that. I am based in Singapore since 2011. I live in Simei. I'm a grassroots organization member there. But I also cycle to us and I know few people come from NTU today. So I'm by chance sometimes dropping by. Originally I'm from Russia. I'm from St. Petersburg. There are only two seasons in St. Petersburg. If you ever plan to go to Russia, it also applies. There is July and winter. So go in July. This is just one question that everyone asked me. I wish to go to Russia. Go in July. Not any other time. And I'm very, very, very big fan of Kaya toes. And like as a as a Singaporean in heart, I really enjoy the cuisine here. I'm currently in the partner solutions architecture role in Databricks and I cover the entire region here. My degree is in engineering. So master degree in radio, links, television. I also work with turbo codes. Those are like the parallel branch of deep learning deep learning networks that are dedicated to signal to noise ratio reductions. And I previously worked for standard shadow bank in cloud and DevOps environment. And before that I would with the MC I salon. This is how I got to meet spark for the first time working with structure data and Hadoop. And before that with a few other vendors. Now I wanted to set the stage for this talk showing you this slide which is coming from the paper Google published in 2015. It's called the hidden technical depth and machine learning systems. You see the greenish dot in the middle, which says the ML code. This is actually the proportion of time. Google's engineers in 2015, I think still till today, spend on developing the machine learning code, the deep learning code versus other things. What are the other things? What are the largest boxes? You can take a look at it. You see that it's data collection, serving infrastructure monitoring process management tooling. So the actual black box of the high word, the artificial intelligence, deep learning ML, that's a fraction of time. Most of the time Google themselves spends preparing the data, mumbling the data, merging the data. So it's the reality of today. So the current state of things is we live in a very siloed world. And you've seen it in Williams in the previous speaker's presentation as well. So there were multiple systems involved in data preparation. They worked with, I believe they had a lot of Google Cloud Platform technologies. The previous speaker shared, they had predominantly GCP based solution. But you may pick up any of the databases, any of the store systems as an example. And they're not really closely connected with all the deep learning libraries, deep learning libraries that are able to create the valuable outputs for the business are not directly connecting to those sources. So there are different types of systems. There are silos in between them. And the good example is, for example, TensorFlow that only recently started to connect to the external sources. We'll speak about that in a minute. But also, the technology impacts the way that we work. And by having those silos, we then organize different teams. When we organize different teams, the teams have to start to communicate to one another in some standard manner. So they create procedures, procedures gradually slow down businesses. And with more and more procedures, businesses have to go for more and more approvals, reviews and exceptions. So the extraction of the value from the data becomes slower and slower and slower. The handles here on this slide between the teams is usually either injurious. And remember, we've talked about, you've seen this slide that was presented by William, I really liked it. There was this Twitter quote that said that it only takes several hours to create the model and takes several months to actually deploy it. Because you need to talk to people, create jurors, approve it, sign off, and then do the pull requests and actually commit it to the masters. Also, by having more and more silos, you're losing the ability to analyze the changes to track the differences. There were a lot of questions today about like, how do you see what changes to the model compared to what changes were in the data? How do you track all that? Well, you have multiple systems, multiple silos, multiple teams, you completely lose this visibility and definitely different infrastructure. With only five systems I've counted in the previous speaker's presentation, you still can kind of live. But in production, for example, on premise environments or heterogeneous environments, you may end up with like 15 or 20 of them. So the data though keeps on emerging and there's a variability of data that grows in different kinds and velocities and types. And the current idea behind project hydrogen, which is the Apache Spark version 2.4, is to start to unify these two worlds together to start breaking down the silos in between big data processing and machine learning. So with all the connectors that Spark has, and Spark has 50 plus quite stable and fast connectors to different storage types, including streaming batch. And even if you can connect to Elasticsearch, someone someone asked me today earlier in the day, like, can we use Elasticsearch? Yes, you actually can connect Spark to Elastic and query Elastic as you wish to with all the capabilities that Spark already has. There are just few things that needed to be solved that would unify the the portion about the total platform that would be capable to do the distributed learning AI and the data prep. So there's there's actually two thoughts. Let me make sure I'm trying to skip a little bit. I think this is the this is the history of how a lot of projects that we're all acquainted with emerged and developed over last several years. So you can see on the left hand side, the Spark community started with the where where Spark started was the MapReduce in memory just to speeding up MapReduce, avoiding usage of the disk and being able to process large sets of data in a distributed fashion really quickly. And it started with the RDD API. It was like a very, very new API. No one was really figuring out how to quickly write a new code for it. And then it started to adopt more libraries and APIs to simplify the usage of Spark. So first, the APIs in Python, in Java, in R started to appear. Then there was the new concept called the data frame that appeared and it made it very easy. It was it was in the unification single common denominator, the unifying pit that allowed a lot of different skills from different types of background working in one team to use the same execution platform. So the person with Python background or with Java background or with our background are now able to use Spark and teach one another, share the skills. So that gradually went into the Spark Spark, in Spark's history in Spark's role, maybe gradually into that gradually went into the production shipping code that was ready and the one that you probably use right now. It then was was extended by structured streaming. So one other interface that was available in Spark from very early days was the SQL interface. So you can actually query Spark just as if you would query the regular SQL NC SQL standard database, you can query it using SQL. And then on top of SQL, this structured streaming API appeared, which was the same approach, but to the micro batches instead of querying the huge data sets, you split them into small tiny pieces and then within each, you can look at them from the perspective of like a second batch interval, micro batching intervals. So this is how you work with the streaming data in Spark. At the same time in the AI and ML community, there were the same development. So there were first foundational projects like pandas and NumPy. And on top of that, there were some performance optimization projects that appeared like XGBoost, GLMNet. Then there was a fundamental big development by the team behind scikit-learn. And slowly gradually we appeared to the TensorFlow's beginnings. Then TensorFlow was a little bit too low level for many of the data scientists. So then Keras started to simplify the way that the TensorFlow models are ran. So it allowed to create the layers in the neural networks in a much easier, much simpler way, although below the scenes using TensorFlow. So you can see that it's all about converging. And definitely then there is a side project in TensorFlow that allows to access the data right now. It's called tm.data, tm.transform. So it all kind of converges into simplification and the ease of use. And what's next, the big double, the two question mark is, we don't know, time is up, but it looks like it all converges into something that would be the best of the two worlds together and the communities start to speak in between one, in between each other. That's something we're observing. The reason why these communities need to speak is because, as you know, Spark is very popular for big data processing, for ETL, for distributed, very distributed with speaking about like hundreds of thousand cores participating in some jobs that one of the large customers of Databricks run. We speak about processing large amount of data, which is very beneficial for DL, for distributed learning. So this picture is actually coming from a research paper presentation from Stanford. And you can see that the efforts you put into feeding your neural net with more and more data, they actually pay off with the performance of the model. So the more you can allow, the more you can allow to train, the more data you can allow to pass to the, to the model pays off overall in the, in the models, in the model's performance and the model's accuracy. So there are a lot of pro projects that we're trying to blend the two, they are not entirely unique to Apache Spark community. So projects, for example, Intel had big DL that tried to do the distributed deep learning using Intel CPUs and leveraged them better. There were several pros, there were several projects from, for example, Yahoo, there were Cafe on Spark, TensorFlow on Spark, and you may actually find a lot of semi orphan packages on sparkpackages.org that are trying to do the same thing. So, for example, around the, around Spark with TensorFlow in a distributed manner, but then people basically gave up after like first several, several commits. So definitely take a look at all these projects. If you have, if you have interest in, in how they develop and where they stop. But what really we're interested in is to use cases to be solved to user stories and they come from a data scientist perspective. So a data scientist wants to build a pipeline that takes training events from a production data warehouse where the downstream kind of data lake ends up and train the model in parallel and also apply this model to already trained model to extract value from the, from the same production data. So basically start inferring it in parallel into the distributed event stream that is already working there. So you've seen in previous, in previous presentation of the previous speaker, there's a whole deployment on top of Kubernetes that they have to work with by start, starting in a completely different, in a completely different source of the data, working with different siloed systems. It all ends up in Kubernetes in the end. This is, this is the example of how segregated different infrastructure layers in that environment are. And the user stories having a, the users want simplification. So the user stories to have one system that allows to do both of these things. So when we speak, when we speak about the distributed training, this is the reinforcement of the same user story. So we want to load the data from multiple different data types. And Apache Spark is one of the best solutions for this user story. And then at the same time, we want to fit the, the model, train the model and do it in a distributed way. And there are multiple frameworks using GPUs right now. For example, Horovod for distributed TensorFlow. And they are then giving us the model that we want to productionize. And when we have a productionized model, what we want to do is we want to have a live stream of data that we connect the model to. But as I mentioned, there is a disconnect. For example, Horovod does not allow you to read directly through Kafka. So you have to create some sort of interim sync, some sort of interim, some sort of interim load step, and also orchestrate this connectivity with some scripting, some additional engineering solutions, some CIC pipelines that are not essential for your extraction of the features, for example. So the afterthought for that is the glue code that we also seen in the previous presentation. So there's quite a lot of, quite a lot of orchestrating, non valuable projects that emerge. And they are sometimes maintained by engineering in larger organizations. They are, they are maintained by the same data engineering team. Sometimes they are outsourced, but that connection is right now not, not realized natively in one platform. The reason why we cannot really, we were not able to really realize the same, realize all both the working with the big data and the distributed training on Spark before version 2.4 is just the way the tasks are scheduled originally in Spark native scheduler. Spark is known for its so-called embarrassingly parallel task scheduling. So when we have a big data set, we partition it into multiple partitions, and then we'll let each individual compute unit of the Spark cluster work on its own portion. And if that particular worker, they're called workers, if that particular worker fails due to some reason, the task is getting restarted and it does not impact the other tasks. It's the idea behind behind embarrassingly parallel scheduling on top of Apache Spark. Now what is needed in distributed training is a little bit different. So to illustrate the task failure in our first distributed system, which was the embarrassingly parallel scheduling, we have a green task that crashed and it didn't impact anything because it was designed from day one to be tolerant to system partitioning. In distributed system, you actually have to restart all the tasks together because they significantly rely on one another to communicate the proceeds, to communicate the, to shuffle the data between them to update them on the stages of their job. So you cannot really restart only one task in a distributed learning distributed, distributed TensorFlow system, for example, you need to restart all of them. And that implies that we need a different type of scheduling. So the solution to this is something that was introduced in version 2.4 in Apache Spark. It happened several months ago, and it's something called a barrier execution mode. So if you go into the Spark's, Spark's homepage, you may find all the release notes always on the Apache Spark project. And then it's the top feature about this release. So it says that this release adds a barrier execution mode for better integration with deep learning frameworks, and introduces some additional higher end functions inside this mode. But not only that, there's also some improvement in Spark 2.4 that allows to use the user defined functions in a much faster way than something that is labeled under the epic called optimized data exchange. And the last but not least, there's a new way to, it's only right now available in Spark's own standalone scheduler. There's a new way to communicate the capabilities of workers, of the compute elements in Spark cluster, so that your task that you schedule on top of Spark cluster is aware about the underlying hardware. So it's aware, for example, that there is a GPU of a certain type, or someone so forth. So these are incremental improvements in 2.4 in all, in all group of them is sometimes referred to as the project hydrogen. So this is the the idea behind it. I will demonstrate a little bit more about the barrier execution mode. But the idea is in the scheduler in the mathematical, in the theory of scheduling, there is something called the gang execution.  That's the idea behind the barrier execution mode. The realization you can, you can take a deeper read about the epic. There's big JIRA that went into the release note. The idea behind it is to allow to start all the tasks in Spark from some point, only from the simultaneous perspective, and wait for all of these tasks to complete, and then get back into the embarrassingly parallel, still operate in the regular Spark mentality. But from some point onwards, you just say, from here, start all tasks together, and look at the way that this set of procedures is executed. And once they're all completed, come back to the regular scheduling. And also it allows to establish a context for the other jobs to be aware what's actually scheduled inside this barrier. And I think it will be a little bit clearer when we go into the demo. But the API's to barrier mode is so far realized in Scala and Python. So there is nothing like that in SQL. If you want to like call out for the user defined function or something like that across the data set, you're still bound to Scala and Python for now. You have inside the context for a particular RDD, sorry, it's inside the barrier for a particular RDD, you have some additional functions that you can call. For example, you can call like for the IP addresses of the workers that are currently scheduling, they're currently executing the tasks in parallel. So something like creating a cluster within a cluster that only does a distributed gain execution job. So I'll go forward and demo it a little bit later. Let's skip this one. Yes, you're able, for example, here is it's important, for example, an API in an MPI. MPI is the way that in TensorFlow, for example, you start the distributed learning, you have TensorFlow, you have one single master host, and then you run you designated it could be not aligned to the regular master host, the so called driver in Spark, it could be actually quite different. And you need to to be able to execute the TensorFlow job, you need to be aware about the, for example, IP addresses of the hosts participating in the distributed TensorFlow job. So you are able to pull and get the information about that sort of like cluster inside the cluster, you're able to get get that information through the context within the barrier, and then launch the usual MPI launch the TensorFlow in the usual MPI way. So so far, as I mentioned, it's only in the standalone, but there are already, you may see that there are several tickets, several, several, several initiatives to talk to the other schedulers where Spark is executed, for example, to yarn to Kubernetes to mess us to also allow this information to get cascaded back into the Spark environment to create the parity in the project hydrogen. Speaking about optimize data exchange, optimize data exchange is when you have a particular Spark type data, and usually it's represented by the data frame API, you you read with all those 50 connectors, you read the data into the data frame API, it's very convenient to work with it. There are very simple functions to change the data to expand it to enrich it to extract features out of it. But most of the deep learning frameworks, they operate in a different data types. Usually they are native to Python, they they are not aware about the data frame API format. So one of the things that could be done definitely is just to write it out, wait for the right operation to complete and then read it back, which is a waste of, which is a waste of time, waste of energy, because Spark operates very fast. And it's optimized to be working completely in memory without touching the disk. So that's not a good idea. The usual approach is to call the transformation to the particular set of data, like, for example, for one row out of a huge one, and then pass it over to Python in the call using the user defined function. So the the calls to TensorFlow, for example, in in Spark, they are also done against the data invoking invoking invoking the data using the user defined functions. And the problem with user defined functions, they were introduced quite quite a few years ago, I think over two years ago, Spark 2.2, the problem with that is the latency of taking a single row of a huge data set, and converting it, and then working on it in Python world, and then returning it into Spark, and then, for example, writing it out with all with all the possibilities that Spark has, or acting at it, at this data, as if it's a streaming data. And when we speak about latency, the measurements that were done is about 92%, 90% to 98%, this was the estimation, is the exchange of the data formats versus the actual execution of the Python code. This is the difference in between the data types inside Spark natively and the world of Python, where most of the distributed learning libraries operate. So the project hydrogen's initiative about the optimization to this problem is to introduce the vectorized data exchange. And the idea behind it is to send the column. So it's similar to column in a database, or column in an exchange. So the user defined function is executed against a set of columns. So why don't we send the entire column out of the data set that already has been prepared, featureized, and stays in memory of Apache Spark cluster. Why don't we send the entire column of this data into Python UDF, and then wait for it to return, and then merge it backward. So the kind of behind the scene realization of this initiative was done using the arrow. The community behind Apache arrow actually has a lot of different transformations already, and they had established the framework for different other Apache projects to start using it. So Spark project just partnered with them and leveraged the transformations that arrow has behind the scenes to speed things up. So when you call the two pandas RDE or two arrow RDD as the user defined function, Spark handles its transparency for you in that columnar manner, so you're not wasting. And when I say Spark, I mean Spark 2.4, Project Hydrogen handles it for you in the transparent manner. It happens all behind the scenes. And the re-usage of the already established protocols by arrow community, and also some additional engineering that was done by Apache Spark committers, allow to improve the calls to use defined functions like 200-fold, 300-fold. So you can see the comparison between a row at a time call to the user defined function versus the call to the pandas UDF with the vectorized columnar format. And yeah, different lambda functions were used like lambda plus one or do subtract mean, for example, from a single column. So I want to switch to a very quick demonstration of the barrier mode. I think it's one of the very important aspects that is there in 2.4. So I have a couple of notebooks here. And I want to want to show you guys also the, is this UI familiar for you? Can you raise your hands who've seen Databricks UI before? Because I know it's an Apache Spark made up, so. Can you raise your hand if you've seen Apache UI? OK, Databricks UI, who's seen it? Yay, quite a number of people. That's pretty cool, thank you. So the idea behind it for those that didn't raise hand is we, at Databricks, we are shipping the unified analytics platform, which only works on the cloud. So you cannot install Databricks on-premise. But the idea behind it is it's a collaborative notebook space that is a very, very powerful way for people to work the code together. And behind the scenes, there is its own cloud-native cloud optimized scheduler and Apache Spark core fundamental with additional optimizations to work with connectors with native Azure, Microsoft Azure storage types with native AWS storage types and additional optimizations from the performance. So that's a very 10-second explanation of what is Databricks for those that are not aware. So you see the notebooks in here. I wanted to, when you go into the notebooks, you have to attach them as usually in Zeppelin notebooks. Can you raise your hands? Yeah? OK, some of you use it. So you attach the notebooks to the cluster so you can execute some jobs. You connect the code to the execution on top of a particular cluster by attaching them. So I have a cluster that has several, at this point, this is a native US environment and it has four worker nodes. Each one has one GPU and 60 gigabytes of memory. The notebooks attached to it, let's go into this first one, distributed training, are executing some Spark code to do distributed learning using Horovod. This is the regular MNIST example. Anyone remembers the MNIST? The digits recognition samples. Sorry, it's the first time that I'm at this meetup, so I'm trying to gauge the audience awareness. That's good. So the idea behind this is we have a set of numbers and they're already being captured as pixels. Like in a picture, you have every black and white dot, and you can create a long list of rows in a very large database to represent the graphical data set on top. And then you can pass it to Horovod to train it against the label set to then output the predictions. So we have a table already. The table has, if we go into the, I need to show you, I guess, the table with the digits, so you trust me, you don't just take my word for granted. So if we go into low world, no Horovod, hydrogen, MNIST, this is what every individual digit looks like. So these zeros are like black dots, and then there are like the other grades of grade. So if we start the job and train the Horovod model using all the four nodes, we can just run them all. What happens here, we read in the entire table into the Spark data frame. So that data frame is then repartitioned into two partitions. And in Spark, the amount of rows, you have a huge data set. You partition it into smaller chunks. And then this is the actual scope of work for every individual Spark executor, the work of the compute unit. So right now, I expect that there will be two executors actually working on the data set. I write it out into an intermediate format here. And then I execute the function against that data set that works on two hosts. So let's see. We need to take a look at this stage. So we have a job stage one, which is just kicked off. I'm just waiting for the second stage. It might take a little longer, but actually it wouldn't care. If I run at the same time, what I want to do is I want to use the same cluster for something in parallel. And I'm not using any barrier execution mode here. I'm just trying to infer the model that I already have. And it's hidden in one of these functions, predict using GPU. I want to infer a model against the live stream of data that's, in this case, it's simulated, against the column that will be the features. And I want to create another column in the same data frame calling it a prediction to what actually this digit means, the hand recognized digit. And then I will call an action to display. So what I'm ideally looking at is a two column table that has the label originally seen. And then based on the column of the features, the prediction to what the real time machine learning algorithm would apply to it. So if I kick it off by running it all, and here in these utils are all of these smaller functions actually, they are all hidden. So I'm expecting the stream to actually fail. So let's see. And the reason for that is that I'm trying to schedule four, there are four nodes in this cluster, four GPUs. And I'm trying to schedule, I took a little bit longer than I anticipated. This is complete. Yeah, I've ran out of time. I need to, I guess, I took a little bit longer. Let me actually stop this job and just rerun it for you for a second. So that happens when you're trying to do the live demo sometimes. And that's a real cluster behind it. I think what happened is the previous job commenced much earlier than I anticipated in the GPU cycle. So what I need to do is I'll clear it all and restart it in a second. So don't take my word for it. The idea behind it is the job, as I anticipated, has to fail here because it would try to use all the GPUs available in this cluster. And there are four of them against the same, against the same, to run this test. And two of them would actually be already occupied running the previous notebook. So let me clear it all up. There is this function, very, very helpful function that is called the KILOL API, just in case there is something running on the cluster. Yeah, so let's kick this job off once more. Doing the prep work, reading in the table with the digits, repartitioning them, writing it into the party format and then reading the same, reading this party database with the, oh yeah, there we go. So I think, yeah, what happened is just, I was a little bit too quick for that. So you can see the usual startup sequence of the TensorFlow here. It does have a lot of details inside, we will not go into it, but basically right now I'm trying to, I'm training the model on the four node cluster. And if we'll look at the event timeline here, enable zooming. So these are the two, on the back of it, these are the two workers that were selected and run the two partitions, repartition two, they run it against the GPUs. So if we go back into the model inference without the barrier mode execution and we just say, okay, run on all available nodes without any awareness about the barrier mode, run this code and infer the model that already exists against the live stream of data. So what it would try to do, it would try to infer also on those nodes that are already got the GPU occupied that tries to schedule four tasks. And at this point it should fail. I want it to fail. Yay. This is the kind of like the opposite example of the usual demo expectations you really don't want for things to fail. So it fails with the, yeah, anticipated GPU occupied error. And the idea is we're like, we're actually not able to complete the task that Spark wants because underneath the hood, below the hood, there's already GPU busy. So let's stop it all. Let's actually go back to this job, clear it all and clear all the possibly running MPI jobs by killing them all. So if we repeat the same experiment with the barrier mode now, the only thing that was added is this. So it says also repetition the same data set into two, but create a barrier for these tasks and distribute the information that you are using these two hosts for the distributed deep learning scenario, distribute the information to anyone who is interested to schedule any jobs in parallel. So it's a cluster inside the cluster. So let's run this. Let's take a look at the execution. So you can see that because there are two partitions to the training set. So yeah, right now there is two tasks going on. And if I'm running the model inference that is aware about the barrier context, I actually don't do anything. So Spark does it for me. So once I created the barrier for the two tasks in my previous parallel job, the other jobs that I scheduled on this cluster, they will be aware, which nodes are currently occupying the GPUs. So when I'm using the different notebooks, in this case, I'm still doing the same. I'm reading the stream and for the every micro batch that I read, I'm inferring it using the UDF. So now I don't want it to fail. So yeah, you can see the number two and number eight in this notebook. And number nine and number 10 are different executors that were picked up. So we scheduled, although we had the, in Spark mind, we had all the nodes available for scheduling. We only scheduled two because we were aware that there is a barrier concurrently doing something on the GPU enabled hosts. So that's the idea of the barrier mode. And it just appeared, like it's like two months, they released 2.4 in November. The first talks about, and I'm not the author of the presentation, the first talks were actually by the committers, like late summer, they were talking about that. So I just wanted to introduce this concept of the barrier execution or two. This is the beginning of a very big progress because you're now able to mix and match the power of Spark with the nature of the distributed deep learning. And the last bit is the accelerated aware scheduling. It's still in discussion. So you can see it's marked as the pending vote here. The idea behind it is you can only specify those particular type of GPU or some technical, some capabilities of the host for further actions that you execute in Spark cluster. And it will execute these tasks only on those hosts that conform to this criteria. But it's again, like it's scheduling, it's actually very dependent on the scheduler. So there is the work going on to talk to the messes community to Kubernetes community and to Yarn community to actually make it happen, to make this awareness presented back into Spark. And the timeline, right now we have the barrier execution mode and it's available straight out. You can also play around with it on Databricks platform. The next big steps will be to integrate more and more functions into the barrier execution mode to make it more easy, for example, to pull out the additional context information. And then going forward in Spark 3.0, there will be optimized data exchange and the completion of the accelerator aware scheduling. So that's the nature of the project hydrogen. It shows you why we actually are rebranding this just Apache Spark meetup to Spark and AI meetup because it's now getting into the single unified platform that merges the best of the two worlds together. I know I was running a little bit short of time and we actually are supposed to end five minutes ago, but still if you guys have any questions, please raise your hand. I'll be happy to address those.


People are obsessed with mushrooms right now in the wellness space but going back thousands of years mushrooms have been used in a medicinal way within ancient China within Chinese herbal formulas. Now today I thought we'd talk about one which is called fuling. Hey guys Dr. Alex Hain, doctor of acupuncture and Chinese medicine. Now before we jump into this video there are two very important links right below. The first is if you'd like to become a patient of mine locally in Los Angeles or virtually via telemedicine. You can reach out to my private practice in clinic right below this video and the second is for a free download which is four daily rituals that can potentially help you add years to your life with Chinese medicine. Now one of our oldest books on herbs is called the Divine Farmers Classic of Materia Medica, the Shennong Ben-Saojin. It talks about the abilities and the uses of some medicinal herbs, stones, plants, animal parts, etc. Now in the entry for fuling it says, Fuling treats counterflow chi in the chest and the rib sides, worry and rage, fear palpitations, binding and pain below the heart, cold and heat, vaccination and fullness cough with counterflow.  Taken for a long time it calms the hun and poa souls or spirits, nurtures the spirit and staves off hunger extending the years. Now that is a one-part clinical observation of its uses as well as a little bit mystical but practically there are a few uses of fuling day-to-day in the clinic. Now day-to-day when it comes to using fuling which often one of the ways it comes is in these rolled little almost little cigar tube looking ways. Sometimes it comes in fuling kwae which are these little squares like this, cubed and sometimes it also comes in these little sheets. So the fuling is very interesting. It's a commensal fungus that grows the base of a certain kind of pine tree in China. It's very interesting there's a video on a fuling farmer and the way he harvest and finds the fuling he was poking at the base of these pine trees that he knows as one of the species that has a commensal or a relationship with the plant and with the fungus and he would stab basically below the pine tree where the fuling ball might grow and if the tip of his little spear came up white he knew that there was possibly fuling there so then he would dig, harvest this ball and then prepare it or slice it. But day-to-day when we talk about fuling the two main uses I would say we use it in are anxiety, bloating and urinary issues. Now when it comes to discussing an iconic formula with fuling there's a modern formula that I don't use but it's quite famous called sujunzitang which is for gentlemen decoction. This formula is comprised of just four herbs renchen which is jinsing, faiju, attractolotis which looks like this, fuling which we just discussed and drigansal which is honey fried licorice. Now sujunzitang is a formula that is a base of what we call the treatment of spleen chi deficiency. Spleen chi deficiency basically as a pattern tends to be someone who has an issue with digestion, bloating, abdominal fullness, loose stools, they can often sometimes have palpitations, anxiety sometimes goes with it, sometimes urinary issues go with it but in general this formula is famous for regulating the digestive function of really what Chinese medicine calls the spleen. Day-to-day most people probably think of this as a formula for bloating or appetite or someone with a lower appetite that's prone to indigestion and prone to abdominal fullness. In that formula there is a power couple combo baiju and fuling that you find in lots of different formulas famous for thousands of years throughout Chinese medicine. Now the combination of these baiju for damp and fuling to drain some fluids is very very commonly used. So for example we say that one kind of low appetite is due to under functioning of the spleen with dampness. So just a little Chinese medicine physiology lesson we say the pathological factor of the spleen is basically dampness. In layman's terms, bloating, abdominal fullness, a lot of mucus or saliva that kind of thing. The baiju and the fuling help with the processing of the water. The baiju is a bitter herb that also will help move things downward so sometimes people who get indigestion a lot are basically the deficient spleen type who just have weak digestive function in general. That's different from people who have good strong digestion but just eat a lot of food or eat the wrong food. But in this case baiju helps expel some of this dampness through the stool, fuling through the urination. Now in the same vein because all of these herbs have multiple functions and even these formulas have a formula pattern. So one formula can treat five or six or sometimes ten symptoms and very often when it's diagnosed properly one formula let's say a formula that moves damp in the digestive system what it will do is people who have brain fog or a constant pressure in the head can be due to dampness. Stuffy noses, runny noses can be due to dampness. Bloating and abdominal fullness can be due to dampness. Frequent urination can be due to cold. There are myriad symptoms that can improve just from taking one formula like one with baiju and fuling and these are some of the most commonly used herbs for modern people especially because of how many people have GI problems and how many people have anxiety. So in the same vein fulings also use for anxiety interestingly enough and it's no surprise that many people that come in have both a lot of GI symptoms and food sensitivities and clinical anxiety. That's no surprise within my field. They very commonly go together. So that is a little bit behind fuling, its uses, the history of it, an iconic formula where we often use fuling but otherwise very interesting herb and very very commonly used day-to-day in the clinic. That's what I have for you today guys. Before you go, check out those links right below the video. Become a patient locally or get that free download and I'll see you guys soon.


Okay, it's time for Up The Press and Chris Kendall and a member of the Chattered Institute of Abiturators in the UK is joining us live this morning from Lagos. Good morning to you, Chris. Good morning, thanks for having me this morning. Great to have you, always a pleasure to have you. So let's just get straight to Brastax Punch newspaper. It's leading with FG denies subsidy return, more fuel stations shot. The writer's NNPCL blames pockets of fuel queues on road delays and 25 refinery licenses idle over subsidy, says Kiari. Yes, you know, this is the geography about where subsidy and no subsidy is occurring in the decimals and you have to understand the basis of May 29th, 2023, the red inauguration of the president, the polar measure, is today categorically that the first subsidy is it's seen of the past and that this government is not going to be part of that and it talked about corruption within the system and for being the reason to remove stress subsidy. And prior to that, before the expiration of the regime of the president, that has already put in place and it was supposed to last till the end of May or June this year. But the president made that category statement and that has led to a lot of issues within the economy. Well, and we are told a few days later that the market force is going to determine the price of petroleum as it were and you can see that in two weeks we had two price increments of petroleum products before the heart cry, but Nigeria and also labor and subsequently nothing like that was not like, but there have been increases in the prices of crude oil across the globe. So if there are going to be, if prices of petroleum products is going to be determined by market forces, so you are expectedly you are going to expect that the prices of petroleum would have risen again after then because the price has shifted in the national market, which is why most people now believe that as the federal government pays subsidy, I know that my decision that Pegasus should look better. So if Pegasus came out to his president to say that the federal government had resumed the payment of petroleum subsidy, then I would believe that. But the NNPC came out yesterday to say that there is no increase, there is no increase. First of all they have not been re-established or returned and there is nothing like that, but I cannot believe NNPC. NNPC is just one out of too many oil companies in Nigeria right now. Don't forget that NNPC has privatized the sooner or later the government agency. So it is a private company. It cannot represent the totality of the oil sector until the Ministry of Petroleum has come out to give a categorical statement or the president of the federation, as well as the truth is that the spokesperson to the president or the minister of information to the categories say that yes, subsidy has removed once and for all. Then it would be difficult for me to be able to believe what the NNPC is saying. That is what it means as of now. But good enough, there is no pricing after the last two. Good enough, no price increase yet. Because that was a major concern, in fact over the weekend it was a major concern, which of course the NNPC dispelled that Nigerians have no cause to worry that there won't be an increase in the pump price of fuel. So you do not believe him, perhaps I don't know, do you believe one other major statement he made that Nigeria would be a net exporter of petroleum products? Do you believe that? Do you think it's feasible? Do you think it's just a political statement? Well, I find it difficult to believe most of this government official because if you look at what happened since 2015, where the president promised that we are going to have revived all the refineries that were going to be more, and the eight years of that government most single refinery was revived. And despite the billions and billions of dollars that was pumped into maintenance of those refineries. Well, if this government have come to say that the tackle refinery would be on stream by December this year, then the others will already have done it next year. I think that what we just saw, but let's wait and see sincerely. I'm not optimistic if they do or will I go for Nigeria, but if they don't do their surprise to me. So if we say that by 2024, Nigeria would be a net exporter of petroleum products. Let me try to take it a bit optimistic and also be a bit pessimistic. So I'm not here, not there. I'll just sit on the fence and see how. December is just about two months from now. Let's see the magic you're going to do with my other expenses. Yeah, but I'm sure even he himself won't be surprised if Nigerians do not believe him. Maybe because he said he won't speak much. The Lord has been said already and he doesn't want to give a date so Nigeria's don't get offended because I mean. The date is no. The date has not already been given. It's not for him to give. 2024, but not a specific date a month. But 2024, 2024 could be November 2024. October. Did you see a date? No, no, no. What I'm saying is that the first 30, the first refinery we start production by December 2023. So the other ones to say 2024. Yes, definitely. That's not it. So if they agree to meet the target of December 2023 for the for the first one then. But what you have to ask yourself, what is the refinery capacity of those refineries and what is our consumption level? If we are just on those three or four refineries, whether they can be able to define enough products is here to be seen. Then whether it's also depending on the one of them that is here to come on stream. Maybe that is also what is what. Don't forget that and they have about 25% equity in the good. Say refinery. I mean the federal government. So because we say that we also share with us in the good refinery. We've been several times that refineries suppose are picking up and they don't go take group up giving us a sort of excuses. In fact to the point that I think you have to be given import licenses for petroleum products. So if that is what the NFPC manager director is looking at. Then our language that what I just want to see is this refineries working. And this is a set is a set decrease in petroleum products which affect every set of Niger like it can be cut short so that we can be able to revive our economy. We want to see same in other sectors not only in the petroleum sector. Yesterday we are talking about Agia Gupta still company that the program was saying that it's going to is projecting up at 50,000 jobs that will be created and I say not again because that is how we have been shortchanged with all these promises. We have took one time that our agricultural sector is going to provide employment for 10 million Nigerians. We never saw anything. We will set it in other sectors. Let this government this government our government not just this government our all government I mean government not just single government. Just let us perform leave all these projections. If it happens we can turn and just go what you see in the US is just say that every month the government will come at the start a group of statistics in the United States who say we have created 250,000 to 50,000 jobs in November and they have statistic to show it for it. But Nigeria all just a history secreted job for this critical job and our employment level continue to rise on a daily basis. Yeah. Okay. What moving forward let's look at the other headlines on the punch newspaper. Above the masthead you have Israeli Hamas conflict. FG1's programs against illegal trips. Legos suspense early lifting. All right. And then for consortia to fix dilapidated pipelines. That's still Melikeari. You want to touch on these two briefly especially the Israeli Hamas conflict. It's getting hotter by the day. The war is raging. Israel is hell bent on getting revenge and getting their people back. Yes the Israel-Palestine crisis has been in the news for over 50 to 60 years now and it has always been that. I remember the seven days before. Yeah but this time around though this time around it appears to be somewhat different. I mean just look at the way Israel was taken on a wares on a very special day of course. They were taken on a wares and we know that Israel when it comes to intelligence who is better than Israel in the world. So for them to be taken on a wares and the magnitude with which Hamas you know carried out this attack is quite strange. Matter of fact Israel has likened what happened to them to what happened in America you know 9 11. Yes I was just trying to lay a foundation to this story there. So I'm saying that it is just like what happened during the hello Israel. Yes I'm hearing you as well as in over 50 years ago and they have been strategic. The Hamas were just been starting if you read the story of the seven day war. It also happened on a Saturday on a June worship day. Saturday was a June worship day in the distant and that is when the Israelis a bit is like our old Sunday where they live lower their guard and go around their business and rest of them. But there was serious failure of intelligence at the part of the of Israeli military. They probably reverted the capability of Hamas while taking off their eyes of the ball and why they have barricaded Gaza. They never knew that Hamas had the supiling rockets arms and rest of them from whatever source which they are the US and the and the Israeli have come to say that they were supplied or we have supplied back Iran. But what happened on Saturday was tragic. That is the worst in the history of Israel. It started by Hamas making an incursion into border lines between Gaza and is the border line with Israel. And they moved into a there was a musical concert was that was going on and they moved into that place and massacred over 260 people young people. As of this month the death toll has risen to about 1000 in Israel. And Israel has also been bombarding the whole of Gaza just this morning. The Prime Minister of Israel said that people should leave because by then they finish with Gaza. It will never be the same again. But what we have already said and which some of us are also students have already said that the social to this problem is a two-state solution to Israel. Why the Palestinians should recognize the state of Israel and give it a due recognition by allowing to use it. Then the Israelis also should be able to look at the Palestinians and also make sure that they also have their freedom. Yes, it can be that they're comparing it to apartheid and this go beyond that. Because on a continuous basis, if the Hamas or those in Palestine continue to pay, send bombs and send all sorts of the same to Israel. Of course, Israel is a nation of which they should be able to also defend itself. But this is the highest bill that have been dealt with Israel since it became independent about 70 or 75 years ago. And this is the premise of Israel. This is just the beginning. What the Jordan was just in the first place. Then there's the possibility that they're going to move in their country. But in all this, it's also the civilians that will suffer. Because the rates at which people are being killed is enormous. And it's quite unfortunate that now and then that we continue to see this. And we don't know whether this conflict will ever come to an end. Because it's probably, if you read the Bible, if you studied the Bible, you would have read that probably it was there somewhere in the Bible that this conflict will continue until Jesus Christ comes again or whatever. Pets, I think it's something that can be solved. If the parties in the country can be pet, this is a very, very unprovoked attack on Israel by Ahmad. Yeah, and they took a hundred of their people hostage. And if they do not stop the bombardment, they are going to kill all of the hostages. Yes, they said that Israel will continue to bombard civilian settlements without warning that they're going to bomb. You know what Israel is normally doing? If they want to bomb any particular building, they send a warning, maybe about one minute, two minutes, so that people can evacuate their pets. From what we have seen now, no more warning is sent. And they said that Israel will continue to bombard to that. They will start killing the hostages. So it's a very, very delicate. Israel will also be very careful in moving into Gaza. Because anything they do, they have all cities that be held hostage. Go by 100 people. And you don't do what this guy is going to do. They just started these hostages and used them as human shields. I don't know how it's going to come out of this, but it's quite unfortunate. Yeah, very unfortunate. Unfortunate that civilians were targeted during this attack by Hamas in Israel. Okay, so let's move forward.  Justice for one of your Leonard Fellows that was slain. What do you make of this? In fact, MBA has welcomed the death sentence for the killer cop. Yes, for us, it's a victory for the lawyers and for MBA. So, but I don't want to limit this to just MBA, all lawyers, lannet colleagues, as we call ourselves, as if it were justice. Because we are mannered, the policemen, shot and kiddie-sooman on the Christmas Day. On Christmas Day. Yeah, pregnant. In the presence of our husband and I think their sister-in-law, whatever, and just unprovoked. There was no provocation.  Before then, don't forget that, within that, Agia has this also. It was said that this notorious police station around there, that was a young boy that was also just about a week, within that, Agia has this by that, from that same division of police office, within Agia, a young man that was just going about it, this is somebody just, one of the people, he just pulled out a gun, killed him. But justice came too fast for me, very fast, and that is what we call, because it's just under one year. And they caught us, they were able to deliver judgment on this. And that's where some that dragged for almost five, 10 years before judgment and listening. So I don't know whether it's because she's our colleague, the lawyer, or what did the government do to put you because they were the prosecuting authority. But he has the same test of death. But I can tell you that that would not be the end of the story. He decided to go on appeal. And if he goes on appeal, and uses that as the cutoff appeal, he also decided to, he made. Really? Can he appeal? He has to make. He made a police, he would appeal, or the police appeal on his behalf. He, the court, the court. Not the police, he has the right of appeal. It's a criminal offense, it's a capital offense. But he has the right of appeal, but the court of appeal, if he loses that court of appeal, he also has the right to go as far as the supreme court. So it doesn't end at just the high court. So if the court is appeal, then we have to wait for the court of appeal to deliver judgment on that. Then if you also, please, I ask you to go to the supreme court. On to the supreme court. The supreme court is the final agenda on issue like this. So let's not say, but the fact is that there has been sentence. And then, but the problem for me, inasmuch as some people are against it, we have so many peoples, are we seeing execution in our prisons? And none have been secluded. Then I dive into that, why we still have that provision in our constitution.  That is what's up. Even the U.S.T., today U.S.T. executes. Inasmuch as with all the human rights records and the rest of them, you still see that U.S. still executes people that are abacus. That is the finality.  So it's better for the governors because the governors are the ones that have the right to sign up on the death sentence for the execution. So one of the governors tried it some years back and that was Adam Mushomura who saw the outcry of Oliver Nigeria and that stopped other governors from signing bets. If you have signed up and that you've been convicted of such in those grounds and you need to be prosecuted again, there's no need to keep anybody irrespective of what the human rights people feel about it. I'm talking as an individual now and not somebody that a graduate of law. Okay let's move to the nation newspaper. The nation newspaper leads with Tinubu I'm focused on lifting Nigeria to greatness and the right of their SGF briefs president their stability under APC government says envoy. This is self-seeking chest beating that stability how many months are they it's going to be in the stable we're talking about how many months about three months four months so I don't know the stability does not take care of the interest of Nigerians. Why are politicians like this Chris? Why do they do that? That's why politicians like why don't they just go and and just say things where they are? As you said how long has the government come in place? I mean with all that's going on in the country right now? Man that's the difference between you and them and between us and them I've said it and when a politician says oh man I suggest you take a good money even before you answer me please open the window by the studio there and take your time Take your time because when there is actually money or not those are politicians you know the electorates and the rest of them the world's scholars think but that's what we they say it is not what's in on crime I think this government should just sit down and face the realities and just smear the coffee as it were that the rajah problem facing Nigeria's especially the economy economy is biting so hard Nigerians are finding difficult to be able to eat they are finding difficult to be able to knead and then we have so much put on our necks and if they can just remove it smaller we know they cannot remove the put to toilet but let the release is small so that we can breathe is there a situation across Nigeria some people find it difficult to eat on a daily business it has moved from one one one to one zero one to one now that is now zero zero zero most Nigerians cannot eat and there are promises made yes we know that it's going to be difficult for them to bring all the promises as a basic one they should start fulfilling which it has to do with food shelter road infrastructure health education Nigeria has not asked for too much once they get the basic things we don't have a well complaining you know everything we leave everything to God let them do what they can do and let's continue opening up God to be able to give the rest for us indeed Nigeria's are just asking for the basics so that they can thrive Nigeria's are very hard working people Nigeria's don't really need so much to to survive but when they find themselves in a situation where it seems as if they are being choked on all sides it becomes a real big problem and that is where governments are coming and creating a nibbling environment for the people to thrive okay yes above the mass head on the nation newspaper you have no return to petrol subsidy says NMPCL we will let sport feel next year we've talked about that and then Christmas day killer cop to die by hanging three thousand two hundred and ninety six on death row you also talked about that then you have EU observers report created says page 27 is where details of that can be found and Kayamal says airlines must pay compensation for flight delays you want to talk about that one yes that's a good one if you can implement that then that is good because that is the standard it's a cow regulation on travel if an airline supposedly deletes your necessary that you need to be compensated that in so many instances in Nigeria where you see airlines delaying their fly schedule for about five, six after at times 10 hours there are businesses some people have been at airport for 10 hours and then before they could fly that is unacceptable so that is one the second part is also that if they delay you for more than one hour then I have a right to get my refund and look for another airline that is buying that is what they don't do here they believe that once you pay you pay that shouldn't be you have a right of fund and that is what it should be because if you realize that even these guys are rich if for whatever reason you miss your flight they say you are forfeited in your tickets but if the delay is from there and then they look your that way so the minister of aviation Festus Kayamal said I hope we can work it out as it were and make sure that some of these regulations that are put in place we cannot be able to continue to behave the airline operators cannot be able to continue to behave there are so many times that people have missed their flights because of their appointments because the essence of flight is because you are here you are time conscious you are time conscious you just beyond the road you want to go to Abuja because I'm time conscious I believe that within one hour I can be able to get to Abuja I can finish whatever I'm doing at Abuja Daddy I don't want to stay I can come back instead of picking up a plane in a hotel but it's a situation where people just get to their point and all of them just say oh we have to tell you due to logistic reasons what is logistic reasons what is so logistic about is their reasons that they are lost and lost everything yes but time for logistic and logistic reasons but what if the weather is bad what if the weather is bad and they are just taking precautionary measures to ensure that you arrive safely no man it's not every time weather which one weather even dry water where are they in the city that is not even if it's weather there's nothing you can do about it because it's a conversion it's conversion across across the globe even the regulations say that when the weather is not good but this one is not they will tell you that the plane we are expecting from whatever has not arrived and this one this one and this one and that is that because it's not I finally enter their plane after waiting for about five hours to six hours they give you one small pure water and say that's good baby I'm telling you and they want one of four so it's very unusual I think you have to try and you know that that it's the cost of a flight in Nigeria is becoming very very expensive one one round flight in Nigeria is about 80,000 to 90,000 no matter how short the distance is that was the time when they used to give Jalof rice even though the Jalof rice runs in 30 seconds so I hope I'm not there was a time there was a time you pass away so always after when you pass and back you say back they can give you that small pure water and they have to say thank you for flying with us you know good okay if you leave me I should be able to give me an apple an apple or a bag to hold you for 24 hours all right so Israel lay siege to Gaza and then 2.3 million to vote in Liberia poll let's talk about Liberia yes what do we know about what's happening there yeah I think the election is holding to that yesterday and President George Roy is going for a second term and there is still opposition as it were from the opposition candidates and then they'll be campaigning rigorously most of Liberia's grip that where George of Pure Water the former world footballer of the year has no leave of expectation to the poor misses but he's also an African politician he's not in fact politicians are politicians not where you find them in the world where they're in America only that our own let's say their own marries to power 10 because we don't have a form of check and balances in the advanced world they have a way of checkmating in this thing look at what happened in the United States past the speaker of the House of Representative was removed because his colleagues felt that he was not he was not performing and it was a bipartisan remover just from Democrats and about eight from the Republican Party and they removed him and that was it and by this week by tomorrow I think they should be electing another speaker for the United States of America so Liberia is the oldest democracy in the whole of Africa I think they got their independence about is the oldest independent country in the whole of Africa and they've been going to the pool today and it should be a tough one for all will this be judge where second term I can't I can't remember right now this is second term there's this second term there's this second term there don't forget that she took over Mrs. Aleph Aleph Saliff yes that and it's at least Johnson and this is second term and I hope that he gets it and to interpret so many of the foreign observers yeah but you know I'm surprised that you know this is the report on where because you know when he was campaigning back then some of us here in Nigeria thought oh he would make a good leader for his people so to hear that you know his people are disappointed this is a major shocker you know that's what I said that's it African politicians they promise you haven't heard at the end of winter when they get there they don't and you don't be delivered don't forget that before that he also contested against Saliff and lost so when it was now when the when he not contested the last time that he won and there was so much because one most librarians read that he's exposed he has seen the way it is done in the in the other part of the world yes and that's what we're able to do and he's young yes and young but most librarians believe that he would be he was not able to deliver on his promises but that could also be due to certain with so many factors I don't know I don't know the GDP of Liberia I don't know what the export I don't know what they make I don't I don't know they are what what they are generally what they make in terms of their GDP if he has the resources to be able to deliver on some of those promises but to so many librarians they say he made promises that he could be whether that will not translate in his losing the adhesion is seen so we wait and see as I was saying before so many observers are already in national observers are already in Liberia former INEC chairman is leading a part of some international observers then also former president Buddha of Jonathan is also leading another international observers so all of them are already in Liberia and that's what happens today after the adhesion all right there's something I almost missed here on the script here on top of the main headline on the nation you have Tinnubu appoints Feladu Rotoye for others as eights I didn't hear about that Feladu Rotoye as eights of course oh he didn't hear about it I didn't talk to him oh wow he was appointed he was appointed and one of us also Linda Yipi of Chinese television was also appointed a special assistant to the president Feladu has to work within the media directorate of the president there was a particular I don't I can't remember what he was giving that is a special assistant to the president I was surprised by that this thing because to you is somebody that's very critical of government and has a kind of vision somebody even that contested he contested yes so I'm surprised I don't know what he's going to do with special assistant as an aide yes but ordinarily if he's another client that's not too wrong with anybody accepting to serve service is service you're responsible for yourself whether he'll be able to be allowed to his his ingenuity or yeah this thing will be allowed to place this probably not good because it's wanting to to appoint you as an special assistant of special advisor but it is also for you for the president whoever appoints you to also accept your advice so that has always been the problem in Nigeria so when you're appointed special advisor in Nigeria how many times you've seen as sometimes it's most times you've seen as job for the boys you know creating something for the boys that's what I'm saying so the I don't see what they are pricing now what they got the best congratulations to him let's see how you go I think it was about five or six or point yes indeed and congratulations to Linda Ibe of AIT yeah all right so south of of channels Linda Ibe is it channels or AIT? should be AIT this channel this is no no this channel oh okay congratulations to her so southeast senate caucus renews push for additional ministerial slots your people are looking for more ministerial slots what say you I just said no I just said we don't pass that bridge I just said we don't pass that that's not it because they are very good or not okay let's let's let's move forward then to the guardian newspaper scarcity of diagnostic services cripple cancer care in southeast states your state is still in the news you have this is their big story here on the guardian newspaper and they are saying that 22 million residents of southeast have limited cancer diagnostics machine in public tertiary facilities residents depend on private facilities or referred to other regions yeah it's a very sad one it's a very sad development the health facilities is it's it's totally that most of them are not industrialized that most people in south we have to go to other regions like south south and people come to the south with their doctors for diagnosis on sicknesses that they that can easily be handled and cancer is one of them if you know the rate of people that get have cancer across the globe then it becomes a problem cancer is no longer a death sentence if you can be able to treat it earlier now and they can be treated so so many people have a death that's the problem in this today and that's what we're talking about infrastructure decodinates we want to say that not this specific in the sartons our governments in the sartons have brought before that was a classical example of what the former governor of my state Eritrean subcontractor did in Moustade he he does and make us to believe that he built one general hospital in every local government if you see the one that built in my village close to my village will be shot just a building there's no window there's no door nothing just the yes it is a seven hour review not my place at a place called seven and a half in Ugo just before my village in Ugo that's what you're just in India and nothing I was shot in fact when when he mentioned that when he started I was on the one of those that present to high health in any degree where I was talking I was talking about oh which answer that he's doing very well and health and result and then we're saying now one chance now we're between that between that and the statue of Zuma which one pains you the most oh no not this one not this head facility that's I was only one that Roger did not move in that but so it is the true that is the true situation of the head facility in the southeast and I hope that southeast used to boast of some of the best hospital in those days I mean in India stated I used to be one general hospital that's a place called Amachara in Umahe hmm very very unique University of Nigeria and Sooka used to have one of the best teaching hospitals hospital in I'm sure you may not remember University of Nigeria and Sooka teaching hospitals used to be it ranked among a almost in equal status with a that of loot are you are you you are that I don't know the situation so what is happening to the southeast I mean yesterday I saw in the news that Avia state governor bought computers for the judiciary I'm like really is this news well to them it's news now it's news the fact remains that Avia state even for Avia state you could remember that for almost one year during the time of is it that the doctors were on strike almost one you remember the number of months that it would and doctor's salary so that is the type of situation and I hope that so that OT in Avia state would make a change because Avia state is one of the states in the south is that I'm really very worried about what I used to be until now that we have someone that appears to know what he's doing and sincere don't celebrate yet I don't celebrate yet because there are so much precisely when they come they come with so many promises and rest of them and end up with all they fail to deliver so let us just wait and see what they can be able to do I'm sure I know that the job is enormous but Avia state is one of the should be one of the most viable state in the whole of the south because if you know economic situation economic potential in our part and the area and market yes as you know that the Avia state you you you'll be surprised the same thing with Anambra state look at the hope with which we held Charles Urudo when he was coming in Anambra state oh he's he has a magic want to be able to look at it but go back to Anambra state and as you said maybe about three years after Charles what is it what has been the change you had the what has been the solution they say it's in Urudo with the solution as they provided the necessary solution that is the problem with governance as it we're in the south but that's how it should be looking to so that it should be looked into exactly what should be looked into all of the south because of medical care indeed they should provide enough to cater to the people especially scarcity of diagnostic services you know it's it's not something that we want to hear yes it's not it's not not the cancer actually with kidney you see the rate at which and ideas have kidney failures now so that means that the people cannot even because if you can go to private hospitals for dialysis then that is the standard and so and that's in itself is a it's a very very terrible okay so we have anxiety in Benwe as Cameroon Shed's excess water from Ladodam we're still having this in the news and then you have Shiite PFN differ as Hamas Israel conflict drugs into third day well the Ladodam is not no longer news one is I've been given months back that an indigance should be able to ask these people to that they're going to release water into into the dam and the Nigerians should be able to get in it and the Nehmer and federal government have once people within those uh in marine area to be able to move up or up and you ask yourself where are they going to move to you say people should move where should they move to and that is a problem so but if the water has not be released or it's going to be released then people within that uh in that area should certainly people around those Lokoja and other surrounding states within those area and then we two times we don't know about that which be able to stretch our own part of the of our own and also build and accompany down to be able to absorb most of this water that the Cameranians are going to release but it's not going to turn our party so every year we continue to talk about Cameroon releasing their waters and every part of Nadiambi flooded that in this I've also have some economic effect in that you come to realize that when this water is released most farms especially within the North Central Benue and the rest of just talk about Benue most of these farms should be flooded and that is a just that Benue is the food basket of the nation and you look at Benue Taraba and some of the states in the north if that happened that also is going to affect our output in terms of food food and that also informed the high cost of prices of food items precious from that part of Nigeria so is a total is a team that we should be looking into the various ministries minister of environment minister of water resources and other federal ministries to in conjunction the state government try to find a solution to this pot so that we don't continue to have this issue every year year in year out okay and market has grown six subsidized effects as FG offers 90 import licenses 90 import licenses how much is the dollar now no no no now where they use the dollar now you build your gun now with the look your face how much is the dollar I don't know I don't know the last we heard it was the last we heard it was one thousand one ten at the black market one basic black market so that I wouldn't only want to it in in the black market and the problem of the the fact is that some people are hoping up the dollars now some people are hoping up I know that some people are just using their money especially there was so-called big men some are buying there in the million some drop 100 million by 100,000 dollars I don't know what they are keeping it for so it becoming a problem and we haven't a serious problem but we have to have this that shortfall if we are not able to do the right if I'm not able to export more to any more and that is what we've always said the time we're done the only way you can be able to m one dollar or in exchange is by exporting more so that you can m more that's exactly what we're cooking the federal government the CPL and the federal government or is it an entity recently those of that they are going to get about is it three billion dollars from from one country I can't remember now they used to question if they you know the swore they or include swore yeah and I would say once we get it it's going to push in the effect on the dollars we didn't say the protein effects there has been a rise you know for about 45 percent 40 to 45 percent in the price of dollar since the government came in and that is not looking good at all that is not looking good at all so those in the man manufacturing association of Nigeria are crying they said that they cannot be good to they cannot be good to breed and people are being sacked on a daily basis because the companies are not producing enough and those that even they produce they can't even sell they cannot compete within the in the time they cannot so a lot of problem with it are economic in fact man says you're having the toughest time in history that is it that is it and the the president the vice president Shethima is the head of the economy team of this government and I hope that I'm able to put a very very not just inviting governors every time to this neck meeting to look at the issue of they should be able to put a very very holistic economic thing just like Opus and your did Opus and your was so creative in picking his economic team and that was what they were able to achieve what they achieved during your time I hope this government can be able to put that we have somebody in the minister of finance now who is supposed to be a technocrat we've gotten somebody in central bank who is a technocrat we've gotten other people as everybody I hope they're able to come together and just give us a solution to the problem as it were but as it is if we're not doing the right to which is producing enough and exporting more then this this the law will continue to go down the way it is okay thank you Chris this is a good place to end the story and we do hope that in your in the southeast that they will do something about the health sector so that they can fix make things don't worry and let me go and let me go so that I can start to make it my own Chris for governor emo state I'm your number one fan Chris thank you so much for your time on of the press thank you very thank you very much have a wonderful day ahead you too Chris Kendewan the member of the Chartered Institute of Obitrators in the UK has joined us here in Lagos on of the press we'll be back in the moment with our first hot topic can Nigeria export good products can we export refined products and make profits is the subsidy up or is it back


Hi, and welcome to the session. Today we will discuss the following question. The question says state whether true or false and the statement is all parallelograms are trapeziums. Let's see the figure of a parallelogram and a trapezium. Alright, here we have a trapezium and a parallelogram. Let's recall the definition of a trapezium. It says in a trapezium one pair of opposite sides is parallel, that is AD is parallel to BC. Now, in a parallelogram we have both the pairs of opposite sides as parallel, that is PQ is parallel to SR and PS is parallel to QR. That means a parallelogram satisfies all the conditions of a trapezium and thus a parallelogram is a special trapezium with both the pairs of opposite sides parallel. Thus our statement all parallelograms are trapeziums is true. So here we finish this session. Hope you must have enjoyed it. Goodbye, take care and have a nice day.