All right, welcome. This will be given all the multi-threaded stuff and all the locks and all that fun stuff. This lecture will be very straightforward and easy. So yay, we've reached the other side of the hill. So today we will talk about basic scheduling and this should be kind of what you're already doing and be fairly straightforward and they're just, I guess, more easy questions to put on something like a quiz. So let's get to it, we can probably go through this. So there's two types of resources. So being preemptible or non-preemptible doesn't just apply to like a CPU, it's a more general term and just means that you can take something away. So preemptible resources are resources that can be taken away given to something else and given back so you can switch back and forth between them. The preemptible resource we're all used to is just a CPU and it can be shared through things like scheduling. So you get some time, you get some time, you get some time and we all get to use the same resource. Well, non-preemptible resources are something you can't take away without at least some type of acknowledgement. So like think about your disk space. I can't give everything full access to the disk. I have to just give it some space on a disk. I can't take it away unless I like do something really rude and delete it. But there should at least be some acknowledgement so you can actually program against it. So another example of non-preemptive resource which would be something like memory. I just can't give you memory and then take it away and then expect your program to actually work. So a resource that is non-preemptible is usually shared through allocations and deallocations. So that's exactly what you do for memory. It's essentially what you do for disk and we're kind of used to it now. So just as a note, if you have super high throughput systems where you actually need to control resources, you might make CPU non-preemptible resources so you just give a program a CPU for the whole time and just let it execute as fast as it can. But you won't see that unless you're in like high performance computing or something like that. So there's two things. There is a dispatcher and a scheduler so we already kind of know what the dispatcher does. It's like the low level thing responsible for the context switching which is essentially like that get context function. You could also call the thing that does the context switching and dispatcher which you've kind of already written for lab two. And then the scheduler is the high level policy which we haven't talked about yet. And it's responsible for deciding what processes to run. So the scheduler runs whenever the process changes states. So for now we'll consider non-preemptible processes. So a process just executes until completion. So it's like the old batch systems of older year. So once a process starts it will run until it is completed. So in this case, the scheduler only has to make a decision when the process terminates and it has a free CPU and it just has to pick the next process to run to completion. So if you have a preemptive scheduler it allows the offering system to run the scheduler at will. That's what you'll be doing in lab three and that's what happens on something like Linux. So if you do uname-v in part of the description it will say preempt and it means it is a preemptive scheduler which everything has been for like the last 30 years. So when you argue about scheduling and want to quantify how good it is there's a few metrics we want to keep track of and try and minimize. So one is you want to minimize waiting time and response time. So if a process comes in you might want to react to it as quickly as possible and that is response time. So it's the time from when the process first arrives until you first give it some CPU time. And then the other one is waiting time. So the total, which is the total amount of time it is waiting and not running when it could otherwise be running. Then if you care about your CPU, right? You want to maximize CPU utilization. You don't want a CPU idle if you have a machine with eight cores you want them all running at full speed if you can. Then you want to maximize throughput so complete as many processes as possible and some of these are at odds with other ones. And then last metric we'd care about is fairness. So you want to try and give each process the same percentage of the CPU ideally if we're being completely fair. So the first scheduling algorithm we can use is not really even an algorithm. It's what we're all used to. It's called first come first serve or FCFS which is the most basic form of scheduling, right? You go to the food truck outside. You are in this type of queue. You line up whoever gets, whoever at the front gets served first and then it goes on and on like that. So it's just first come first serve. So in this, the first process that arrives gets a CPU and the processes are just stored in FIFO order and something like a queue. So we'll use Gantt charts to illustrate schedules. So everything will look like this as we go on and this is the type of questions you'll be asked. So you'll be given something like consider the following processes. So in this case, in the first column we just have a process where we just give it a name. So we have process one, two, three and four and then the arrival time. So at what time does the process arrive in this case, they all arrive at time zero and we'll just start everything at time t equals zero. And then burst time is just the total amount of CPU time this process needs to complete, which is pretty much just for arguing about the metrics of this in real world situations you wouldn't know the burst time, right? Your kernel, whenever you start a program has no idea how long a program could last and it has no idea how to predict that as well. So for this example, let's just assume that process, so they all arrive at time zero and we'll assume, it's somewhat atomic that we have some order where it's ordered by one, two, three, then four. So if we schedule it at time unit zero here, the first process that arrives given that order is process one and it's burst time or the amount of total time it takes to execute is seven time units and we'll assume that you can only break things down to the smallest unit you can do is one time unit. So here it would take seven time units, so that's the P one here in the darker color that takes seven time units to execute and that t equals seven, it's done executing because it's had at seven time units. So now at time seven, the things left in the queue are process two, three and four, so two was at the head of the queue, so at t equals seven, we would schedule process two and then process two, if we look at the chart, takes four time units, so one, two, three, four, takes us to t equals 11 and then process three is next which only takes one time unit which takes us to t equals 12 and then it goes on and then it takes 16 time units to complete everything. So the average waiting time is just the amount of time each process is waiting for, so process one is waiting for zero seconds because it comes in at t equals zero and gets scheduled at t equals zero, so it's waiting time is zero, then for process two, it's waiting time is seven because that's how long it takes to start and then p three is 11, p four is 12. So average waiting time, nothing special about it should, so it should be zero plus seven plus 11 plus 12 divided by four which is if we want our average wait time that should be 7.5, all right, make sense for everyone? All right, so if we just assume they all still arrive at the same time unit so they all still arrive at t equals zero and the only difference is we'll just assume that they're in a different arrival order, so it goes three, two, four, one instead. So now our scheduler would look like this, again first come first serve, so we do p three which would only take one time unit, then p two which would take four time units so it would complete at t equals five, then p four which again takes four time units so it completes at t equals nine and then we have finally p one which takes seven time units so it completes at t equals 16. So our average waiting time now would be zero plus one plus five plus nine divided by four. So if that's our average waiting time that would be 3.75 which we decreased our average waiting time by half just by changing what order the tasks arrive in. Okay, any questions so far? We got way shorter waiting time just based off the order so there must be something to this. So if we want to actually minimize the waiting time we have a slight tweak to it where if we have a bunch of processes we can do the shortest job first. So as we saw before what kind of happened before is we were always doing the shortest job first in the previous slide so we always did the shortest job first and then that is a way to minimize the average waiting time. So we'll always sketch so instead of first come first serve yep, yes, yep, yeah so shortest job first we'll have a tweak and we'll see the tweak where we have different arrival times, yep. Yeah, like I said before the kernel would not know so this is just so we can argue about different scheduling algorithms and actually show what trade-offs are but realistically the kernel is not going to know. Okay, so we'll still assume no preemption in this case and we'll slightly change the example so that we have different arrival times. So now not everything arrives at t equals zero so on the Gantt chart here on the top row I put in all the arrival times so p1 arrives at t equals zero, whoops, p2 arrives at t equals two, p3 arrives at time equals four, p4 arrives at time equals five so I just put the arrival time at the top so we can keep track of it. So if we're at t equals zero the only process that has come in at that time is p1 so we have no choice but to schedule it so we would schedule it and again we're assuming no preemption so as soon as we schedule it it runs until it completes so at t zero we pick p1 so we're stuck with it until it's done after seven time units so while it's executing all the other processes arrive so we have p2, p3 and p4 at t equals seven whenever p1 is done so if we do shortest job first the shortest job is the shortest burst time so we would pick p3 so we would pick p3 to run at t equals seven it would run, finish then at time equals eight we have a choice between p2 and p4 and at this point it doesn't really matter because they have the same burst time so they have the same completion time so if there's a tie generally you just pick doesn't matter but generally you can just pick whatever one arrived first and if there's a tie for completion time you can fall back to first come first serve so in this case if we compute our average waiting time again it's going to be slightly different because we have different arrival times so for p1 our wait time is zero came in at t equals zero and it got scheduled at t equals zero and then for the rest of them because we're non preemptive you essentially just count the difference from when it arrived until when it started executing so for p2 that would have taken six time units because it arrived at t equals two and didn't execute until t equals eight so it waited around for six time units for p3 it only waited around for three time units so it arrived at t equals four got executed at t equals seven and then for p4 came in at t equals five and couldn't get executed to t equals 12 so if we do our average waiting time it's four any questions about that? Nice and simple finally right? After multi-threading so this is not super practical so you can provably minimize the average waiting time if you have no preemptions but again like you commented that you don't know the burst times for each process you could do something like you use past executions to predict the future that's not gonna be perfect you could try and stick some AI there too again that's not going to be perfect and then you also might starve long jobs so they may never execute so assume we had a schedule like this where just one task takes like a thousand time units and in this scenario it would never get executed because you'd still get tasks with like a bunch of fours and a bunch of tens or something like that so your longest running task might never get executed and that's called starvation and then the first tweak we're gonna do is we're gonna make it difficult now and say that we can preempt processes so if we wanna tweak shortest job first to work with preemptions we can just do shortest remaining time first and again we'll assume that the minimum execution time was just one time unit and similar to shortest job first this will minimize the average wait time so here's the same thing again but now we can preempt whenever a new process comes in and immediately schedule it as long as its remaining time is lower so we'll have the same arrival times as we saw before and the same burst times so now if we do shortest remaining time first at t equals zero we only have p one so we would schedule that so it would run for two time units and then process two arrives so if we do the shortest remaining time first for p one it takes seven and we execute it for two so it has five time units left to execute and whenever p two arrives it has a total execution time of four time units so p two is we'll finish faster than p one so we just kick p one out and schedule p two instead so everyone follow that so we have five time units left for p one so we would schedule p two for two time units and then when p three comes in we might have to make a decision so we can reevaluate so now p two has two time units left to execute and p three just arrives and it only takes one time unit to execute so we would go ahead and switch over to p three and execute it for its one time unit until it completes now at t equals five p four also arrives which will take four time units but now our shortest remaining time is going to be that p two we kicked off that only has two time units remaining so at t equals five we would just schedule it for its two time units and let it finish off that t seven we'd have p four which takes four time units left and we'd also have p one which still has five time units remaining shortest remaining time first we schedule p four so we schedule it for the four time units and then finally at t equals 11 we only have one task left we'd schedule p five for the last five time units so if we do our average waiting time we have to just calculate the total number of time that process is waiting when it otherwise could have executed for p nine it's waiting around for nine time units so it got started immediately at t equals zero but between t equals two and t equals 11 it was just waiting around because process two, three and four all executed to completion during that time and then if we look at the waiting time for process two it only waited around for one time unit right here while process three was just sitting around while process three executed and then process three waited for zero time units it got scheduled immediately and ran to completion and then finally p four waited for four time units because it arrived there at t equals two or t equals five and what finally began executing at t equals seven so if we do our average waiting time now we got it down to three when before it was at four right yep yes so the comment is does this still have that starphasian problem and it does where you would start long jobs because essentially we just tweaked it to run with preempt we just tweaked it so it can run with by preempting processes okay now we get to the difficult one yep so the waiting times so you just take the total time from when it arrives until it completes and you can just subtract out the total time it takes to run and that's how long it's waiting for so like the total waiting time for p one so it comes in here and it doesn't complete to here so and it's not executing here yeah so nine time units so it's the total time it takes if you don't wanna count like the individual squares easiest way to do it is just time it comes in time it finishes and then minus how long it takes to run and that's how much time it was waiting for okay so now is our hard tweak which isn't very hard and it's the first scheduling algorithm that is actually used in offering systems and kernels and I guess lab your labs so it's just called round robins because so far we haven't handled fairness we just did the shortest thing first and we just tried to minimize our waiting time so in this case we're going to make it fair but there's gonna be trade-offs with other metrics if the other metric is probably reduces the average waiting time then if we make it fair it is not going to have the lowest waiting time so we are going to have some type of trade-off here so what the offering system can do and what happens in lab three is the operating system could just divide the execution into time slices and this is called quanta don't ask me why they wanna seem harder than it actually is so an individual time slice is just called a quantum and you do essentially the same thing where you maintain a FIFO queue of processes really similar to first come first serve and then you just serve them in order but they only have set time slices so you try and be a bit more fair so you just preempt the process if it's still running at the end of the quanta and just re-add it to the back of the queue and it just takes the next turn so there's going to be some practical considerations for determining our quantum length anyone care to guess what metrics we'll be trading off here yeah so one of them is the number of context switches we'll have so the shorter it is the more context switches we'll have and then what could be another one in terms of things you might care about for processes which is kind of related to the polling question before yeah responsiveness right so if we have it super super short we're going to have a lot of context switches but we're going to be very responsive so whenever a process comes in there's a set time that you know it will take until it actually executes and again drawback of this is if the quantum length is too long so if it's you know essentially if the quantum length is infinity this is something we've already seen if the quantum length is infinity it is first come first serve so there'll be no difference so let's see that so here's round Robin we'll say there's a quantum length of three time units so the most any process can execute is three time units so again we'll put a rival in the top and then I'll show at the bottom here all of these that is the current queue just so if you write this out it helps keep track of it so same processes same arrival time same burst time same everything we're just changing the scheduling algorithm in this case so for process one it comes in at t equals zero it'd be the only thing in the queue so it would execute and our quantum length is three time units so it would be able to execute for three time units and then we'd preempt it and then check our queue so at t equals two p2 arrives so that would be at the front of our queue process one is still executing and then at t equals three we would re-add p1 to the end of the queue which now has p2 in it so at t equals three we would schedule p2 everyone on the same page alright so we schedule p2 we schedule it for three time units it gets its three time units it still hasn't done yet and in the meantime our queue grows at t equals four while p2 is executing p3 arrives so it would go to the back of the queue so it would execute next after p1 and then at t equals five p4 gets added so it would be at the back of the queue again and then at t equals six we put p2 all the way at the back of the queue and then for the next thing the next process to run we just pick off what's at the front of the queue so we execute p1 for another three time units one, two, three which takes us to t equals nine and it's not done yet so we re-add it to the back of the queue so at t equals nine our queue looks like p3, p4, p2, p1 so p3 is at the front we schedule that it gets a maximum of three time units but it only takes one time unit to execute so it just executes for its one time unit and then it's done then at t equals 10 we get a new chance to make a decision so we'd pick off what's now at the front of the list which is p4 it runs for four time units or sorry, three time units so that takes us to t equals 13 where it gets re-added to the back of the queue then our next process is p2 that only has one time unit remaining we schedule that p1 is at the front only has one time unit remaining schedule that and then we schedule p4 which again only has one time unit remaining so does this make sense to everybody? this is literally as hard as it gets as a nice break for us oh sorry this is the tricky part where we have to count so we have to count the number of context switches so it's just the number of times we change processes so one, two, three, four, five, six, seven no tricks, no anything we're just counting the number of context switches so we can compare the difference between different scheduling algorithms so there are seven context switches if we take the average waiting time it's going to be eight time units for process one so it doesn't finish executing till t equals 15 and it starts at t equals zero so if you take 15 minus seven it's waiting around for eight time units and we can verify that by counting it like one, two, three, four, five, six, seven, eight so it's waiting for eight time units until it's not done executing then similarly for p two p two is done at t equals 14 and it comes in at p equals two so it's waiting for one, two, three, four, five, six, seven, eight waiting for eight time units and then process three waiting for five time units process four waiting for seven time units so if we calculate our average waiting time it's seven and then if we want to calculate a response time it's going to be the time from when the process first comes in until when it first starts executing so for p zero its response time is zero came in at zero, got executed zero for p two there's one time unit came in at two and started executing at three and then for p three it came in at four and started executing at nine so it waited around for five time units and then p four came in at five and didn't start executing in 10 so it waited around for another five time units before it started executing so here's all our numbers if we write them down so most of the time when you compare scheduling algorithms you count the number of context switches the average waiting time and the average response time and then quick note here if there are ties so a new process arrives while one is preempted generally you always favor the new process in if there's a tie and the rationale behind that is well if I favor the new process instead of the old one all I'm doing is reducing response time for the new process, right? The first one already had its response time so, oh sorry this is as hard as it gets so we'll just reduce the qualm length to one time unit so we just write over and over again but it's not any harder so now if we have a qualm length of one time units we just have to argue our queue over and over again so at t equals zero p1 is the only thing there so it would execute then check the queue again p1 is the only pass there then at t2, p2 comes in so we would favor that because p1 would end its time unit so we'd put it at the back of the queue so this is where our tie comes into place so we would execute p2 and then they're just going to go back and forth and around Robin so there's only those two tasks so we're going to get p1 then our queue is going to look like it's only going to have p2 on it and then p3 comes in so there's a tie between re-queuing p1 and then the new task p3 so this is our tie so we'll favor the new task which is p3 so at that point we would schedule p2 and then p4 would come in at t equals 5 and there's now another tie with two re-adding itself to the queue so we'd put four ahead of two and this would be our queue so this is just going to be our whole queue we're just going to round Robin at one process at a time so you'd have p3 get p3 executed it's done it only executes for one time unit then the next process is p1 and then p4 then p2 then p1 then p4 then p2 and then at that point p2 is done executing then you have p1 p4 you can see is the round Robin and then p1 then it's done executing then p4 then it's done executing so any questions on this? so there's just a queue it keeps on re-adding, yep so the question is at t equals 4 why didn't we favor p3 and we did so right at the time here right at the time here when p1 is executing there's only p2 in the queue so right at t equals 4 when p3 comes in we would just add there's a tie between p3 and p1 re-adding itself to the queue so that's why we put p3 here ahead of p1 so that's our tie so we do that we just go on and on and on again and you just have to pay attention to you just have to pay attention to how long each process has left and when to throw them out of the queue because they're done executing so if we go through again and calculate all of our numbers again so p1 if we do it's well first we do the number of context switches so 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 right you just count the number of context switches so in this case there's going to be 14 context switches instead of what we saw before which was only 7 and you'd calculate the average wait time and average response time so if we do that these numbers should make sense that the average waiting time went up a little bit so now we're at 5.5 as our average but because we use a shorter quantum length we can actually demonstrate that our response time is better so we're at 0.75 instead of before when we were at 2.75 so we had and nicely enough to our waiting time went down so we got a lower average waiting time and we got a lower response time and our drawback was we context switched twice as often so before we had seven context switches and now we have 14 cool so here's it again same question but now we have a quantum length of 10 units so if we do round robin with that again q at the bottom and schedule at the top at p t equals zero we only have p1 so we schedule it and it gets up to 10 time units so it only takes seven to execute so it would just be scheduled for all seven and in that time p2 comes in which would again the q's at the bottom so p2 would be at the front of the q then p3 comes in so it would go to the end of the q at t equals 4 then at t equals 5 p4 comes in and it's at the back of the q then time seven when process one is done executing we just pick off what's at the front of the list which is p2 it executes for its four time units then it would execute p3 that's the next thing in the q it's less than it's less than the uh it only takes one time unit to execute which is less than the quantum length of 10 so it would just execute to completion then we only have p4 to schedule we would execute that to completion and if we run the numbers on this this also is exactly the same as our first come first serve schedule right because our quantum length was bigger than the time the longest process takes so so it's not going to do any better but it is going to further reduce the number of context switches because in this case we only have three context switches but if we crunch the numbers our average waiting time is now somewhat better than before with our lower with the lower quantum rates it's 4.75 but our response time is now terrible yeah yes the average wait time isn't necessarily dependent on it and like all with these like with every scheduling algorithm it's a hotly debate topic because it's all trade-offs you can't have a perfect one that reduces everything so then there's tons of fun scheduling names and people just make up their own schedulers all right so in this case it was the same as first come first serve so your round robin performance is going to depend on the quantum length and also the job length which most of the time you can never predict the job length you can only analyze it after the fact to see how well you did so round robin has low response time pretty good interactivity it has fair allocation of the cpu a fairly low average waiting time when the job lengths vary so you can kind of game it to make the waiting time as bad as you want but the performance really depends on the quantum length if it's too high it becomes first come first serve if it's too low there's just going to be too many context switches which is just wasted time that your cpu is just wasting just to swap registers and context switch between programs so if you actually go through a bunch of examples typically for this they'll just simulate a bunch of schedule like a bunch of processes and then see how each individual algorithm does so if you do this and kind of figure out trends round robin has a really poor average waiting time when all the jobs are of similar length which kind of intuitively makes sense all right any questions about that so scheduling is like super really dependent on your load and just a whole bunch of other factors that you can't predict and there's lots of trade-offs you can do so we looked at yeah so linux has two main scheduling algorithms one is round robin which we just went that we'll see in the next lecture which is a bit more advanced but actually not that not that complicated it's called the completely fair scheduler that tries to be more fair and divvy up cpu resources a bit differently there's also a few fun schedulers one is called brain f and that's a fun scheduler that people seem to like like you can create your own schedule for the linux kernel and see if that works any better yeah yeah so that's a good question for the next lecture is what if jobs have priorities you might want to complete one over another and that's that's the next lecture so next lecture we're basically just going to add in priorities and see what the linux actually uses yeah so you'd care the average response time if it's like a gooey application and you're a user so more a faster response time it's just going to seem like the system reacts instantly and it just feels a lot better so there's always going to be some type of trade-off so sometimes like windows will do something different and a lot of the feel of different programs just is how it's scheduled and there's no right or wrong reason right or wrong answer just depends so scheduling involves a lot of trade-offs we saw first come first served the most basic scheduling algorithm that we're all used to in everyday life then we tweaked it to do shortest job first to reduce the average waiting time then we added preemptible processes and we came up with shortest remaining time first and that optimized the lowest waiting time so which is also called turnaround time that you might see and then we saw around robin which optimized fairness and response time so that's it nice we can take a breath just remember I'm pulling for you we're all in this together and let me


Hi, Steve. This is Ashley with Augsmore Toyota. Thank you so much for your recent interest in our 2016 Avalon. Went ahead and answered your questions and sent over an email with everything. When you get a moment, please give me a call. My number is<PHONE_NUMBER> or just email me back. Thanks. Bye.


I think that talking is quite paramount and I think that school doesn't encourage enough of it and I think that if we did more talking around mental health when your mental health was in a good way and when you weren't struggling particularly then it would encourage more people to talk about it when they're not feeling so good and when actually they need help so I think that school should encourage being open about the state of your mental health. And how could they encourage that? I think that we should do more on PSHE because we don't do anything and I think that that would help to reduce the stigma but I also think that they should be more open about the services they offer rather than only offering you counselling if you ask for it and even then you only get a 15 minute session with the counsellor as Zach and Ellie said it's in that horrible room so I think that they could improve that quite a lot too. I think schools should, specifically with a school nurse in the house office maybe put more in the environment where you feel more comfortable don't surround it by school work or anything that reminds you of the stress because that can be an issue and also people can usually see in and it sometimes doesn't feel as confident that it should and the atmosphere may not always. Having that safe adult there maybe we get to choose rather than having a select person that is of a specific gender and a specific age that maybe we don't want to talk to necessarily. To like multiple options? Yeah rather than just the one which may not get along with like people in therapy don't always go with their first therapist they need to take time to choose to trust someone to build up a relationship. I definitely think so people with anxiety stuff like that when you're having maybe like a little bit of a breakdown in class you need somewhere to go after because normally say if I ran out of class you'd probably get a detention for doing that so I think if we had somewhere to go that was comfortable and felt like you felt good there would be really good. Definitely the teachers have made, well some of them have made a difference start of term checkups like I had won yesterday and they basically ask you if you're worrying about coming back to school if you've been to Ken holidays and I think that really helped to maybe improve a bit more on that. Help sort of ease you into it and like show that there's a support system if you need it and that actually you haven't just been forgotten about and it's not a chore to sort of look out for you. In school there are many many things that can happen in exams so you can be referred to have a separate room for your exams you're not having to be in the exam hall with everyone else which I think is a very very good idea for some students in school. The stronger minds the school facilitating that's been particularly good because I mean when you're there you're like I can't think of a time that I've been with more like minded people that are going through similar things to me and I mean I felt quite on my own a lot when I was struggling with anxiety so when I went and talked to all of these people that were going through similar things that felt the way that I felt but knew how it was in the life that we were living it was a good place to be. Yes you got kind of mutually supported did you find that as well? I think there should be more staff training on a regular basis to do with mental health so teaching staff how to cope with students who walk out the lesson having either a panic attack and they need that support from their teachers which teachers can't give because they're unsure of what to do. I mean they always seem to presume that it's about the education well do you not understand the maths but I feel like they lose sight of the bigger picture sometimes and that actually being in this maths lesson isn't the most important thing going on in your life at the time so I feel like they need to realise that maths isn't actually the priority when you're going through things. They don't even have to offer support they just have to like sort of establish that they are sympathetic and that they're not actually trying to make anything harder for you because sometimes I feel like it's the way it is when they try to stop you from going out of class or that sort of thing. So I think it would be useful to do practical workshops and like examples and stuff and show them different statistics and how other people feel and obviously hearing it from a younger person's perspective they can understand how we feel as them talking to us about it their sort of them hearing it from us is helping them because we're the ones experiencing that so it can help them better their understanding. I feel like the teachers just need to be more open to talking about it because I feel like they are worried that they'll say the wrong thing or worried that they'll react incorrectly but I think the most incorrect reaction is to not offer support because that just makes you feel like you're not being heard.


What does repentance mean? In the previous video I explained the problem with the repentance of your sins to be saved Gospel message. Mainly that there are many verses in the Bible that say repent but don't go on to say of your sins. So we need to understand what repentance actually means. Now we could go to the dictionary but the problem is with the dictionary is that it's based on how we use words in language and so unfortunately religious dogma on this issue has tainted how repentance is defined in the dictionary. So we're just going to stick to the Bible and allow the Bible to tell us what it means. So we want to understand the core word repent and remove this silly man-made context of your sins after it. What does the word repent mean? So repent is a verb and that will include past and present tense such as repented or repentance or repenteth. The action word is doing the act of repenting. It can act as both a transitory and a non transitory verb. Repentance in the noun form is the state of being of somebody who has repented or describing the act of repenting in the noun form. So to start let's take a look at something people are very familiar with John the Baptist's repentance. So it says in Matthew chapter three verses one and two repent ye for the kingdom of heaven is at hand. Now what does John the Baptist mean by this? Well Jesus tells us in Matthew 21 32 for John came on to you in the way of righteousness and you believed him not but the publicans and the harlots believed him and you when you had seen it repented not afterward that you might believe him. Paul further qualifies this in Acts chapter 19 verse 4 in saying that John verily baptized with the baptism of repentance saying unto the people that they should believe on him which should come after him that is on Christ Jesus. So in summary John the Baptist is pointing people towards the coming messiah and in hindsight both Jesus and Paul understood this as saying that the people were supposed to repent by believing on the Lord Jesus Christ that John was pointing them to. This context tells us that repentance means to turn towards something. In this case John the Baptist told us to turn towards Christ by believing on him. This then helps us to understand repentance when people are called to repent towards something. For example in Acts 11 18 God grants the Gentiles repentance unto life or repentance towards life. So the repentance is turning towards life or in context eternal life. Now let's look at an example of using repentance in a different way. It says in Revelation 2 21 22 and I gave her space to repent of her fornication and she repented not. Behold I will cast her into a bed and then that commit adultery with her into great tribulation except they repent of their deeds. So in this passage Jesus is giving messages to several churches and Jezebel was part of the Church of Thyatira. Jesus commended the church for their works but there were a few issues that Jesus had against them including Jezebel's fornication and the deeds of the people who were seduced by her. This context tells us that repentance means to turn away from something. In this case Jesus told Jezebel to turn away from her fornication. In this context it is a sin issue but there are passages where turning away from something is not a sin issue. For example in Jonah 3 9 it says who can tell if God will turn and repent and turn from his fierce anger that we perish not. In Jeremiah 42 10 it says I repent me of the evil that I have done unto you and this is not talking about sin. Now in these passages God repented and God has no sin. So we see that repentance cannot automatically mean to turn from sin unless sin is explicitly stated as the context of what is being repented of. So a most basic definition of repentance would be an about turn. A turning away from one thing and towards another. It's more specific than the word turn though because it typically refers to two opposing directions. Sometimes repentance is a change of action. Stop doing one thing start doing another but it can also mean a change of mind even when there is no obvious change of action to prove or demonstrate that the change of mind was genuine. Let's look at a couple of examples. It says in Exodus 13 17 and it came to pass when Pharaoh had let the people go that God let them not through the way of the land of the Philistines although that was near for God said lest peradventure the people repent when they see war and they return to Egypt. This is an interesting example because this would have been a bad repentance once again proving that repentance does not automatically mean turning from sin. In this case if they had repented there would have been a change of action. In Luke 17 3 and 4 it says take heed to yourselves if thy brother trespass against thee rebuke him and if he repent forgive him and if he trespass against thee seven times in a day and seven times in a day turn again to thee saying I repent thou shalt forgive him. This is an interesting example because if your brother sins against you seven times in the same day he only has to say that he repents. He does not actually have to demonstrate his repentance with fruits by not sinning against you and yet you are commanded to forgive him no less. This is an example of a change of mind irrespective of whether it results in changed actions. Now let's look at a couple more verses before we finish so that we can apply more definitions to the word repent. In Job 42 6 it says wherefore I rapport myself and repent in dust and ashes. You may well be familiar with the concept of sackcloth and ashes in the Bible that people prayed in this way when they were extremely sorrowful and remorseful. So this would be an example of where repentance means having sorrow or remorse possibly including guilt. In Job's case it wasn't so much his actions he had to repent of but rather his ignorance and lack of understanding. In Psalm 110 verse 4 and this is quoted in Hebrews 721 the Lord has sworn and will not repent thou art a priest forever after the order of Melchizedek. Now this would be another type of repentance referring to God and a bad type of repentance proving that once again repentance does not automatically mean turning from sin. This verse has nothing to do with being sorrowful or remorseful it means changing course or rather not. Sometimes God repented due either to man's wicked behavior or man's appeal to God. Other times God cannot or will not repent because he has eternally purposed and promised to do something irrevocable. So in summary we see that repentance means to do an about turn from one direction in another direction in other words a change of course a change of action a change of mind. It may include sorrow and remorse and guilt but not always. So we see that repentance is not a doctrine it does not demand a 12 paragraph article about your incredible testimony of weeping over your sins before you surrendered everything to Jesus. It's a simple verb as in walk or see or do and it needs context to understand how the verb is applied. When the Bible says walk in the spirit your first reaction is not to put one leg in front of the other in repetitive succession to visit the girl somewhere because in the spirit is the context which changes the application of the verb walk. Likewise the surrounding context of the use of the word repent indicates how it applies. In Mark 1 15 it doesn't say repent of your sins there's no mention of sin sin is not the context it says believe the gospel of the kingdom when it is preached on to you that's the context of what repentance means in this verse. In Ezekiel 1830 God will judge the house of Israel according to his ways and he is telling them to repent and turn yourselves from your transgressions so iniquity will not be your ruin now transgression or iniquity which are synonymous with sin that is the context in this verse it is about repenting of sin. In Acts 19 4 when it says John verily baptized with the baptism of repentance again it doesn't say of your sins there's no mention of sin here so sin is not the context it does say believe on him which should come after him so believing in the Christ that was preached and be baptized is the context of what it means to repent in this verse. Hopefully I'll talk more about baptism and repentance in a future video but baptism follows belief as is demonstrated in multiple places in the bible so in the next video we're going to explore the question do we have to repent to be saved this is no nonsense Christianity reminding you that nowhere in the bible does it say repent of your sins to be saved


The Senate President, Ahmed Lawan, has advised the incoming Thent National Assembly to maintain a bipartisan approach that prioritizes the national interests that made its deliberations. Lawan said these when he received outgoing British High Commissioner to Nigeria Katrina Liang in his office in the FCT. The Senate President said that the relationship between the National Assembly and the executive should be based on national interest. He also commended the outgoing United Kingdom Ambassador for her work with the Ninth Assembly in ensuring the passage of the electoral act of 2022. The call I would make here is for the next National Assembly to ensure fast and serious capacity building as quickly as we can because we need to bring into form that the members of parliament will be able to kick start immediately. We have to make every possible effort to provide for capacity building for our colleagues, especially those who are new. It's almost 70 percent. That means we just have to spend a lot of resources and we'll take this opportunity to say that the National Assembly needs a lot more resources for it to be in the form and shape that will provide the kind of service, parliamentary service to Nigeria. Speaking earlier Liang described Nigeria's politics as interesting while expressing joy over her stay in the country. The outgoing ambassador said she made very good friends during her stay in Nigeria. And I've had overall a wonderful experience here in Nigeria. I've made many very very good friends, have developed some really important strong working relationships including with you personally and I want to thank you for all the support you've given me and my team, all the many bills you've helped navigate through parliament which is of course the foundation of the whole governance process. It's what goes on in the parliament and the senate is really the foundation of a democracy and I think you've done an amazing job and we've worked very well with you on all of that. So this is really just for me to say farewell, to say thank you, but not the end. I hope we'll continue to whatever role you move into. I'll be certainly watching Nigeria very closely and I hope my successor Richard Montgomery will be able to forge a strong relationship with you in your new position.


You're creating this to disperse. I'm going to need some assistance down here. An impending confrontation of potential threat. Every day all over the country, law enforcement officers are placed into situations such as this one, where they must take immediate action to neutralize the danger. It's a way of life for everyone with a bag. Split-second decisions they must make regarding the use of force necessary to control a situation that can change people's lives forever. The responsibility is monumental because human lives truly do weigh in the balance. But the training professionals at the Federal Law Enforcement Training Center recognize the critical nature of these day-to-day decisions. As a result, they've designed a use of force model, which can help identify and interpret various levels of resistance within a given situation, and which suggests corresponding standardized responses to each of these perceptions. The purpose of this program is to provide officers and students alike with a graphic illustration of that model. Now the use of force model is a graphic illustration, which will be referenced by all of the FLETC divisions throughout your training here at the center. It describes the escalation or de-escalation of the required force based upon a demonstrated level of compliance or resistance from a subject. Its purpose is to help create standardized responses based on an officer's reasonable perception of a threat. Now as you can see, it's a colorized five-tiered model illustrating five levels of threat and their corresponding responses. The bottom level is blue and illustrates the lowest level of activity in the model. This activity includes normal day-to-day non-threatening activities such as verbal communication with complaint citizens. The green level describes the perception of passive and nonviolent resistance by a subject, which calls for an increased level of verbal communications by the officer in conjunction with the physical response of the contact controls. Moving up the model to the yellow tier signals the need for increased officer awareness due to a recognized threat of active resistance. At this point, there's been no assault on the officer. Therefore, the use of control techniques only should be employed to handle the situation. The orange tier defines an officer's perception of imminent bodily harm to himself or to others based on an assaultive posture or hostile resistance by the subject. This level calls for self-defense techniques along with threat elimination strategies. The highest level of threat is depicted here at the red tier where imminent serious bodily injury or death may result. If a threat on this level is perceived, the officer must maintain the highest level of risk assessment and be prepared to use survival skills, including the use of deadly force. As you can see, the model also supports double-sided arrows running from the blue tier all the way to the red tier. These arrows represent a logical and legal cause and effect based upon a reasonable perception of risk. They illustrate that an officer has the option to escalate, de-escalate, jump between tiers, or maintain the level of force that he or she deems appropriate to gain and maintain control of the subject and the situation. It's a dynamic model, isn't it? In theory, it effectively defines enforcement electives as a function of a reasonable officer's perceptions. But what about the real world? Does the use of force model apply in real-life law enforcement operations? Let's continue the scenario we saw earlier to see if it does. Save our jobs Stop management greed Save our jobs Stop management greed Save our jobs Stop management greed Save our jobs Stop management greed Save our jobs I'm special agent Dave Hale can y'all quiet down so you talk about this quiet quiet ma'am Listen, I don't like the idea of the plant closing any more than you do and you all certainly have a right to voice your opinion in this matter But this is not the place to do it. You're gonna have to take your signs and move along. You're creating a hazard Not a chance. We're not going anywhere until we get some answers. Yeah, listen. I understand how you feel I know the times are tough, but there are better ways to deal with this right now What you're doing is you're breaking the law and if you don't leave now, I'm gonna have to arrest you What do you know about tough time?  Yeah, what about our families? What about our retirements? Yeah, what about you? I know that you're upset by this, but there are better ways to deal with this problem You can take it to the courts or you can hold legal demonstrations in authorized areas But you cannot demonstrate here. It's illegal and it's creating a hazard to other people Don't listen to this guy, Harry.   All our lives we've been doing things the right way, and why did it get? Unemployment. Well, we're here to do something about it now. Listen, hold on. Let's just cool down and let's talk about this We're through talking about it, mister. We're here to do something. We have a right to do something For the last time, I'm telling you to move away from this area now Get your hand out of my face. All right, that's it. You're under arrest.  This is a free country. Hey, come on, man.  You can't arrest me. I haven't done anything. I'm on my own Back off now.  I've got every right to be here No, sir, you don't it.  I'm a taxpayer. I pay your salary, pal You're supposed to be protecting me. You don't even do this harassing me.  You're under arrest. Turn around and put your hands over your head I'm not going anywhere, man. You want a piece of me? All right, drop the knife Drop the knife.  Come on Drop the knife. What are you gonna do, shoving? Yes, sir. Well, if I have to, that's up to you. Drop the knife. Now I don't want to do this. Keep my job. Drop the knife. I'm working hard on my life This isn't fair Drop the knife now. Cross the knife over there. Now I put your hands over your head. Turn around Don't move Well, were you able to distinguish the tears of the model? Remember them. Compliance, passive resistance, active resistance, assaultive with a threat of bodily harm and assaultive with a threat of serious bodily harm or death Let's look back and see if we can locate them Save our job Right at the beginning, when our demonstrators have just arrived at the steps we are at the first tier of the model Officer Bryson utilizes her communication skills in the hope they will be compliant and leave the area without incident She is not successful in that attempt When Special Agent Hale arrives on the scene, he again starts on the first level trying to resolve the problem by gaining their compliance We know that didn't work either. The demonstrators continue in their posture of passive resistance However, one of the demonstrators crosses the line, becoming actively resistant But by escalating his level of enforcement tactics in response to this active resistance Agent Hale was successful in taking control of one of the demonstrators A second subject also becomes actively resistant and charges Agent Hale After being pushed away, the subject becomes extremely belligerent Agent Hale continues in his effort to gain compliance, but to no avail The subject escalates the situation to the fourth level of the model by pulling a knife Recognizing the risk of bodily harm and that intermediate weapons such as batons or chemical sprays are inappropriate due to the level of threat our officers draw their weapons in response to the assault The threat de-escalates quickly from here Our demonstrator becomes compliant and our officers take control of the situation Or do they? Remember, there are five tiers to the model So far, we've witnessed only four In addition, orderly escalation or de-escalation up and down the tiers is not always the way enforcement electives work out We all know that situations can begin at any one of the levels of the model Or move from one level to the other in an instant An officer must be prepared both mentally and physically to administer the proper response Based on the level of resistance he perceives With that in mind, let's rejoin officers Hale and Bryson and see if the situation is really under control Get him inside, get him inside now Give my brother a lot, he didn't do anything Good It's not often that an officer is forced into a response from the fifth tier of the model But he must nonetheless be ready to take that action If an assault by a subject places an officer or anyone else in danger of serious bodily harm or death He has the right and the commission to protect himself and the others around him The use of deadly force is a serious matter But it's an option which sometimes becomes necessary So in the final analysis, the goal of the use of force model is to help officers maintain a high degree of enforcement readiness That readiness manifests itself in two ways First, in a willingness to keep enforcement responses on the lowest tier possible through skillful use of physical and communicative strategies And second, in the resolve to escalate to the appropriate level of response whenever necessary to protect human life and to maintain control of the situation It is absolutely essential that all law enforcement officers understand the precepts of the use of force model And are fully confident in their ability to navigate up and down and in between its five tiers So let's briefly recap what the tiers represent The blue level is the lowest level, representing day to day non-threatening activities The green level represents passive or nonviolent resistance and calls for an increased level of verbal communications and contact controls if necessary The yellow tier signals active resistance and calls for increased awareness and compliance techniques Orange warns of hostile resistance and imminent bodily harm to the officer or to others and calls for self-defense techniques And the red tier represents the threat of serious bodily injury or death and calls for the use of deadly force if necessary These standards defined by the tiers of the model go to the very heart of the law enforcement philosophy taught here at the Center And so you will see this illustration often during your training here Learn it well. The decisions you make regarding the proper use of force will play a major part in your career as law enforcement officers


This next one actually gives me a chance to gloat a little bit, which I would feel kind of weird doing that, but let's go for it.  Everyone that knows me knows that I'm big on Carson Wentz. Before the season started, he was in our bold predictions video, and we said that he was gonna throw 30 touchdowns this season, already on pace to do that 17 touchdowns through seven games. We said that he was going to be an MVP candidate on pace to be one. Also said that, hey, he's getting drafted. His ADP, average draft position, is in the 12th round.  He's a fancy football sleeper, and we predicted that. The question is, how'd you know that he was going to have such a big year? It's the parallels between him and Derek Carr and Marcus Mariota. What Philadelphia did for Carson Wentz, it's so similar to what Oakland did for Derek Carr. It's so similar to what Tennessee did for Marcus Mariota, because you look at those three quarterbacks play, Carr, Mariota, and Wentz, you see them play in the rookie year. You see, okay, it's decent stats, nothing special, but it's the most overused cliche saying, and I feel like in sports casting, but it's all about the eye test. It really is, because if you see those three quarterbacks play in the rookie year, you saw the potential that they had. You knew that one day they were going to be polished NFL studs, and that's exactly what is happening with Carson Wentz. What Philadelphia, what Tennessee, what Oakland did for the respective quarterbacks said, hey, your rookie season, we believe in you. You're going to start all 16 games. You're the cornerstone of our offense in the future, and we're going to build around you. We're going to bring in a run game. We are going to bring in wide outs. We're going to bring in so many targets to help you out. We're going to make you feel comfortable. You're going to learn with us, and we're here to help you out, and that's exactly what Philadelphia did for Carson Wentz.


So today my talk is about building and measuring a valuable ecosystems. My name is Mark Odden. I am the CTO of WeTrade. WeTrade is a joint venture owned by 11 banks, 11 European banks. I developed and execute the technology strategy. I designed the operating model of the company. And WeTrade is an innovative trade platform based on blockchain in 15 countries in Europe. WeTrade is the first blockchain digital based trade finance platform in production. Today I'm going to talk about an ecosystem and what's the definition and why an ecosystem and why we talk about ecosystems. I'll talk about blockchain as an ecosystem enabler and knowing your participants and effectively how we actually measure in some KPIs in terms of measuring ecosystem performance. So why an ecosystem? What has changed? The first point used to be data is oil. I think in this green world, we can actually really and in this changing world, we really see that data is a valuable renewable resource. It can be pooled. It can be shared, reused. But we also want to make sure that we make the most out of the data while securing that we can trust and we can protect that data from misuse. The companies no longer are willing to pass that value and the value of the data over the intermediaries. And new business models are being driven based on data insights and even other new technologies such as AI. Why does it matter? We all need trusted data, verified data, non-repudiated data, immutable data. Blockchain is an environment in where collaborators can come together and they can actually put good, stronger governance around the data. Particularly in terms of what data needs to be shared with who and what time during the process. With blockchain, we have a great potential for creating marketplaces and new marketplaces based on new business models. And then again, people can actually use the data for driving insights and innovate new ideas, new products, new services. Why start or why join an ecosystem? Common problems exist. Common problems in trade, common problems in logistics and climate action and in accounting. So effectively, organizations are actually, as they start to collaborate and start to understand the existing business models where they are and they identify problems, then effectively they can come together to solve these problems in a better and more efficient way. They can share risks. There's obviously a lot of inherent risks in terms of trying to go your own and then trying to deliver value, incremental value to customers. So there's a lot of risk involved. So why companies come together or why they start an ecosystem is so that they can actually share that risk. Obviously, finances and even in today's world were from a financial perspective, given the pandemic and the outcome of the pandemic, pooled finances. So being able to, to be honest, work with this new technology, even though it's fantastic that Hyperledger and Fabric and Sawtooth and various other DLTs and tools and components are open. It doesn't mean that it all runs for free, especially when you're trying to actually pool together and create a network or a platform in which a lot of participants actually come together. It can actually be able to run and operate a service with the security, with all of the underlying infrastructure that's required, with actually all the digital development tasks that are required. It becomes very, very expensive very, very quickly. So again, you know, by creating an ecosystem, you can actually share the cost of that across the initial ecosystem members.  You can actually, you know, you can, you know, and one organization doesn't need to take on the expenses all by themselves. When we create value, and we're talking about digitizing and optimizing efficient, you know, the existing processes, but we're also looking at deriving value from new business models, new products and new services, whether they are part of the core offering of the ecosystem, or whether they become value added services. And you kind of look, and you can listen across the ecosystem participants and the users to actually see, you know, to solicit feedback in terms of where do they, you know, where do they see new value being derived, you know, or potential value being derived from the ecosystem. We talk about building and strengthening relationships. And this is a case of, you know, with customers being able to, you know, offer your customers new services, new products, but it's also been able to, you know, derive, you know, to build and strengthen relationships with ecosystem members like like-minded members, where they have the same or very similar appetite for risks, where they're willing to put in the same level of, you know, of investment and pooling that, you know, the finances, where they all have this shared vision of about the value that they're actually looking to drive and deliver to their prospective customers. You know, with the ecosystems, we look at about increasing efficiencies and minimizing governance costs. Again, just, you know, being able to, well, you can pool the finances to generate the initial, you know, the product, the services, the ecosystem, but it's also a case of being able to run and govern.  And we're really creating a distributed shared source of truth without the need for intermediaries. We've all seen, you know, over the last number of years, we've blocked in the whole idea of disintermediating data, disintermediating the service.  And then they can actually really understand it at a very early stage. What can you do? You can join an already existing ecosystem. So, and that's really looking at, you know, optimizing for yourself.  So it's really kind of, you know, you could, you know, one of the options is that you could join an already existing ecosystem. And again, depending on, you know, what, you know, what domain that ecosystem is, again, you could get help and assistance in terms of whether it's business case development, value and really even understanding and learning. One can build a new ecosystem or a new consortium from scratch, you know, and there will continue to be new ecosystems built throughout the couple of years. But again, again, we talk about network of networks. We talk about one network not ruling the world. We talk about one ecosystem, you know, you know, there won't be one, one place. So there will always be places for collaboration and interconnectivity and interoperability between different ecosystems and not just ecosystems of the same discipline, such as trade or logistics or, you know, carbon, you know, climate action and accounting. But again, we'll actually be connecting some of these different ecosystems together to drive even more value, more business models, you know, more benefits to the end users. Or one can expand, you know, so you can join an existing ecosystem and also then offer new value added services to those ecosystem members. And one can then connect to that ecosystem member. So really, I mean, when we talk about expanding, we're talking about really, you know, either joining where you have, for example, you know, you know, a finance trade network, and then you kind of take in a logistics network, because you actually want to be able to take in or you connect within the logistics network because you want to be able to take in the information of and the traceability tracking of the goods. And so there's two ways you can do that. You can have the logistics providers come in as value added services directly on to the trade into the trade ecosystem, or you can connect a trade ecosystem and a logistics ecosystem to arrive then that even expanded value. Blockchain as an ecosystem enabler, multi-party manage, multi-party workflow management is really all about managing the workflow and the data between the multiple parties of the ecosystem. For example, you know, the process between a trade, you know, the trade between a buyer and a seller where the buyer has sought a bank guarantee and the seller has also sought an invoice financing. We talk about the disintermediation. We're talking about really blockchain inherently because, you know, comes with immutability and then non-repudiation, but often as a core driver within a blockchain ecosystem is the disintermediation of data, control and management of the data and removal of the intermediaries. When we talk about governing and the network democratically, it's really all about change. For example, onboarding new ecosystem participants, deploying smart code contracts, agreed by consensus between the multiple parties within the network. From, you know, starting small scale and, you know, with electricity, you know, as members and transaction volumes increase, it's really starting with the whole idea of, you know, starting with a minimal viable product, with a minimal viable ecosystem and then looking to create and achieve value from that ecosystem sustainably while laying the foundations for the ecosystem to scale. And scale and growth will evolve on a continuous basis based on customer and client feedback. And the goal is really to achieve, you know, an organic self-sustaining ecosystem. When we could talk about immutability and traceability, discussions can come, you know, discussions really come about, you know, the dispute resolution. And here we can leverage immutability and traceability of the data to aid in dispute resolution on a blockchain-based ecosystem. And a single source of truth. Really, data is shared and it can be segmented through ledgers, channels, private data collections, but it's really about, like, you know, that privacy, you know, and, you know, providing access to that data on a need to access and a need to know basis. Blockchain business progression. So companies or organizations maybe start to, you know, start kind of a little bit ego system-centric. In other words, they're kind of building it for themselves, maybe as a POC, they come up with a minimal viable product, you know, and effectively, to be honest, this is how WeTrade started. It started with a POC for one individual bank. They had a minimal viable product. They had a very good concept. It was very much at the core, you know, it was actually built in a public permissionless blockchain. It was built on Ethereum originally before we switched over to hyperledger fabric. But again, we just saw, you know, the value of the POC in terms of what it could do in terms of evolving and revolutionizing, you know, trade and with the use of blockchain. And then we actually, a number of members actually came together and they saw the potential in terms of building an ecosystem. So this is where we talked about a minimal viable ecosystem. And again, the arrows really indicate, like, you know, where you had initial core idea and the arrows are different tangents. There could be new opportunities, new ideas in terms of how we could actually evolve and offer new products and services on that minimal viable ecosystem to members of that network. And then kind of we're looking at really, you know, the whole industry utility where, you know, blockchain and at least in some aspects and for some ecosystems will be kind of considered very much a utility. You know, whether it's in food, whether it's in agri, whether it's in maritime, whether it's in trade. Some of these, you know, you know, we are kind of providing utilities and has been able to connect these, but it's all about really kind of designing for a market. We'll also talk about that, you know, putting in incentive models, you know, how do you incentivize new ecosystem participants to join? How do you incentivize the existing ecosystems in terms of to increase in terms of transaction volumes? So really looking at how we can actually look at creating these new incentive models. And again, by actually looking to incentivize, we're looking to create even new digital products, new services, new value added services, some that we thought, OK, would automatically fit in terms of the domains that, you know, where some of these original ecosystems, you know, start out from. And others are really kind of looking at new digital products or services that could be offered, you know, through the ecosystem. And then we really kind of get to the ecosystem centric, you know, as we kind of move over to the right and we talk about token economy, business networks and connecting all of these business networks. Know your participant members. You know, this is actually it was taken, you know, this is taken a reference taken from leading digital, which is really about was a book and written in 2014. So before blockchain, but I was really talking about like, you know, business transformation and turning technology into business transformation. But the matrix actually fits even today, you know, from from a blockchain perspective, I'm still very, very relevant because when we talk about beginners, you know, they may be skeptical of the business value of, you know, blockchain, for example, they may carry out some experiments, but not really fully committed at this stage.  And, you know, they're not ready or other waiting and seeing and they adopt the way to see strategies or approaches. As we moved on into conservatives, you know, they may have an overarching digital vision, but it's underdeveloped. And a few advanced digital features exist, although traditional capabilities, you know, may be more mature. They have strong governance across the silos and active steps to build, you know, and they activate steps to build digital skills and culture. Fashionistas, fashionistas, you know, they advance many digital features such as social media, blockchain, big data, AI, IoT, and they're working on all of them in silos. They have no overarching or they may have no overarching vision on how to connect them all underdeveloped coordination because they're stuck in silos. A digital culture exists, but again, it could be siloed based. And again, these, you know, some of these guys might try multiple ecosystems, even of the same domain or same type. So because they want to be in everything so that if one succeeds, they'll always say, well, we were part of the winner type of thing. And then you've got digital masters, strong overarching digital vision. They're able to get, you know, they've excellent governance across the silos, bringing about the organizational changes necessary when adopting new technologies. And many digital initiatives generating business value and have strong digital vision. And it's really a case of when, you know, from experience, you know, in terms of building an ecosystem, you really want to have a look at your digital vision. You want to have a look at your digital masters because they really help you in terms of steering where, you know, the most benefit in terms of where your ecosystem or where your platform should be evolving. There are a lot of characteristics and you know, I've picked this particular one ecosystem member velocity. Some members will move at the speed of a scooter and others will move at the speed of a locomotive. And again, this is across a number of different areas, whether it's knowledge and expertise, whether it's training, whether it's a risk appetite, whether it's the adoption of new technology. And, you know, three years ago or even two years ago, blockchain was relative, was very new, of course, especially enterprise and permission blockchain. And we're really talking about here in terms of also in terms of cooperation and collaboration as opposed to competition and protectionism. So, you know, really have a look at, you know, ecosystem members who are open to new business and new models. Critical success is success factors. We talk about incentive design. So each marketplace participant, you know, having a fair and reinforcing incentives to want to increase, you know, trading activity on the marketplace with an easy on board and immediate effect. Network effect. We really talk about the network effect becoming the dominant service provider for target markets for both traditional services and new innovative services would rapid adoption from a governance design perspective. You know, each participant must have a fair role in defining and redefining marketplace rules, including the incentive models, data access, really for about privacy control. And it's monetization in a competitive market. A degree of decentralization is required. For ecosystem KPIs, we talk about growth. So really growth of the ecosystem, whether it's the number of specific stakeholders or members is really the density of ecosystem members. Members of the same segment industry or, you know, even just, you know, value added services members growth and online engagement. We really talk about common understanding of what is a transaction and what we may start out as a core transactions.  We talk about then the number of meaningful connections within the network. We're really talking about providing a platform or an ecosystem where members come together to create new connections while at the same time strengthening or reinforcing existing connections. A lot of business today, you know, as we know, it's been really done, you know, paper based, and we're kind of using in a paper based or in a, you know, centralized way and really what we're kind of doing with blockchain is decentralizing, digitizing, optimizing.  And we kind of, because change is hard. So we need to make sure that we kind of bring them along in terms of as we're looking to digitize, you know, already existing paper based processes, or even go into new market places and new areas, you know, for new revenue generation. Ecosystem density, the number of nodes or relationships within the ecosystem. This is similar to LinkedIn. How many people like, you know, what are the engagements and how are the engagements between the ecosystem members, you know, whether it's, you know, end users in we trades case of its buyers and sellers, how are they engaging with each other on the platform. Yeah. Successful conversion of business opportunities between members. So again, this is all about, you know, successful transactions in a multi-party system between the parties, for example, in a trade network, the number of trades between counterparties. Are they trading just doing a one off trade? Are they doing repeat trades? Are they actually moving more and more of their business on to the, on to the platform because they actually see this success. So it's really looking at monitoring that because then, you know, you've got a really good viable ecosystem that one can build on as, you know, as part of a foundation. And we're talking about income. So income stats. I mean, okay, everybody's looking at investors, partners, sponsors, even members, they're all looking at what's the return on investment, you know, what's in it for them. How can we derive value from the ecosystem, making sure that the, you know, whether it's operating costs in line with revenues, making sure that, you know, in terms of as you deliver value added services. What is the cost of actually delivering that service versus the income of actually delivering, you know, the, you know, the income that you can derive from offering that value added service from stakeholders perspective, you know, as they deliver capital, you know, as they, as they give capital investments.  So it's really kind of looking at making sure that you deliver and you provide a viable, reliable, self sustaining ecosystem for all of the members because, you know, as we look to some of these ecosystems, nobody wants to be joining an ecosystem for a year and then they move their business over and they have the cost of moving their business over to that new ecosystem, only for that ecosystem not to be to be there 12 months later, 18 months later. So it's really making sure that's where you need to make sure you've got a good viable ecosystem that is self sustaining and then can grow organically over over time while delivering value at all aspects. I've got a Q, I'm going to So presentation, I'm going to kind of open it up for a Q&A. So by all means, I will have a look at the Q&A if people want to ask questions. Okay, so Benedict, can you talk a bit about democratic management tools used at WeTrade? So, yes, so WeTrade, so as people may know, we are a consortium of banks. And primarily we started in the trade and trade finance area, and we use, you know, so WeTrade is a separate entity. Our shareholders are banks, but we're a separate entity. The governance in terms of how we evolve the product is with WeTrade. We listen to our banks and effectively their customers to derive, you know, what features, functions, what capabilities they want to see. And effective, you know, from that point, then we really kind of, we set out the roadmap, obviously making sure it's in line with the overall strategy of the company, with, you know, in line with the board of directors and our shareholders. So from a WeTrade perspective, we govern, you know, the evolution of that platform. We have, we had, I mean, we do have various work streams or councils as we talk, as we talked about, so where we have members of, you know, whether it's technology, we used to have, you know, technology councils, security councils, legal councils. We used to have, you know, product councils. And these councils would then meet with the various stakeholders, you know, from each of those disciplines. And then, again, WeTrade was the glue between all of these councils where we would then kind of listen to all of the feedback from all of the council members. And then we would, from there with them, we would drive the overall approach and strategy of, you know, what we need to do from a product, service, build, legal compliance, regulatory, all of these different aspects. Benedict, I hope that answers your questions. If it doesn't, feel free to post another one. How do different intermediaries, I'm just going to go to the next one in line, it was anonymous. How do different intermediary laws applicable around the world could be enabled? Is there any option to incorporate financial legal aspects across partnering nations? There are quite, when WeTrade started out, we designed, and there was one of the councils that we talked about, we actually incorporated and came up with our own rule book. And that we went to then, which, whether it was the EU or the bank, you know, EBA and the European Banking Association and, you know, and the EU regulators, financial regulators. We also then, through our member banks, went through each of the local regulators of each of our banks, because we were looking at offering new services that there were no standards and no rules or laws that could actually really govern it.  We have a pretty solid, you know, rule book and, you know, so that can be used in other jurisdictions outside of Europe.  We see UNICEF-LAR with their laws and their model laws. So there's more and more laws that are actually coming, you know, that are being generated. Quite a lot of them are, you know, a flavor of what's already been there before and just, you know, just applying digital, you know, digital connotation to some of the existing laws. We trade so far, you know, with every new region that we've had to go to, every new country or whatever, we've been working with the local regulators with our rule book. And so far, you know, what we've actually designed from our rule book perspective has no changes. So whether we were going to, and, you know, I have sensitive information that I can say, but we were going to other regions outside Europe. And from the feedback that we've had so far with all of these local regulators, no changes required in terms of, in terms of changes on the rule book. Anonymous, I hope that answers your question. 10 or 10. Do Enterprise ask for concrete business value of blockchain solutions? I think everybody is asking for concrete business value and business cases of any solution, not just blockchain solutions. Do you use specific tools to quantify blockchain business value or is it qualitative statements? No. So we use effectively, as I've said, we have an operating model. We have a revenue. We have a charging model based on transactions. Effectively, what we need to do is we need to make sure that we trade as an organization is sustainable, you know, and resilient from a financial perspective. But also in terms of our ecosystem members, we've been working with all of our ecosystem members to ensure that the, you know, to help them through the business case that they could derive by joining we trade to make sure that for ecosystem members that they're also able to run and to join we trade and to actually derive the value from it. We have very much analytical, very much qualitative, you know, and very much, you know, we have all of the data there in terms of transactions, you know, foresee, you know, forecasts, actuals, you know, the charges for all of these.  We have quite, we've amassed quite a, you know, amount of data over that over the last two and a half years to really help new members and new ecosystems, you know, to guide them on the path of actually, you know, driving the most value that they can out of out of we trade and out of joining we trade. Eugene, what kind of challenges does we trade and other finance or trade finance industries face? Very short answer in this because I know we're tight on time and we're over time regulatory financial regulatory guidelines or legal, you know, so really much. What are the matrix Peter, what are the matrix for blockchain operations we measure value to the customer metrics really is all about. Density of participate ecosystem members. Because if you got more more ecosystem members, more business and number of transactions, so volume of transactions, number of actual participants users, you know, whether they be companies or whatever on the platform because then it can actually derive new, you know, new business relationships, as opposed to just exist, you know, on trying to just digitize existing business relationships. So really it's all and then, you know, it's all about then how much of a business a customer can actually put through through an ecosystem member and how efficient we've seen that from a we trade perspective we've gone from processing of several days of a loan request down to an hour. You know, so again, that all that also brings efficiencies down to, you know, to the end and customer and it also helps to manage liquidity. Okay. Tan, I can, if you want to reach out to me if anybody wants to reach out to me privately, I think we are over time. So, but if anybody wants to reach out privately, then, you know, please do and I'd be more than welcome to to answer questions.


I think the biggest risk or concern that people have about investing is that it's a rigged game. Ultimately, the biggest thing we get wrong is actually the biggest thing that we're addressing, which is, is this market fundamentally fair to all who participate? And that's the thing that we have to continue to emphasize and focus. For a retail investor to trust financial markets, they ultimately have to believe that they have a right to ownership of what society is progressing. And I think that's a very important fundamental value that we'd like people to believe and for them to trust us with. Now, we have to show that trust in turn back to them. In a period of extraordinary societal, geopolitical and macroeconomic stress, financial markets broadly worked. And I think that's a great reflection of the investments that have been made in the technology and infrastructure that drives capital markets over the last many years. Now, that said, no market is perfect, so we're all obliged to embrace the latest technology, really invest in resiliency and make that a priority, and ultimately ensure that from the experience of an average investor, that they truly understand how that market works, what are their rights and obligations, and that ultimately their interests are aligned with the interests of those who serve them. One of the most important things we can do as a group of financial institutions is help people interpret their own data for their own purposes, make it easier, make it more transparent, make it more actionable. The concept of whose data it is, who has rights to it, and ultimately what is the quality of that information is going to be one of the central issues of our time. Allowing investors to invest in a wide range of asset classes that diversify their risk is also going to be a critical step as we think about the longer term. Historically, most retail investors invest in largely traded equities. Fixed income hasn't received the same level of attention, and until recently, private markets have been inaccessible to the average retail investor. Giving that retail investor a broad access with transparency, a clear understanding of the economics and risk, I think it really is the mandate for our stakeholder group. Capital markets reflect the best intent of a society. We're going to tackle our most important issues, whether it's the climate transition, geopolitical strife, by increasing the flow of capital around the world and making sure that it gets to the right people to do the right things that ultimately serve our society. And for people to be owners of their society, they have to have ability to invest in a transparent, fair, and economically viable way. One of the most important things we can do is help set a pathway for our world that is generally based on more inclusion, fairness, and ultimately serving societal goals at the most important level that we can.