What's up everybody and welcome back here to the channel torque drift on a mold platform We said we were gonna be doing some more torque drift So we're here with more torque drift today We have a new chase challenge that we're gonna be trying to accomplish I did try it once I did get one of the three So we're gonna be trying to get the chase challenge and see if we can't complete the chase challenge here today And try and get on that leaderboard So we got mad Mike Woodette following Vaughn getting junior at Long Beach Vaughn getting junior versus mad Mike Woodette. We got to try and be on Vaughn's door Got a good start there Kind of knows the tail on Vaughn's bumper Would like to be a lot closer, but we are pretty close To start don't don't hit it. Oh man Throwing it in there. I believe 30,000 is the third final score You need to try and get All right, we got a long way to go So we got to make sure we initiate nice and tight on Vaughn and definitely try and get this We're chasing that leaderboard today trying to get up there got to get the all three completed before I can Even see where on leaderboard I would stack Definitely been not waiting for some sort of challenge to come about That was probably not the best way You go from fifth We may have to go in the fifth Think we may have to go in the fifth year Let's know we're near it. So we're just gonna give it a reshot So it looks like we should probably go in the fifth before we enter Here Initiate in with them Closer or maybe go in the fifth This year seems to be where we need to be for this part. So we're gonna have to switch up After the first turn fourth gear is good on the initiation, but I believe this is where we need to be on the up and up Ah too much of a throw man had my scar definitely Definitely a fun one to try and drive for sure Let me share this on me in the scrimmage with all of us front in the scripture box below Bit of a better initiation On the fifth No, we're near Still tickling around that 30. No, we're even near getting those three TD crates We got to get those three TD crates before at least the end of today's episode for sure Because if we don't get on well I'll be very sad with myself. I believe we should be able to be up in the 40s Or 50s here on this chase challenge long beach definitely a very Good track to get some really close chase points There we go. That's a little bit better Through it a little bit too hard there And we're nowhere near him there Oh come on Oh, I need to be really close to gone his door through that big sweeper through, you know that outer zone Otherwise, this is uh, not gonna work Got the beginning down pat There we go Oh, that was much better. We had a little bit of frame rate lag We didn't want to smack into his door, but we did I think we got at least a 30 We but 30 9 So it's over 30.  All right We got the 3d TD crates, which we are gonna open up for our skyline But we're gonna give this one more shot before we go up in those crates So we're creeping probably nowhere near Uh, the highest, you know Anywhere on lead were probably at all But we're gonna try Try our best here to get closer It's not smacking to him that time Oh, I feel like this is a much better run. I mean, it's more than it's more than 30 It's 31 So that runs better We we up by a thousand the long way to go to get that 43 So let's go check out those three TD crates See what we got for the skyline because that's the car we only have in the one we're building right now And then we'll go back and give this another shot Let's go to the garage See what we can get For the TD crates here We get some, uh, whole stacks of tires So that's unfortunate. I mean I have most of the pro cars. So that's something we get. Ooh Ooh custom part Custom part. What is it a custom ECU? All right, I'll take a custom ECU I'll take a custom ECU for the skyline And we got some more pro tires. We got a pro part Pro clutch. I don't think we had a pro clutch and some blue neon. So I will definitely take a couple of those parts. We will take this ECU We're gonna install that ECU for sure And then I believe oh, we do we can get the pro clutch installed. All right so that's good pro clutch installed pro ECU and, uh We'll throw on uh, we'll throw on some important eons Here we go So kind of happy with uh getting some things that I'll actually throw on the skyline here So let's go back and try this again We're probably gonna take the skyline out to back. Ooh and kelly banks We're gonna hit kelly banks real quick with the skyline with that pro ECU See how she rips and then we're gonna try that chase challenge again 800 horsepower now in the skyline with the body kit I guess I followed last episode and I bought that but was waiting on it Going against the 1200 horsepower skyline. See that slap logo on the front Big thank you to slap chain for the uh the shout out on one of his recent videos All right, so I probably should have started in second gear Definitely should have started in second gear and uh Upshifted in the third Oh too much of a wall tap so The skyline. Oh, that's just a little wall grace Skyline is handling pretty well recently With the uh adding of the body kit and stuff like that and lowering the suspension and softening it up Not the cleanest of kelly bank runs but it can run nonetheless over 100 k I'm happy with it. Let's see what we're going against.  All right, so we're gonna start in second. We're gonna go up into third Yeah, yeah much much higher acceleration that way Throw it in no Oh, man, I did the same thing he did. I was trying to be too uh Too saucy on that bank Little wall grace be good Not as close as I'd like to be But we will Get the tandem points Oh, I feel like that was I feel like I got robbed on that. I feel like I got robbed on that wall tap I felt like I shouldn't have lost those points But we did take the win And I don't think even got a chase did he? Zero point chase he did quit Well, that's unfortunate. So we're gonna go try the mad mic chase challenge and one more time here for today One or two more times We got to get up there trying to chase that leaderboard something I've been on once or twice pretty up there in the like 10 or 11 But never been super high. I wish we could change cars Wish we could chase him down with like Chelsea to know if his car or something like that But this is what we're given and this is what we are going to be using Some deep on the fun haver It's that bank that's killing us or not that bank. It's that sweeper that is definitely killing our chase run That sweeper we need to get we need to be dialed on his door if we want any chance To get this challenge completed because we need to be on his door pretty much dragging bumper on the wall not like that And getting those wall drags and the close chase So if you guys can please this let me know down in the comments how many times it take you one two three fifty who knows Definitely need to start playing torture more and more while I'm at work when I got nothing going on or something So like get back in the swing of this game Okay, so that's much better We increased it above the 31 now Still not 43 though Still ain't no 43 darn so let us Try One more time We're gonna send it again here for today You guys know if you guys are not subscribed to the channel I would appreciate the support tap that subscribe button hit that bell notification so you'll be part of the notification gang And get the videos as soon as they come out I felt like I should have been getting I should be getting points there Let me know if you guys think I should have gotten points there. We're gonna give it one more solid effort here for today And then we will end this episode off here for today Because they're having a good week more torque drift coming as well as some rfx or two other styles of games And of course we set a course of VR content and everything So let's send this one final time Nope and we bumped it We done did bumped him so that I'll do here for this episode here on torque drift So as always thank you guys for watching. I'm evil rabbit. I'll see you guys on the track


And for me, like, nowadays, you know, I do meditation with a coach. Like I do like a yoga flow, I go for walks every single day at 11 to get outside, breathe fresh air. And I would say like right now, like my schedule, I make sure to go fishing one time per week. I make sure if I'm trading at the setup behind me, I make sure to get off the desk 11 or 12 max for three years. I didn't have any fun. I make sure to have fun. I make sure to go on trips and make sure to eat healthy food, spend time with people that I love and good quality time and really just have fun and laugh things off.


We are August 26, 2015 at the Fairmont Hotel in Toronto and the interviewer, as usual, will be William McCrack. I'm just going to start with a few quick questions. Could you please state your full name? Indira Vasanti Samarasekar. And your age, please? I was born on April 11, 1952. I'm 64 years old. And where were you born? I was born in Colombo, Sri Lanka. Okay. And as a child, what did your parents do? My father was a surgeon. He's no longer alive. He was an auto-learningologist. And my mother was a homemaker. We had four children. I have three siblings, so pretty busy. She had plenty to do. And as a child, what were your passions or what did you do for fun, your interests? I enjoyed reading a lot. And I played a lot of sports in school, as well as debating teams. I participated in debating, so that kept me pretty busy. And so where did you, in school, I guess, at a young age, what subjects were your strengths or interests? Yeah, I was good at math and physics. So that started at a very young age. Physics, I started thinking physics. I think it was in grade eight, perhaps, but then really significantly in grade 10 and 11 and 12. Okay. So when you went to university, it was just obvious that you were going to go into the sciences? Well, no, yeah. I went straight into engineering. Okay. Yeah, so that was pretty, I knew I wanted to do engineering very early on, so there wasn't a lot of uncertainty around that. I did worry about the potential that there would be no women in the class. Yes. And so the last one day, I was getting a little bit of cold feet, but my father and my mother said, well, let's go find out whether there are women in the class. Then you don't have to worry about that. And it turned out that he had about 12 out of 150. Okay, so still small, but still significant. Most of the time there used to be one or none or two or something like that. Yeah, what kind of engineering? I did mechanical. Okay, because I know chemistry, for example, usually has more women. Yeah, mechanical, there were no women. So when I went into engineering, there were 12, and then we streamed into second year. So when I went into mechanical, there were no women. But by then I was used to being around guys. Yeah, I know even when I was not that long ago, when I was looking at universities, I'm from New Brunswick, and the University of Monkton, I don't know if they still do, but a few years ago they would give scholarships just for being a woman for engineering to encourage numbers, essentially, in the department. So yeah, tell me a bit about your education and what you did afterwards. Is that your first job? Yeah, so I did most of my education in Sri Lanka. Basically those days I was not co-ed, so I was in girls' schools all my life. Then I worked for the Petroleum Corporation Refinery. It was a small refinery. I was a mechanical mentor and engineer. But I always knew I wanted to do graduate work, so I applied for a Fulbright Scholarship, which is an American exchange scholarship, which I received. And then I got married. My husband and I went to the University of California in Davis, California, for about 18 months. And then after that he was lucky to get a job in Canada. We were looking to move somewhere where we could maybe stay long term. And so we got immigration to Canada and came to Canada in 1977. Why Canada specifically? Well, you know, it's interesting. My father always had a great admiration for Canada. He'd say, you know, it's a beautiful country. It's a big country. It's a country where immigrants seem to be welcome. They seem to integrate well. Pierre Trudeau was prime minister and he was well known around the world. So he always encouraged me to think about Canada as a destination. So maybe that affected my decision. So then you made it to Canada? And then I did a PhD at the University of British Columbia from 1977 to 80. It was at that time that I switched from mechanical engineering to metallurgical engineering. And what was your thesis? It was mathematical modeling of continuous casting of steel, which was a very new process at the time. It had been commercialized in the mid-60s, but it was still quite underdeveloped, I would say. So I was doing more research. So a lot of research beginning to happen in that field. And so I did research in that area.  So at that time jobs were somewhat hard to come by in academia in the 80s, believe it or not, because they had done a lot of hiring in the 60s. So not many people were retiring. I was hired as a lecturer for two years on a temporary basis at the University of British Columbia. And then when that was over, there was a semi-permanent job as assistant professor, which I took. And I was in that position for about nine years before someone retired and I got a permanent job. And when teaching, are there any standout or memorable classes? I enjoyed teaching mathematical modeling of processes, because that's what I did for my PhD. And computers, believe it or not, at that time were just becoming useful. Just to give you a sense, people don't realize that the entire mainframe, because we basically had mainframe computing, there were no mini computers or even PCs were not yet developed, was eight megabytes, the entire machine for the entire university. So today you have eight megabytes just on your own. That's just a few word documents. So you began to develop tools and techniques to use the computers to solve large-scale problems. Throughout your career, did you join any organizations or committees? Yeah, well I joined the metallurgical society of CIN quite early on. I think I was on the membership committee or something. And whenever there were conferences I would help with organizing symposium. I also joined the iron and steel society of the AIME and also TMS, which is the metals, minerals, materials society also of AIME. And I was on committees and things like that. Any of those help you early in your career? I would say becoming the president of MAPSOC in 1995, which is now almost 20 years ago, was a really important turning point for my career, because I was quite diffident about leadership positions, particularly with mostly male colleagues. But I was encouraged to take that on and I'm really glad I did, because I had to organize a conference, I had to run the society for a year. It really helped me develop my confidence. And you're talking about roles as a leader. Tell me a bit more about that, because eventually you became quite a leader. Well, after the metallurgical society CIN presidency, I began to participate even more so on various university committees. This was sort of routine perhaps as part of their job. But I was invited to serve on, you know, search committees for deans and committees for research and this and that. And then about 1998, the university was looking for a vice president research. Now at that time, I had not had much administrative experience. I was an academic, but I hadn't become a dean or department chair. I was department chair for six months. And interestingly, some colleagues of mine, one of whom was Ray Medecraft, who used to be the VP at Stelco, he was my department chair, encouraged me to put my name in for this job. And I was mostly convinced I wouldn't get it, because I didn't have any sort of traditional experience. It turned out they offered me the position.  I mean, I went from being a professor, director of a small center to being the vice president of research for the whole university, reporting to a president. So that was my first really big executive position. And then five years later, I became president of University of Alberta. So it was a fairly rapid set of promotions. And you were, if I'm not mistaken, the first woman president in any Alberta university in history. So how was that? By that time, one stops thinking about the first woman business, because the first time you think about it is when I became a woman, a mechanical engineer. After that time, it's not very noble to me personally, maybe to others. But I never found that a particularly remarkable thing. There were many other women who were presidents of universities. Martha Piper, my boss, had been president of UBC. Heather Monroe Bloom had just become president of McGill. Shirley Tillman had become president of Princeton. She's a Canadian. She's becoming university president. So I didn't see that as particularly remarkable. Yeah. And now there's more women in the public world than now. Exactly. What would you consider to be the big bulk or the important aspects of your work as a president, but also when you were working in research as well? Yeah. Well, let me speak about the research first. Arche2 have got into, as a mechanical engineer, into continuous casting of steel, because the big issues of the day were making high quality, steel casting going from liquid to solid in this relatively new process and not having cracks and defects. And so the work that I did with my supervisor and mentor, Keith Premakom, who was an unbelievable giant in the field, was some of the, I would say, fairly pioneering work to understand the relationship between the design and operation of the machine and the defects in the steel product. So very proud of that work. We worked together as a team for pretty much 20 years because he died prematurely in the 1997 heart attack. And over those 20 years, the work that we did really became, I would say the gold standard for industry in terms of making billets which are small sections and casting them without defects. So that was perhaps a wonderful development to be a part of and to help drive. So it enabled us to do consulting around the world. We were giving courses in different countries, consulting with companies who were having problems, trying to help them solve them. So it was a good innings. And your work as president, could you elaborate a bit on that? Yeah, running a large university is a highly political job because unlike the CEO of a company, we have technically what is called shared governance, which means everybody's in charge and nobody's in charge. And so you have to really lead the institution by consensus. And you have a lot of stakeholders. You know, you obviously, your number one job is to ensure that the student experience is the best it could be, that you hire the best faculty, and ensure that they can do their best work and that staff feel challenged. And then you have to deal with the government who funds most of the universities and alumni. You have to be in the business of fundraising. And then you have to work with other stakeholders like the city and the province and of course the federal government. So I would say my time at University of Alberta, there are a couple of things that I'm particularly proud of. First of all, the University of Alberta is one of Canada's leading research universities. I mean, if you look at all of the indicators at the time that I took over, we might have been ranked about fifth or sixth on a lot of them, but also relatively unknown in Canada. People knew University of British Columbia, they talked about Toronto and McGill, but U of A was never really mentioned in that league. So one of my goals was to ensure that the reputation gap between University of Alberta and its peers, who are basically UBC, Toronto, McGill, and University of Montreal was closed, and I think we did that. Secondly, we worked on creating a vision that brought the university community together, and it was called Air to Discover. And that vision basically set up some very high-level goals like increasing the percentage of international students, which was about 4% when I arrived. It's now close to 15% building international partnerships around the world, and renewing the physical infrastructure that had not... We had very few new buildings over the 90s because of the oil prices and recessions and whatnot. So we added about a billion and a half of new buildings while I was president, and hired probably about 400 professors from 200 additional to what we originally had. So it was a very satisfying time, but a challenging job, highly demanding, and not one you could do forever. Yeah, I'm not kidding. And then you, after that, you moved back to UBC? Yeah, so I've gone back to Vancouver, which is where my children live, because obviously they were born and raised there. At this point, I call myself busily retired because I don't have a job and I don't want a job, but I have a portfolio of projects that I'm involved in. I have an appointment at the Liu Institute for Global Issues, which is a think tank located on the campus of the University of British Columbia. I'll probably spend a day or a week there. I'm also going to be an advisor for a law firm called Bennett Jones. That's an Alberta firm that's in the energy industry, and they've opened an office in Vancouver, and I'm going to be in that office as a policy advisor a couple of days a week. And then I'm on a couple of boards, for profit and non-profit. I'm on the board of Bank of Nova Scotia, which is a very exciting place to be because the bank is one of the most international banks. We are in South America, so a lot of strategic planning. How do you get on a bank board? Well, it was interesting. It goes back to my steel connections. John Mayberry, who used to be the president of Defasco, I got to know because I held the Defasco chair in steel processing at Vancouver at the University of British Columbia. And I wouldn't say I got to know John that well, but of course his company was funding the chair and I'd meet him occasionally, and I'd send him Christmas cards, and we kept in touch. He was on the board of Bank of Nova Scotia, and they were looking for a board member from Alberta at the time because Alberta was growing rapidly and they obviously must have wanted more women. So he called me up and asked me whether I was interested, and then I went through the process where you have to be interviewed by the governance committee and there was a headhunter involved. So that's how I got on. So yeah, so semi-retired but still pretty much full-time. And then I'm on the board of Magna International, which is again back to my steel processing roots. Magna, of course, is one of the largest auto parts manufacturers in the world. It's number three. Canadian Company, started by Frank Stronach in Aurora, Ontario, run by a great engineer called Don Walker. And Magna supplies auto parts to the Detroit three and the German three. And we also have plants in China. We're growing rapidly in China. And Magna is very much at the frontier of the move to make cars lighter, smarter, you know, and more fuel efficient. So it's a very exciting time to be on a company like Magna. So I'm on that board as well. And then I'm on the board of the Asia-Pacific Foundation, as well as a couple of other small boards. Oh, there you go. Yeah. You also served as both board member and chair of the National Institute of Nanotechnology. That's correct. Could you explain a bit more what that is and what you do? Yeah, the National Institute of Nanotechnology is a joint venture between the University of Alberta and the National Research Council. And this was a lab that was started just before I became president. I think around 2004, and it was completed during my time as president, 2006. And as part of that, either the president of the university or position as a result of the fact that I was president of the University of Alberta, the chairmanship of the National Institute of Nanotechnology was a position that was held either by the president of UOB or the president of NRC. And so we rotated every two years. And so basically you provided oversight and direction for NINT as we called it. And I thought this was interesting. So I thought I'd ask, you served on the Prime Minister's advisory committee, right? Yes, yes. So how did you go from, I guess, your science background to the Prime Minister's advisory committee and what was your role in that? So the Prime Minister's advisory committee was on the public service. And Kevin Lynch was the clerk of the private council at that time. I'd like to know Kevin very well because Kevin had been Deputy Minister of Industry Canada and Deputy Minister of Finance. And as Vice President of Research at UBC, I did a lot of lobbying, really going to Ottawa and making the case for investments in R&D. So when Kevin became the clerk and created this council, he wanted people in the public sector who understood humans. It was really about helping to modernize and streamline the public service and new ideas around performance management and direction. So I was one of, I can't remember, 10 or 12 members. And the two co-chairs of the council were Darnas and Kowsky, who was the former Deputy Prime Minister and the Malroni government, and Paul Telly, who had been also a clerk of the private council. And I knew Darn quite well because he's an Albertan. So probably all of those things led to being on that committee. And we'd meet the Prime Minister once in a while, I would say, once every couple of meetings. He would come, he'd join us for dinner and give us his sense of why he thought this was important, how the world was changing and how the public service needed to be providing the kind of thought leadership to the government. Were there specific goals the committee had to reach in regards to the public service? Yeah, I mean the goals largely were around, in my recollection, was around ensuring that the public service broadly represented Canadians and attracting more people into the public service from outside Ottawa and ensuring that the government of Canada's public service had a presence across the country as well as ensuring that there were more young people coming into the public service and that the public service dealt with the rapid changes taking place in technology. So there were some specific goals in some of those areas that we were to meet. I asked the next question once in a while. I like the different answers and I find it quite interesting but with you it will be interesting because you have the science background but then you also have, you worked a lot basically in many, many other facets of Canada. The question being do you think there is still today a disconnect between the natural resource world, whether it be metallurgy, petroleum, mining and the general public? I would say yes, there is a disconnect. I think the disconnect is because the general public first of all doesn't understand that natural resources have been an important part of Canada's prosperity and history. Secondly, natural resources require high technology to advance whether it's oil and gas or steel or mining, metallurgy, all of these things. Huge amounts of technology and innovation have taken place. Many of them have been driven by Canadians and underappreciated, there's no question. That's the first thing. The second thing I think Canadians don't understand there's a sort of a gap in the science literacy in Canada and unfortunately the environmental movement while an important group, climate change is for real. This is not something we can just ignore. There's been a lot of hype around greenhouse gases and pollution and all of that and unfortunately many Canadians rightly or wrongly point to the natural resources as being responsible for the large amounts of greenhouse gases and not being good stewards of land, air and water. I think what people forget is that all human development has an impact on air, land and water and all human development has attempted to mitigate it to some extent and perhaps we absolutely do need to do more but I think the natural resources has gone through periods in which they've been very negatively perceived by the Canadian public. There was a time when acid rain and all of that stuff, big news and now we're in this business where the oil and gas sectors are taking a lot of negative publicity. It's unfortunate because if you ask any single Canadian whether they're willing to give up their car, their heat, air travel all of the modern conveniences they won't and yet they are the first to sort of criticize other people. So there's a high degree of hypocrisy around the whole question of who the villain really is. The villain is a consumer. Yeah, I had asked that answer to someone else who had worked a lot, Mellige, but a lot in mining as well and his point was you often hear people or the media criticizing a new mine or something but you never hear about because it ruins the force of the land it's around but you never hear about them criticizing a new housing development that ruins an entire forest or a new shopping centre I guess it's because it's also so exoteric for most people, it's not something they understand how it works down to the core it doesn't relate to them one really absolutely everything they touch comes from the natural resource industry Now a question as a woman we've seen now today and we talked about it a bit there are many more women and even more women than men in academia who go to university as well there's a steady incline in the STEM department for women as well but there still seems to be even though there are many many women in school who take STEM there seems to be still less women who end up in the natural resource industry I guess could you able to give some insight on that? I think there's a couple of things first of all I think there's no question there are more women doing science in many disciplines and yet women seem to gravitate towards what I call the sort of caring professions they tend to want to do medicine and nursing and well there's quite a few more women in law but engineering still is stuck at about 20-25% and those numbers haven't changed a lot so that's point one secondly I think women even when they do go into these fields tend not to go into the natural resource sector if it requires them to move a lot or go out into the field or into areas where remote locations field work partly because they have families who's going to take care of the kids even if their husband is willing to share the workload you need two people to have jobs so it makes it more difficult and women tend to be less reluctant to be the person that says we're going to up and relocate somewhere else so I think those are some of the reasons and of course the natural resource sector from time to time does do a recruitment of trying to attract women but generally speaking back to the question of the public a lot of young people don't actually know what the Korean natural resource sector would look like and so more outreach continues to be necessary good answer looking back throughout your career so not necessarily directly having to do with your career per se but with your experience and expertise in your opinion are there definite changes or advancements or anything really that you think has improved or advanced Canada for the better in your field oh yeah there's many yeah sure like notable ones fairly notable you know in Canada certainly you know Bob Lee we were talking about Bob Lee earlier you know the submerged two year which enabled certainly enhance the ability to make steel at high temperatures you know increase the productivity because you could run these things longer it was a huge breakthrough for the worldwide steel industry the second one that people may or may not know is the work that was done a long time ago by Jerry Heffernan and Lake Ontario Steel and the whole electric furnace steel making which is the recycling of scrap and making steel by re-melting scrap was a huge innovation and led the way to what is called the mini-mail industry all over North America Jerry is still alive you know I hope you'll interview Jerry I mean he is you know unbelievably you know an entrepreneur in that sense of the word the work that was done by my late colleague Keith Bramocom was a pioneer in the mathematical modeling business and really I think was one of the world leaders in design mathematical modeling to many processes not just steel but he worked in just about all the metals you know that really advanced Canada's stature and also the industry both nationally and internationally those I would say are the ones that come to mind Alex McLean again did some pioneering work in the business of tundish design which is in the still continuous casting business using a fluid flow models again a pioneer better understanding how steel flows and how defects are captured so Canada had many academics who were working in the sort of steel industry which was really thriving in the 80s and the 90s and a lot of new developments were taking place globally and Canada making I would say significant contributions and we'll finish with a few questions first what would you consider to be your biggest challenge throughout your career I think the biggest challenge really was really changing fields and then trying to juggle so I changed from mechanical engineering to materials and metallurgical engineering so I was very difficult because it was a whole new discipline that I had to grasp and then trying to develop a research area that was not my PhD thesis but something else in order to be an independent academic since I was in the same department so my colleague I couldn't keep doing the same things but the other big challenge was really juggling family and career it was very very challenging I mean I have two children who are very little and my ex husband a wonderful man it's been a great parent so we shared the parenting but it's very demanding trying to travel the world consult publish stay on top of your field and ensure that your two children grow up to be successful I think that problem is still there today for most families very engaged I watched my daughter my grandchild is only three years old and it's challenging for her parents and what would be the proudest moment in your life and if you want we can split it into it could be the proudest part or moment in your life and also your professional life I think my proudest moment is the two my son and their daughter my son and daughter have done very well they both pursued interesting paths to their careers my son did engineering first and then went into medical school which surprised me and of course he's today a urologist practicing surgeon in British Columbia my daughter went into biochemistry which was kind of an odd choice then she became a lawyer and now she's working on becoming a writer and proud of she spends many hours just on her writing career in addition to her legal career she works for the BC Securities Commission as a litigator basically inside of trading I would say by far that's given me the most pride I mean my career is great I've had a great career but ultimately it's your children and your relationships that really are lasting absolutely and last if you were speaking to someone much younger for example you were surrounded by students for a while so let's take students for example what would be the the biggest most important piece of advice for life lesson you could give them looking into the field I would say to be uncompromising in pursuing and focusing on something you really love but doing it at a very high standard I mean to succeed you have to focus laser like on excellence you can't do anything in half measures and just because you love it that doesn't mean you'll be successful there's a huge amount of hard work and relentless I mean there are days when you almost want to give up but if it's something worth doing you really have to put the time in I mean Malcolm Gladwell's notion it takes 10,000 hours is absolutely right and it's not just 10,000 hours of sloppy work it's 10,000 hours of diligent pushing the frontier so that's my advice to people you have to decide what it is you want to do you can change your mind as my kids did many times but once you've got to something you have to pursue it with 1,000% well thank you good honest answer is there anything else you'd like to add to that well I think I'd like to say that you know the Canadian metallurgical industry that I was part of was a great industry to be part of as a woman they were very encouraging I think I was certainly a great debt of gratitude to many leaders who have mentored me over the years as I mentioned you know Keith Bramaco, Alex McLean, Bob Lee people like Jerry Heffernan Jerry Hatch colleagues like Chris Twig-Molsey Sam Mccusen Carlos Diaz all these people who were colleagues you know they all probably never realized that they had a profound influence on my career by first of all being very inclusive and enabling me to succeed as a woman in a group of men so I just wanted to say this opportunity to say thank you to the community of mostly men who were great friends and colleagues and I'm very indebted to all of them for having had an extraordinary career all these years well thank you appreciate it


Whether you've experienced it personally, or know of someone who currently is or has struggled with drugs, this epidemic affects most walks of life in many ways. I had a chance to speak with a few employees of the Williamson Health and Wellness Center to see how their new plans on how they are helping combat drug addiction. Williamson Health and Wellness Center developed a partnership with Marshall University when the initial grant was written for SAMHSA. And we received a huge grant from SAMHSA to expand medical assisted treatment. And so, in part of that grant, we agreed to do forums and things like that. I think it's important just because you need the whole community to get on board. So, you know, we've invited a lot of leaders in the community to come to the table with us today to really brainstorm and try to come up with some solutions. I think more than anything, the important part is the team building process and breaking down the barriers of each agency willing to work with each other. It's crazy because the rest of the country is kind of lagging behind where we are in this region because we're almost coming out of the opioid crisis and moving into the methamphetamine crisis. This initial forum is going to be a small forum to develop a bigger forum in February that we plan to invite people who's in recovery right now to get their input because we don't think it should be anything for them without them involved. Plus, it'll be other community leaders. So, this initial forum is for us to come together to how can we heal? How can we move forward and how can we help this population and serve them the best? People can recover. People do recover. Me, a lot of people know my story. I made some mistakes in my life and I've rebounded. We have some success stories here. It's just not about medical assisted treatment. Some people can do abstinence based and really for us here at Williamson Health and Wellness Center it doesn't matter if it's abstinence based, medical assisted treatment. We just want people to survive. I'm actually a recovering addict. I have a little over a year and a half clean and so definitely some of my patients, you know, we've kind of gotten them from all walks of life whether it be starting fresh from an inpatient program or whether they're just walking out on the street, came in the door and said, hey, I want some help. We have started from the bottom up and we are trying to work with everybody that we possibly can to try to end this stigma of addiction whether it be jobs, transportation, any barriers that may stand in the way of success for recovering addicts.  It's nothing of the sort. We want at the end of the day for us to really make a difference. All hands have to be on deck. And that's the main end goal is to get everybody together and let's make a difference. That's first and foremost. If we don't have the service we'll be glad to refer them to where they can get what they need but<PHONE_NUMBER> and hit extension 500 and somebody on the other end is more than willing to help. We're not going backwards. We're definitely moving forward and that's the end goal. So, yeah, it seems like that everybody's going really good especially I've been here for about a year with Williamson Health and Wellness and I feel like even from the beginning when I started to where we are now it makes my heart happy. It makes my heart happy truly.


Okay. I am back. Hello everybody. I realized that I probably didn't introduce myself earlier. My name is Evan Gilman. I am one of the maintainers on the Spiffy Inspire projects. So I, you know, I'm just trying to kind of help facilitate today, give the opening remarks, and I'll kind of kick off the day of talks here with an update on the Spiffy project. Earlier, I described some of the velocity, like high level velocity and growth metrics, adoption metrics from the Spiffy project as a whole. This is going to be a little bit more zoomed in on specific things and activities that we've done over the last year and where we've come from. So when I was doing these slides and I looked back on the last year and I was thinking of like, you know, what themes are there? The only thing that I could really come to my mind is this idea of stabilization or maturation. The last year in Spiffy, both the Spiffy project itself, the specification set and across all the sub-projects including Spire. The last year has been a lot of kind of like standing down rough edges, making it more usable, making it more performant, making it more stable, all that kind of stuff.  This year, we had a little bit of that, but it was mostly around stabilization. So what do I mean by that? I think probably the biggest and most meaningful part of this stabilization story is all the governance work that we've done over the last one year. This is really, really, really, really important and critical to Spiffy Inspire as a whole. It's kind of this dirty work that nobody wants to do, but it must be done because if you cannot show continuity, business continuity in the project, if you cannot make people feel that, hey, this thing is here, it will be supported if something happens, I can rely on it, how can you adopt it in any meaningful fashion if you cannot depend on a project in this way. So having that governance be really, really strong, having strong bust factor, having good rules in place and policies and the good structure, all that stuff is critically important. So in that, you know, we did a lot of things in that of that ilk in the last one year. One of them was that we renewed the charter or actually wrote, wrote charter for the TSC. You know, this group is a group of people that have oversight over all kind of the Spiffy Inspire sub-projects. Oversight is not maybe not the right word, but they're there to help support the projects. They're there to help connect people, to help spot gaps. They're there to help unblock, they're there to help facilitate across all the Spiffy projects as a whole. One way to think of them is kind of like the CNCF talk. CNCF talk as it is to CNCF projects is a similar body there to service Spiffy and its sub-projects. So we put this new charter in place that defines, you know, the mission, exactly what this group is going to be responsible for and how to get on it and people who are on it, what all the responsibilities for them are and is all clearly codified. So that was a pretty big, pretty big deal for us. People with sharp eyes may have noticed in that last screenshot that one of those changes was that we actually renamed the group. Oh no. I'll go back.


What I want to do is show you guys how to kind of get that gravity point of the haircut to the center. You want to find kind of where you want this triangular shape to sit. So what I'll do, I'll kind of comb the hair forward and then I'll take a look in the mirror and see exactly where I want that weight to start being removed and where I want it to build up to. So now, what I'll do is I'll separate the front and the back. So right here, down to the ear, that's separated, and then I'm going to go right here in the front and I'm going to decide where do I want to bring that taper up to. What I'm going to do about right here, I'm going to comb this hair over, port it, and then I'll slide a clip up on the other side. Now I'm going to do the same thing on the other side. Alright, and I'm not worried about the back so much right now, behind the ear, kind of follow the hairline up, that's about it. Now I'm going to blow dry underneath here. I'm going to keep this wet on top so I'm going to be cutting that in my hands. Now as I go in and clipper, I'm just going to pull away right around the ear. So I'm not going to take this all the way up. I'm not trying to go that tight all the way through this haircut, just around this edge. I'm going to start off right here and then just flip my wrist out. I'm going to get up to that line, so in and out, and then we'll continue into the back. And basically what I want to do is I'm putting this weight line here, back into the back in that occipital bone area, so I'll flip out right when I get to that point. First off, I use a wider tooth comb to do my scissor over comb. As I work up the head, I'm going to start here, start removing some of this bulk, and then I'll go back through and fine tune it a little bit more. I actually want to bring this weight line as close to the head as possible so then I can start my triangular field. So going nice and close to the head so you can kind of see how that's starting to blend up. The key thing with scissor over comb is to really just keep the one blade up against the bone of the comb.  You don't want to be chomping at it like this because then you'll start pushing hair or cutting hair weird. This keeps it more mechanical. I'm going to let this top out, not because I'm going to start cutting it right away, but kind of want to see how it's all falling together. What I'm going to do to cut this faux-hawk is I'm going to start by creating a guide right here going short to long and I'm going to start bringing everything over to that point. I'm going to travel a little bit towards the beginning and when I get about midway between center back and behind the ear, I'm going to stop and I'm going to start over-directing to the center because I want to start building up weight in the background. Now a key move would be to start point cutting from that short point to the long. This new section is going to go halfway through the previous. Now we're halfway there so all these new sections until I get to center back. Now that pushes some of the weight here to the bottom of the long hair. I'm going to do the same thing on this side then I'm going to blend that. Now the very center of this section is the longest point. When you look at it, it can start to give us this kind of mohawk feel in the back from a short to long point. I comb it down. I've got a nice blend on both sides, rounded feel to it. But I can just take this little point and cut in just to blend it a little bit more so it's not disconnected. Now we're going to go into the front. How do we get this triangular shape on the top? I'm going to take center parting like this and we're going to start by cutting the right side. Our guide is going to come from this back point we cut prior. So that becomes section one. This is all of our sections. What I do is I cut one and that's now my gut. Then once you cut one and you grab section two, you pull section two back to one and a half. If I pulled section two all the way back to one and cut it, now section two is a lot longer than section one. Then I pull three back to two and it's even longer than two. Then all of a sudden you end up with a line that quickly goes like this. Your lengths start shorter and they quickly get longer. The way that we're trying to do this is because I bring it back, you're going to get more of a line that looks like this. Still a triangular line still has kind of an upward effect to it, but it's not like this. Which is too much length if you start over directing too. I'm going to cut short to long. Longest part being in this section. Now the cool thing about a faux hawk is it doesn't need to be super long. Now I'm going to bring this section halfway back to the guide. Continue doing that. Take a new section. I'm going to halfway back to the guide. Now all this came back to the previous guys, but here's the thing. It came back to the previous but once you get to the front of the head, the head starts to curve away. So the over direction becomes more extreme even though you're not really doing it. That's why I don't like to start over directing back too quickly. So you can see my smooth transition through. Now we're going to do the same thing on the opposite side. You would imagine the longest point is in the very front. I'm going to blow this dry. This is long. It's longer than I want it to be. I want to go through and I want to cut the top here and I want to take quite a bit of weight or length out of the top.  Now that I have my overall shape, I don't really have to be so perfect in everything that I do. I can literally lift this hair up. You can see the point right there in that point. I'm going to point cut into it. Don't make that point go away. Just point cut into it to soften it. You can also remove a little bit of that length. Same thing on the opposite side. Just put one up some of this hair. So like create this pretty blunt line using more precision cutting techniques. All I want to do is just take the edge of that blunt line and soften it. If I were to point cut a wet line, it would be chunky. If I just want to soften a line, I do it dry because the hair becomes more separate. Alright, so now what do we do with this front length? What I like to do is just kind of mess around with the hair, move it together in my hands, and if it will actually like stay up with just a little twist of the hair, then I know it's at a pretty good length. That's if they want to wear it up. So I'll hold this hair up. I'm going to go off to one side and I'm actually going to remove some length from this so I'll shift my scissor over just a little bit. Like this. Cut up through. Now that we've got everything laying flat from your head, I'm going to come in here. I'm just going to slide and cut. So I've got tri-razor, right? Tri-razor has three different cutting sides.  So I like to take diagonal partings, stick the Velcro clip in the hair like this, see how well it holds the hair back, and then I can take my Texture 25 side of the tri-razor and hold the hair in my hand and I'll just take that Texture 25 side and work it through either mid-shaft or towards the base, depending on how thick their hair is. Slide it down and you remove 25% of the hair. And I'll do this all over my guy's hair. This creates all of that like kind of jagged, up and down movement throughout the haircut. And I love doing that on this edge of a triangular haircut because it just skims it up even more. And then I'll take a little bit of product, fingertip fold together in my hands. And then what I like to do is start with my fingertips and kind of work the product in the hair. If you go right in with your hands all in the hair, you're going to get too much product in one spot.


The Burukabugu Hydrogen field in Mali provides a unique opportunity for geologists to study the characteristics of natural hydrogen reservoirs. The field consists of two types of reservoirs, carbonate rocks containing dolomite and sandstone rocks. The carbonate rocks contain higher levels of hydrogen than the sandstone rocks, and the neutron tool was used to identify the presence of hydrogen in both reservoirs. The carbonate rocks are highly porous and contain secondary porosity created by castification, while the sandstone rocks are less porous and contain primary porosity. The presence of hydrogen in these reservoirs indicates that they are dynamic systems that are constantly being replenished with hydrogen-rich gas. This article was authored by Omar Mega, Eric Deville, Jerome Laval, and others.


Alright, welcome. My name is Nico Riecke and I'll be presenting about checking your project for non-inclusive language. I was raised to be open to everyone except them as they are, but later in life I found that this is not enough. Because often there are hostile structures in society that are hostile to certain kinds of certain people. And one of those structures is language. So it's important to check your project for the presence of non-inclusive language so you can improve it. But you are aware of this issue already as Git changed the branch name from master to main. That's one example. But there are much more examples. And now I'm not a native English speaker. I'm Dutch. Which means that I look for expert information to see if I can prove some things. And looking for expertise I found the Inclusive Naming Initiative under the Linux Foundation. Which is a great community which also has a Slack channel you can join. They are very welcoming. And they have an open process to evaluate words that might be considered non-inclusive and determine if this is the case, why this is the case. And they recommend alternatives and also prioritize them with a tiered list to see what are the most things that need, are urgent to replace and what might be more like a suggestion to replace. So this is one source for expertise. Another I found is by the University of Washington. Which is a very extensive guide with many words. And it starts with an introduction about writing plain non-collegial language. Which is a good read to explain why this is important. Now, I work at an open source program office. And even if I were to check this on the dozens of projects we have open source, that will take quite a bit of time. If I would do that on our internal projects without talking about thousands, this would be an immense amount of work. So I need automation. Looking for automation, I want a tool that can discover words that are considered non-inclusive to explain why this is an issue. Especially if teams start using this tooling. I really want to suggest alternatives so they can take direct action and prioritize whether they need to focus on what is lesser of a priority. And maybe even correct it automatically if that is possible. Looking for tooling, first I thought maybe I can write something myself using regular expression. How hard can it be? Or maybe repurposing spell checkers like C-Spell. And finally I found Woke, which is a dedicated tool for this purpose. But thanks to Chris Ward who reached out to me yesterday. There's another tool I became aware of now just that. Veil, which is able to do much more like checking for grammar and spelling and also issues like non-inclusive language. So maybe that might have been a better use, but I went with Woke. Woke was developed by Caitlin Elfring at her time at the Render Runway and she introduced it in a blog post. It's really great that she did this with the motivation to get it out to as many people as possible. And Woke is a command line tool you can use to check your project. And as you can see down there there's a file that contains the word white list. This is considered non-inclusive. And so it highlights it, explains why this might be insensitive, suggests alternatives, and also has a level, in this case warning. So it's very explicit and actionable. And once everything is fine, according to the rules, then it says so. And all this is backed by rules that determine what terms to look for, what are the alternatives, and a node to instruct the user. Also, there's the option for categories to filter out. So you have a long list of rules within your organization or your project. Maybe say, okay, I only want to check for these types of rules. Looking to implement this, I saw the need to expand on the quite limited rule set that is built into Woke. I want to add more words, languages, Dutch, for example. I want to add jargon. I work in the energy sector. We have specific words that are far too complicated to include in documentation. And a lot of this is also subjective. Words matter more than others. So it needs to be configurable. And that brought me to build the Woke config builder, as I named it. This is a repository you can find to take and make your own. It's a hierarchical structure of rules that is composed and rendered out to a single Woke configuration file. And you can make your modifications, add things, and render out. Publish that somewhere. And then you can directly import that public URL in Woke for usage. Also, it has some ignore file definitions. So it doesn't check if you have dependency specified somewhere with a name that might trigger sort of a false positive that is also ignored. These are some of the categories started there. So you see a reference to the inclusive naming initiative definitions. You see some about the languages and all the various types of non inclusive boards. So you can actually filter them. Now, this is what I want you to take home. So please take a picture. If you want to take something from this talk, these are the resources that help me along the way and can help you. And please also check fail as an alternative to Woke. And please set aside somewhere 30 minutes like next week or the week after in your schedule to work on this, take a project, a project you like, and start checking it because then you have to read up on words that are non inclusive. You can go through whatever project you want to check. And this will be a very learning exercise, whether you do this manually or automatically to really get your head around why this is important, how to act on that. Thank you. Yeah, I don't have time for questions. Yeah. Yeah, the slides are already online. Yeah, yeah. So you can check it there. That's good point. Then you don't need to take a picture of only you can account or it once you go scrolling to your academy. Yeah, indeed, language matters. Yeah, I learned that. And especially if you go through the list like the University of Washington list, including naming initiative list, you see the words and why they matter. It's all explained. Yeah, you learn a lot, even just reading that material. All right, thank you.


Hi, everybody. My name is Ipsen. I'm a senior consultant at Red Hat. Today, welcome to the open source summit in Japan 2020. The presentation is about how to handle telematic data with OpenShift. What is telematic? Telematic is a simple concept that combines telecommunication and data processing together. Telematic relies on using wireless devices that were embedded into your vehicle and transmit the data in a real-time to an organisation. A couple of tactics include personal vehicle with certain collecting information. So what is the purpose of telematic? Well, we use telematic to increase the operational efficiency so that when, for example, when the truck is going to a different direction, different route, we identify that so that we could improve digital efficiency and identify the optimal path. We want to improve the customer base. So for example, when the customer do not receive the deliverable on time, we want to tell the customer where exactly the truck is and when do they expect to receive the deliverable. We want to protect the driver. For example, when there is an accident on the freeway, we want to alert the driver and try to avoid the accident by taking a different route or taking a different time shift. We also want to come with safety compliances. For example, when certain direction and certain route is not compliance to specific deliverable objective, we would use that to do the safety measure. Telematic data includes the wireless communication location, usually based on the GPS location data and some in-vehicle electronics. It is also used to integrate to the cloud from the automobile. And it is also used for data analysis. For example, when we could use machine learning to do different types of predictability to calculate the sales event and plan ahead on how much data, how much truck load, how much deliverable we need to deliver from destination one to destination two. An example of telematic data lifecycle, it contains the first step where the data is generated from the vehicle, from the telematic device. The data will get transmitted to the cloud. The cloud will do the data collection and aggregation, meaning that it will convert the external data object into internal object and you could aggregate the data together based on custom, based on data similarity. And after that, the data will be analyzed and sometimes it could be used by machine learning or it could be done by manual analysis to analyze the behavior of the data. And at the end, we would go into some sort of evaluation. For example, the driver's performance, the truck's performance, are we delivering as an organization?  Commercial telematic contains onboard tracking systems that send GPS location, wireless communication, truck data, and also it contains jurisdiction and tax management. The driver would have some interfaces. The interface could support the voice communication to talk to a dispatcher and it also support the text message communication. The onboard sensor is embedded into the truck, monitoring the truck load, the capacity, the temperature of the container and the tire, and the weight of the vehicle. All these different real-time information will send to the cloud. This is an example of a telematic system integrated into a container truck. At the end of the truck, you see that there is a door sensor that will keep track of how many times the door of the container has been opened, how long it takes for the operator to load the goods from the truck and out of the truck. The door locks is also monitored so that we could track how many times the lock has been engaged. Is the lock secure? Is it protecting the item? At the bottom of the truck, you see there were monitors for the tire, including the tire pressure, the temperature of the tire, the rotation of the tire, how many times it has been rotating, the RPM and all that. The truck ID is also monitor integrated as part of the truck. Other temperature sensors and also the weight sensor were also integrated. This is a complex system. You see there was a lot of data that we could use from all these different types of sensors. Similarly, in the passenger vehicle, you can see that when we are driving, we have the emergency call. The call could keep track of how many times we are calling for emergency for roadside assistance, navigation GPS system, tracking our driving behaviors, device-to-device communication. For instance, when there is an accident happening ahead, the device could communicate to other devices embedded into the vehicle to send our alert and notification. We move vehicle access, so it will allow you or other people who are allowed to access the vehicle, access to the vehicle. Vehicle tracking and information, tracking the owner of the vehicle, the wireless phone integration, tracking your phone activity, tracking your phone number, radio system and other user preferences. This is a picture of our telematic control unit. The control unit contains different integration points with different ports. Each port will get some information from other integrated systems. You can see the microcontroller in the middle can take the information about the battery, about the memory, about the GPS, about the car internal system, and all that information will get consolidated through the telemetic controller unit. The purpose of this unit, as you can see, is to create and collect vehicle data from the ports. It manages the information from the communication interface. It manages memory and battery. It manages the communication to the cloud and also manages the communication to the dashboard in the device. This is usually embedded into your Edge devices. We will talk about the meaning of Edge devices in the following slides. An example of telemetic data category, you have the vehicle system category. This data will focus on the engine, the transmission, the OBD, the weight, the lights and the tyre of the vehicle. Now on the right-hand side, you have the category for the driver. That usually keeps track of the driver's identity, the driver's driving behaviours, the hour of services, the speed, the acceleration, the idling time of the driver. Then in the bottom, you have the operation categories. This is usually used by the organisation to plan and optimise the operation to usually reduce operational cost and increase performance and efficiency. This includes tracking the location and navigation, the dispatcher schedule, the roadside assistance and then the delivery time. Now we have a basic understanding on the telemetic data. Let's look at how OpenShift could help with the telemetic data. OpenShift is a distribution of Kubernetes. It's optimised for continuous application development, multi-tendent developments. OpenShift contains developers and operator-centric tools on top of Kubernetes. It enables rapid application and development, easy deployment, scaling and lifecycle management. Architecture of OpenShift will contain full stack end-to-end automation. On the top, you can see it manages the Git repository, the infrastructure, the databases, the release cycle and the deployment. All of these were managed by OpenShift using the OpenShift web console. In the bottom, you can see that OpenShift supports the multi-cloud platform, the hybrid cloud platform and edge computing. We will talk about edge computing in more detail in the following slides. First, before we move to OpenShift, we need to make changes to the telemetic system to make it as a cloud-native application. Usually cloud-native applications come from the concept of DevOps, continuous delivery, microservices and container. Regarding the OpenShift cloud-native app, the benefit is that the app is scalable, is self-healed based on health check and monitoring. The port-dev-off approach is continuous delivery and it decomposes microservices with API and this is also leveraging the container architecture. DevOps processes usually involve continuous integration, continuous testing, continuous delivery and continuous monitoring. As soon as you check in your code into Git, the code will get deployed to the cloud and using the health check and monitoring, we can continuously look at the container and make sure that the container is up and running. As soon as the code is checked in, as long as all the integration tests and unit tests are passing, it will be integrated into production and that becomes the next release. The Jenkins CI CD pipeline will get the latest code from the Git repository, dockerize the microservices with the Docker registry and images, hook up the unit test, integration test and automation test to the Jenkins CI CD pipeline. With the Jenkins Gator check-in, you can recheck, for example, any code change that failed the test or that did not meet a specific code coverage number. The build artifact will get pushed to the artifactory, such as JFOB. The Jenkins pipeline also supports statistical analysis, security scanning, so that it can cache for any security vulnerability as part of the code change. Jenkins also supports deploying to different OpenShift environments and then after the deployment, it will do the health check and smoke test to validate the deployment is successful. The migration to microservices is the idea to break down telemetric services into microservices by function. The microservices is to contain logical blocks of function. Each surface is self-contained, self-deployed and they work independently. So each surface you can think about it as a quad operation and, for example, we may have a location surface that deal with the location data endpoint. So it's a self-stand-alone small microservice. When there's a high peak going into this specific surface, it will automatically scale up by OpenShift and it would also scale your databases based on usage. Other benefit coming with OpenShift is the operational readiness. We talk about health check. Health check have the lifeness probe and readiness probe health check. It would be able to ping the surface endpoint and verify the dependent surfaces are running. Other instance such as the VictorOps integration allow error and alert notification to send out to engineers when there's a production issue. It also provides integration with Slack and email so that the engineer will get notified when there's a production issue. Comifias is also integrated with OpenShift. It allows us to track the API calls, the request, the response time, the error rate so that when there's a performance issue, we can use Comifias to identify the problem. For example, if a specific error or message is happening too many times, Comifias will be able to flag the alert and send out a notification. OpenShift also has sprung integration. All the warning and error logs will get sent to sprung so that you can query and look at this log in details. Yeager tracing, for example, is another integration with OpenShift. It allows us to group surfaces together using a common request ID so that we can track the surface orchestration, the surface call workflow and use that for debugging and troubleshooting. Now you understand health check and monitoring are all tied to a container and we could also use metrics, logging and tracing to get it from the container. So now let's introduce an idea called IFTAR, I-F-T-A. IFTAR stands for International Field Tax Agreement. Each day has its own IFTAR. Basically, the idea is that when you are driving in a truck, going through a different state, you have to pay tax, right? And then the tax is a lot of time based on the mileage or based on how much time you have spent in each stay. And this calculation is super complicated because each stay has its own IFTAR log. So this is an example, micro-surface architecture where you can see we are using the telemetric surface, generate the telemetric events and feed the data back to IFTAR so that IFTAR can use that information to calculate the tax for the payment for the stay. And then at the same time, you have a vehicle surface that keeps track of other vehicles and they all feed into different databases in the bottom and then on top of the database there was a layer, a surface layer to get the data from the database and feed that information back to the UI and the gateway. So the IFTAR calculation is complicated. For example, if you are driving from Trinidad, Colorado to Threadford in Texas, you could easily go through four different stays based on your GPS calculation. But this calculation is changing at real time. So for example, if there's an accident on the freeway or if there's a construction in the freeway, the route from the GPS could change. So any change will result in different IFTAR calculation. You may end up not going to a specific stay or you can end up going to more different states. So this calculation is dynamic and it rely heavily on the real-time telemedic data. So for IFTAR calculation, we need the real-time data from the telemedic surface to perform the actual calculation. Any change in the GPS calculation could result in different mileage and time span in different jurisdiction. A lot of time operation would be focused on how to optimize the operational cost by going through the state with the lowest tax and lowest fuel tax. In this situation, the edge computing could help. So now we understand the big picture of OpenShift. So how does edge computing come into OpenShift? Edge computing is the idea to place the workload as close to the device where the data was created and the action was taken as possible. So the idea is if you put your calculation and computational power and resources closest to your device, you would get a much faster response from the calculation so that you can take action a little bit faster. The telemedic data gathered from the device will be made available to the cloud. So how does that work? We leverage the 5G network to open up the computing capacity into the devices. This is a picture of edge computing using 5G network and in the bottom of the page you see there are different edge devices that were embedded into the truck and different trucks were driving. As soon as there is a 5G network available, the data will get transmitted to the edge node. Edge node is a layer, is a sub-layer on the cloud that's specifically configured to interface with the edge devices so that you would do do the computation at the lowest level and as fast as possible. And then the edge node also integrate with the rest of the cloud system by submitting the data to the cloud. The cloud will do the data backup and do further data analysis and generate the user dashboard and interfaces for the operator at the organization to look at the data. So this is a high-level architecture. Sometimes you have more than one tiers of edge nodes and we will talk about the benefit of doing that in the following slides. In the edge node this is just a piece of IT equipment built for IT workload computation. It could be a four-blade server. Usually it means to be lightweight.  In terms of the edge devices you would have a piece of IT equipment built for gathering different telematic data in your vehicle including the sensors, the GPS navigation, the radio, camera, internal card system and its CPU. So on average we have about 20 to 30 different CPU in it. So you see there was a lot of computational resources. We could take advantage from within the vehicle. And for the edge devices the computational capacity has increased significantly in the last decade. Many of them were running in Linux. That means we could deploy, container-wise, work low into the edge devices as long as the edge device support Linux. So now the next question would be how do we manage these different environments and ensure that the white workload will deploy to the edge devices and the edge node at specific time? The answer would be using OpenShift. OpenShift builds the workloads as containers. Containers are deployed for scaling and availability. It enabled the Kubernetes to run on the cloud and then we basically use the same concept to manage and deploy workloads into the edge devices. Some technical problem that we have seen the number of edge devices are very high. It's estimated all together 50 to 100 billions of edge devices in the fields. These devices are coming into many different forms and configuration. Security is also a concern because the edge devices are outside of the boundary of your data center. Therefore, the data associated with the edge devices need to be protected. And it is not possible to do any manual deployment or configuration on the edge devices because there's just so many. So we need a way to distribute workload to billions of edge devices in a massive scale. So how do we use edge deployment with OpenShift? OpenShift ensure the uniformity, consistency, security and reliability of the edge node and edge devices. OpenShift define and provision standardization in edge node and edge devices. We could deploy the appropriate application resources based on their purpose. So for example, the GPS has a specific purpose of gathering and tracking the location. So we would only deploy workload that were related to locations to the GPS device. When we deploy new edge location, OpenShift would ensure that the new primary standardize compliance and secure so that it would keep the custer to be uniform across all business footprint. So uniformity is really the key. You want to make sure that the same container-wise application could be deployed to all these different platforms using a container-wise architecture. So earlier we had a diagram about the edge deployment. So there are actually more than two layers. Sometimes in this situation, you have five or six different layers of edge tiers for deployments. So on the far left, you have the device layer. This is where you deploy the container-wise application to devices. The thing about this device layer is really limited computational power with sometimes you don't have any availability. You have one instance that was deployed to this edge device. So on top of the edge device, you have the edge node layer where you could have some simple infrastructure configuration. You could set up high availability, scalability, geo redundancy on that layer. At the provider layer, this is the layer where you would communicate between the no-edge device and your data center. It has different provider layers set up. For example, the provider layer for the far edge, the provider layer for access edge. For example, you have different types, different tiers of the data. You have different accessibility. This would be the way the level, the layer that you set up the access edge. At the end, you have the provider aggregation edge. You aggregate these different data together into an aggregation layer so that you could do layer surface orchestration. In the core circle in the middle, you have the provider enterprise data center. Your data center could be a regional data center or could be on the cloud. This layer is also deployed using OpenShift. So looking at this picture, you can see that as we are moving from the edge cluster layer to the right to the device layer, this configuration would change significantly. At the edge layer, if you deploy with OpenShift 4.5 it automatically goes with three master nodes and three worker nodes. These are set up for high availability and if one of the worker is down, the other worker will step up and continue to support the workload. As you move to the right, you see the remote worker node will be configured a little bit differently. You have one worker with three master. As you go further to the right, you have a single node single edge node server. It is basically one worker and one master. On the device, you don't have any more replication. You only have one single container deployed into the device because of the limitation of the computational resources. From the left to the right, you can see this also depends on the network connection. The edge cluster with three nodes have more reliable network. As you go all the way to the right, the edge device which basically depends on the 5G network has really weak connection. The idea is that if the connection is not available, the workload won't be able to pass on to the left. So some retry logic needs to be happened so that you can cache the data locally on the edge device and as soon as the 5G network is available, it will push the data up to the next level. We have an example project where it takes the highway, the road condition and send out alert based on telematic data. The truck camera will keep track and capture the road condition as an image stream, as a video stream. Video stream is a collection of images. Each image is tagged with the location let long and the time frame. When the image was saved and gets submitted to the edge node, we have some machine learning program to run and flag the images with hazardous condition on the edge node. So for the images that were flagged, we will trigger the alert and notification based on the image location and based on the image timestamp and send the alert back to the edge devices which are within the location and the timestamp. Once these different edge devices get the notification, it will trigger the GPS to recalculate the route so that it can take a different route to avoid the accident. And then similarly, the machine learning layer will also communicate to the cloud layer and talk to OpenShift when OpenShift notify about the images that were flagged for accident. It will update and notify the operator the telematics dashboard will also get updated so that the operator can go in and manually configure the driver to take a different schedule or go to a different destination. Basically, the alert will allow the operator to optimise the activities and minimise the operational cost for the day. The telematic image with OpenShift is based on the telematic image from the edge devices and as you see, the world condition will get analysed at the edge node and alert will get triggered to notify other devices. So the edge computing solves the computational resources and latency. The image will be able to transmit through 5G network when the network is not available, retry will happen. If after a couple retries and it's still not available, the edge device will cache the image and wait for the next availability of the 5G network. The telematic image will be back up in OpenShift when it receives the data from the edge node. From the dashboard, you can see this is an example where it would update the accident indicator based on the image, based on the location and the timestamp. From the operator point of view, the operator can come in and look at all these different incidents from the dashboard and then try to plan ahead for the day. So as you see, our application is using containerized application OpenShift Support GPU graphical processing unit. So the latency of the image data going from the edge device to the edge node is 400 ms. The latency for the machine learning for scanning each image is less than 300 ms. We have a bandwidth of about 5 mbps for upload of the telematic image. This is a layer of machine learning configuration that we use. This is basically an artificial neural network. Each image coming in, we can use some image feature extraction and based on the feature, we can predict the true or false whether the image is an accident or not. These are some of the factors that you could consider when you are doing the image extraction. You could look for the accident cone, you can look for specific accident sign, look for police car, ambulance and so on. One means the image signifies a prediction of a potential accident. The alert will get triggered based on the image location. A score of 0 would signify the image does not associate with an accident. The cutoff value we set off is 70%. If it is lower than 70%, it is not an accident. Based on the model, we can see that the prediction could reach 97% accuracy. The last piece that we want to cover is the real-time telemetric. We want to execute jobs within a minute or a mile when the real-time data comes into the telemetric system. How do we do that? The idea is that we could use change data capture on the applications. This is a design pattern for event-driven cloud native application with OpenShift. Change data capture continuously identifies the incremental data change. The real-time data replication across databases and WebPica were done through the transaction logs. We triggered events based on data change. We captured and propagated data in the events to the microsurface within the system. Based on the change data capture, it is based on the upstream dPCM project. It integrates really well with Spring Boot and OpenShift. The purpose of using CDC is to increase the data collection where the entire database becomes really expensive as your data size is growing. Backing up database to a full scan on a database is also expensive. We would make the real-time data replication possible. The idea is that we have the telematic database on the left. We have different query coming in to do the insert, update and delete. When this query is finished, we have the transaction logs. These logs will be standing and waiting at the event bus. We push it to the CDC relay, which is a dPCM and then the dPCM will notify the Elastic Search engine on the right-hand side which contains the telematic index. Once this data is updated, it will trigger the notification to the dependent services and it will update the telematic dashboard, the operator dashboard, the email notification and all that. You can see that this is a really effective architecture because every time there is a new change coming in and if the changes are insert, update and delete, we will do some action based on the query type. To integrate the dPCM into Spring Boot, all you need is to open the dependency com.yaml file and update the dPCM dependency. The CDC Listener is basically a Java program that is set up to take the constructor to load the configuration and set it all back on the handle event method. The handle event method is then getting invoked when the transaction is performed. The embedded engine is a web part of the connector that manages the connector lifecycle. This is an example of a CDC Listener that will perform on the telematic connector and the telematic surface and it will notify the handle events when the transaction happens. The start method is called when the dPCM engine is initialised and it started asynchronously using the executor. The end method will be called when the container is destroyed this will stop the dPCM and merging the executor. Handle event will be called when the transaction is performed on the telematics table and handle event method will identify what operation takes place and call the corresponding telematics service to perform the create, update and delete on the elastic search. This is an example of the start method on the CDC Listener and the telematics connector will listen to the change from the telematics table using the Postgres connector class from the dPCM. It will have the offset storage to keep track of how much data has been processed from the transaction mark. It can resume from the failure point when an error has occurred. Usually this type of error means there's a connection error, the database went offline. When it goes online again, it will be able to resume. The file offset backing store is used to store the offset in the local file so that you can have a cache copy of that offset. The connector will record the offset in the file for the new change. Then the dPCM engine will flush the offset based on the flushing interval. This is a dPCM connector configuration based on the name of the connector, the flush interval database name part number, username and password. The telematics surface maintain the read model method and the update, insert and delete on the telematics data. The telematics repository is an interface to perform the quad operation on the telematics database. The telematics surface if the operation is deleted, it will call the telematics repository and delete the transaction based on the telematics ID. If it is not deleted, it will save the telematics object. At the end today we have gone through a lot in this presentation. You learn about OpenShift, edge computing, telematics data, and CDC. You can see that the telematics data integration with edge computing on CDC worked really well with OpenShift. It enabled easy, highly distributed event-driven, transactional, log-driven, stable microservices development. The edge computing with 5G network offers fast and reliable solution. The event stream created is based on the lock change. The application could listen to the event and perform action based on data change. That makes it super useful to deal with real-time data. It also performs consistent data manipulation using edge computing. The data will be processed at the edge layer before it will go to the OpenShift layer. You can see the distributed connector cluster also provide high availability, scalability and improve the performance of the overall system. With edge computing and OpenShift and CDC the architecture is open source supported by the open source community and support any programming languages and development framework. This is really useful. So, for the next slide thank you again for attending this conference. We enjoy talking with you. If you have any other further questions please reach out to Red Hat Consulting. We would be more than happy to assist you in any architecture related to OpenShift and edge computing. Thank you again. I hope you have a great day at the summit.


So with the sunshine double over for another year, it's time to go through the predictions for both Indian Wells and Miami. We'll go through Indian Wells first, considering I did so bad at the Australian Open, it's only can be up from here, so let's have a look. And yes, I do have a pimple on my face. I reckon Goff's gonna do better than Rabakina this year. So what a start! Rabakina didn't even play, so I got that one without even having to watch any matches. I mean, Goff just played one match, Rabakina pulled out, easy win. And this is a tricky one, but I'm gonna go with Vilch on the men's side, he played really well on the quality. And on the women's side, I'm gonna go with Perra. So my Vilch pick was great, he actually took out one of the seeds, Hashinov, in the second round. He did lose in the third round in Marijan, and then Perra only made it to the second round, but no qualifiers got out of the second round. So she was the equal best, because everybody lost as well. Now there's about two or three countries I could have picked, but there's a lot of withdrawals coming out of the Czech Republic. So I'm gonna go with the USA to have the best tournament. So you can't go wrong when you pick the home country. I mean, Indian Wells, it's in California, it's in America, a lot of wild cards. And they did do the best out of any other country. They got around 30 wins, with Russia being the second best at around 20 wins.  And I'm gonna go with her catch on the men's side. So of course Rabakina pulled out, so she hit no Aces, which was very unfortunate. Her catch though, he did play, and lost. So both didn't hit that many Aces. All right, the big one, Djokovic's sinner, who's gonna do better? I'm gonna go with Djokovic in this one. Okay, so, uh, this might be the last time I ever doubted Sinner ever again, because Djokovic of course lost to a lucky loser, Nardi in like the third round. Sinner, of course, made it to the semi-finals. Djokovic just completely fell apart and of course had that massive upset. And there are a lot of dangerous unceded players in the men and women's draw for this one, but I've got to go with Pleskovac. She's played so well over the last couple of weeks. And I'm gonna go with Mensik, so a little bit of a Czech flavor. I'm gonna go with him on the men's side. All right, so not very good with these two. They didn't do as well as I hoped they would do. Mensik did lose a shelter in the second round, so a little bit of a tough draw for him. And Pleskovac lost a blink of her in the first round, who then went on to take out Pagula. So again, tough draw. Maybe I should have picked two different players, but I had some faith in them, because they'd been so good over the last couple of months. Top 10 upsets. I'm gonna go with Anstjaber, her knee problems. I'm a little bit worried about those. And I'm gonna go with Rublev. I don't know why. So I got them both right. They both lost in the third round. In the second round, Anstjaber lost in her first match to Volleynets, and the knee did prove to be a problem. And Rublev ended up losing their lahetka in the third round. So two massive upsets, and there was actually a lot of upsets, but they were two of them. Dark Horse picks. I'm gonna go with Dasher. I don't care what anyone says. I'm gonna pick Dasher this time. And I'm gonna go with Umbair, which is probably the obvious pick on the men's side. So Dasher made it to the fourth round. Big run there. She outlasted players in her section, like Zhang, as the rank was in there as well. So made it to the fourth round. Lovely run there. Umbair, super disappointing. Lost in the third round. He did lose to Tommy Paul, who ended up making to the semifinals. All right, the 1-2. We've got Shriyantek. We've got Sabalenka. I'm gonna go with Sabalenka in this one. Man. What was I thinking? I mean, Sabalenka did lose. I mean, she almost lost in the second round, but she did lose eventually in the fourth round. Ended up going to a very good informed Navarro. Of course, Shriyantek won the whole thing. So, I mean, just terrible pick all round. I'm so sorry to all the Shriyantek fans. My hot take for this one. It might be more of a lukewarm take. I don't reckon we're getting that many upsets. I think that the top players are gonna do well here. So this was a weird one, because, I mean, we did have upsets. You know, Djokovic lost, of course. We had Rubelev losing early. Jabero on the women's side. Sabalenka didn't make it as far as I thought. But we did have a lot of big names in the finals. Like, we had the two versus number four in the final for the men's. We had three of the top four in the semi-finals for the men. On the women's side, it was a little bit different. We only had half the top ten playing in the quarter-finals. And then, again, in the semis, we did have three of the four players in the top ten. So, there wasn't as many top players as I thought making it through. All right, so one of the toughest parts of these predictions is the semi-finals. But on the women's side, I reckon we're gonna get Shriyantek versus Kallonskaya. And down at the bottom, I'm gonna pick Goffas Sabalenka. On the men's side, I'm gonna go with Djokovic versus Memvedev. And at the bottom half of the draw, I'm gonna go with Sinar, taking on Diminor. Okay, so actually, that was good. Kallonskaya didn't make it as far as I had hoped. She ended up losing to Paolini. And Sabalenka, of course, had lost to Navarro, who then lost to Sakri. So, I was two out of four, I'm pretty happy with that. Again, just like the ladies, you know, two out of the four. Djokovic obviously had that shock loss. Diminor, unfortunately, lost to Zverev.  And then, of course, lost to Elkres. So, you know, two out of four, I'll take that as well. And the winners for this tournament. Again, boring picks. I'm gonna go with Sabalenka on the women's side, made the final here last year. And I'm gonna go with Djokovic on the men's side. Alright, so Djokovic's losing third round. Sabalenka losing before the quarter-finals. Both terrible picks, because Shriontek won. And so did Elkres. I should have just picked them, because they've won it before. Why wouldn't I just pick the easy ones where they both win? I mean, of course, they're defending champion on the men's side, and then the player who won two years ago. You know, it was a no-brainer in hindsight. But, you know, who would have thought that Djokovic was gonna lose a lucky loser? So, I actually did better than the Shrion open in this one. I had a hot start, though.  Then they all just fell apart. I mean, look at the other predictions. I've got less than that. In fact, the Shrion opened.  Let me know down in the comments below if you did any better than I did.