Okay, we made it to our rock-hounding spot today. Today we are getting some Wonderstone near Salina, Utah So we just took a drive up the canyon. That's what the time-lapse was so we're in the location now right here so we took the right fork that heads up toward some weird named Meadow and mud mud springs or something and You just come up a short distance to this little turnout right here And what we're looking for is Wonderstone. That's gonna be up on the hills so if you look back on this way you can see where people have been digging right up there and Also, if you look back this way You might be able to see some other places people have been digging along that edge It's hard to tell though with where the Sun is right now We're gonna be just hiking along the Layer that's all over these hills that layer that you can see there. We're gonna just See what we can find in it and different spots will have different colors and patterns of the Wonderstone So this is rhyolite ash mixed with mud and sandstone Probably a couple tens of millions years old. I'll find out exactly how old but That's what we're doing up here, and that's what we're looking for Digging over there. I Decide to come over here a little bit Think obscene some promising things or put a few things out right here that might be nice and I like the look of that one with this thick layer right here. That's kind of a light tanish green gray green Whatever you call that Okay, we'll try and Get some of this out and dug for a little bit And I pulled this one out just now it was It was originally like that busted it kind of nice looking We'll take some of that home and try and cut it Yeah, dig here a little bit more get a few more pieces here, and then try a new spot So I was just over there came here a little further just to test it out. It looks very similar though And it's just okay I'm gonna keep going further All right, I was over there on the other side of this little hill here, and I followed the van over here and It doesn't look like it's been dug nearly as much and Right here seems to be the layer again, although I think it's more than one layer but here it's the Kind of layer above that looks kind of nice but The thicker one underneath where you'd expect something is Nothing really There's not much of anything on that so The colors come and go All right, we'll go a little further Came over here just a little further. I was just right there and I busted this one open and it's that dark stuff, but now we're getting the black again But it's darker bands not lighter color ones like we got when I was digging over there The last time I was here Which is the only other time I've really been here We ended up just going over there, and that's where we found those ones that I cut open a couple a couple weeks ago If you've seen that video where I cut open the Wonderstone, and I thought that was just okay stuff Turns out that might actually be the better stuff over there. I don't know. We'll keep looking around though All right, I'm leaving further over and more black But the black is starting to have more contrast, and this is the kind of stuff we were getting Kind of over there some of this it was kind of the black, but also some lighter colors Yeah, we might need to head back over there Plus that one up. Let's see how she looks inside. Well, hello Yeah, that's kind of cool, but I think the stuff on the other side was better Interesting well, I've come a little further along this hill actually very distance further and Other than what that black stuff. I was sitting back there haven't found the greatest looking stuff It seems to be losing a lot of its pattern The swirls and stuff are disappearing like and it's becoming thinner I'm except maybe that one bust that one up and see So if you remember That's what I was finding further back that way, which is definitely better looking found that sitting there I thought that was really cool. It's just so perfectly flat and smooth The way it broke off Just really cool Yeah, just naturally come off that way might take that just because it looks so cool It does have some lines in it All right, I'm gonna try and bust that thing now Let's bust this thing open. It looks like anything inside So I guess it's gonna be mostly black Yeah, I can smell that sulfur every time I hit it The high sulfur Volcanic ash looking stuff. That's gonna want to break. It doesn't want to break. It has high silica Yeah, it's kind of just looking inside like it does on the outside. Not very good Well, I'm back at where we started My dad went over and dug over there Found a thicker vein, but it was just solid black. There was no variety or pattern to it So we are gonna go to the other hill on the other side where I was before and probably Find better stuff because we definitely found better stuff on that trip than what's over here Maybe I hit the Good zone the first time I was here and didn't even know So there was where we parked So we're gonna head over to the north slope, okay, let me point it out that is where we're gonna head Hiking up the hill to get up there Right here from interesting piece of agate. I made it over here I were parked over over there and I searched all that Black stuff I found was right in that area And here we are over here This is where I was last time I came here about four years ago the only other time I've actually been here So I found those pieces in here and I found that one Sitting there right on the surface somebody dug up that didn't I didn't want to keep Yep So Go get into this a little bit Dig down in there and see what we can find. Okay. I just finished digging all this out This is the layer right here. It's dark stuff. So we'll try getting some of those pieces out Break them open and see how they look Okay, just pulled the first one out Let's break it open and see how it looks. Ah, yes, that's what I remember Interesting stuff here pretty cool So one thing that we have noticed throughout all the layers is Some of them are much thicker than others This is one of the thicker layers like this and these ones tend to come out in bigger sheets And a lot of the other places some of them are very thin like this very small layers They were the ones that didn't Work out so well. That's why we're over here and hopefully get some good stuff All right, I'm gonna try and get this one out and see how big it is All right, I was digging for a bit getting a bunch of dirt out of the way and look how thick the layer is right there So it's still there. It's getting really thick right here. So these are very promising So I want to try and get these out in as big pieces as possible You can see the regular fracturing going on regular jointing in the rocks and frost wedging Okay, like a bunch. You can careful. This is a curious overhang here That should be enough now Loosen a lot of these pieces up. We'll just pull them out. Yep There's one It's kind of cool to have these little things of bits of gypsum That grew in natural fractures right going through the middle of it that piece Pieces came out lots of small pieces mostly All right been digging for quite a bit got quite a few pieces out now All right here. We'll get in the bucket. Look at that one though Some pretty gypsum on it Sparkly in the sun Just been hard at work removing some bigger pieces Got this one out right here. It's a nice big piece of Wonderstone A lot of gypsum on it Looks neat the way it is Does have a fracture going through the middle of it. So it's technically two pieces. They're still pretty big That'll be a good one. I've got enough stuff out of my area Mostly fill the bucket So we're gonna see what my dad's got. He just finally hit the layer further down here We're gonna see what the stuff over here looks like some of it out My dad got his first few pieces out and there were two very different pieces So the first one we got was this just that purple right in the middle and then the second we pulled out was black The white one. Very good looking. This one had a little bit of green in it What a deep hole right here. Oh, that's a good sized one I'll break open the little one to get a fill, but we'll definitely want to keep the bigger one Break open the small one and see how it looks.  It's gonna maybe try and get that one last piece down there My dad's pretty much done in his area. It's just getting too precarious. It took a load down. We've got a bunch more. I was just digging this spot and Look at this broken one that just came out. Oh Nice I'm not sure if I broke it if it was already broken like that, but look at that So I'm gonna get the rest of that but Looking good here Looking good All right, so I just wanted to explain a little bit about the layers and how you get this stuff out of here So this is the Wonderstone right here. Usually it'll have this nice yellow on here probably sulfur and It'll have gypsum Also, the layer above is kind of a brown and it's very loose So we dig the the layer on top out of it and then right below it it turns to white ash and To get this stuff out Dig the top out and then bust into the ash underneath because that's easier and Once you've busted it out and you've got some space then it becomes easier to get these pieces out So we're gonna go ahead and demonstrate that here That's gonna dig the top layer a little bit Once you've got a good amount of it off the top right there And he's gonna go ahead and try and bust into the ash underneath See the ash kind of comes out in these little layers. It was fractured. That's too bad Well, we've been digging quite a bit and I think we're gonna call it there My dad's on his way back up. We have limited buckets and We've already taken one load down and we're gonna sort through that and figure out what of that we're gonna keep We're gonna get those down We'll get them all loaded up And then starting the eyes We'll head back home and I'll cut some of the small ones open so we can take a look at those and The bigger ones are just gonna have to wait to have a bigger saw. That'll be something down the road All right, I'm back from the dig. Let's get to cutting some of this here Wonderstone I'm just gonna cut some of the smaller ones today. The bigger ones. I'm gonna save till I have a bigger saw So let's get at it and see what we find That's pretty Look at that. Look at that. That's pretty in it Nice one that one That's a pretty one too, isn't it? You want to see it? Yes, that's beautiful. There we go some more Wonderstone Without some special That pretty Okay, look at that one That one's got more black in it. It's really pretty when you get the black with a nice contrast So that's a really pretty one Look at that one. That one does not have any black and Has a lot more bands in it That's pretty cool, too nice Hey Another one that looked very similar to the last one a little bit more white. Don't see any black Lots of bands in it. Oh, hello. This one has a lot more black in it So I can tell when it's gonna have black because it makes a black Mud that I can see and I can smell sulfur So this Black must have been deposited with high concentrations of sulfur from whatever groundwater was going through it Pretty neat. Well, that looks like a couple other ones. We've already done Still got that same White barely any black Yeah, not as many bands in that one All right, so everything we've cut so far was from that last area where we found all that good stuff In the first area that we were where we didn't find a whole lot. There were those few pieces that I found decent This is one of those We'll cut this one. See what it looks like Well, that's quite pretty too. Look at that That's not too bad It's just different different from the other it doesn't have the black Or the purple So not as good but still quite pretty Okay last one I've got tons of wonders done like I said, but I'm only cutting a few today the smaller ones And the bigger ones I'll save for when I have a better saw. All right. Here we go last one Well, this one had a fracture going through it. So there's one side the other side As you can see broke. Let's see how it looks along the break That's how it looks along the break Still a pretty piece Not bad All right, that's it for this episode. I really hope you enjoyed the wonder stone from selining utah It's a beautiful area I really like the stuff that comes from there and I plan on making some really cool stuff with it Eventually down the road And for now, I'll just enjoy it as it is So I hope you really enjoyed it and remember there is treasure everywhere


I look at Bitcoin as becoming more and more a leveraged version of Treasury bonds and goals, some form of combination of that. This year, central banks' tight and monetary policy caused a massive crash in crypto and other risk-on assets, but macro factors are already shifting and in the second half of 2022 we may see a completely different scenario. I think we're heading to one of the greatest reversions in history in terms of risk assets. In this interview, we talked to Mike McGlone, senior commodity strategist at Bloomberg to understand what macro factors will impact the second half of 2022 and also why we should prepare for one of the greatest bull markets in the history of Bitcoin. Before we start, don't forget to subscribe to our channel if you haven't done it already. I'm Giovanni, your host, and this is a Cointelegraph interview. So I wanted to discuss with you the outlook for the Bitcoin market in the second half of 2022. You said that the risks-verses-rewards ratio are tilted towards one of the greatest bull markets in history. So can you explain what you mean exactly by that? A resumption of, that's the key thing to remember, is Bitcoin has been by definition probably one of the greatest bull markets in history and it's just backed up at the highest velocity to the greatest discount ever versus it's 100-week, 50-week, 200-week moving averages. So it's pretty well discounted. What I see it's doing now is it's probably building a base around 20,000 similarity when it did around 5,000 at the end, at the bottom in 2018 and 2019. The key thing we have to think about is why is Bitcoin down? Now the whole market's corrected a lot. It really gets really speculative expensive with all cryptos. And it's correcting now because the whole tide's going out. And we've had one of the biggest corrections in the stock and bond market in my career. And I've been in the pits since the 80s, I've been in the business since the 80s. So the whole tide went out, Bitcoin and cryptos went down with the market. And now it's the question of which comes out ahead. And I see Bitcoin coming out ahead because I see it transitioning to be more of a risk-off asset like bonds and gold and less of a risk-on asset like the stock market. Looking at the second half of 2022, you see a higher probability that this greatest bull market in history will resume than a prolonged bear market until the end of the year. Yeah, well, so if Bitcoin does what it normally does, it's going to recover from these levels. It's just a question of time. Now if the Fed keeps hiking at the highest velocity, the greatest pace since, it's basically since 1980, yeah, that's a sledgehammer for all risk assets. But I think in most scenarios, Bitcoin will do better. So let's look at the best case scenario, say everything recovers, the stock bear market's over and starts to trickle higher. And that environment cryptos and most normally Bitcoin, most normally cryptos like the Bloomberg Galaxy Crypto Index will outperform most assets and continue to do that. That's what it has been doing. The other scenario, which I think is more likely, the stock market continues to go down, we head to this pretty severe extreme global recession. And we end up with bond yields declining and gold rallying. And I think Bitcoin will probably go towards that path because basically what's happening this year is people are recognizing that Bitcoin is one of the most significant leading indicators ever. Trace 24-7, it's no one's project or liability and there's no central exchange controlling it, trades on weekends. And by Monday morning, you get a sense of what's going to be happening in the world with markets by Bitcoin. It's never really happened that way before, but it's been showing us risk off. Now I think it's transitioning the other way. So that's where Bitcoin is becoming clearly unique. I sense what's happening is there's bids below the market in Bitcoin and offers above in the stock market. But I think we're heading to one of the greatest reversions in history in terms of risk assets. 2022 might be similar to 1929. We see the global markets, the world heading towards pretty severe recession. And most central banks led by the Fed are still tightening. That's pretty bad. So when you mentioned this great reversal that you are waiting for, what would be the main trigger that could mark the beginning of this reversal? So the great reversal of 2022 is something I've been speaking about since December of last year because it's don't fight the Fed. The Fed is being aggressive. And they have been much more aggressive than most people anticipated. Just the 40 days between the June hike and the July hike was the highest, 150 base points. That's the highest pace of hiking since the Volcker in early 1980s. So that's the great reversion I'm speaking to. And we've had a pretty substantial correction in the stock market. We basically wiped out $25 trillion from global equities. Crypto's wiped out maybe this year total of a trillion, a rounding error compared to what's happening globally. That's the great reversion that I think it's going to continue. Basically the pendulum of risk assets swung way too far one way. And now it's swinging back the other way. Cryptos were a leading indicator. And they're the hottest, fastest horse in the race, the hottest markets. Now they're coming back. So what I see is happening is we're going to continue that main reversion from prices of condos in Miami to the stock market. And the bond market probably do weather. If we head towards deflationary recession, that's my call. Commodities should collapse. That's not profound. That's typically what they do. The Bloomberg commodity next dropped 20%. It's dropped 50% twice since 2008. And I fully expected again, crude oil should collapse. And Bitcoin should come out ahead with gold and treasury bonds. That's my vision. That's my base case. A key aspect that you point out when you mention your bullishness is the declining supply of Bitcoin and the increasing demand. So increasing demand should not be taken for granted. What are the indicators that you look at when you say that there is likely to be increasing demand for Bitcoin? So the on-chain metrics are pretty positive. We've seen different addresses used. Zero balances addresses. My colleague Jamie Douglas Coots, based in Singapore, has been digging into that. A lot more of it's more functional and anecdotal. And what I hear from inside scoop from people saying, well, OK, well, this is becoming global digital collateral. And it's probably greater risk to not have part of it. And we all know what's happening with supply. It's clearly declining. So that, to me, is the key thing. It's basically becoming competition for bonds and competition for gold. That's nothing new. It's continuing. It's just continuing trends that every day that goes by to this space that doesn't fail becomes more legitimate. And I've been saying those same things for years. So to me, now that we've backed up to the most significant discount almost ever in terms of a 200-week moving average, 100-week moving average, then I think that's going to be more responsive buyers. The key thing what's happened is the opposite is happening in the commodities. You look at commodities like crude oil. It's becoming redundant. And it bumped up. I look at that as that's an endearing bear market. They got very expensive. It's coming back. Bitcoins are enduring bull market that got very cheap. Expect reversion to favor Bitcoin versus things like commodities and crude oil. And to me, that's the key thing I think to expect in 2H. Bitcoin to continue to recover, not trade much below 20,000. Basically, it could obviously get hammered like it did around 5,000. It bought them in there three. But it basically built the base to around 5,000, starting in 2018. And it to come out ahead. And eventually, at some point, if we expect it's a non-trajectory to get to $100,000, it's just a question of time. Because I don't think the adoption is going to do anything but increase. And we know supply can't do anything but decline. To be clear, do you expect this turn to happen within the second half of 2022, right? So Bitcoin recovering, commodities crashing. Yeah, I do. It's already started happening. Copper, the Bloomberg Industrial Metals Index, was up 40% in the year. Now it's down 10%. Copper was up about 10%. Now it's down 20%. Lumber, one of the biggest darlings of the commodity bull market, it's down 50% on the year. Yes, Europe's having some major problems with natural gas, which is more local, but on a global basis. We fully expect energy to do what it always has in history. When it pumps this much, it dumps just as much. It's a question of time. And what we're seeing now is the beginning of a pretty severe global recession. The narrative out of China is very negative. The narrative out of Europe is as negative as it gets in still early days, and they're tightening. And the US is just catching up. We've had two quarters of negative GDP growth. I fully expect we're going to have a pretty severe recession globally, which probably will make Bitcoin shine. Gold and potentially US Treasury, along with gold and US Treasury long bonds. The way I look at LNU and with this Giovanni, I look at Bitcoin is becoming more and more a leveraged version of Treasury bonds and gold, some form of combination of that. So Ethereum is about to undergo one of the major upgrades in its history, which is the merge. So how are you looking at this event? What kind of impact is it going to have on Ethereum, according to you? So in terms of Ethereum, it's much less macroeconomic. And it's much more disruptive technology. And it's in the interest of most people who understand how rapidly technology is advancing to not fade what's happening. So the key thing I'd like to point out about Ethereum, first looking at price, it got to near $5,000. It's corrected to near $1,000 bonds since then. So it's had a substantial correction with an enduring bull market. And the question is, is that sustainable? Now, we fully expect bumps in the road with this rapidly advancing technology. I'm a markets guy. I let the tech people explain it to me. And we're getting some of that. We've had a pretty substantial discount for the switch, for the transition from proof of work to proof of stake. But let's think about what it does and what it's going to do. First of all, it's going to put Ethereum in the space of discounted cash flow. That doesn't exist in any other cryptos. Like, Bitcoin doesn't have cash flow. Like, it's like gold. But once you do that, it brings it into the realm of the world, the masses, who really understand cash flow. The financial analysts, the people really track equities. And that's what we've been proving. We've seen that in the Blomark term. A lot of interest when we wrote about Ethereum becoming part of that discounted cash flow analysis. We can actually get positive cash flows from holding this asset. That brings a world of institutional investors in this space. And that's why I look at Ethereum. It's OK. It might get below 1,000. But I fully think it's going to just backed up with an enduring bullish trajectory. So do you think that with the merge, Ethereum might have a larger upside potential than Bitcoin? Oh, sure. Well, Ethereum's leveraged Bitcoin in a way. I mean, if Bitcoin goes up 1, 2x, Ethereum goes up 4x. I mean, I expect that to continue. And you know, Bitcoin's basically beta in the space. And it should be. But it's macroeconomic beta. Ethereum's more related to what you see in advancing technology, like from the NASDAQ. So yeah, on a bull market, Ethereum will probably outperform. But I think they fit well into a portfolio together with the diversified portfolio of other cryptos. And I believe that's going to be the case. And that's the way I think institutions are looking at it. Why put your eggs in one basket? You might buy Bitcoin as kind of a complement to your bonds and maybe your gold. But you probably want more of a basket of cryptos so you don't have to try to pick out the next winners. Awesome. OK, Mike, that was great conversation. Thanks a lot for coming on our show. Thanks for having me, Giovanni. It's good to talk to you again.


All right, welcome, thanks for joining us again here today. I'm Lawrence Buja, Director of RAL's Climate Science and Applications Program. If you're in the audience today, then you're probably already familiar with Patty Romero-Lengkau in her Cutting Edge Urban Futures Program. But for those of you who are new to Patty, well, today you're in for a real treat. Patty joined NCAR in 2006, and since then she's been a leading voice in expanding the scope of NCAR's interdisciplinary research areas, collaborating with both NCAR and external scientists on high quality integrated research at the intersection of urbanization and environmental risk. Patty tends to work at a very high level and has exercised strong scientific leadership on for many significant local, national, and international efforts. These include such global initiatives as Future Earth, UJEC, which is WCRP's Urbanization and Global Environmental Change Project, and the UN Habitat Program. Patty was also a co-leading author for Working Group Two, the Nobel Prize-winning IPCC Fourth Assessment Report, and was a convening lead author for the North American chapter of the more recent IPCC AR5. And yet she still takes the time to meet with and carefully explain the impacts of urbanization and climate change to the first graders in the elementary schools. So whether it's participating in international negotiations, like her presentation at the recent COP 21 in Paris, or working out one-on-one with residents in Islam in Mumbai, India, Patty brings a keen scientific curiosity and a deep theoretical background combined with an amazing level of passion and energy to her cutting-edge urban research. This keeps NCAR at the scientific forefront of this area. So at this point, I'm gonna turn it over to Patty for her presentation. There'll be a question-and-answer period at the end, so I'll ask you to hold your questions to that time. Thank you.  Well, it is known for many of us that cities are crucibles of innovative experiments and interventions seeking to reduce the impacts of floods and heatwaves and to increase the capacities of populations to mitigate and to respond to environmental change. What is missing is our interdisciplinary efforts that help us understand how effective, how sustainable, how resilient those interventions are. In that context, the mission of urban futures is to explore key intersections between urbanization and environmental change in order to inform action and world views across scales in ways that foster sustainable and livable cities. And livable is very important, believe me. I am from a non-solivable city. We have focused our research in three research, or three research foci. The first tries to address how urban development impacts the environment. The second tries to understand what factors determine vulnerabilities and resilience across and within cities. And last, but not least, we also want to understand what factors, what limits, what barriers are out there to enhance decision makers and actors capacities to respond to these challenges. I won't be able to talk about all these three themes. I will focus on the second one. Let me insist that something I have engaged in since I moved to NCAR are research efforts that go from the global to the country to the city to the neighborhood and household level and backwards. And I will tell you why. It's very important for social scientists to understand how the history of a city and its institutions, which I will define later on, define current patterns of vulnerability and risk. But when I came to NCAR, I realized that we were faced with a challenge similar to the one doctors face. A doctor needs to be able to say whether someone is healthy or not. We social scientists need to also be able to understand what factors make populations and cities vulnerable, sustainable, or resilient. So our challenge is an upscaling challenge, which is similar but different to the downscaling challenge. Many modelers here at NCAR are faced with. And I really have learned to respect what they do, by the way. I will focus my presentation on five themes. First, I will share with you the rationale, the motivation for this work. And our conceptual framing of risk. Then I will use three examples of our research to highlight how this framework has been tested. The first focuses on the factors, the determinants of urban populations, vulnerability across and within cities. The second focuses on unpacking urbanization, because we all talk about urbanization, but sometimes we don't measure it and we just say blah, blah, blah. So what are the links between urbanization and risk? And this is an effort that we did focusing on global and the country scale. And then I will go into a case study, one of the most recent we have been working with with Joshua Sperling on a key dimension of urban development inequality as it relates with vulnerability of households. I will use these three examples to lay out my ideas, my suggestions for future research directions. Let me start with the first, with the motivation. Cities already face risk from climate relevant hazards. This is a map I did with Alex DeCervini for the UN Habitat Report on Cities and Climate Change. And what we measure here was the hazard risk of each city that represents a cumulative index based on the risk of impacts from exposure to four hazards, cyclones or hurricanes, flooding, landslides and droughts. We know from our work with Cynthia Rosenzweig that and also from my work in the IPCC that human activities are expected to change the Earth's climate in ways that can increase risks to cities. I know also that it is very difficult to attribute many of the dynamics of hazards to climate change. And therefore, and because I have learned that by working within the IPCC and collaborating with my colleagues here at NCAR, I am convinced that an integrated risk approach will be key to understanding and managing sustainability challenges in a changing climate. Equally important will be to understand the development context in which actors and decision makers make decisions. If we don't do, we will make many mistakes. How do we define risks? There are two big approaches to urban risk. For the first, top down, risk is the probability of a hazard occurrence multiplied by its consequences. This is the most used here in NCAR. Modellers use scaled down models to estimate future hazards such as floods, heat waves. They explore adaptation options under different climate and socioeconomic scenarios. There is also what I would call the bottom-up approach to risk. This is more of a social science approach for which risk is the potential for uncertain outcomes where something of human value, livelihoods, life's property is at stake. We need to keep that in mind because sometimes we think, oh, let's move that population from there. Well, that's not so easy. It's quite complicated. This approach uses vulnerability frameworks that are similar to the models that we use in the top down approach. It combines quantitative and qualitative methods and data, but it's very important. It helps us understand the culture, the history, the institutions of a city. And it combines that with quantitative methods. And I will refer to examples of how this is done. The framework we suggest integrates both approaches. And let me just ask you to remember that the top down approach tends to focus on the dynamics of environmental change as they affect hazard exposure. Why the second approach tends to focus on the societal factors, governance, one of those that explain differences in access to assets and options to respond to hazards. In this approach, which builds on the IPCC reports, the one on risks and the number five, a hazards are stresses such as flats and social unrest because people do not only deal with the environment, people, economic, social, and infrastructural assets are exposed to. The final impacts of exposure to this depends on societal factors and ecological factors. And those are defined with the concept of vulnerability, which is the propensity to be adversely affected. The contrary of it is resilience, the capacity to perceive risk and effectively adapt. I want also to insist that the exposed units can also be sensitive. And that we define as a degree by which they can be negatively or positively affected. The capacity of sociological systems and of populations, which I will focus on, is a pool of assets such as education, information, and social networks. Actors can use to manage risk while attending their development needs. Actors, who are those actors? Not Angelina Jolie, but actors are the government, the private sector, NGOs, scientists, and the media. These assets and options are unequally distributed and therefore we use the concept of social inequality defined as a condition that answers when assets are distributed unevenly. These actors do not work and operate in a vacuum. They operate in what we call governance and we define as a set of formal and informal. I want to insist informal rules because I will come back to that. Rulemaking systems such as laws and regulations, actor networks at all levels to steer cities towards or away from sustainability. With this framing, let me now show you how we have been testing it and what our findings are. And let me start by the first example, urban populations vulnerability. That's something I did with Katie Dickinson who is there and who is watching. We are aware that context matters, but we have hypothesized in this project that was awarded by NSF that it is possible to identify patterns of populations vulnerability across urban centers and what is equally important research approaches. We develop a meta-analysis and meta-knowledge or sociology of science, I'm a sociologist, so that's why I did it, right? Approach and we focus on temperature-related hazards such as heat waves. Why did we focus on those? Because they are associated to large impacts because they are clearly tied to climate change. We were able to assess a large number of studies and to cover 224 cities. So, okay, what is urban populations vulnerability? Also, everyone defines vulnerability the way I already share with you. Not everyone measures vulnerability equally. We identify three approaches to vulnerability. For the top-down applied by 80%, 88% of the papers and which is mostly used by epidemiologists, climate modelers and some natural hazards communities. Vulnerability results from exposure to a hazard and they measure it by exploring quantifying the temperature, health outcome and also quantifying confounding factors such as age and such as education. Let me drink some water.  Okay, for the second approach, also remember I am mapping my framework and you are seeing how I'm going from concepts to methods to data to results and backwards. So, for the second approach applied by 11% of the papers and which is mostly a social science approach. Vulnerability is defined by differences in capacities that are driven by what we call structural factors such as inequality, governance and urbanization. While the top-down approach focuses on the individual without considering the social environment, the social science approach focuses on social dynamics and sometimes forgets about the environmental conditions. Therefore many scholars, particularly some of us at CISA have developed integrated approaches that integrate both approaches to risk and that focus on both the social and ecological determinants and also the underlying drivers of vulnerability. Sadly, this approach was only applied by 11% of the papers we analyzed. We also applied the IPCC approach to assessing uncertainty and what we did was to quantify the evidence, meaning the number of papers, identifying a factor as a determinant of population vulnerability and the agreement among scholars. And how did we measure this? It was hard, right Cathy? It was not an easy process, but we did it. And what we did was to identify those hazard indicators such as the timing of a heat wave, the levels of temperature and what is very important, the thresholds which we call temperature magnitude. We also use indicators of exposure such as population density, total population, vegetation and indicators of capacity such as access to education, social networks. And we also use a symbology to define whether the indicator is positively related to vulnerability, meaning increases it or negatively, meaning decreases it or there is no agreement such as the relationship is a no relationship. We found that 13 factors which are mostly located in these quadrants account for 60% of the tallies and we also found that only two determinants, two of the many determinants that play a role in defining vulnerability are analyzed by studies, H and temperature magnitude. These findings result from the dominance of the top-down approach. I mean, we need it, but it is not enough. That is our point. We also found and we mapped the cities covered in the studies and we found no surprise that most of the studies focus on the US and Europe. What we have found in our IPCC reports that most of the vulnerability tends to be located here. In summary, we found that it is possible to identify patterns of vulnerability across cities and research approach and also to make the city, the approaches, the research approaches comparable which is a huge challenge for scientific progress to happen. We also found that knowledge has examined only certain aspects and that scale has been key in defining also why some aspects are neglected. And this was the first time I was confronted with uncertainty linear. I couldn't believe it. Uncertainty given by the approach used that focuses on some things, forgets others, the methods and data and the scale of analysis. We were able also to justify why an integrated approach is needed. Let me now move to the second example. The urbanization dynamics shaping risk. Our hypothesis states that a focus on only the urbanization exposure interactions is not enough to understand urban risk. First of all, we need to really unpack urbanization. Second, we also need to include indicators of sensitivity and capacity. What we did, and I did this with a colleague, a very young and promising scholar from Germany, Garchagin, what we did was to use two indicators of urban levels or urbanization levels and economic growth and two indicators of the rate of urbanization and economic growth. We applied a hierarchical clustering analysis to group countries in 10 groups. And we correlated these numbers, right? These indicators with indicators of exposure, such as populations in presence and in contact with affected by storms, floods, sea level rise and droughts, sensitivity, such as population undernourished, dependency ratios and poverty indicators, and indicators of lack of adaptive capacity, such as the corruption perception index, which in our country is very important. But also in the US, it's very important. Access to medical services, gender equity, access to education and quality of ecosystems, we also use indicators around all these. And you will see that these indicators appear over and over in our analysis. What is what we found?  For sure, we were able to create the OECD group, which registers high levels of urbanization, low levels of urban growth, high levels of GDP per capita or GNI per capita, low levels of economic growth. We also have a typical case represented by Southeast Asia with high levels of urbanization. I mean, I think that China and India are on steroids. There is no other way I can describe it, for good and for not so good life. So again, high levels of urban growth, in this case, there are high levels of urbanization but low levels of economic growth. Which really, I mean, these are indicators that we get at one of the two of the key elements of urbanization. So we also found that rather than exposure, sensitivity and capacity indicators contribute to overall risk. And let me just show you how is it that we could test, prove this. These are plots where we have here on the Y axis, the index values and on the X, the country groups. And we found that in terms of exposure, there is not so much difference between country groups. That's not the case once we include indicators of sensitivity and lack of adaptive capacity. Once we include those, which are indicators of development or lack of. Once we include those, then this group, this group and this group are particularly vulnerable. We also found that rather than urbanization levels, it is a race of urbanization that influence risk. Of course, and this is what I summarize here, right? Again, rate of urbanization is more important. It's a key driver of risk. Sensitivity and capacity indicators contribute more to overall risk. But let me tell you, this is just the beginning of the conversation. Why? Because urban development is more than economics and more than demographics. It's also built environment characteristics. It's also governance and it's also equity. So there is a lot of stuff to do. And I will give an example of how we address the links between social inequality and vulnerability in the city of Mumbai. This is part of an NSF Pire Award I engaged with. And I'm really happy I did with George Sperling. I really learned a lot from you and by being with you, George. And I hope we stay together. So our hypothesis is similar to the prior one. Under current conditions, current climate conditions, poverty and capacity contribute largely to overall vulnerability. Why Mumbai, India? Well, I like large cities, I guess. No, not only that. I mean, Mumbai is one of the 10 largest cities depending on the boundary we use to define the city. It has between 18 and 22 million people. It has high levels of risks of impacts from sea level rise, storm surges and flooding. And that's why we did our field work during the monsoon. And these risk results not only from hydro meteorological conditions that are changing with climate change. It also results from human action. A key element here is a huge reclamation of large areas of what used to be seven islands of land just above sea level with many of these areas below the high, high level. And just now we are mapping this, this. But I cannot talk about that just now. So besides that, the city is faced with high levels of inequality. I am from Mexico City. I'm used to deal with poverty. I grew up in a poor neighborhood and I used to engage with how to address poverty. But once I moved, I started to work in Mumbai. I said, okay guys, we need to address this issue. The levels and qualities of poverty in Mumbai are special. Some data, some indicators, 70% of the population engages in the informal economy. Defined by the government as non-conforming with the regulations. One out of four children is stunted. And informality, remember the formal and informal rules. Informality is a key institutional component defining social inequality. In many ways. Informality is a state of regulatory flux. And I know it from being with my mom when she was selling on the streets. I mean, where the legal and the illegal are up for negotiation, contestation, and corruption. And this informality has implications for vulnerability as well. Because by not having access to secure land tenure, many of these populations are criminalized. And do not have access to many sources of capacity such as infrastructures and services. A key other finding, and this is for Caspar who is not here, Caspar Aman. And I will share that, we will see whether we can do something together. Although Caspar is already finding that heat waves will be a key hazard facing cities such as Mumbai, the levels of penetration of air conditioning are very low. Of between 8% and 12%. Equally important is the fact that the study, the analysis of the influence of wealth and vulnerability indicators offers a lot of methodological challenges. Not only because these are multi-dimensional concepts. I have been talking a lot about vulnerability but let me tell you poverty, which is one expression of social inequality, can be measured by income indicators, expenditure indicators, and assets indicators. And each approach offers very different results. And I also found that, again for you, it's a present to you, Linda, that uncertainties are key challenge when dealing with this. Why, to what is this challenge related? To indicator selection, to index construction, and to an accurate understanding of the influence of indicators on the outcome we want to explore. And I want to give you here an example. And I will use an image. To study vulnerability, we usually use the same ingredients, more or less the same ingredients, which are our indicators. But the characteristics of the indicators vary across context or with context. In Latin American cities such as Mexico, access to improve toilet facilities is of the essence. And it is mostly related to connection to the sewage system. In India, Indian cities such as Mumbai, besides that, a key challenge is given by the fact that between 40 and 80 households share these toilets.  Guess what? This is our challenge. How do we deal with that? Okay, how do we address that? Well, we develop, again, our indicators, and I already have talked about indicators of exposure, sensitivity, and capacity. I just want to say that we capture some indicators of hazard impact, such as the number of hazards people experience, households experience, and whether they suffer a health impact from exposure to those. I just want to insist that we added these indicators, which are access to material possessions and access to physical assets, which in all urban centers is key, right? And what we did was to combine qualitative methods, meaning knowledge from local experts with quantitative methods, which allow us to embrace the subjectivity involved in waiting indicators, and to build, and we combine that with the fuzzy logic to build four household classes. The low, moderate, high, and very high vulnerability class. The first finding is not surprising. 55% of our households are highly vulnerable, and 28% are very highly vulnerable. The two groups make two thirds of the population. That's not surprising, but what is surprising is that with the exception of the very high vulnerability class, which is faced with very high levels of exposure, the other groups, the other household classes are faced with similar levels of exposure, and what defines vulnerability are differences in poverty, which increase, yeah, when we go from the low to the very high vulnerability class, and with capacity indicators, which decrease when we go from the low vulnerability to the very high vulnerability class. Let me just really tell you a little bit more about the capacity indicators, which really define not only access to assets, but also agency, which for us social scientists is so key, because people do not only receive hazards and say, oh, let me go and be killed. No, people are active, and they really are very creative. Okay, so we found that in terms of awareness and priority given to mitigation, risk mitigation and adaptation policies, our household classes are more or less similar. But we also found that, and this is a finding that is similar to what we found in the other for Latin American cities I have studied, while the very high vulnerability class is very likely to rely in family support and able to respond to emergencies, the other classes are very likely to use social networks, such as NGOs and religious groups. Well, the education assets are really related to class, the socioeconomic status, not surprise, but interesting, and this is important for us at NCAR, with the exception of our very high vulnerability class, the other classes are very likely to use warning sources, such as TV and radio to inform themselves about risks. This is a very challenging table, and I'll see whether you like it this time. So, there's so much conversation about thresholds or points after which heat waves move from some conditions to the others, right? What we did with this figure, which is only one example of three we are working with, what this figure shows are threshold points for capacity and exposure. As we move from high to low capacity, a point or some points are reached in which levels of vulnerability start to ramp up. We move from one regime to another regime. Likewise, when we move from lower exposure to high exposure levels, in each case, a level is reached. At which further increases do result in higher vulnerability regimes. But what is interesting, and this is really bringing me back to one hypothesis I love so much, which is the idea that also the reach will be at risk. And why is that important? Because I have seen that historically, only when the wealthy are affected, they take care of things. So, there is also a point at which, and this is our next regime, there is also a point at which increases in the exposure effect populations equally across capacity levels. To summarize, under current climate conditions, poverty and capacity indicators contribute largely to households overall vulnerability. Another important conclusion is that these methods which are really a combination of qualitative and quantitative methods are key and complement what we do here at NCAR in that they capture the multidimensionality and uncertainty in vulnerability and race analysis, help us evaluate the contribution of wealth, sensitivity, and capacity indicators. And what is very important, they support policy interventions that target attributes under different combinations across household classes. Let me close with some conclusions and some considerations of what could be done with this area of research. I'm convinced that both approaches are needed. Both approaches to race are needed. One shed slides on some aspects, but it's not enough. Either one, even if it is social science or physical sciences that are coming together to address this issue. We need to combine qualitative and quantitative methods. Only by being and doing free work and interviewing people and listening to them and going to meetings and going to meetings and going to meetings, we get the trust we need to really get to understand the role of power, the role of governance, and how people really feel whether their interests are taken care of. I mean I have some quotes of how people from low income areas in Buenos Aires, Mexico City, Santiago, and Bogotá are aware of the fact that without governance, without governmental support, they won't be able to make it. Equally important is the fact that, and this is something I will include in another paper addressing some other data about Mumbai, the economics of this are key. And why are they key? Because you need also an economic threshold in terms of levels of income, city authorities need to invest in things. And when you have high levels of informality, you don't get those sources of income. And that you only understand if you go to the places and listen to people and you combine all these with quantitative methods. And also you learn to be humble about things, right? So I hope I was able to show how multiple scales bring shared light on different components of vulnerability and resilience, and also how some patterns cut across scales, right? That's interesting. I was like, wow, I cannot believe it. Wealth and capacity indicators contribute at least in Mumbai, I think also in other cities, contribute more to overall risk and vulnerability than exposure indicators. It's true that that might change and it's already starting to change under that changing climate. Before I go to the future directions, I want to insist that I only present our work on this theme. It is by working on the three themes that I have been able to find other connections and I will refer to those. What can I say about future directions? First of all, it's really important to unpack the key dimensions of urban development. We already have some work dealing with demographics and economics. We need to also deal with governance indicators with built environment indicators, such as infrastructure, the quality of housing. For instance, in Indian cities, having a separate cooking space is key. That's not the case in Latin American cities. We already have that. So we also need social indicators such as inequality indicators and I think that in this context, where everyone around the world is starting to say enough inequality has increased, we need to also address issues of inequality. Another important point is the issue of thresholds and tipping points and let me tell you, a threshold is defined as a point at which one relatively stable socio-ecological and governance regime gives way to another. What is interesting is that while socio-ecological thresholds are contingent upon the interaction among physical, hydro-ecological and social processes, governance thresholds are integral to the way societies work, with limits contingent upon ethics, meaning values, politics, knowledge and culture and politics meaning power. So, I want to move forward and I'm working on that with colleagues from the Future Earth Orban platform, UJEC, which is transitioning to Future Earth. And what we want to do, I know it's crazy, but what we want to do is to connect the problem space with the solution space and what I mean by that is that we social scientists, physical scientists, engineers, tend to always focus on, okay, how urbanization comes together with hazards and institutions to drive risk and resilience. That's nice, but once we come to decision makers, they say, well, what do I do with all this information? Well, we also need to understand how people make decisions. And I see ourselves inserting our groups in this solution space, but what we need to do is to understand whether there is a fit or lack of fit. I think there is a lack of fit between the two domains. And we are studying that in two projects. One is called Unicorn, urbanization, water management and flood risk across mountain regions. I'm working with Andy Monaghan on that and we are waiting to see whether the Belmont Forum will award our proposal. And with Unmask, a proposal on urbanization, food, energy, water systems, and extreme hazard risks that we will submit. So besides working on this, well, I'm working with Olja, which helped me and we try to understand uncertainty in urban vulnerability and risk. Linda Mearns is our mentor in this. So I mean, these are, again, just some examples of future directions. I wouldn't like to finish without saying that my ideas wouldn't be possible without the beautiful projects and collaborations and partners I'm engaged with at NCAR. They are with RED in the US and globally. Without them, I wouldn't be able to be challenged, particularly if engineers and physical scientists challenged me and I want to kill them. But next day I say, I need to see how to do this. They are right and let me get it, okay? So I also want to really thank my postdocs. I mean, I called them mine, but they are not mine. Because they also have been a source of inspiration for me. They always have good ideas. They come with interesting stuff and I also want to thank them for being part of this. I also want to insist that many components of what I do are the result of awards, but others are the result of just sitting together after meetings and saying, hey, I listened to what you say. Why don't we do this? For instance, the survey we conducted in Mumbai was a result of a collaboration with Indian partners who were conducting an atmospheric field campaign. They had 90 students. They said, if you train my students, we conduct the surveys. I said, voila, I'll do it. So there is a combination of many things involved here. And again, I want to thank them and thank you for taking the time to be here with me. So I'll let you choose who you want to answer the questions from. So we'll open it up to questions from the audience. Hi, I'm Daniel from Amsir Chemistry. Very inspiring talk. I really like the slide about the moving the solution space into the problem space. There are more connections. Do you have any examples of what that would look like specifically or any situations where you've seen a shift happen? Right. In my work on governance, I learned that there are always misfits in terms of the spatial and temporal scales at which vulnerability and risk operate and the temporal and spatial scales at which decision makers operate. I give you an example. For water management, you use the basin. I mean, as a researcher, right? Well, decision makers have made, I mean, there are some exceptions to that. But usually, they have many jurisdictions coming together to deal with that basin. And they overlap, they are fragmented, et cetera. That's a problem of fit. Another problem of fit is given by the fact that usually decision makers work at a temporal scale that goes from, I mean, if you are lucky, 10 years are the maximum. Well, many processes we are dealing with here are 50 years processes. For instance, just now, even if we were able to mitigate, to introduce mitigation policies and move us to a trajectory, it's a 4.5 trajectory. Whereby we could reduce, bring our temperature to 1.5 to 2. Even if we were able to do that, the effects of what we will do will be manifest in 50 years. Many decision makers tell me, Patty, I'll be here for three years. What are you talking about? So those are two examples. But now under a changing climate, there are two other issues of a fit that I want to work with. One is thresholds, meaning are we really identifying thresholds or not? And cascading effects. And what happened with the floods in 2013 here in Boulder is an example of that. I mean, the impacts of the floods were not only the result of the huge amount of precipitation we receive. But those were also the result of some ecosystem areas not working as multiple use areas, such as the walking and biking spaces we have in Boulder, that during floods can also help you mitigate the impact of floods. That together with how people act and operate creates a series of effects that we call cascading effects that perhaps we are not ready to deal with. So what I consider is that we need to add those two to our efforts to support decision makers and to engage with them and learn from them. I'm sure they will talk about more, but I really think we need to bring those spaces together to really connect the dots. So there's a lot of unauthorized construction so these cities, how do you get data and things like that? For what, again? Unauthorized constructions. Well, there are some methods. One is participatory GIS approach whereby you go with people.  It's in general a problem. So I mean, you can use participatory GIS now, but when I used to do it, we just walked with people and we really swept around places. And they started to say, look, this area is not acknowledged, but we have so many houses here, et cetera. So there are many approaches. And now again, the GIS is really making us do things. But what is important is you might have all this information if decision makers don't want to listen to you. Believe me, they won't listen to you. So the challenge is how you frame issues in such a way that you can engage with them. And that's not a challenge of us telling, you know, decision makers, you are not getting it. Because they will say, you know what, go away. I cannot, you know, you really need to. For instance, with George, we are just now analyzing our interviews in Mumbai. We were looking for climate change policies. And we found development policies that are driving risk. So. We go back to time for the. Vivek, from UIL. Next. Yeah, here, before that, yeah. OK, in the fourth one, you say, well, then capacity indicator contributes to more than exposure indicators. Could you differentiate between those two or the exposure indicators you meant? What connected somehow? Right, I will. Let me give you examples here. Capacity indicators include such factors such as education. We have found that people who are educated are more able to look for sources of information to respond to challenges and hazards. Also access to governmental support in general and during emergencies, and access to family members, to community grassroots, to church organizations, which are elements of social institutional capacity. And last, but equally important, are also indicators of information. How people perceive risks and whether they use the TV, the internet, or not to respond to hazards, to respond to early warning systems, are key elements defining vulnerability. And then in terms of exposure and sensitivity, people wouldn't be vulnerable if they were not exposed to something. You need to be exposed to a heat wave, to a flood, to be vulnerable. So we measure that by asking people to which heat waves, air pollution, and other hazards they have been exposed and whether they can identify impact levels and health outcomes from that exposure. And we have found in the literature and in our prior work that the elderly, for instance, are more sensitive to heat waves and that women are more sensitive to floods in some areas, particularly because they cannot go out. I mean, there are cultural issues that constrain them. And pre-existing medical conditions, if you already have a heart condition, it will be very hard for you to deal with a heat wave. So those are the exposure capacity and wealth indicators we use. So what we found, again, is that in terms of exposure, which we have here, these are our index values, average index values, and we have levels of uncertainty, by the way. In terms of that, many of the households we interview are more or less equally exposed, with the exception of the very highly vulnerable household. That's not the case when you include indicators of capacity. The more vulnerable you are, the less capacity you have. Remember those that we use. And then the poorer you are, the more vulnerable you are. So that's why we concluded that. Thank you. Marcus Mench, have you thought of taking and adding into your kind of key research issues the question of surprise? Because I think in terms of thresholds and both the cascades, where the relationship between wealth capacity might come apart is when you have things that are outside. And the boulder floods might be an example of that on a small scale. Right. Yes. And that's why, as you can see here, independently of your capacity levels, once you reach the 0.8 level of exposure, then you are at risk. So this is something that I already have been talking about when dealing with exposure to air pollution. And particularly atmospheric modelers really pay a lot of attention to this. And in our analysis of thresholds and cascading effects, we are including the issue of surprise. But again, this is a project we are to start. So yeah, we are including that. But that's a very important comment. So following on, does that go into the exposure index then, the surprise? Yes, it is here. And it was interesting in three cities of Latin America, Santiago, Bogota, and Mexico. We ran some Poisson regressions to examine the links between exposure to air pollution and health outcomes. We used mortality indicators, which are extreme, by the way. That's a caveat with that. And then we correlate that with spatial data, vulnerability or vulnerability indicators. And we found that impacts from exposure to air pollution in these cities cut across socioeconomic status. Only air pollution. And I'm only talking about extreme impacts. We are also aware of the fact that these wealthier households have a lot of assets to respond. But still, I mean, I think that we need to start pointing to the fact that wealthier populations are not spared. All right. Thank you for joining. Again, thank you. Linda, sorry. You blended. I blended in, as usual. Some Linda Mearns from NCAR. It's probably a tricky question to answer this late in the time. But I wonder if you could succinctly. I think the audience would probably be rather interested in some of your activities at COP 21, in terms of the side event of cities, if there's a few pearls that you could throw before the audience. Yes. I worked together with Cynthia Rosenzweig. And we put together a synthesis book, a report on cities and climate change. And what we did was to engage with stakeholders and to really also make them part of all these synthesis efforts where we did not only explore what will happen with climate hazards, but also how authorities are responding to these problems in the water, health, build environment, I mean, in many sectors. But what was interesting was that we really have been engaged with the Covenant of Mayors and with the ICLEI and with key authorities that are really addressing these issues at the global level. But it's also nice is that I am involved with the city of Boulder. I'm going to their meetings, learning a lot from them. We are having a very nice dialogue about the COP. So there are many things that are going on. And I'm very excited about them and what we could do here at NCAR to inform those decisions. All right. With that, I think we'll close today again. Thank you, Patty. Thank you for coming.


What is going on guys welcome back to the channel and I appreciate you being here today I bring you another destiny through video and today I bring you a complete list of recipes what's needed in terms of ingredients where those ingredients are found and who to give these baked cookies to but before we go any further guys if you do enjoy the video and would like to show you support you can by hitting that like button and if you are new around here and enjoy daily destiny through videos be sure to subscribe okay so the main point behind the dawning is to bake cookies and deliver them to certain vendors for those juicy rewards as you know there are plenty of ingredients out there plenty of recipes too today I bring you guys the entire list of recipes where to get each ingredient and which vendor has what so let's go okay so the first recipe is for the infinite furries cake and this is for failsafe so once you make this you take this to failsafe you need vex milk and impossible heat and add the essence of dawning then you just bake away the next recipe is for the vanilla blades and this is for lord shacks what you have to do here is mix cabal oil sharp flavour and add essence of dawning then you just bake the next recipe is for the gentleman's shortbread and this is for devrim cake mix the ether cane and perfect taste and add the essence of dawning then bake the next one is for the liksney bird seed i believe that's pronounced and this is for hawthorn here you have to mix the ether canes and personal touch and add the essence of dawning then bake the next recipe is for the burnt edge transits and this is for master all the cryptarch of the tower what you have to do here is mix two ingredients that do not successfully combine then add essence of the dawning then bake it's important to note as well this is actually not a recipe so it will not count towards that triumph of creating recipes the next one is called the traveller doughnut holes and this one is for what i call a ray here you have to mix cabal oil and a flash of inspiration and add the essence of dawning then bake the next recipe is for candy dead ghosts this one's for spider mix a dark ether cane and flash of inspiration then add essence of dawning then bake the next recipe is for the telemetry tapioca and this is for banshee 44 the gunsmith within the tower mix vex milk and bullet spray then add essence of dawning then bake the next recipe is for the radio larion pudding and this one's for ash and mer here you have to mix vex milk electric flavour and add essence of dawning then bake the next recipe is for strange cookies and this one is for ze what you have to do here is mix taken butter and electric flavour and then add the essence of dawning then bake the next recipe is for the galadoodles this one's for savannah which you've probably already done here you have to mix ether cane and the delicious explosion then add the essence of dawning then bake the next recipe is for the chocolate chip cookies this one's for amanda holiday mix cabal oil no taste and essence of dawning then bake the next recipe is for the al kindrego cookies this one's for saloon here you have to mix kiting powder and bullet spray then add the essence of dawning then bake the next recipe is for the ill fortune cookies this one's for petra revenge mix dark ether canes and impossible heats then add the essence of dawning then bake the next recipe is for the javelin mooncake and this one's for anabrae mix kiting powder and sharp flavour then add the essence of dawning then bake the next recipe is for the dark chocolate malts this one's for the drifter here you have to mix taken butter and no taste then add the essence of dawning then bake people so those are the entire list of recipes in the game but how do you get each ingredient well that's what i'll showcase to you guys on the screen now the first ingredients are vex milk which you get from killing vex enemies ether canes are from killing fallen enemies cabal oil is from killing cabal enemies kiting powder is from hive kills taken butter is obviously taken kills and dark ether canes is from scorn kills the second ingredient we have delicious explosion this is from any explosive kill sharp flavour from any soul kills impossible heat from solar kills electric flavour from art kills no taste from void kills flash of inspiration create orbs of a light personal touch melee kills perfect taste precision kills or sniper rifle body shots bullet spray any automatic weapon ie machine guns smg's auto rifles and just get kills with these either from hit fire or aiming simple as that people and the third ingredient is obviously the essence of dawning this drops from any activity you'll get between one and three from patrols three from escalation protocol around you'll get five from public events five from flashpoint completions six from heroic adventures 10 from her work well 15 to 17 from her strikes 22 from gambit 23 from nightfall strikes and i believe 29 from the 100k nightfall strike but yes guys i'll leave all ingredients and all recipes and all that on the screen now and also link an image within the video description if you do want to just save that to your desktop or your phone for later use that is completely up to you guys but guys that is it that is a complete recipe list i hope you guys enjoyed the video and i know we have come to the end of the video if you did enjoy it leaving a like really helps out if you're new out here and enjoy daily destiny to your videos like guides top fives gameplays reviews just about everything make sure you subscribe and if you never want to miss a video i upload two notifications and by hitting that bell button but again thanks for stopping by and hopefully i will see you on that next one


Hello everyone welcome to lecture on channel capacity. At the end of this session students will be able to calculate channel capacity and also channel efficiency. Now before starting with the actual session let's pause the video and think about what is a mean by mutual information. If you remember in the previous video session we already saw that what is a mean by average information and what is mean by mutual information. So it is nothing but the difference between the two end copies. So you can see over here average mutual information is nothing but equals to difference between initial entropy and final entropy. In this case initial entropy is a source entropy, in this case initial entropy is a destination entropy and these are two nothing but the conditional entropy. Now first this is a noise free channel. So you can see this figure 1, the figure 1 shows noise free channel is in that which is having 1 to 1 correspondence between input and output means x1 is connected to y1, x2 is connected to y2 similarly up to xm connected to y, right. Now for that from this noise free channel you will get the two probabilities. One is a joint probability matrix which is nothing but having a diagonal form, another one is a conditional probability matrix which is having a unity diagonal form. So diagonal form means this diagonal form similarly this one is a joint probability matrix probability of x given y or probability of y given x which is having also diagonal form but unity means the value is 1, conditional value at p x1 y1 or p y1 given x1 is 1. So diagonal elements are 1, so that is why it is called as a diagonal unity matrix. Now from this we can have a entropy equation. So entropy equation that is a joint entropy h of x, y we have the equation already did have in the previous videos. So double summation from j equals to 1 to m and k equals to 1 to n. So in this case there are two different, j is different and k is different having different count. In that case there are two variables taken j and k. So in this case both the source and y are destination having same numbers. So we have only one summation symbol over here. So j equals to 1 to m and the probability of xj, yj, right. So this one is a joint probability log of joint probability that gives you the joint entropy, right. So from this equation if you observe this matrix diagonal form joint probability matrix you can see only for diagonal element the joint probability value is present other than that it is 0. So when it is j not equals to k it is having 0. So from that we can see that the joint probability matrix equals to nothing but the source probability of destination probability. So from this we can write joint entropy equals to source entropy or destination entropy, right. Now from this matrix conditional probability matrix we can say that h of x given y or h of y given x equals to m minus m into 1 log 1, y 1 log 1 because of probability values at every diagonal is 1 and other than diagonal it is 0 and y m so there are m elements sources are m also destinations are m count is m so it is multiplied by m. So log 1 we already know that value is 0 so 0 multiplied by 0 and then it is multiplied with anything it is 0 so that is why the value is 0 for conditional entropy. Thus we can write for noise free channel the mutual information equals to we have equation now in this case this one is 0 so which is which is equals to h of x which is equals to h of y or which is equals to h of x comma y means for noise free channel conditional entropy is 0 joint entropy is equals to source entropy is equal to destination entropy this is the equation. Now symmetric channel now what is the mean by symmetric channel it has two conditions first condition is what this conditional entropy h of y given x means you are obtaining y by observing the source is independent of j that is the entropy corresponding to each row is having a same value and second property is what the sum of all the elements of the column is the same. Now let us see the example so there are these two conditional probability matrix given each one is having 3 rows 3 column right so as per the first condition this one entropy corresponding to each row is the same if you find the entropy of each row that is a summation average of this you will get the same value similar in this case also means that this symmetric matrix let us check another condition second one the sum of all columns of is this supposed to be same now if you follow that one in the both the example if you found that this conditional probability matrix gives you the same value when you add the column and for this that is not a same so means that only this is the symmetric matrix this one is not right now for that symmetric matrix we have to find the mutual information equation. So we already have equation i of x y equals to h of 1 minus h of y given x this is a destination entropy this one is a conditional entropy now in this one this conditional entropy as said over here it is independent of j is written as this summation of j equals to 1 to m and probability of x j now this is a constant term a equals to which is independent of j is taken as a common outside so summation of j equals to 1 to m equal probability of x j is remaining. So this is nothing but value gives you the 1 so final equation for the mutual information of symmetric channel you can write i of x y equals to h of y minus a now let us see what is mean by channel capacity now channel capacity is nothing but the maximum of the mutual information so that is why we in the previous slide we derived the equation of the mutual information for noise free channel and symmetric channel. So this can be denoted as c equals to max of i x y i of x y so for every different channel the value of i of x y is different so i of x y is nothing but the difference of entropies h of x minus h of x given y or you can write h of y minus h of y given x there are two different equations you have now as I say mutual information is nothing but the difference of entropies the unit is bit per second we already derived the unit for the entropy in the previous sessions. Now the transmission efficiency or channel efficiency is given by the equation eta equals to actual information divided by maximum information now in this case actual information is nothing but your mutual information which is nothing but i of x y and maximum information is nothing but the max of i of x y so which is nothing but the c now and the redundancy is given by the r equals to 1 minus efficiency that gives you the redundancy. Now what is the channel capacity equation for noise free channel and symmetric channel let us see for noise free channel we already derived the equation for i of x y c equals to nothing but the max of i of x y so i of x y is equals to nothing but the h of x or equals to h of y or equals to h of x y which is nothing but the joint entropy. Now max of h x equals to nothing but your c that is channel capacity but we already derived equation for max of h of x equals to nothing but the log m so channel capacity for noise free channel equals to what log m bits per message similarly symmetric channel we have the equation for mutual information i of x y equals to h of y minus a so c equals to max of i of x y so max of h of y minus a so max h of y minus a that is log n minus a this is the equation for the symmetric channel now let us take a example so find the channel capacity for p equals to 0.9 p equals to 0.8 p equals to 0.7 p equals to 0.6 and p equals to 0.5. Now to find the channel capacity when p is 0.9 0.8 0.7 individually so for that we require the channel capacity equation so c equals to max of h of y because we have equation of symmetric channel c equals to max of h of y minus a log equals to log n minus a n is number of bits or number of channels two channels are there so log 2 minus h of y given x j now here in this case y is independent of j so we have equation minus summation j equals to 1 to 2 there are two sources the probability of y given x j log of probability of y given x j so this one is a conditional probability this one is also conditional probability now from this diagram we can get the values of conditional probabilities so one equation is log 2 plus minus minus plus probability p log p plus 1 minus p log 1 minus p so this is the final equation for channel capacity now we have to find c for in every value of p so let us find every value of p when p equals to 0.9 c equals to 1 log 2 having value 1 why the value is of log 2 is divided by it is a base of 2 so that is why log 2 divided by log 2 gives you the 1 so that is why it is 1 plus 0.9 p in this case is 0.9 so 0.9 log 0.9 plus 1 minus p so in this case p is 0.9 so 1 minus 0.9 is 0.1 log 1 minus p is 0.1 that gives you the answer this 0.531 bits per message now for p equals to 0.8 so in this case it is 0.8 same this one is 1 minus p that is why 1 minus 0.8 gives you 0.2 so log 0.2 the answer is this you can verify the answer similarly you have to find it for 0.7 0.6 and 0.5 now 0.5 you will get the answer is 0 right because the probability of transmitting 0 from source 1 to the destination 1 is 1.5 and to destination 2 it is 0.5 right so 0.5 and 0.5 that gives you the answer 0 if you solve that one right these are the references thank you.