Greetings and welcome to the introduction to astronomy. In this lesson we are going to look at some of the properties of the stars and in particular how we determine the masses and the sizes of the stars. So how can we determine these in detail based on measurements that we can make? So let's start off looking at kind of a census of the stars. What kind of stars are typical? And what we find that these are not the stars that we see in the night sky. When you go out at night and look up at the stars, those are not the typical stars that exist in the universe. These are very large and bright stars that can be seen over tremendous distances. And many of those stars are hundreds or even thousands of light years away. The better idea of typical stars is the stars near the sun. So the average star is actually smaller and cooler than the sun. Now if we look at stars within 21 light years of the sun, these are the nearest stars within our solar neighborhood. We find that there are two class A stars, 1F, 7Gs, 17Ks, 94Ms. So if we look at the typical types of stars at the normal spectral classifications, remember that there's also O and B stars? They do not exist within 21 light years of the sun. They are very, very rare. Even A and F stars, I mean among the over 150 stars that are listed in this table, only three are A and F. When we get down to stars like the sun, we see a few more, but far, far more of the much cooler M-type stars. We also see white dwarf stars and brown dwarf stars. These could be, well, the white dwarf stars, especially the brown dwarf stars, could be underestimated. These are very hard to find. Even 21 light years away, while it's easier to find a star, an object that is much cooler is very hard to detect. So these brown dwarfs are very faint, and we are still detecting more. So likely this is even larger, and I would suspect that the number of brown dwarfs actually exceeds the number of M-type stars. That there would actually be more brown dwarfs within 21 light years than there are cool red dwarf stars. Now, why do we see this? Why do we see when we look out at the sky all of these bright stars, but they are not typical? This is an example of what we call a selection effect. A selection effect happens quite often. Typically what it means in astronomy here for stars, bright stars can only be seen, or can be seen, even if they are hundreds or thousands of light years away. So we can see incredibly distant bright stars, but faint stars at the same distance would be almost invisible. As an example for this, our own sun, which we know is decently bright as a G-class. It is not one of the coolest stars that is around, but it would not be visible if we took it just 30 light years away. It would be one of the fainter stars seen in the sky. It would not be a very bright star, even if seen from 30 light years. And if we went out to a hundred light years or a thousand light years, it would be invisible to the naked eye. So even our sun would not be that easy to see, so you can imagine these even fainter stars are really hard to see. Some of them, within even 20 light years, have to be seen through a telescope. They cannot be seen at all with the naked eye. So what do we see in the sky? Again, are the more unusual stars because of this selection effect. We see the O and B stars. We see red giants and super giants. But the nearest red giant is a hundred light years away. The nearest O-type star is 500 light years away. They're not common because we don't find any nearby. But they are visible across very large regions of space. So we can see these stars even though they are very far away. Whereas a typical star, or even a star like our sun, would be invisible at these distances. Now let's look at how we can go about determining masses of stars. And that can be something difficult because it's important to know, but it can be hard to determine. In order to determine the mass of an object, we need an orbit. We need something orbiting it. And we can do that for binary stars. Now we find that probably half the stars in the universe are part of binary systems, which is great for determining orbits, but it still is not an easy thing to find. So we need to use that, and we not only need to be able to determine that it is a binary, but we need to be able to measure the properties of the orbit. Now there are three specific types of binaries that we want to look at. First is the visual binary, which we see here where you can see two very distinct stars. So when we take an image of this part of the sky, we could see two stars, and we could note that over time they may be revolving around each other. We could track out that orbit and be able to determine the orbital properties so that we could figure out the masses. So a visual binary is one example. You actually see two stars over tens or hundreds of years you could follow their orbit. Now another type of binary is a spectroscopic binary. In this case, we cannot see two stars, so we cannot separate them visually, but we can see two different sets of spectra, and we can watch their motions. And what we would see is if we're looking here at various times, we would see this star coming around, and here it would be coming towards us, and here it would be going away from us. So in this case, we would see a blue shift as it's coming towards us, a red shift later as it's going away, and that would shift all of its spectral lines. So when things are going across our line of sight like this, there is no Doppler effect, so we would see just one set of lines. Other times we would see a sets of lines are split apart, and we can use that to be able to help determine the orbital parameters. So that's an example of a spectroscopic binary, and that is the most common type. The most common type that we see is a spectroscopic binary. So another and the third type that we want to look at is the eclipsing binary. Now the eclipsing binary, again, we cannot see the two separate stars, but we can see their effects. In this case, we have it in a very edge on view, meaning that one star passes directly in front of the other. So as this orbits at position three to four, then this star passes in front of this other star and dims its light. Here on the back, at position one to two, this larger star actually blocks out the entire star and causes it to dim. So we can actually look at what we call the light curve, which measures the brightness over time. So at position one, we are seeing the light from both stars, and we get the full brightness. At position two, the very small but bright star is blocked out, so the brightness drops significantly at that time. As it comes back out, then we get the normal light curve again. And at position three, we're seeing the light of both stars. At position four, now the brighter star is blocking out part of the fainter star, so you will get some drop in brightness, but not as much as you had previously. So these are the three different types. One, the visual binary. Two, the spectroscopic binary. Three, the eclipsing binary. And the spectroscopic binary is by far the most common, because all you have to be able to do is get a spectrum. In order to separate the stars in a visual binary, it has to be relatively close to you. And in order to see an eclipsing binary, the stars have to be lined up properly, so it has to be tilted in exactly the right direction. So how can we use this to determine the mass of a star? Well, we have to go back to Kepler's third law. And Kepler's third law told us that the cube of the semi-major axis is equal to the period of revolution squared. So if we use d to represent the semi-major axis, p for the period of revolution, Newton modified this to give us that d cubed is equal to the sum of the masses times the square of the period. So if we can determine the semi-major axis and we can determine the period, then we can calculate the sum of the masses of the two objects, or m1 plus m2, equals d cubed divided by p squared. So if we can measure these two, we then can get the total of the masses. If we measure d in astronomical units in p in years, then the mass will be in solar masses. If you want to use other sets of units, there's actually a constant that you have to put out here that I'm not going to go into at this point, but there is a way to be able to determine if you wanted the mass in grams, you could put a specific constant out there that would convert the solar masses into grams. But the easiest way to do it, and to get a number that we typically use, would be just to use the period in years and the semi-major axis in astronomical units, and that gives us the answer in solar masses. But it only works if we can determine the orbital parameters. We need something orbiting. If we have a star that's sitting there all by itself and we cannot detect a planet around it, we can only compare it to similar stars to get an approximation of its mass. We cannot actually directly measure it. Now, what kind of masses do we find when we do this? Well, typically, largest stars are limited to about 120 solar masses. Why? Why can we not make a star that is 1,000 or 2,000 solar masses? Well, as that star forms, the radiation pressure will exceed the gravitational force. So if the gravitational force pulling material in is exceeded by the radiation pressure pushing outward, you have gravity pulling down, you have radiation pressure pushing out, then the radiation pressure will be able to overcome gravity and will be able to expel out the outer layers and prevent further material from adding to it. So there's a limit at about 120 solar masses, although theoretically very early stars could have been about 300 solar masses because those very early stars would not have had any metals. Recall that metals means anything other than hydrogen or helium. So you technically could have made in the very early universe even larger stars, but now they're limited to about 120 times the mass of our sun. Now the lowest mass stars are about one-twelfth of a solar mass. If we get below that, there's not enough mass and does not heat up enough at the center to fuse hydrogen to helium, and it becomes a brown dwarf star instead. So this is the ranges that we see. The smallest star is about one-twelfth of a solar mass. The largest star is now about 120 solar masses or so. Now how about determining the diameter of a star? Well we can do that by a couple of different methods, and in fact when we just look at stars, the vast majority of them only look like points of light even through a gigantic telescope. So no matter how much we image them, we still only see points of light. There are some very large stars that are relatively close to us that we can actually see sizes of and measure a diameter directly, but those are very, very rare. So the methods that we use to determine the diameter include lunar occultations. If the moon passes in front of the star, so as the moon is moving across the sky, it may pass directly in front of a star blocking out its light. We can then time how long it takes that star's light to disappear. If the star is so far away that it's just a point, it will just blink out. It will disappear immediately. However, if it has some size, even if we can't measure it with a telescope, it might take some tiny fraction of a second for the star's light to disappear, and that, depending on the distance, would then be able to give us a diameter for that star. We can do a similar thing with the eclipsing binary stars. If you note we looked at this light curve earlier, but as it eclipses, it does not drop straight down. It does not just disappear immediately, and then does not come straight back up. There's actually a slight angle to this. That tells us how long it takes that star to actually be completely eclipsed by the other and gives us a measure of the size. So we can determine the size of one star here, in this case the smaller star. Over here, we can determine the size of the other star by how long it takes, so we can determine the sizes from eclipsing binary stars as well. And a third method we can use is the radiation law. If we can measure the luminosity of a star. Remember, that's difficult because we need to know a distance to get the luminosity. We can use this equation, which relates the luminosity, the radius, and the temperature. Temperature is not too difficult to get. Luminosity, if we know a distance and a parent brightness we can determine, and that means that we could then calculate the radius and the other numbers in here, the 4 and the pi and the sigma, are just constants. So if we can determine the luminosity and the temperature of a star, we can then use those numbers to estimate the radius of the star. What do we find when we calculate radii of stars? Well, there's a big range. So the smallest stars are actually the size of Jupiter. They're not much bigger than Jupiter in size. Remember, they are still more massive than Jupiter, but not much bigger in size. These are the most common stars that we see. Largest stars, much, much larger than the Sun. We have giant stars, which are defined to be between 10 and 100 times larger than the Sun. Supergiant stars are more than 100 times larger than the Sun. And the very largest known star, known as Vy Canis Majoris, is 1,400 times larger than our Sun. That means it would extend out beyond the planet Jupiter if we placed it at the center of our solar system. So Mercury, Venus, Earth, Mars, and even Jupiter would be inside this star if it were placed at the center of our solar system. So that gives us an idea of how tremendously large these stars can get. So let's finish up here with our summary. And what we find is that stars do have a wide variety of masses and sizes. So they vary very widely from very small mass and small diameter stars to very large mass and large diameter stars. We've talked about some of the various methods that we can use to determine the sizes and the masses of stars. And typically what we see, the large stars are more commonly seen in the sky, large size, large mass. But it is the small size and mass stars that are really more common in the universe. The difference is we can see these stars from very large distances. So we can see them even if they are not close to us. And the small stars can be hard to find even if they are close to us. So that concludes our lecture on determining sizes and masses of stars. We'll be back again next time for another topic in astronomy. So until then, have a great day, everyone, and I will see you in class.


Good afternoon. I'm Tim Apachele. I'm your host with Moving Hawaii Forward. And today's show is titled Aerial Mobility, Hawaiian Skies or the Limit. And as the title would suggest, we're going to talk about mobility through the skies. And that's our traditional discussion about rail or bus or bike or walk. This is an innovative topic and I'm very much looking forward to it. With me today I have Nicole Horie and she is actually a board member of Think Tech Hawaii. But she's going to talk about one of her passions and that is Aerial Gondolas. And so, Nicole, thank you very much for coming on the show. Appreciate it. Thank you so much for having me. Yes, this is going to be good. So I know you did a lot of research to find out about the Aerial systems that are existing around the world before the show. Yeah, there's a lot out there. There are. And what I'm noticing is that Europe and all over the world, they're using and implementing these systems of mobility and the United States is not so much. Well, there's definitely projects that are being proposed. So I think everyone's excited about the proposal for Austin. There is a proposal for the Georgetown area near the capital where it would be a system with three stations. And that's actually just been approved as well. The feasibility study has been done and it's been shown to be feasible. So that's exciting. Well, in my research, I mean, I was just constantly amazed on some of the advantages of to an Aerial Gondola or I've used the term tramway. But one of the things that I thought was very interesting is that you have consistent travel times. You have a continuous transportation of it. You have smaller capital investments. You have operating costs. And the thing that really caught my mind is they're quick to build. Yes. Yes. So tell me a little bit more about that. So generally they say the construction time is between 18 to 24 months. But there is a system that just went in Berlin. They're having a flower show and there's a large park outside of Berlin where they just installed a monocable gondola. They started constructing it. I think the first tower was being put up in March last year. And then by September they're already doing test runs. So it's pretty amazing. That is amazing. They're planning to bring that on service for the 2017 show. So just over a year. That is unbelievably fast. It is. When I think gondola, obviously, I have a stereotypical image of my brain because when I go skiing, I'm usually in a gondola. But it's far beyond that. I mean, it's much larger. In fact, these gondolas sometimes go hold how many people? Well, 35 passengers. 35 passengers. Yeah. So you only have seating for either 20 or 24 passengers, depending on the design. I'm sorry, 24 or 28 passengers. So if it's full, actually 80% of the passengers would be seated, which is a big advantage. And then if you have a smaller system, you could do 10 passengers. But for what we're proposing here in Honolulu, we would go with the 35 passenger car. That would be unbelievably great. I understand it can move so many people per hour. Do you know what that number is? I've seen different numbers on that. Depending on what settings you choose, it could be 5,000 to 6,000 for the maximum capacity of the system. But if they don't expect demand to be that high, a lot of cities will just have the cars coming less often. So you might space them 90 seconds apart instead, and you have a much lower ridership capacity. But as long as it meets your needs, it's still a system that can be very practical for the local residents. And is there a distance? Is there a maximum distance that these things can be implemented in? So I think one of the longest systems that's being proposed right now is actually something that the city of Branson, Missouri, just signed a memorandum of understanding with some developers for. And that would be, I believe, 9 kilometers. 9 miles? Well, 9 kilometers. Oh, sorry. We get to go up. Yeah, that's right. Yeah, so that is a very long system. And then it would have multiple stops. And it's more oriented towards tourists. Right. But there's other ones such as the peak to peak at Whistler that have enormous spans. It's possible to have a distance between two towers that's about 3 kilometers. Do you need to have it from an upper elevation that goes down to a lower elevation, or can you have it as a constant elevation? So if we're looking for where to place the towers for a constant elevation system, we would place smaller towers closer together. They would hold the two travel paths closer together as well, so that we minimize the footprint and the air rights that are required. We don't want to affect the buildings on either side, but it is something that's feasible for an urban environment. Now, a lot of these, obviously, you go on websites and you look at the one, the Matterhorn, it's 12,000 feet up in the air. I mean, that's phenomenal. Yeah. Particularly in the mountains, you have this airstream that's 12,000 miles, or 12,000 feet up in the air. So they hold up the sustained winds then quite well. Yes. The manufacturers say up to 100 kilometers an hour. So even when we have high winds here on Oahu, it's very rarely... So that doesn't pose a problem at all. And how fast do these things move? So the ones we're looking at are 8.5 meters per second, which translates to about 19 miles an hour. And you have to add in time to stop at the station or just slow down at the station for people to get on and off. Right. But it's still a pretty quick system when you compare it with things like the bus or... No, I mean, I've really never thought of this before, and I've been in the transportation business for 17 years. So aerial mobility is the new concept, and I find it quite exciting to research it. So let me ask you this. Is it a point A to point B, or can you have stops in between? Is it really designed for kind of a one-stop method of transportation? Actually, maybe we can go to one of the pictures at this point. Okay, let's take a look at them. There's a route map that I've developed. So basically, looking at where the demand is for people coming off the rail, you'll have a lot of people who are headed either for Waikiki or who are headed for university. So in order to connect those points, in order for us to also serve the convention center in Pucks Alley Area, we can put in stations that are midpoints. And are those relatively low to the ground, or is there a stairwell system that will need to be... So they could be elevated or they could be low to the ground. And there are some pictures later on where you can see the different options that designers have. I know with the transit system or a transit development or rail development, we always have the challenge and it's a good challenge of American Disability Act access. Right. And so I was wondering maybe how that would complicate matters with a system like this. Yeah, so that's actually one of the reasons to get with a larger 35 passenger car system because then it's large enough that you can't have wheelchairs and strollers and bikes, probably not during the two hours of peak travel time, but you could have a way to carry your bike at other times during the day. Oh really? Yeah. Well, I'm familiar with a little bit because I have family in New York and I've been to the Roosevelt Island area and have ridden that tram. And I didn't realize that tram actually started back in the mid-70s, back in 1976. How is that different for that? In fact, that's one of the very few systems in the United States that actually use it for mobility rather than in mountain resorts and things like that nature. So what do you know about the Roosevelt system? Roosevelt Island system, excuse me. That one's a tram. So basically you have the cars trading places and instead of a gondola where you have many cars traveling at once and then them just attaching off the line once they get to the station, you'll actually, I believe, have the trams fixed to the line. So when one car is moving, the other car is moving, but then you can have much higher capacity. So in terms of the number of passengers in a single car, in some cases it's up to 200. I believe the one in Portland has about 75 passengers per car. And you know, sometimes there's a coffee shop inside. Really? Yeah, yeah. Coffee shop. So it's a much bigger technology, but then you also don't have departures as frequent. Oh, I see. So the advantage of gondola for an urban setting is that as soon as you get to the station, you just walk on, you might wait 10 seconds on average to get to the next car, and then you just board, there's no, you're checking a schedule or any of that. Right, you don't need a schedule. So that's where that concept of continuous transportation plays in. There's always a car and it's always continuous. So you're not at a particular time point. There's just always a car ready to go. Yeah. Fascinating. Well, you came here with some photos, so let's take a look at some of those and be interesting to have you walk me through those. Okay, so here is a proposed system for Chicago. You can see how they envision it fitting in with the urban environment. It's relatively low compared to the buildings around it. And it really, for them, would be a great sightseeing opportunity. I'm envisioning it as much more of a commuter thing for Honolulu, but it does look like it would be fun to ride, and their renderings are just gorgeous. There's also a picture here from a new station that's being planned for Toulouse, France. So this is actually the fourth largest city in France, and connecting to a metro stop would be this three-station system. They actually just signed a contract for $57 million to have it constructed. So it is... Did you say $57 million? $57 million. That's nothing. So it's three stations, and then it's going to be three kilometers in total length. It's going to go from a university up to a medical facility, and then there's also a tech park or research park that's the third station. So right now it takes them about 30 minutes to go from one end to the other from the university to the tech park, and with this system they'll be able to do that in 10 minutes. And it's not even running particularly fast. You could speed it up if you wanted to, and I'm plotting on our system being a little faster to go between the stations. I mean, in relative dollar terms, $57 million is a drop in the bucket compared to what numbers we've been running through. It seems quite reasonable. This one is in Berlin. So this is the one I was telling you about that they were constructing a crazy fast speed. It is part of a garden exhibition. So you can see that they used the green roof technology and gorgeous architecture since they are planning on this being a tourist event. I'm actually not sure how much it costs because this is something where the manufacturer is putting it in for three years and they're covering that through a share of the ticket sales for the show, but also as a demonstration project. And then after three years they'll revisit it. I don't know if they'll actually end up keeping the system, but as you'll see with the Koblenz system, which was the site of a Gondola for a previous garden show, the community decided they liked it so much they wanted it to stay. Here's another one that's for a ski area. This is in France, and it's just a good example and a good view of what the station size could be. That's fairly low impact. Not a lot of real estate there. And here is the Koblenz station. So this is actually the station that is the background for us right now. This is just a different view of it. And you can see that there's a tower at the mouth of the station that helps it get up from ground level to the proper cable height. Okay. And how well is that received in the community? Oh, it was received very well. It was supposed to be a temporary installation, just for the Flower Show exhibition, but they decided to keep it around. And it's just a good demonstration of what an urban scale Gondola could look like. So let me clarify something. That was put in place as a temporary... That's right. And it was that simple to put in as a temporary could be dismantled. Yes. So the idea with the Berlin one in particular is that if the Berlin area decides not to keep it around, they'll just take down the electromechanical portions. They'll take down the cables. They'll take down the cabins. Obviously, you can't take away the buildings. But then they could always install that equipment somewhere else and still a lot of the values retain. So even as a temporary feature, it's reasonable to do. I find this conversation absolutely amazing. One, because of price. Two, because of speed in which to build it. Three, if the community doesn't like it, it could be dismantled. I mean... That's true. That's true.  So there's going to be more to look at here. So we're going to take a quick break. I'm Tim Apachella. This is Moving Hawaii Forward. I'm here with Nicole Horie. And we'll be right back. Thank you very much. Hawaii covers stories that matter to tech and to Hawaii. I'm Elise Anderson. And I'm Kaui Lucas. For our show next time, we're doing a think tech special. Home Alone and Homeless Alone at Christmas. We want to learn more about the isolated, disconnected people alone in our community. Lots to come on think tech. Tune in 10.30 p.m. this Sunday. See you then. Welcome back. Thank you. I'm Tim Apachella, host for Moving Hawaii Forward. Today's topic is aerial mobility. Hawaiian skies are the limit. And I'm here with Nicole Horie. And she's discussing aerial gondolas and tramways. Thanks again, Nicole. Thank you. Well, thanks for coming back. It was a quick break. It's a pleasure to be here. Yes. It was a quick break. So let's take a look at these pictures. Let's continue on because I find them fascinating. I really do. So this is where we are on our picture here. So this is a picture of the interior of that Koblence car. And you can see it's a much more public transportation oriented design than some of the other ski area designs. But the station, the cars themselves actually have a lot of variance. So because this was a demonstration system, they were able to show multiple versions and, you know, just experiment a little bit. Well, the design is sleek and looks nice. Now, if we were going to get really fancy, it would be lovely to have one like Chicago's proposing. They're actually proposing custom, you know, glassed-in cabins. They look absolutely phenomenal. But, you know, it would increase the cost. Right. So you have trade-offs there. There are some really nice options that are just available off the shelf. If we look at the next picture, this is the Symphony Cabin, which is partly designed by Pininfarina. This is a famous designer from Italy. He does the Ferraris? Yes. Does he not? Okay. So... Got a Ferrari in the air? Yes. And the interior views of that are also just really luxurious. Here's another one. So this is the plan system for Toulouse and what the cabins would look like for that. So just to give people an idea of what that experience would be like, we're expecting, you know, 35 passenger cars coming through the station about once every 20 seconds. So you can easily transition from the rail to getting on to Alamoana, and then you go to your final destination. So I... And what kind of total cost are we... Okay, so that alignments about how many miles? Less than eight? Oh, okay. So actually to go with that design would be about 2.6 total miles. Okay. So... Yeah. So it's not a long distance, but actually the cost is in the stations. So depending on how fancy the stations are, if we have, you know, a lovely green roof and, you know, phenomenal curves, it could be absolutely gorgeous, but it could be really expensive. We do think it's going to be something that could be quite iconic. So you don't want to skimp on the design, but you also want something that, you know, fits in with the rest of the transit infrastructure that fits in with the rail stations that, you know, is very open air and, you know, very place appropriate. So I am looking at the rail system design for some ideas. So let's talk about Hawaii and these sort of systems. What kind of reception are you receiving for them? Oh, everyone's excited about it. You know, we do have... Excitement is good? You do have some people who mentioned that some people are afraid of heights, which is true. I don't expect necessarily everyone to want to ride it, but I think people in general are looking forward to benefiting from lower street-level traffic and less congestion. Yes, ma'am. You also have, you know, a quiet system. So even if you're not riding it, it's not going to be something that interferes with your enjoyment of, you know, your neighborhood. There will be the potential for, you know, tinting as it goes past people's windows if it's in an area with towers. So hopefully you can enjoy the view where there is a view and then not be looking into people's windows as you go past apartment buildings. That's an interesting point. I didn't really think about that, but now you've raised it, and so... Yeah, yeah.  So that is something where you would have to, you know, consider the possibility of slowing down. Portland does slow down their system when they have high winds. But if we look at the 3S system, which has the three cables, it's much more stable, and that's what enables it to operate even up to 100... Is that the Leichner or Leichner system? So Leitner-Poma does the system, and then Dopamirah does the system. So, you know, there's a lot of similarities between the two, and you just have to see, you know, how the things came in. You mentioned Portland. Do you know how long there's been an operation and how many people are they carrying? I know they've carried over 10 million passengers. Really? Yes. So, I believe there has a kind of challenging configuration because it's on a steep hillside and it's, you know, fitted in with the hospital complex at the top. So I know that wasn't trivial for them to build. But... Was it a government works project or is it kind of a partnership, a private government partnership? So I believe some of the funding came from the hospital complex. Right, okay. Because they wanted to increase mobility up to OEQ. Okay. Interesting. And do you know when they installed that system? I think it might have been 2008. Oh, okay. I think you should quote me on that. Oh, that's okay. I'm not going to hold you anything here yet. Okay, so tell me about what kind of reception you're getting from Hawaii Department of Transportation on this concept. Well, I don't know that I've specifically spoken with... You know, I have spoken to various civic leaders. So, you know, they've expressed interest and they do have some good questions for me. I mean, this might be an opportune time and we don't know where funding is going to go as far as our light rail project. We don't know if it's going to really end at Middle Street and that's going to be the end of the day. We don't know if it's going to have increased... It's going to increase funding to get us to Alamoana. We just don't know. And I would think this would be an opportune time for folks, you specifically, to introduce this idea as an alternative and a viable alternative. And I don't know... You're probably a one-person lobbying effort, I'm sure, but maybe you need some help. Certainly the success of the Honolulu Aerial depends on the success of the rail. And I think having the Honolulu Aerial in place to deliver passengers to the university and to Waikiki, where they really want to go, is really something that will help the rail and increase the ridership. And then just you make the entire system more cost-effective. It's one thing that you can see with every successful system is that usually every aerial system is connected to a metro or underground line. The one in Brazil, there's an underground station, right where the first station starts. There's a couple in Paris, the Toulouse one, and then one being planned to connect to the metro. So having that additional ridership and just using the aerial to expand the reach of that system is really important. So this is an integrated multimodal concept that can work. That's right. And it can work here in Honolulu. It can. It can. And it's something that currently I'm planning to do on a private basis. I think that just makes things simpler and still looking at potential federal sources of funding and things like that to support the project. But it's not something that I expect the city to necessarily be on the hook for. And then I am hoping that there would be some operating profits. So the system in Bolivia, Mito e Ferrico, just announced that they had a $3 million profit for the year. They carried 77 million passengers and also came out with a profit. Because even your best transit systems, I'll say bus systems, you're lucky to get 29 cents on the dollar for a fare box recovery. You're lucky at best. Yeah. And most systems only obtain about 4 cents, 5 cents on the fare box. So you're looking at a 95-cent deficit for a lot of transit systems in the country. And you're telling me that there's a system out there that's operating with a profit? Well, with their operating profit. I'm not sure what their investments initially were. But the cost of a system, and going back to the earlier question you had for me, depends very much on the architecture. So if you have a building with retail integrated into it, maybe some office space or community space, and then you have the aerial as just part of that building, it's hard to say how much of the cost of that is the aerial. You can see from some of the station's costs that are even being built here in Hawaii with our hard construction costs. For instance, there's Ho'opili. So Ho'opili is supposed to cost $14 million. And if we look at the picture for that, you can see that you actually get a lot of station for your $14 million. Right, right. Well, don't start off on Ho'opili because I went off last week because there is a planned development of almost 12,000 houses. And we're talking between Ewa and Kapole. And the bottom line is, I just don't know if there's any more room to put more houses so that these people can get on, you know, on the highway and come into downtown. And in my mind, a lot of times, I'm hoping that our transportation systems that we're trying to put in place are not a warrant for developers to say, look, we have this in place. Now give us the permit and we're going to convert our precious agricultural lands into, you know, planned communities. And I'm hoping that these great ideas are not being used as a basis for us to get approval for permits and build more and build more and build more. So that's just a cynical side of me and I'm sorry I introduced it today. But I came up last week with Jay Fidel and it has merit as far as why are we putting more houses on precious ag land? Yeah, well I think it is a different case where you have an urban area that's already primarily built up. I think with the aerial system you will have some different uses of land. I think there will be more people from Waikiki coming out to explore restaurants and shops along the line. But I don't think it will change the character of the neighborhood too much, honestly. What is the harshest criticism you've received? Is it aesthetics? Is it logistics? I think the greatest skepticism is just in terms of cost. Though when you look at, you know, the potential revenues and the impact on the city I think it's easy to make the case for it. So, you know, it is a lot of money to have to raise. Right. Do you see this as a mixed mode of transportation for both the commuter and tourist or commuter and... Yeah, so I think it's most likely that we'll have a... Most of our riders will just come off the rail and have it integrated as part of their rail fare. Then you'll have other people who pay for a ten ride pass and let's say they pay $25 to ride the aerial ten times. You know, for locals who just ride it intermittently it's pretty cost-effective. For tourists who are just going to be riding it once you might sell them like a $6 single ride pass or maybe a $15 day pass. So you'd have to figure out what would make sense. But it's not something that's cost prohibitive. And certainly a lot of the aerial systems that aren't targeted at tourists charge more on the order of $30 or $40. We wouldn't charge that much but we do see ourselves foremost as a commuting tool. Okay. A couple of weeks ago I had... Mark Garrett, he was the transit director from DTS and he's talking about a project that they're working on. It's a smart card technology. But the nice thing about smart card technology is you can integrate the fare structure between rail, bus, and potentially an aerial system. So that would be very nice if this all could come into place. What's the next move? What's the next move to try to see if this thing could be realistically implemented in Honolulu? Well, certainly I think looking at what's going on in Missouri where they have the memorandum of understanding with the city that provides the developers five-year exclusivity on the concept of building an aerial system that enables them to invest in engineering to develop the routing and just make sure that you have the correct locations. The memorandum of understanding assumes that the city will also tell them what the city is looking for. So if the city is planning on having a lot of visitors go to one area or another and they do have a lot of visitors, they have about eight million a year which is on par with Hawaii though, I assume they most only stay for a day. But they're expecting to stop off at multiple tourist locations and then the city can help them figure out which places make the most sense. So they do have stations mapped out theoretically but they're going to be working on their engineering and design now that they have the memorandum of understanding. Fantastic. Well, fingers crossed. Would you come back and give us an update as we hopefully move forward with this? Of course. I'd love to have you here and hear more about it. But we're out of time and as usual I have more questions than I have time today. So I'm going to say that it's the end of 2016 and I'd like to give a special thanks for those that have helped me hobble along with putting the show together and a special thanks to Zuri Bender, Ian Davison, Robert McLean and Nick Sexton. This is Moving Hawaii Forward. I'm Tim Apachella and we'll see you in 2017. Happy New Year.


Ac mae'n ddiogelio i fyny ni rwy'n dod, a ong yn cyfreidio'r ystod i vaeth yn digwydd, felly, os drafodd o'i dechrau, fe ydych yn frysgol. Llywodraeth ni'n ddiddorol o ffocüsol o'r tref, o'n ddiddorol o ffordd o dechrau a'r mynd i rydym ni'n llei i'r ffordd o'r ddweud yn Hopebryd. Rwy'n gweithio i'r ddweud yn gweithio yma. Rwy'n gyfreidio'r hynny yw unrhyw dros cynnwys of getting around the problem. Some of them are just problems that are open for discussion and a lot of this is reflecting what's happened in the other talks today. So I'm going back in time a bit with this one. This was a piece of work as part of the Seaware project and our goal in this was to try and come up with something similar to a credit score but to do with your energy consumption. So credit scores take data from various different sources, different credit cards, different financial services you've signed up to and try and give you a day long or a kind of holistic view of your spending. With energy we have smart meters coming in now. I'm sure some people in the room have smart meters. I was talking about my smart meter and how rubbish it was earlier but the meter we have energy tends to be for one place. We have our home energy monitoring which was great, tells us about home potentially. Some of our workplaces have energy monitors but these are not brought together in one place. So the point of this project was just trying to bring together all that data in one place so you can see your route through the day. This wasn't because we necessarily thought it was going to work it's just we hadn't seen someone else do that so we want to see what it was like to try and bring that data together. In one hand that was a technical challenge so some of us on the team were computer scientists so I'm a computer scientist not necessarily a very technical one by am one. We had sociologists and psychologists in the team as well so part of it was a technical challenge and part of it was a social science challenge to see what happened when we put that data from one place and see how people reacted to it. So it wasn't a very big study this was really a test for a PhD student but it was hard to bring that data together so it meant instrumenting a few people's houses it meant getting access to data from their workplaces and it meant instrumenting some workplace as well to try and create a place where all that data could actually be harvested. It's very hard to do. We found some things out and again luckily I'm not going to focus on the results so much today I've put the links to the papers in these slides and I've sent them out at some other points so hopefully they're easy to go and find if you want to see the results. Some of it was as you'd expected so the top graph shows for one person I think where they spent their energy during the week so that the red spaces in there were communal spaces like workplaces blue spaces were personal spaces was their home and green spaces were spaces in between so this was travelling this was shops things like that so they spent less time in communal space at the weekend so they spent a lot of time at home. The graph below so I've basically said time spent in that top one not necessarily energy spent the bottom graph shows energy spent and the amount of energy you spend in different spaces is quite different and I'll come to that in a second so this is it boiled down to pie charts just to make it even easier so time spent on the left a lot of time spent in personal space this is what it looked like when we divided up the energy based on what space they were in so in the workplace you're using a lot more energy this is if you take the workspace's entire energy and divide it up by the number of people they're in that space now obviously in a workspace you've got a hell of a lot going on compared to the home you've got a lot of these people were office based you've got things like servers sitting there in the background you've got air conditioning systems which don't necessarily have a home you've got a whole load of other systems that you're using as part of your daily work that if you just purely divide up by the number of people you end up looking like you're spending a hell of a lot more energy in those spaces doesn't necessarily make sense but the whole point of this was just to see if we just divided up really simply energy what's it look like on paper because in reality that's what a lot of these services into each other might just do they might just divide things up because that's the easiest way to take this data so you can imagine the kind of things that led to we get nice things here like this was a quote from one of the participants so he said looking at his work consumption he may as well give up a home he's using so little at home it doesn't really matter he can do what he likes there maybe he should make a bit more of an effort work maybe not because it's so much energy consumed at work I can't make possibly an impact on that um it's like this I mean again people will recognise this there's a done work in workplaces people don't feel like they have a lot of power in the workplace so people quite quickly worked out they have a lot going on around them lots of systems things they have no way of influencing we had people working in offices where they couldn't touch the thermometer they couldn't change the thermostat they cannot home they can't at work they're not instrumental at work so despite the fact a lot of energy consumption is going on there I can't do anything about it so why bother why care what we also did was have a few people in the study that were friends of one another um and what we let them do was to see their impact on their friends home so they could see how much energy they spent when they're around each other's houses so we again in the behind the scenes got them to do a time you start and got them to divide up and say when they're at each other's houses and we were kind of intrigued to say well your day long footprint doesn't just involve your home or your workplace it involves energy you're consuming other people's homes and workplace as well so if you go to a friend's house and you turn on you ask them to turn up the heating you're kind of responsible for that okay they're being a nice host by allowing you to do that but you're sort of responsible for that consumption um and people started to say that they start saying well someone's come around and left the light on in the toilet and they've gone to the kitchen and left that light on as well they're ruining my data they're increasing that and I really want them to be accountable for that data this was neat one as well um people started to make really I guess simplistic rational decisions about how they were spending their energy and started to try and draw conclusions from it so I guess my point with this one was we did this as a really simple experiment but people got quite invested in the results and started saying they would change their lives based on these results we kind of had to step back quite quickly and say and kind of explain the simplicity of what we were doing so these are very simple calculations and we're doing it to provoke responses but these are not things that you should be basing life changes around um and it's something that's come up again again in these talks is that context really matters and our lives are really messy and we do things that can't easily be explained in data or you can't apply simple functions to the data to make decisions based on and obviously the psychologists in the study were really intrigued to see where this data could be used to change behaviour and like I've just said on one hand it ended up being quite dangerous and you couldn't you just couldn't do things based on this you could people were starting to contextualise themselves but the amount of missing data or data that didn't make sense because of the way we'd processed it just undermined in those decisions they could have made um and I sort of mentioned this this is a really interesting part of it was we we kind of threw in the ability to see each other's data just to see what would happen um but turns out if you're good friends with someone you can almost read their data as well as you can read their own you or at least you think you can this is one thing we found we had a couple of people in particular who were very quick to say they knew each other very well and knew exactly what the data said and of course if you think you know someone well it it starts to reinforce what you're seeing in the data so if you think you see something you kind of change the way you see the data so that it fits your view of that person and this data was showing them entirely what they wanted to see and they would say exactly the wrong things they're making conclusions that we knew were completely wrong um but they thought this was a great view into this person's house as they expected it to be so I'll leave that one up is a study we did a while ago it wasn't very big one but it it it starts the story neatly um so we I guess thought what can we do what sensors what other data can we collect to uh regain some of this missing context that we thought we weren't getting in that first study so we moved like a few people did I think onto wearable cameras um so if you're not seen wearable cameras I don't know how experienced people are with these sorts of technologies in the room we used one called Autographer and that came from one called Sensecam which is from Microsoft at Cambridge uh and rather than me just talking about them these are some nice product pictures from Autographer a guy on the left is what they want people to look like when they're wearing it these two were the faces we got and people gave them the Autographers really um this was the wearable camera here so this is the Autographer and it is how it looks it's basically a USB stick stuck to a tiny camera and you could open and close the lens how you wanted to so this guy here's got his lens closed that yellow bit is the lens cap on it she's got it open there and the idea is you hang it round your neck or you clip it to you and it's got a PIR sensor there so it detects motion in front of you um it's gone accelerometer in it so by default they can capture a picture of what's in front of you every 30 seconds but it tries to use those sensors to be a bit more clever about it and save battery so if it's not moving if it's not seeing motion it captures pictures less often if it's moving a lot it captures pictures more often now this was sold this product was I mean Sensecam was sold as a research tool Autographer was them trying to clamber on the bandwagon of kind of quantified self and people wanting to log their entire lives it's a life logging tool really it's a consumer product where it was until it was cattened um no one really bought it as far as I can tell except researchers because it was slightly more well packaged than Sensecam and that's this sort of slightly devious thing about not actually being very visible so I know what it is because I'm looking for it most people had this on we could put them in a room and other people wouldn't really know what it was um it's kind of and the marketing around it was sort of done so that it's like it fits into your life and it doesn't interfere with things and people won't know and yeah it's slightly devious marketing around it um so we thought uh let's give that to a few people and see if it can complement time use diaries so we know time use diaries they're not necessarily 100 accurate um sometimes people invest again their sort of view of themselves in the time use diaries so when you look back at the end of the day and you're tired you think oh what did I do oh yeah maybe it sound nice if I did this so you kind of start to to to it no longer is objective so we thought why not put some of these pictures alongside the time use diaries so this is the sorts of pictures that we captured uh these are the non blurry ones hell of a lot of pictures are blurry it's not the best camera in Sensecam so you get hell of a lot of just motion blur and blackness someone this is the person that you like to pretend they read at their desk at lunchtime but actually played candy crush and things like that inside their book that's this person eating at their desk that said they took a break from work while they're having lunch they didn't they worked while they're eating lunch um just two ways that those pictures contradicted completely what people put in their time use diaries we gave people an app to go alongside it so we didn't do paper based diaries um well we did start off with but then we developed this app which you at the end of the day you plugged your autografer into the app did some processing across the pictures that it captured and the data it captured and then prompted you for what you were doing at certain times of the day based on what the app could see in the photos and in the data so it tried to do some processing to pick out activities significant activities different changes in the data and prompted you for what you were doing based on the pictures at a time of day so it's kind of neat the app can say I'm seeing a lot of motion at this time of the day pulls out the picture that's timestamped at the same time and said were you doing something interesting at this point in the day so we did that we gave it to people our focus was on uh hey just because the funding we got was on how we use technology at work and whether this was having an impact on their well-being and their productivity at work so we were using the the wearable cameras and the and the time use diaries as a way of studying that so we gave it to not many people to be fair I think some of us doing 2030 got that many images out of the cameras that wasn't fun we had a summer intern who had a great load of fun going through 160 000 images alongside the data and what she did she coded all those images and went through and tried to pick out what she thought the activities were going on in those pictures this was our this was our attempt at ground truth in those images but given the amount of pictures that were blurry and given the the kind of other factors that are going on that was quite hard to do so again I'm not going to focus on that on the the results I've just put them in here because they're quite fun so we only had people wear these cameras during their working days we said when you start on the road to work turn it on and when you get back on the road the end work turn it off so we don't want any of your home life we just want your working day here um I'm sure people can recognise themselves a bit in this so we found on average people had about an hour of non-work activities and this is everything from eating lunch to going to the loo to going to walk outside to have a smoke like some people did but 30 minutes that was digital in some way or another so that again as you might imagine that might be catching up with people on WhatsApp it might be doing some shopping on the internet now what it'll let us do is to try and cluster people into behaviours so we again not going to go into this in detail but we had we kind of made distinction between people that are grazers and we're constantly doing some sort of digital non-work and this might have been blended quite well in with their other activities so it may have been doing something on their phone while doing work on a screen we had people doing that it may have been people walking around sort of looking like they're taking physical break for again doing something on their phone versus people who batched their behaviours people that would not touch their phone until lunchtime when they would maybe watch a video on iPlayer and then go back to work it said they'd clearly segment their behaviour really quickly to us it seemed like what we considered traditional recovery activities they were disappearing people don't do them so much anymore so how many people in the room think they take a significant break away from all kind of technology and work at lunchtime anymore anyone think they fall into that category anyone take a real break at lunch yeah and the people that did kind of like it was a real life statement they were like I'm going to get away from this and I'm the person that does that most people that seems to have disappeared completely digital things become more attractive as day wears on you get more tired more bored with work you start looking at the internet more you start faffing around with your phone a bit more that's just natural it happens you had interesting correlations between our measures of well-being and how much people did non-work digital things so the more you did the more constantly you were checking your phone again these are sort of intuitive that the greater fatigue you felt at the end of the day and people that spent a lot of time on facebook during the day people were not describing this as a break people were describing this as another type of work they're saying this is me managing my social network it's me making sure the kids doing what they should be doing it's me doing my shopping it's it's not a break um so again i'm not going to push on with this too much i want to get to this as a method um so what we were basically doing was we were turning our participants into researchers there were no longer participants we were sending people out with quite a sensitive device to catch pictures of the world around them this is no longer a study where they are kind of ignorant participants they're doing the work for us really um this forced us into doing a lot of things we don't normally do so walking around a workplace wearing a camera capturing everything that's going on is quite a sensitive thing you can walk into a meeting you can capture who's there you can capture documents on the desk in front of you you can capture things on screen so we had to have our participants knowing quite intimately what it meant to get informed consent and knowing when a sensitive situation was happening and that basically meant doing ethics training with participants because he quickly is moving a long way away from the the uninformed participant into being a researcher um and we had to be quite picky about who we were recruiting to see whether they were understanding that process and understanding why we were doing it something i didn't mention with the app was we kind of wanted to capture some of those images we didn't necessarily want 160 000 images except for the coding we don't want to keep those images they're not useful to us um but before we even got to coding it was the the participants responsibility to censor was in those images so quite often this was happening retrospectively and you could close the the lens cap and we were saying to people if you know you're going to go into a sensitive thing if you're on a meeting with people that that you're not allowed to photograph put the lens cap on we don't care we don't mind that we're missing that data it's fine for a lot of times they'd do it and then they'd remember five minutes later they've been in the meeting that was sensitive or hey where people going into the loo remembering they'd forgotten to close the lens cap when they did it there was a need to retrospectively censor these things that's a really big deal at the end of the day these people are taking tens of thousands of images on this camera to go back and start deleting those was onerous work but it was something the participants do not ask there was this real concern so as soon as people start to feel like they are a researcher rather participant and even if they're a participant i'm sure people have seen this in the room there's a concern that you're producing the data the researchers want is it good quality data is it showing what that what the researchers want to see in the data as soon as you start telling people what the aim is of a study are their head goes into that space and saying oh i should be adhering to these things people are wanting to find so this was a real issue for us and it it but it just meant we really had to embrace the fact there are researchers now not participants so in the end uh just another weird technical thing about these cameras the autographers had a usb slot on the top of them if you put your camera down on table someone else could pick it up plug it into the computer and they had all of your images there's no kind of encryption there's no password protection there's nothing on it it's literally a usb stick so one of our real concerns was people would put it down at lunch and then their whole day was captured there and ready for someone else to pick up and just have a a quick glance through um so we the the kind of coding app or the the time these diary app people had was sort of an encryption app wrapped up into that as well and we had to really get people into a habit of plugging their autographer into their phone and running that app quite regularly just so it encrypt their images over and over it seemed weird to me that wasn't built into the autographer it's literally just an open usb stick that anyone could pick up but that's autographers decision not ours so jumping on again this i think is probably the only thing that hasn't really been discussed today this was a more recent study i was only tangentially involved with this but was hearing the stories quite regularly so it's a nice case study this was a collaboration between computer scientists and social scientists to look at um deploying community-based wi-fi into a into a deprived communities the idea was they'd put together this uh technology package that if you had broadband in your home you could replace the route you had with this router and allow you to share your wi-fi with anyone else that's living nearby the idea was there were some people in that community that could afford a wanted broadband lots of people that couldn't afford broadband this was just a nice way to spread that uh digital capability throughout the community or at least that was the plan anyway um okay this is where so this is all about recruitment again we've heard some nice ways that recruitment has been done today this one probably wasn't well it definitely wasn't successful um so we just worked down the list so we had research students go out and knock on more than 2 000 doors in this community so it's a fairly big area um 730 of those were answered so people actually open the door and talk to them uh of those that that's not a bad number above 10% wanted in some way to be involved in the project um so actually more people asked sharers so we share us with the people that had broadband already and citizens were the ones that didn't have broadband but wanted to to you know start using it somehow um of people that then actually took in one of our hubs not very many that's fine 21 people uh obviously you have to have someone living living near you that wanted to use that wi-fi for it to make any sense so we ended up with 17 pairs of those and only two pairs actually took part in the study so we didn't have many people involved in this and out of more than 2 000 that's not great numbers but and part of the problem here was just it we sent people out basically cold calling on doors and that was a stupid idea in retrospect but it was a fairly short project this was the sort of interaction that happened i'll let you get to the end of that quote happened quite a lot and we we're talking 2017 in this case you'd think i don't know my my observations of the world now is that internet is pretty well embedded in in my everyday life i'd struggle to go without it now but for a lot of people in this community and this was a i mean nottingham doesn't really have inner city areas but it was an inner city area in nottingham and i think back on the previous slide does put the stat in there it's only about 70% penetration of broadband or any kind of internet access in that community so that's that's that's low of the uk now but not i guess not unheard of around nottingham that that wasn't the lowest level of penetration in a in a area of nottingham we didn't pick the lowest because we wanted one that we thought was going to be suitable but but still approachable so this slide's going to be offensive whichever side you fit on one of the main things to come out of this so we forget about the results because there weren't any results we know how to speak each other's language it works great this team didn't um so what you had was sociologists that were no longer being sent out to study they were being sent out to bug fix because this is the thing the sociologists had the great relationships with the a 17 people that wanted to be involved um we're learning about their daily lives we're learning about their struggles uh but we're having to do that alongside trying to fix their router and with all the will in the world we had some of our sociologists going out trying to kind of bug fix security certificate issues and things like that while listening at the same time to what was going on these people lives and it just wasn't working so the burden of that was really heavy and just made it not work other side things this is my side um turns out academic computer scientists don't know much about actually fixing bugs so academic computer scientists can talk at great length about these things and this is a neat quote this was from well this was from one of the computer scientists on the project and they were kind of trying to lay out why they shouldn't be responsible for fixing these routers and this was actually just trying to fix the and I guess everyone here probably knows this you put a wi-fi in your home it's actually really hard to know where you're going to get signal it's kind of a black art it's just it just is but the computer scientists were being held responsible for the fact that in this case someone next door couldn't share this person's wi-fi it's wasn't going through the walls and wi-fi just doesn't do that very well so they're kind of I guess their their response this was not all computer scientists the same even in computer science you get a whole load of people with different skills and no one has all the answers so we tried to break this down in three takeaways so the top one is I guess everyone knows this this is I think this is the Facebook saying that Facebook engine is move fast and break things that's their kind of philosophy why it's not their philosophy anymore because it'll be here but it was until about a year ago was that hey we're this cuskiness company and we'll just do things and some some good things will fall out some bad things will fall out and we'll keep the good things when you work with the deprived community that doesn't work you can't move fast and break things because hey you get kicked out pretty fast almost literally in some cases we've talked about kind of robust technology we've talked about needing to switch things on often people do that themselves often I think the work we do we put in technology that we think works fine in the lab sort of if we don't stress it too hard and kind of hope that people's houses will be that way they're not that way the other thing is that again this the kind of I feel like I assume now that I have all of these technical systems that work around me and they're all there so I can buy new products off the shelf and I have Wi-Fi I have a broadband connection I know it's got good uptime I've got my phone that I can connect to mobile internet and fix things if my broadband's not working there's hell of a lot of digital plumbing that goes around now to make these things work and there are people that don't have that digital plumbing around them so we were making assumptions like there was enough broadband around or there was always uptime or people had accounts on social media and things like that and it just wasn't the case here so we built all these assumptions into this technology that we deployed and then we're having to instill other things for people as well so we're having to go in and give them other technologies to fill in the gaps we're having to set up accounts of them they didn't have so we're having to do a lot of digital plumbing to make this work the final thing on this one so I said we had this sort of disparity between the two disciplines what we actually found was that the sociologist here was starting to buy into what the computer scientists were saying which is really dangerous on the best things on it on a cross disciplinary team is that you have some kind of scepticism of each other so you can say I have much more experience of doing field work your technology is not going to work it needs redesigning in this case that wasn't happening it was kind of getting sociologists that were becoming amazingly excited about these technical ideals that just weren't going to work in the field um that just wasn't a good thing and the sociologists ended up being as surprised at the technologist when it failed whereas if I think they felt they'd had that scepticism from the start they could have intervened much earlier and said this isn't going to work so saying no and saying you're stupid and saying you're impractical is not a bad thing in these projects I think and like I said I think it's one of the great things in having across disciplinary projects is you can be sceptical of each other so very last one this was us going back to energy consumption and actually before I go on to that the last thing about its previous approach I didn't even really mention how this was a sensing project but part of this was seeing how people shared this infrastructure but also seeing how the technology performed while it was sharing so it's one the technical side of this was saying if you've now got a in our ideal world 10 families sharing one wi-fi hotspot how well does that perform and having a discussion with people who are already hostile towards the idea of the internet about a hub that will monitor what you're doing and what other people are doing around you that again is another kicker for that initial discussion it doesn't work because it's about coming with a language in a way to to explain research studies to make it work so very last one c-tech like there's another energy project this was looking at energy consumption in workplaces and these are workplaces at least in the UK there's a it is regulated that that organizations with a certain number of people or a certain size building have to have half hourly metering so it's the kind of thing we're going to get through smart meters anyway which has been the case for commercial premises for a long time the problem with that is that really only mandates that you need one meter on a building of thousands of people capturing data every half an hour and if you have a thousand people's energy consumption on a single line on a graph you can't really tell much you can see when the working day starts and ends but that's about it so this project was all about trying to do some sort of social apportionment of energy consumption was to say right we've got that data great that doesn't really tell us much but can we get the people in the building start to break it down into manageable chunks and say who's responsible for what and how much more technology would do we need to put in to make that feasible so i'll see again breaking down a thousand persons data without any kind of extra help is really hard work but can you put in maybe one more meter or two more meters or 10 more meters to try and make that a manageable task so we took one building this is a council building it was an interesting building anyway so this is a picture of it behind so it's a very old building that's a listed building joined up with a very new building to make one new mega building so this was one of the sort of commonplace uk council things where they'd merged about three different councils into one and decided they need to expand one of the headquarters of those councils to make it suitable for all of them to live together so they've done this crazy new building that looked lovely but had one side that performed really badly one side that performed really well and for some reason it'd been given a energy rating of really well despite the building that was sort of leaking energy out of one half so they were having really bad performance in that building and we're going to lose their energy certificate because of it so they wanted us to step in and aid a research project would be trying to work out what was going wrong so what we did um yeah we're almost there so we kind of just showed them the day to start with and wanted to see whether if we get all the decision makers in the room it's not necessarily office staff but people were responsible for changing the systems in the building and deciding where people sat and what went on the building could they come up with anything so we took data from their building management system so again on the top we've already seen graphs like this so uh blue line is temperature outside green is the internal temperature and red is how much gas they're using so you can see when the temperature goes down they use more gas to maintain that nice uh static temperature which when you start to look at these you already wonder why isn't temperature really going down outside of working hours they need it on all the time but there's the sorts discussions we had uh we then stuck in a few meters and tried to sort of break down electricity consumption by part of the building and again just showed this to people uh it got a bit heated in this room so these are the people sitting around the table looking at these graphs and we tried to get people to sort of discuss with one another what they thought the energy was going on what was consuming the energy who was consuming it and just basically ended up in a big argument with lots of people saying what they thought consumed energy and other people saying you're stupid that makes no sense it's all about kind of these turn to a storytelling session but the end of each story someone said you're insane that doesn't consume energy there's lots of people shooting each other down what we did get were lots of errors in the data um and by errors i mean things like inefficiencies and inconsistencies in the data so we saw that the building was consuming energy at the weekend no one had ever seen that before but they there was gas being consumed so i say for no reason this building wasn't used at the weekend despite people telling stories about kind of legionnaires and things like that there was no reason for that to be on the weekend so they just switched off at the weekend and that was saving them 10 grand a month and it's kind of these things were kind of obvious since you looked at the data but that was the easy cases and with these projects you always get a few low hanging fruit at the start what we kind of moved on to uh what we ended up doing as well as them saving a bit of money we ended up with one very angry person or one very angry set of people which were the facilities managers are these people their remit is to look round and find inefficiencies and solve them in reality their job was to make everyone happy and try and tell everyone the right things they needed to know to keep them happy without ever causing big ripples or making really bad things happen they don't want to get people pissed off at other people that was basically their job was firefighting so all they did was sit in the middle and they had a very different language for talking to the managers to talking to the everyday members of staff and they felt like they'd created this wonderful equilibrium where no one was angry the building was going just enough for it to keep going and that was it and we'd stepped in and we'd shown people the data these people were theoretically building decisions on who were causing real problems for them it's kind of my takeaway for this one is when you get into big systems like a workplace you've got to be really careful about the ripples you make for this kind of thing we thought we're doing people a favour by showing this we thought the facilities managers would love it because hey this was their data and they could show people what data they were making these decisions on it turns out they weren't making decisions based on the data they were making on it who shouted at them the most and who had the biggest clout in the workplace so i think we're going to end there a few more slides but i feel like i've gone on for a while now this project continued and we i guess took a we basically end up identifying that man in the middle that person in the middle and them being the first people we talked to in the project so we went to a building we looked for that middle person and started off with them and the study became more about what they did and trying to build systems around them so i think that's where i'm going to end


Today, I've got some retirement certificates to present first of all. I'd like to call up Kurt Roster Kurt grew up in Sheboygan County graduating from Woosburg High School in 1986 after high school Kurt enrolled at UW Oshkosh where he earned a bachelor's degree in criminal justice while working at the Appleton Police Department as a CSO Kurt was then hired by the Sheboygan Police Department in May 29th of 1990. During this time with the department, he had the opportunity to serve as a DARE instructor, emergency response team operator and team leader, field training officer, crime scene reconstruction unit, Honor Guard, peer support team, and Meg Unit investigator. In 2005, Kurt was promoted to a lieutenant, was assigned at the Meg Unit commander. He was selected to attend and graduated from the United States Drug Enforcement Administration's Drug Unit Commanders Academy in 2005. He was later transferred to the Criminal Investigation Division as a lieutenant. Kurt was selected to attend the 129th administrative officers course at the Southern Police Institute in 2013 and graduated as a Dean Scholar. Recognition of earning a grade of A in each of the graduate courses that he was enrolled in. On August 4th of 2014, he was promoted to captain and assigned to the Criminal Investigation Division. Kurt has a long record of achievement within the department and has led or contributed to numerous process improvements within our investigative division. Additionally, he's well respected throughout the county and the state. He has been commended repeatedly throughout his career for his compassion that he demonstrates to victims. He has a strong sense of humor and an easy-going personality and is appreciated by all. Kurt shared with me that he is grateful to everyone in the department for the positive impact that they have had on him in his career. And he is proud of the work and grateful to have the opportunity to work with everyone in the city of Sheboygan. Chief Domagolski told me that he is grateful for the leadership, the service and hard work that Kurt has shared with the city and his department. His leadership, his willingness to take on challenges and the strength of his relationships throughout the state and this county have helped him to improve our investigative capabilities and eliminate silos and improve collaboration. Kurt, on behalf of the city, we're thankful for your 30 years of dedicated service and the positive contributions that you've made to the police department over this time. Please accept our best wishes for a happy retirement. I'd like to give you this certificate of appreciation to Kurt Rasser for his 30 years of dedicated service from May 29th of 1990 through January 29th of 2021. Kurt, thank you so much. Thank you.


Another running theme that I heard was that I've been to the farm where they produce the cheese and they let the calf stay with the mother and I'm like, okay, yeah, I see that. So I said, like, what happens to the mother when she no longer produces milk for the farmer like and becomes a nuisance? Do they go to the slaughterhouse? Well, I don't know about that. I said, okay, yeah, yeah, well, you know, that's the process. There's a fire inside your hut. And the cows are all, they're sexually abused as well to make it even more sick. Perverted. It's a word for it. How is everything going, Cristiano? Have some good conversations? Yeah. Yeah. So I've had some really good conversations. Again, she just started activism. It's really amazing, like, vegan starting activism. It's good. It's just as nearly as good as that. It's actually better than turning someone vegan because you can turn 1,000 people vegan with an activist. Yesterday I was talking with Yolanda about karma points. You know, when you turn someone vegan or vegetarian. Like, you earn some karma points, I guess. When you turn it, when you turn people into activists, though. Like, it multiplies. By 1,000. Exactly. Where's my karma points? When are they coming? I'm still waiting. No, only joking. God. Yeah. This is all plant-based. Made from plants, vegan, vegetarian vegetables. Everything from vegetarian. Everything from vegetables. Yeah. No, oh, vegan. No, animal. No, no. I can taste one of these. Yeah, yeah, yeah. This is sausage. Fromage. Good conversation. Some even better than I had yesterday on AV. Just different, a different type of conversation, you know. Scented around food. You've just got a different vocal point than the cruelty. That's just, you know, if you can get good tasting food without harming animals, why wouldn't we? Positive reinforcement. Yeah, exactly. Interesting. This is a great... You can't tell the difference? It's with... Spices, herbs? Spicy, yes. Yeah, salty, spicy. I don't go all positive about the taste. You know, you need a good blend. A good blend. But no one died. No one is murdered. No one is killed. No, no, no. No animals slaughterhouse. No slaughterhouse. No slaughterhouse. No blood and no organs. No organs, just plants. Better for the environment, better for the animals. Yes, yes, yes. Look at animals. And also for health. Better for health? Much better for health. For your heart? For your heart, only fat. You know the heart disease? No. Number one killer of human beings on earth. It's not due to vegetable fat. It's due to animal fat. Cholesterol? Yes. In animals? Bad colors. It's bad fat. Bad fat. Animal fat. Collects in your heart and arteries? Vegetable fat. Yeah. Quite better. So the animals they suffer and then the human beings eat and then they suffer with heart attack? I do understand. Yeah. I stopped eating. Animal. I stopped eating animal. Good work. Very, very positive. Yeah, no, it's cool. We're actually on this street because they sell lots of cheese and animals and we're just promoting a compassionate way of eating. A different way. No one was harmed. No. Yeah, we've got vegan foie gras. Obviously foie gras is they force feed the animals and cut their liver out. Yeah. You told me about that last night. But all animal products are inherently cruel and slaughterhouses only exist because we eat animal products and cheese and people actually don't connect the cheese to a slaughterhouse. No. But this whole slaughterhouse is dedicated to slaughtering dairy cows when they don't produce milk anymore. I do know that. Recently I stopped it. I think for health it could give a lot of improvement. And also I slimmed down doing this 12 kilos in four months. Wow. Amazing.  It has been one year. That's amazing. It has not been back due to don't eat meat. Meat? Wow. You're from the country. Yeah, I grew up on the farm. On the farm. For what reasons? It's just too cruel. It's too cruel. It's too cruel.  So they're suffering. And they suffer. And people forget that when you transport animals for slaughter they lose two, three kilos. Yeah. And drop their weight so fast. Do you think... So it's the suffering that... It's suffering. It's cruel.  Like they'll just drop it. They're scared. They're scared. They will literally just die out of fry. And that is cruel. They've mentioned that being like... I just don't agree with live exports. So in Australia we slaughter the sheep as well. Like before they go on...    And they are decapitated. And in those last moments of them, you know, suffering in their own blood. I would still say that that's suffering. Oh, yeah. 100%. Yeah. So like for the same reasons, like live export is bad. I would say eating lamb chops in Australia is still morally... It's still... Yeah, it's pretty cruel. I'm not going to... But there's like a spectrum of cruelty. You're saying there's... They're going to suffer for three weeks on a boat and then be murdered. But in Australia they only suffer for that 10 minutes. It's still... They're both still very cruel. But I'm probably more about... Like I don't agree with live export. I don't think as a first world country we should be doing that. Like we just don't agree with that at all. But that's just... That's just my opinion. So then you would agree that animals deserve moral treatment then?  So in a slaughter house, I would say that that's sort of inconsistent with moral treatment. Like even your own standard, like they deserve moral treatment. But when we put them in a slaughter house... Oh, yeah. Yeah. So that's what like veganism basically is. Red meat. No, no, no. Chicken, sometimes I cheat. It happens because it's so often chicken.    Anyone. Yes, birds. Anyone. Anyone. And birds are someone. Fish. Fish are someone. Yes. And they suffer, they're hooked out the ocean. They, you know... But eggs. What's about eggs? Eggs. Eggs are interesting because they breed the chicks, okay? The males don't lay eggs. So they'll be disposed of by the egg industry. They usually put them in a big blender in their first day of life. They kill them.  The females, they'll produce eggs. Okay? And when they can't produce eggs for the farmer anymore, they're useless. So they go to the slaughter house and they're killed. So same process. Unethical. And we don't need eggs and they're high in cholesterol. Yes. Very high in cholesterol, bad for your heart. Bad for the animals. So yellow over there. Yeah. So we stay away. Choose compassion.  So... Choose compassion. Yeah. It's like...  No way. I used to eat so much meat, all the... Every single meal. It's ingrained in this culture. Bacon, eggs for breakfast. Nearly every morning, huge omelettes and then I have chicken flesh and then I eat steak and I wouldn't even ever connect it to the animal. No. No. Even while I was... You asked me where animals come from. I'd say, oh, cow. But I never connected it with someone. Yeah. Like some animal probably had their own personality. Probably terrified on the way to the slaughter house, got killed. Didn't do any of that connection. Yeah.  Yeah. And I have the connection. Yeah. Which is something we all lose, I think. 100%. 100%. Yeah. No, it improves my health. Good. So being kind has improved your health. It did. There we go. I just have to go a little further. Really, it's like the deadline. Don't take even one gram. Don't take one gram. But I've done a lot of it. You don't miss out on anything from... Don't miss out on any flavor from... You don't miss out on any flavor or any taste. Beans and legumes and nuts and seeds and fruits and vegetables, pasta. It's just the meat, though. Well, the non-meat, actually. And you know, do you hang out in Australia a lot? Is that where you spend most of your time, Australia? We live in London now. Are we living in the UK now? Dude, I'm a UK citizen as well. I spend heaps of time in the UK. UK is one of the mechers for plant-based vegan stuff everywhere. Yeah. So... The Club and Commons got vegan places out there. There's an app called Happy Cow. Yeah. What you could do, possibly, is... There's a challenge that we recommend to people, and it's free, and it's on Facebook, and it's called Challenge 22. Challenge 22. Yeah. So if you go Challenge 22 on Facebook, and it will come up, and you can do a little three-week challenge and see if you can eat plant-based for those three weeks. Yeah. And then, like, there's a little forum so you can go, oh, look, I'm in the UK. Yeah. Where do I go? Yeah. Tesco, Sainsbury's have vegan options galore. Yeah. Where is that going to start? It's just ingrained meat. But you come to France. It's ingrained cheese and meat. You go to Asia. Yeah. Everywhere, yeah. Like, every single culture has baseline meat-eating ingrained. Yeah. But no one really has the connection between the animals, the suffering and blood in the slaughterhouse, and the plate. It's always happy farm plate. Oh, yeah. Yeah. That's not how it goes. It's not happy farm butchered in slaughterhouse. Yeah. You're eating the remains of someone who didn't want to die. That's okay. She took me. Oh, don't do me a bad word. No, you're famous now. You're famous. I have no time to do it, but she did it. It's good. You can be a good spokesman. You can be a good spokesman for plant-based, healthy, helping animals. But it's my experience. Yeah. Nobody told me. You should see for yourself. It's the way to go. Yeah. That's excellent. Now I go to sport before. 300 metres. No energy. Can't run. Yeah. Now 800 metres. Oh, I don't feel. Full of energy. I don't feel. Amazing. No, because there's no body parts in here. But I am lucky. My wife is cooking vegetables all the time. All the time. Lucky. No fat.  It's the same problem. For health, it's not good. But for the animals, oil is okay. As long as there's no animals. But if you want to be healthy as well, you cut out the oil. You want to be ethical? Cut out the animals? Just leap over consumption. Oh, it's just documentary in the U.S. And something like 30,000 pcs is slaughtered every day.  I think even one. Think about my dog at home. If someone killed that one animal, I would feel like that was a massive injustice. So 30,000 is horrible. But even one.  That one injustice for that poor animal, that one soul, when I could have eaten this sausage. So we just feel like it's so unnecessary. We're not starving. We're not trying to survive. We have all these other options. Yeah, more ethical ways. Thanks for... So what was it called again? Challenge 22. I was going to say Judge 22 there. Challenge 22. Peace. Interesting. One from a farm, one from Adelaide, and they live in London. London's a mecca for vegan stuff. We'll see how they go. You never know. They could be new vegans. And yeah, that's good. Well, I am going to ping pong, huh? Have good fun. Take care. Really nice to talk to you. Take care, my brother. You speak a good English. I'm from Australia. Mate. Cheers, mate. This is my mate. Cheers, buddy. Well, he's like so healthy from just cutting out some of the animal products. And I think we just gave him that push to cut out all of the animal products, which is what he needed. See, some people are almost already there, and if you give him that little push in the right direction, he's going to go off and he's so motivated. So motivated. And he's going to be a good spokesman for this message. So let's just hope that that seed flourishes into something amazing. You know, handing out food is... I guess people's senses going, they go, wow, this tastes good and murder tastes bad. Murder feels bad. This tastes good. Compassion tastes good. But the slaughterhouse footage should leave a bad taste in their mouth and the vegan cheese should leave a good taste in their mouth.