Hello everybody. It's a pleasure to be here. Today I'm gonna talk about practice, but first let's start talking a bit about myself, because my name is quite weird to pronounce, so I will just present myself. I am Guillen Rurambajaste. These are my Twitter and GitHub accounts. When I was younger I wanted to be a hacker, so that's why I studied telecommunications engineering. But for the last five years I've been more focused on learning Python and data science and trying to invent my own artificial intelligence algorithms. I'm currently working in Madrid source, which is a real good startup, but I am from Mallorca and I'm actually really proud to be one of the organizers of the meet-ups from BiData that we hold there. Why here? Well, now it works actually better. Do you hear me well? A few months ago, Bial Estella, who's a friend of mine, found inside the bmb.com, which is a website that periodically scraps Airbnb and makes the data available to the public. He wanted to study how Airbnb influences the real estate market, but he has no way to make full visualization about his findings. You can see here how inside the bmb collects data sets from all over the world, but mainly from North America and Europe. And, well, we also have some data sets from Australia and Hong Kong. Each one of these data sets contains 92 different columns with a very wide range of features such as how the information was scrapped, listing features about the daily price of a renting, how many bedrooms or bathrooms it has, and so forth. And also has location features like the GPS coordinates that I displayed on the map in the inside Airbnb webpage, the booking requirements that an owner makes to be able to rent his listing, and also information about how the people who rented the listing reviewed that listing. We will be using data from both Mallorca and Barcelona. And, well, each one of these data sets contains 14k columns and 17k columns respectively. So it's in the range of what a browser can handle easily. So this could not be considered big data. This is one of the visualizations that the inside the Airbnb webpage has, which shows how the Airbnb listings are distributed in Barcelona. Here, for example, you can see in green listings that correspond to a whole apartment or house in red. You can see the ones corresponding to one room, and there's a few ones here in blue that corresponds to rooms that I shared if you go to the website. You can see the same representation, but for data from Mallorca. And you can expect it to mean it actually works quite well when you have enough RAM and has Google tooltips and filtering options. But I think this is written in JavaScript, and this is EuroPython. So I wanted to make a tutorial on how to display this data.  So I wanted to make a tutorial for people that wanted to try to plot geographical locations, but had no idea how to start. So I can at least save them a few hours if they just download the repository and clone it. So today, we will talk about how to plot Google Maps using Bokeh, how to work with shapefiles, which are a data format to work with geographical locations, how we can plot the shapefiles using all of yous and geo views, and how we can use big data to make cool representations with data shader. So let's get the hand on the notebooks. These notebooks are meant to be used with any data set available in insert BNB, but as I am from Mallorca, I will be mainly using the data set from the island. So how many of you know Bokeh? Please raise your hand. Okay, there's a lot of people here. Well, the ones that you know, please check the documentation. It's an awesome package for making any kind of visualizations. And the ones who already know it will find it extremely easy to work with Google Maps. It is basically like building a regular figure object, but with additional features in order to indicate how we want our maps to be displayed. We will need to input the latitude and longitude coordinates of the center of the map, the zoom level ranging from one, which is for the whole world to like 20, which is the match zoom you can get on Google Maps. You also will need a JavaScript Google Maps API key, and you also need to state which kind of layout do you want your map to be displayed. For example, we have the roadmap layout, which is like the map that already know that we all know. We also have the satellite layout, which is more like a Google Earth style and the Hebrew layout. Then it is just like the satellite layout, but it also has an additional layer. Well, let's wait for the internet to load. Let's get the cable. This is the last layout, the terrain layout, which is almost like the roadmap layout, but with additional features that highlight terrain features. And the one that I wanted to show you before, which is the hybrid layout, is like just like the satellite one, but with an additional layer which shows roads and names of places. So once we have created our plot, we will go for the first example. In this example, I will try to recreate the plot from inside Airbnb. So now that we have the roadmap layout, we need to embed data on top of it. This embedding will be done by creating just a regular skater plot. Well, we will assign visual properties to each one of the points. So first, we will select the suitable color map in order to mimic the plot that we are going to replicate. So we will select a diverging green, white, red color map, and assign it three colors. And now we only need to create a skater plot where the X and Y coordinates of our plot will be the latitude and longitude coordinates from each one of the listings. And we get a pretty cool plot, which given the amount of code that took to build, looks actually pretty similar to the one that we had here. Of course, this can be improved, having filtered in kind of additional and more worked tooltips, but I think it's actually pretty good. Well, we're working with a number of points that can be managed by a browser easily, but sometimes it's coming handy to aggregate data over regions. So we cannot trust the region data that it's embedded in the dataset. We will have to download shapefile with all the geographic data of all the regions in Spain. Shapefile is a vector format for geographical data representations. Each one of the shapefiles can contain different records. And its record contains both a geometry and different attributes. This geometry thing is handled with shapefile, which is a Python library that allows to work with planar geometrical objects. And each one of the attributes is stored in the form of a Python dictionary. As we are getting all the regions in Spain, we would like to filter out that ones that do not correspond to Mallorca. So looking at the record attributes that we have, we find this code node three code, which turns out to be a standard way for representing geographical regions in Europe. Here you can see all the code nodes that belong to Europe, the level three. And it turns out that Mallorca is assigned the code ES532. So we only need to iterate through each one of our records and store the ones that belong to Mallorca. Now we have on the one hand all the shapefiles, all the shape records corresponding to the island and a lot of GPS coordinates. So if we want a match, one with the others, we will have to think some way to do it. What will we do? Okay. We will take the shapefile library and create a point object using the GPS coordinates of each one of the listings. Once we have created this point, we will be able to calculate distance between that point and each one of the regions that we already have stored. When we find one which distance is zero, we will just store the name of the region that we found inside the column of the data frame. This way, we will be able to aggregate the data by the regions that we just calculated. And now that we already have the shapefiles of the regions that we interested in plotting and the aggregated data, we will use all of us and zero views to plot it. Please raise your hand, the ones who know what is all of you. No one? Oh, great. And zero views, have you ever used it? Okay. They are actually two really cool libraries which allows to do really complex visualizations really easily and they can use as a backend, matlilyp, bokeh or plotly. And well, zero views is an extension of all of views that allows for also working with geographical data. So, we will load all of you and zero views and we will create a data set from the aggregated data frame that we had. Now, we only have to select the shapefiles that we want to plot, the data set and a series of attributes that will create the visualization. In this case, we will choose on which is the common key on we will join the data, a key from the attributes of the shapefiles and from a column in our data frame, value which is the column in the data frame that will be used to assign colors to one of the regions, index that are at least containing the name of the columns that will be used in the tooltip, group which is used to state the name of the plot and finally CRS which is the kind of projection that we will be using. In this case, it's plate query. Once we have learned that, we can use cell magics to state how we want the visual options of the plot to be displayed. For example, we can choose to eliminate viatsis, use a hover tool, the size of the plot or even how we want the colors to be mapped. Here, we can see how we started with a scalar plot of a lot of points and we were able to map them into this shape plot where each one of the regions is colored according to the number of listings that it has. For example, we can do more things with that like aggregating the data and coloring by different properties. Here we have a comparison, perfect. A comparison of the number of listings in each one of the regions in Mallorca against the median price per day that each region has. For example, here, which is Palma de Mallorca, the capital, has the most number of listings but it also turns out to be the most cheaper one. So, when you have a lot of money and want to be alone, just go to the northern part of the island to the end. And now, before moving on to data shader and big data, I wanted to show you one cool thing that my friend B.L. Stella did. He used the Barcelona dataset and various data sources to try to figure out how many listings in Airbnb there was compared to the total number of houses in Barcelona. Airbnb usually states that it cannot influence the real estate market because the proportion of listings that it has to the total number of, but to the total portion of the real estate market is really low. That is true unless you group the data by different neighborhoods. So, he first created a plot of all the different neighborhoods in Barcelona and then merged different data sources. He calculated the proportion of Airbnb listings to the total market of real estate market. And we find that, okay, it's true that if we take the whole city, the proportion of Airbnb listings are really low, but if we go to the center of the city, we found that, for example, in El Barrigotic, there's 14% of all the houses available that can be rented using Airbnb. And the colliding neighborhoods, you find that this proportion is about 10 or 11%. So, it really has a great impact if almost one in 10 houses in your neighborhood are occupied by tourists and available in Airbnb. Well, now that we've finished with HoloViews and GeoViews, let's talk a bit about DataShader. How many of you know DataShader? Have you ever heard this word before? Whoa, 8.5.1. Nice. Well, DataShader is actually a really good package which is meant to make easy loading big data. There's a lot of examples in the form of Jupyter Notebooks that you can just check here. But I will explain a bit its inner workings. It mainly consists on a three-step pipeline which allows to turn big data into images. How is this process accomplished? Well, first, we have a projection step in which we will define an image container. This image will be treated sort of a two-dimensional histogram and assign different bins. In the next step, the data will be aggregated in a meaningful way such as that can count or you can use any aggregation function you want in order to map the data into that bins that we created in the first step. And finally, we can choose how we want the visual properties of each one of the bins to be displayed. Using these three steps, we finally get an image which has all the data that we wanted aggregated. For example, if we wanted to show a DataShader representation of listings, in Mallorca, we first define a canvas as a projection, then we use CVS.point with the listings, X and Y-axis, and the function that we will use to aggregate the data. And finally, we pass that aggregated data and select the kind of color map that we will use. In this case, it's hot. And we will indicate that we want to map the colors logarithmically to each one of the bins. And then I also choose to have a light gray background, so it's easier to see. And this is what we get. In order to understand better how this binning process works, let's compare a regular skater plot with a representation of with a DataShader image. A moment, this, okay. So, as you can see here, where in this skater plot, a high density of points is just represented by a blue mass, DataShader allows to change the color according to the density of points. And if we zoom in, we can see how the bins, how the data is mapped into bins. These are each one of the bins created with an heuristic and each one is colored according to the number of points that fell into that bin. Of course, this is a pretty aesthetically unpleasant effect. So, if you change this dynamic attribute here to true, all the plotting paid line from DataShader will be recalculated each time. So, when you zoom in, these bins will automatically adjust to the data that is included in the image. You have to take into account that the coloring is also taken into account only the number of points that are included in the image and not the whole data set. So, when you're mapping data and zooming in, you can see how the map changes according to the points that are displayed. So, now that we already know how to display big data, with DataShader, we will explain how it can be overlaid on top of a map. Unfortunately, here, we do not have Google Maps. So, we cannot access the Google map plot like we did before, and we'll have to think some other way of doing it. In this case, we need to do a few steps, because we will be using something called tile sources, which is basically a class that, if you pass it a proper rule as a parameter, it will just download an image each time that it is called. So, in this case, creating the class argis from WMSTY tile source and passing this URL, we will later get an image representation. The downside of it is that it uses a different cartographic projection. So, if you want to use OpenStreetMap instead of Google Maps, we will have to transform the coordinates from one projection to the other. In this case, we have created a function that takes DataFrame as input and transforms the longitude and latitudes from the plate carry projection to the Google Mercator one. In order to create the plot, a map plot with DataShader, it's quite similar to the way that we did it with Google Maps, but you will need to figure out which are the coordinates that correspond to the center of the map. Sorry. Instead of the center of the map, you have to define the ranges of geographical coordinates that you want to have displayed. So, it takes a little bit of tweaking and it's not easy, but once you have found the right numbers, it looks like this. So, this map is not as cool as the Google Maps one, but at least you can still zoom in and all the pipeline is recalculated each time. And the more you zoom in, the cooler you get the map. You have to take into account that even though these are just 17k points, all the time it takes to aggregate the data and use it, it's pretty much the similar from this size to like 100 or 200 megabytes. But it's also building an image with big data, like using the touchy data set which is included in the examples that I showed you before, or the sensors data, oh, or the sensors dataset from the United States is actually quite fast. It's a bit painful to install all these examples, but you have a really nice tutorial there on how to do that. It takes a while to download all the data sets, but you should totally check them out because it's totally worth it. But, well, what are the downsides of using Data Shader to work with big data in the notebook? Well, unfortunately, everything related to interactivity does not work pretty well, unless you are using the options that are included in Data Shader to build widgets, but you have no total control over what you are plotting. For example, I tried to build here a dashboard that showed how aggregating data works with Data Shader, and if you are just using this image, it works quite well, but, for example, if you want to change how you aggregate the data, for example, if you want to aggregate the data by the match price of each one of the listings that fall inside each one of the bins, you get errors that you are not able to silence until you get it right and the image is recalculated. And, also, please do not use the reset tooltip because it messes everything up. Well, now it will be really nice. I can talk a bit more about JSON data and it can show you some examples, but I would really like you to ask questions. So, I will just finish my talk here and let you ask whatever you want. I'm not really an expert on this topic, so maybe you think of a question which is quite difficult. I won't be able to answer it and you can laugh at me. So, thank you very much for being here and it's been a pleasure to be talking about maps. Thank you. I would really like you to ask questions. I can keep talking if you want, because there have been, like, 27 minutes and I have more material, but please, come on, participate a bit. There's a lot of people in here. So, what would you like to know about plotting data on maps? Well, hands up, please. Stand up. That's better. My question. Thank you very much. It was very useful. My question is regarding the shapes when you painted, like, the shapes of, I don't know if it was the different regions of Mallorca and the density of points in that area. I had to do that at wall scale and from the files I've got, the precision was very accurate and the data was massive and it was impossible to plot anything. So, I mean, the shape of the United States, I don't know how many points there are there, but if you have, like, one point every two meters, it's, like, huge and I never managed to plot anything with that data. Do you have anything to just compress this or did you have to use anything like that? I think if you're using shapefiles, you are screwed, because I also try to do that with the whole regions in Spain, which is not a lot, but it was totally impossible, because I got a lot of lag and the browser just cannot handle so many points. So, you have to resort to data shader, but the good thing is that it can be used to plot data from all over the world. It actually has one example in which it's used data from satellites that shows really well how all this works, but, yeah, unfortunately, if you are using shapefiles, you are screwed, so you have to figure out some way. You could aggregate the data and pass it, try to create an image using data shader for the data you have aggregated, but it won't be easy. So, maybe you should contact James Bender, who's the main maintainer of these packages, and ask him how to do it. They actually answer really fast in GitHub, so when you open an issue, there will be a few hours until they respond. So, first are questions. It's a Python conference, so you presented Python solution. Is that the best technology to display such solutions, such information? I did not understand you, because when you talk with the mic here, it reverbs a lot. Can you please come down and ask me? Python conference, so you presented Python solution. Is that the best technology to display that, or is there some alternative? It depends on what you want to do. If you're using the notebook, you can achieve almost anything, but if you are a real pro and have a lot of investors worried about how you're displaying the data, maybe you should use JavaScript, because it will be easier to do some really handmade JavaScript tricks that are really specific for the kind of representation that you want. But if you want to build something a bit more general really easily, just fall back to all of you, because it's really easy, and it takes a few lines, of course, to do something really complicated. For example, the plot here, a few years ago, I tried to do something similar with plotting all the patches, and it was an example on the first versions of Bogey about the sense of the United States of America, and there were 70 lines of code which had to be really carefully thought, because it was really easy to mess up. So this is getting every day easier and easier to use. Now the most pro thing is JavaScript, but if you wait a couple of months or years, you will get the same things here in the notebook, and this can also be used using the Bogey server, so if you don't like the notebooks, you can try to do it there. It's a pain, but it can work. Come down. I almost heard you. Yeah, I would definitely use this to prototype the visualizations, and if you need something really specific, you can resort to JavaScript. But even with things like the tooltips, Bogey allows to play a lot with it, so you can embed arbitrary HTML code inside of them. So almost anything can be accomplished, but yes, if you are a pro, go to JavaScript. With the next piece, can you come downstairs? It's much better. We need the question on the video tool, and also he wants to understand you, so anyone with questions, please come down. Can you show us an example of how to visualize time series data, visualize time series data? Yeah, I don't know how to load it here, but there's an example. Here are a few examples. For example, here, she's a visualization. Let me zoom in a bit. On the population of some cities, you just enter a proper formatted data frame, and as I told you before, there are custom widgets that can be used with all of you, so you can use them to visualize how things are evolving in time on top of a map. Depending on the specific kind of visualization that we're going to do, I can point you some resources, but here you can find a few things about how to do that. And also, in the examples from all of you, I think there was, well, merging these two examples, you should be able to work with data maps and make cool dashboards. We can talk later about it if you want. Hello, thank you for inspiring talk. I got a question maybe unrelated to geodata, but is there any convenient way to create a custom shaped map, for instance, from a TFI, a TIF texture, to get the custom map view, for instance, for some game data, game level data? I mean, is there a way to create a custom map to load and visualize the data over it? Yeah, what kind of custom map are you referring to? I mean, for instance, you have an in-game level and you have a player activity data and you need to visualize it, so you have an actual geometry with some coordinates, so is there any way to load such a level right into these tools? Yeah, there are actually very different ways to deal with all these things, but there's a package called Cartopi, which allows you to get images in all of the coordinate representation that you need. Let me check for an example. Well, for example, here, there are different types of visualization using Cartopi. For example, you have, like, the whole warm-up here to be put with maplolip. Thinking to account that all the weird projections and all that, I'm not sure if it's work with Boke, maybe you have to do it by hand and be converting from one coordinate system to the other in order to then plot them as a skater plot, but Cartopi has not only everything you need to switch from one coordinate system to the other, but also has stored visual representations of maps, so in a similar way as we did with the Google map plot, you are able to select how you want your maps to be displayed. Wait a moment, let's see if I can find... Yeah, here you have a lot of different map representations, so it's really possible that you are able to find the one that you need, and if you're not, write James Benner, bother him. Okay, can you come please down for questions? Do you have one? No? You want me to talk to you with J.O.J. some data? No, that's just a joke, I won't. If it's possible to integrate OpenStreetMap with Boke, for instance? It should be possible, I have not tried it because I think that Google maps are way cooler, at least the point in which they are now, but I can look at it and tell you later. We'll try to search it, I have never done it myself before. Okay, next question too, any questions? Okay, can you come and speak to the microphone and directly to him, otherwise it's difficult to understand for him. Yeah, it's like the mic is for the people in YouTube, but if you use that I cannot hear you well from here, so... If you put the data into Boke, what do you get? Is it some kind of JavaScript or I didn't understand it really? I'm not understanding your question. I saw those Boke calls, something like this, and some map mysteriously appeared. What do you get if you put data into Boke? Is it some kind of JavaScript that you can put on a website? Yes. Well, if you wanted to study thoroughly all this data from Airbnb, first, go to the repository of my friend who's this one, they did an in-depth study on how data from Airbnb affects the market, and if you wanted to plot it outside the Jupiter notebook, you would have to use the Boke server. I don't like it because it's really difficult to configure for me at least, but it can work really well if you know what you're doing. So, you could also be using data shader, all-of-use and J-of-use, using Boke as a backend, and using the Boke server to render the plots, and then send them as HTML code and JavaScript into a regular website. Because Boke is using JavaScript as a backend, so if you're using the Boke server to render the plot, a plot from Boke looks like regular HTML code that you can embed in your website. Last chance for a question? Yeah, you can find all these notebooks on GitHub, so when you want to plot maps, just clone the repo and copy paste, and if you want to find, if you want to know the findings from my friend when he analyzed the Airbnb data, then go to this repository. Mine will be in my, what is this one? My username in GitHub is and the project is Insight Airbnb, and here you will be able to find everything that I explained here. Please come down side. Please speak to the microphone back directly to him. I wanted someone to ask me this. I did not forget to put the data into the repository. It was too big for a GitHub repo, so I have uploaded that into my Google Drive account, so I don't know which is the link, but I will put it in the repository, okay? I don't know what it is, not here, see? I have all the data inside the C file that I will commit to my repository when I finish talking about 10 minutes or so, okay? Last chance. Otherwise, get him for a coffee or so. It's been a pleasure having you as a public, because it's like the time that I have been asking questions in my entire life, so thank you very much, everyone.


Stool DNA testing has been shown to be more effective than traditional methods such as FIT and GFOBT in detecting colon cancer and advanced adenomas. However, there are currently no studies evaluating its effectiveness in a European context. Therefore, further research is needed to determine whether it should be incorporated into existing screening programs.


Good morning and welcome to the chart of the week video with me. David Madden today's date is Thursday the 23rd of July 2020 and the time has just gone 1150 British summer time and this week's chart of the week is Euro sterling and As we can see here from the upward trend from this nice upward this chart here Daily chart we can see there's been in a nice upward trend for the last number of months in fact last month In late June it is highest level Since March it's come off of it since But but we have been pushing higher yet again And this video is going to discuss potential price points to keep an eye out for in the medium In the short to medium term So the wider upward trend Is it remains intact and and so if we do press on higher from here We can look at retesting the highs of late June And that comes into play at zero spot 91 75 And should we take off that area? We could then be testing up towards this zone here in around zero spot 92 up towards zero spot 92 39 And if you go beyond that we could be looking at having a potentially up towards the 94 area If you do move to the downside Work we potentially find support from we could find some support from this blue line here the fifth Daily movie average and that comes into play at zero spot 89 69 So 80 zero spot 89 96 and we can see that this metric Only only a few days ago and also last Last week and also back in mid-May acted nicely as support and if a metric is acted Nicely as support of the past and makes it more likely it'll be Active support in the future, but obviously there are no guarantees So that's something something to keep an eye on and even if you do drop below the 50 moving average Support could be found from this yellow line here at the 100 a moving average And that comes into play at zero spot 89 26 Or we can see here that When the market had a an aggressive sell-off into uh in april into going to may the market didn't quite get down there But but it was going to got close enough to 100 a moving average But we can notice back in february in late february that metric actually has both Support resistance and then also support. Um, so if the metric once again has been important in the past It could be of importance in the future and even if you drop below that We could head back down towards this zone here down on zero spot 88 now if you aren't going to be trading euro starting today or any of the Euro euro and and or and starting crosses keep in mind that today is the last day of planned EU-UK negotiations in relation to what the UK's Post-transition period relationship with the european union is going to look like not to say that or the talks couldn't be announced But today is the final day for planned talks So we're always seeing a bit of a bit of pressure in the pound hence why we're seeing the the the euro continue in its upward trend because The traders are kind of anytime. There's been a kind of uncertainty of the UK winding up in a no-deal scenario. That is typically Pop pressure on the pound and therefore has pushed the euro higher I also want to talk about Is how the euro is fair against other currencies and how the pound is fair against other currencies because One of the things I like to look at is doll theory What doll theory basically says is that the averages must confirm each other what that essentially means Is if you're looking at a certain market for example euro sterling and you see it's an upward trend You're you're seeing that the pound The euro is gaining against the British pound But you also want to see is that true across other markets? Here is euro dollar euro dollar. Is it a nice upward trend? We're just we're not too far away from the one spot 16 mark Yesterday we traded above one spot 16. That was euro euro dollars highest level Since october 2018. So given the occasion of how strong euro dollar is So we're seeing the euro again it gained against the British pound But we're also seeing the euro gain against the US dollar. So it seems fairly clear that there's a broad Move to the upside in the in the euro. So therefore you can be more confident that the um The euro sterling is going to push higher, but obviously there are no guarantees This here is the pound sterling now And we can see in the last couple of sessions only on the only in tuesday pound dollar hit its highest level In over a month. So there is a bit of strength in the British pound But we can see if you look at the candles on the last couple of days The long wick on this candle the narrow body in this candle You know, it seems to me there's not indecision tied up with with the with pound dollar at the moment So if you know if you add it together we can see that the euro is gaining ground against the the British pound The euro is certainly gaining ground against the um The US dollar and euro sterling. I'm sorry and pound sterling seems to be a bit indecision. So We can say we can be more confident that this upward trend in euro sterling is going to continue But obviously there are no guarantees Thank you for listening. Um, stay safe. Have a good trading week and good luck


Welcome to Science Lab Equipment. Part 1. Equipment used for containing and dispensing chemicals. Each piece of lab equipment is designed to do a different job. Some, like the beaker shown to the right, are used mostly to hold liquids. And others, such as the spatula, are useful for transferring small amounts of solids. It is important to know the names and functions of different kinds of laboratory equipment so that experiments are done safely and experiments are completed successfully. A beaker can hold liquids or solids. Beakers are sometimes used for pouring chemicals into other containers. Heat resistant beakers can be used for heating liquids. There are several types of flasks, including the Erlenmeyer flask. The Erlenmeyer flask can be used to hold and heat liquids. This flask has a triangular shape when viewed from the side. Boiling or Florence flasks can be used to hold and heat liquids also. This type of flask looks round when viewed from the side. There are also several kinds of flasks that have special functions. Test tubes can hold smaller amounts of chemicals. Some test tubes are made of heat resistant glass and can be used for heating chemicals. The watch glass and the evaporating dish both allow the solvent of a small amount of solution to evaporate. Evaporation can be helped by careful heating. The crucible is made to withstand high temperatures as when heating solids and may have a cover. The mortar and pestle are used for making and mixing powders. A short stem funnel is used for pouring liquids into containers. A thistle tube is another type of funnel and is long to make sure liquids get to the bottom of a flask or bottle. A dropping bottle and dropping pipette are used to dispense small amounts of liquids. Reagent bottles hold larger amounts of chemicals until needed. Other bottles are designed for generating or collecting gases. Stoppers and caps help keep contents safely inside, so replace them after use. Small quantities of solids can be transferred with a spatula. It is often helpful to handle solids by placing them on filter paper. Now you should be able to identify these different kinds of laboratory equipment. Congratulations! You have completed this learning activity. This lab equipment.


Our first keynote this morning is by Attila Lazar, who's a research fellow at the University of Southampton, and his title is as you see up here. Attila, yeah, how to run this, but did you hear us? Okay, so you got it? Yeah, I got it, thank you. Well, good morning, everyone. During the last two days, we have heard many interesting presentations about complex system modeling and complex issues. Well, today I would like you to introduce to another kind of complex system modeling, which is gonna be quite different from the previous presentations in many aspects. One aspect is that we are working with multidisciplinary research teams and try to integrate the results together. And also that this project is ongoing currently, so we are just halfway through, so I can't provide you final results. I can only give you the vision, the tools that we are using, and what is gonna be the output, why we are doing all this hustle around this project. So that's the outline of my presentation. So I'm very briefly talking about the project and the integration names, showing some results, talking a little bit about uncertainty within the integrative tool and model testing and just wrap up very quickly. So this is a four year long project financed by the UKA, the UK Environmental Research Council, and the Economic and Social Research Council. The aim is to provide the novel agent tools to the Bangladesh decision makers to make more informed decisions. So this is a study is in the present. We're trying to look into the near future, so next 50 years, and try to do some policy scenarios within the work. We have a large consortium to do this ambitious job. We have 20 some partners from three different countries, and on a regular Bangladeshi consortium meeting, we have 60, 70 participants normally, so it's a large group to work with. So as I said, we are located in coastal Bangladesh, the tidal influence part of the Ganges-Brahma-Putscha-Magnar river data plane, and as a result, we have a focus on the coastal zone, but to be able to tell anything useful about the coastal zone, we have to really consider what is outside of the study area. So we are considering exogenous drivers where the Bangladeshi decision makers have no influence on, or at least very little influence on, like upstream floater version in China and in India, or climate change, some macroeconomic changes. We also consider the upstream river basin to provide boundary conditions to the study area, so we are doing detailed hydrological and sediment transport modeling. Also colleagues working on the Bay of Bengal modeling, what's going on with the tidal ranges, sea level rise, fishery productivity. When we do, yeah, sorry, I forgot to mention the Andagina's governance, which is the national scale of the project, that's where the Bangladeshi decision makers can make a difference too. So what policies they create, how subsidies are formulated, what they support, what is the flood protection initiatives, infrastructure development, so these aspects are considered to some extent in this project. And when we zoom down to the local scale to the study area, really we are interested in morphodynamic changes, land use, land cover changes, and also the changes of the productivity of the system, so the relationship of the environment, how that changes over time and space. But we don't stop at the biophysical environment, we are really would like to couple what is happening in the environment together with what is happening in the social system, how demographic changes are likely to take place, what are gonna be the likely market changes, and how security and livelihood and well-being are changing at the rural population, at household level. This slide is really just to point out two things, that governance is in working together with every project element, and from day one we have a stakeholder engagement with national level decision makers, they are working together on creating issues, what they are interested in, developing scenarios that we are test with the models, and when this integrative tool is developed, then we are gonna have an iterative learning loop together with the stakeholders, we try to learn together about the system, how it works. So just very briefly about the project elements because just to show you how multidisciplinary this project is, the first pillar of the project is governance research. Our lawyers from Dundee University assessing the laws and policies in Bangladesh, what are the gaps, implementation efficiencies, conflicts in between legislations, also working together with the stakeholders or during the entire project period. And then the second pillar is the social science, how I call it demographics, household economics bit. We collect primary data at household level, qualitatively and quantitatively about how they live. We also create population projections and create a statistical associative model that I will introduce a bit more detail later. And all these feed into a dynamic and quantitative understanding of what is happening on ground in terms of people and households. And the third pillar is the biophysical modeling, practically the environment and entire system, starting from climate models, projections, hydrological sediment transport models, modeling the Bay of Bengal, modeling the data plane with F3 command F3D. Those feed into the morphodynamic analysis and the land cover model and all these together affect the productivity models that we are applying in this project. But we don't stop here, obviously, we would like to integrate this knowledge together. So it is a very ambitious aim to bring these disciplines together, working at different scales, different timescales, spatial scales and create something useful, useful information for national level decision makers. So I just would like to highlight that we have a special focus. We have a hypothesis within this project that rural population in Bangladesh is highly depending on the ecosystem services. So the quality of the environment, they actively modify it and the quality is affecting their livelihood. So that's our hypothesis that we would like to test with this integrative tool and predict what is gonna be the impact of any change in the system. The change can be governance decision or climate change or environmental change or a change in the behavior of the people. As I said, it's a large ambitious project. We have multiple working groups working on different elements of the system and they are working together. So they pass information to each other, mostly as a unilateral flow of information. But what we would like to create with this integrative model is a more rapid assessment tool that encapsulates all this research that is going on within this project and create a dynamic framework which allows a forward-stepping feedback in the system and which allows a more rapid and more numerous testing of different scenarios. Yeah, so this work is mainly building on the in-depth research of our colleagues and trying to encapsulate and make the calculation faster. So how we do this, we are gonna use simple process-based models if they are simple enough to include in this integrative framework. If they are too complicated, like the DAF 3D model results, then we are aiming to reduce the complexity to Bayesian emulators. So just capture if the inputs change, how the output is gonna change and that is gonna be used in the integrative tool. Just keep in mind, we are trying to say something about the cost of changes. So some of these models, in the integrative tool, we don't need the detail, why is happening in that, what causes the change? We just need to know in the integrative tool if something change happens, how the output of that model is changing. So let me just briefly introduce this DeltaDM model framework, which stands for Delta Dynamic Integrative Emulator Model. It's designed to be a holistic tool and encapsulating both the biophysical environment, social behavior, livelihoods, and some governance issues. It's a meta-model which practically just harmonizes the run of the different model elements together in an efficient way and a harmonized way. And the model elements are gonna work at different spatial and temporal scale and have different complexities. Some of them just statistical relationships, some of them probabilistic emulators and some of them, especially for the household level decision model, is probably gonna be an agent-based type model. So actually this DeltaDM model is gonna have two versions. One is what I call a hybrid version. When we estimate the environmental changes with process-based models, some demographic changes considered governance actions and try to estimate how land-used land cover would change and using statistical associative model directly estimate livelihood and poverty changes. So this is based on, I will talk about this later. And then the second approach is a more process-based version. When we don't stop here, but we apply productivity models which feed into a household level livelihood model and that will give us indication about the well-being of the whole population. Hopefully these two approaches will support each other, but if not, again, we can learn something from the differences. So we will see in a year's time what is happening. So this hybrid model, as I said, is using this statistical associative relationship. And this statistical model is trained actually based on land use, land cover information, shown here, environmental quality information like soil serenization and poverty information which is coming from the census data that is available for three years over the last 30 years. And this is a map created based on the census data. The red means that it is high poverty, green means that it is relatively better off. It doesn't mean that they are rich, just better off not necessarily at heart or poverty. So what our colleagues do in Southampton actually applying a number of statistical techniques to create relationships in between those two, actually those three, we also consider actually, I forgot to put it on, the infrastructure and network, road networks. So here are two examples. The first one is really self-explanatory. So it is the fact the relationship between irrigated land and poverty. It is not surprising that there is a linear relationship. The more you irrigate, the higher your productivity and therefore you are getting better off. The second is a bit more thought provoking. If you have high larger of mangrove area around you or if you live in cities, you tend to be poorer. If you are in between, you tend to be better off again. We don't know why this is just a pure statistical relationship in between different input and output variables. But these are just two examples. There are other on salinity and other aspects that we would like to combine in a statistical model that will be applied in this data VM framework. The more process-based model is simplified here. So we will have again, the hydrology sediment transport models. We will emulate the coastal inundation and salinization. That will feed into a land use land cover model together with the demographic changes. We run productivity models, household level assumptions and we will estimate human well-being. This is a simplified version. This is a bit more in depth what is gonna go in going in this model. But there's not much time for that. So rather, I will show you some expected output. What kind of information we are gonna get out when the model is fully operational. So first is the demographic changes. We will be able to track in space and time how people move in between districts, how population changes in terms of sex and age group, distribution of the population. So this calculation is actually done at district level, we have nine districts, but the results are gonna be downscaled to union level, which is the smallest planning unit in Bangladesh, having an average surface area of about 26 square kilometers. And we have 655 unions within our study area. You will see some maps about that in a minute. Like here, so these are the unions and the output of the model is gonna be at union level. We not necessarily present the results to stakeholders at this level, but the calculations are done monthly and at union level in the more process-based approach. So what you can see here, actually it is the changes of crop productivity under just one scenario. If there is no irrigation and the soil salinization increases continuously over time, then you can see how the staple foods would change, the productivity of the staple food would change in space and time. We have about 36 different crops in our crop library, so we can do complicated cropping patterns with this model. But as I said, everything is preliminary. It has to be updated when the researchers, colleagues have updated, finalized their results. We can do household level indicators and indication what is going on with the people. So for example, we can do profit margin calculation that what is the fraction of the revenue that remains in your pocket when all the cost is paid. You can see that preliminary results shows a quite distinct difference in between regions of coastal Bangladesh. We can check this at agent types. So these are my farmer agents, large landowners, small landowners or shalcopers and landless laborers. Each of them have different characteristics and behavior how they operate. What you have to know here is one doesn't mean that they are rich, that means that they are okay. They don't have too much cost in relation to their income. When they close to zero, that means that they are in big trouble. So they need either loans, so loan schemes are gonna be important in this research and or they have to do multiple jobs to survive. And finally, some likely indicators that we are planning to include in this integrated framework output. These are just some of the indicators when on which my social scientist colleagues are working on. And these are the ones I have selected that are likely to be included in this framework as an output. So for example, income expenditure ratio, we are gonna have a routine to estimate the likely diet of a household based on wealth and background. And also based on we can estimate the calorie and protein intake and food insecurity and hunger periods over time because we are considering seasonal changes in the system. But there are also other indicators that we are thinking of. Some of them are just an empirical relationship developed based on primary data that we collect on the field. So uncertainty, well, as you can see, it's a complex model. It causes me a big headache how we are gonna do this formal uncertainty analysis of the model. But I have a simple plan so far that we are doing formal uncertainty analysis on each of the process based models, creating these tornado charts if they are not exist already. And we are gonna have an automated built in Monte Carlo sequence in the integrative framework. So when the integrative model run once, actually this crop model, crop productivity model is gonna be run 16 times varying those most important parameters and estimating the uncertainty around the mean output of the model. When we have a more complicated model like the F3D, as I said, we will simplify it to an emulator. And when we create the emulator, we keep track of the error around the mean output of that emulator. And again, that mean and the mean value, the error and the mean value is passed on in the model chain. And we're counting all the uncertainties all along the model chain. So when you have stakeholder agent types, you always have an envelope of response and an envelope of livelihood indicators. Validation is another key issue. We have known for a long time that we never be able to validate environmental models and no mere askation. Not if you are familiar with this paper. It's a theoretical paper, but it's worth reading if you're not aware of it. She argues that in life sciences, you will never be able to fully test and fully validate and fully verify your models. And this is certainly true for our case, which is not just pure biophysical processes governed by physics, but we also have people inside in. But still, we have to do some kind of validation and testing of this model. So the first and most obvious step is checking the code and then checking each model element separately. So productivity tools like the agriculture crop productivity tool, checking it against published lens suitability maps, published farmers yield data in statistical yearbooks and check if your results are resembles the observed pattern. If this is done, or for all the elements, we are moving on to the entire model, testing it in a full extent and checking more global variables like land cover changes, poverty levels, inundation as a result of a historical floodiment. So these testings are on the historical period. And the final step that we would like to do is really compare the hybrid and the process-based results because the hybrid statistical model is 100% representative for the study area. The census data is 100% representative. If they support each other, that's good. If not, we are trying to learn what makes the difference. And these results actually are discussed with the stakeholders. So every time we produce a result, we take back to the stakeholders for discussion and in the policy cycle, we will allow them to test ideas how they would tackle with some of the results that we have in the modeling. So we try to learn together with the stakeholders how the system works. But why do we do this? So this last slide that I was planning to show you, just give an indication what are the questions that we would like to answer and assess with this integrative tool and with all the in-depth research about what the colleagues are doing in our project. And to remind you with this integrative tool, we are not aiming for 100% accurate to three precision results. We are aiming just to give trends, likelihoods and robustness of different changes and different governance decisions on the different elements of the system. So these are just some of the questions that we had in mind when we started the project. What will be the extent of inland flooding on a hypothetical events, storm surge or river flooding? Where will be the isoline for threshold salinity lie for different crops in the future if we assume different scenarios, what will happen and different management interventions like more polders, less flow diversion or more flow diversion in India and so forth, and so forth, how productivity would change over time and space under different scenarios and how poverty levels would change in relation to productivity changes and environmental changes? What is gonna be the effect of further a more significant flow diversions and reduce sediment transport? Also, what does drive migration and where would you expect higher levels of migration in the study area? What would subsidies and remittances, whether do they make any difference to the poorest of the poor in coastal Bangladesh? And whether global commodity prices like diesel, rice, shrimp, do they have an effect on the lives and the environmental change in coastal Bangladesh? So these are just some of the questions. We have actually a four-page long document just listing different questions and grouping them into sections whether they are depending on just one discipline, so output of one research group or whether it requires a full integrative model to answer those questions. And finally, just a summary that we are aiming for a holistic generic tool which the methodology can be applied as well. Obviously, the details have to be updated. We are aiming to link the environmental change with livelihood change of the rural population in coastal Bangladesh. The in-depth research is still ongoing, so both the biophysical models are still being calibrated and tested. Hopefully we will have the final outputs by summer this year. The quantitative social research is just started and having the first results in the next month or so. So everything is ongoing and the integration is ongoing, so hopefully by the end of this year we will be able to start doing the simulations and running the full model. So the hybrid model actually is expected to be operational by November this year and the full process-based model early next year and we are hoping to start the policy cycle in March, April, next year in 2015. So that was it, a brief overview and the vision of our project and the elements of the project and I'm happy to answer any questions if you have. Thank you. Thanks, Attila. I have a couple of questions. First you said that my first question is about the uncertainty and you are doing these uncertainty and sensitivity analyses. Wondered how you intend to communicate that to both the Bangladeshi government as well as the stakeholders and if there's any sort of plan in place for how you might handle any pushback on the uncertainty that you produce in the models and the second question is about the stakeholders and once you go into the field and present your model results to them with or without the uncertainty, will you use their feedback as inputs or will you modify boundary conditions based on the stakeholders' comments? Yeah, difficult questions. So communicating uncertainty is always a difficult issue with stakeholders but we can't avoid. So your model result is as good as your input data and your assumptions. So how you can reduce it, you can partially reduce uncertainty if you work together developing your assumptions based on the stakeholders. We just had a stakeholder meeting three weeks ago in Bangladesh where we had about 80 participants from different ministries and national level organizations and research institutes and we are going through with all the assumptions that we make for business and usual in the future and we try to implement those assumptions like flow diversion rates or changing in diet habits of the people or every aspect. We have actually 104 items on the issues that the Bangladesh national level people identified that they are interested in. We can't model all of them. We try to include as much as we can in the model framework and using their knowledge on those issues, we are setting up the model. So that's how we work. So, but yeah, we have to communicate uncertainty because otherwise it can be misleading. Question is similar to the other one but I'm interested in the level of information particularly on that you're gaining and monitoring and particularly on local climate. And I note that the kind of precision that you're talking about for your interests is overlaps very much with mesoscale models as opposed to global models and getting information back and forth to the locals is very helpful for locals but also hugely helpful for those of us who are interested in understanding the climate and being able to get information back. So I'm curious what your method is of communicating data in that case. Communicating data to actually monitoring a long-term modern thing of any of the components that you have but also particularly of climate variables. Well, in terms of climate variables, we have a difficult situation because we were not able to get any climate data from Bangladesh. But we have a partner, the UK Met Office, and they provide us climate projections for both the historical period and for the future to 2100 actually, so it's much longer that's what we need. And we are using these actually to drive the models. So these are done-scaled regional model outputs of climate models. So we have selected three ensemble members of a 17-member ensemble and tailored to our needs. So those are the future flag changes and would highlight uncertainties. I'm not sure if I answered your question. We can talk about it later, but it has to do with monitoring, local monitoring of information, particularly taking advantage of cell phones and other ways of transferring information different scales. As I said, we don't use local monitoring data. We have a grid cell about 50 square kilometer and we have climate data at those cells. So that's what we use.


and Sergeant Fitzgerald of the Department of Justice are baffled by the trend of events in the airmail mystery. Sent to investigate a strange series of airmail robberies, Ms. Delroy and Fitzgerald find themselves involved in a startling crime which has just resulted in a murder. Andy Andrews, pilot for Trans-American Airlines, is found dead at the controls of his plane with a bullet wound in his head. Later, the Trans-American radio operator, Roberts, is found in his room at the airport, apparently a victim of suicide, but Ms. Delroy refuses the suicide theory. While investigating Roberts' death, Ernest Powers, general manager of the airline, reports someone snooping around in the radio transmitting room. Surprised by Ms. Delroy and Jimmy Gifford, a reporter, the man in the radio room escaped by airplane but is followed by Gifford in another ship. A new pilot, Lewis, has just disclosed an important bit of information for Ms. Delroy, as the two wait for news of Gifford's flight. Would you repeat that, please? Certainly. Andy Andrews is not dead. I'm the man you thought you found in that ship out in Devil's Canyon. But the sergeant knew Andrews. He identified him. The sergeant thought he knew Andrews. The man he knew as Andrews was the man you found dead in the canyon, but he is not the Andy Andrews who was transferred here from the eastern division of the line. I'm afraid I don't follow you, Mr.... Lewis, that name will suffice until this business is cleared up. How much do you know about this business, Mr. Lewis? Plenty. But there are also a few things I don't know. I was hoping we could get together on the facts. Perhaps if I knew your story, it would be helpful to both of us. It'll be hard to follow, Ms. Delroy. I believe I know more of this business than you seem to give me credit for. Very well. But for personal reasons, I'm going to ask you to keep my story in my presence here secret, at least for the time being. Very well. You can rely on my discretion. I am the same Andy Andrews that hung up the Los Angeles, New York record two years ago. I made quite a bit of money through promotion schemes following that flight. Then I got an offer from the Lufthansa Lines in Germany as a sort of advisory pilot. On completion of that contract, I returned to New York and applied for my old job with Trans-American. I found mostly a new crowd there. Who was this, Mr. Lewis? Three months ago. And the applications? Was approved because of my past record. Well, I was transferred to the Western Division, which had been just opened recently. And you came out here immediately? Yes. I'm coming to that part of the story. I'm warning you, you'll find it hard to believe. There will be no more difficult than several other features of this case. I was sent to Toledo to pick up a plane for use out here. I hopped by way of Chicago, St. Louis, Tulsa, Oklahoma City, and Albuquerque. I spent a night there, and the next morning, I left for here. And for some reason, you didn't show up here. Is that right? Very right, Ms. Delroy. About an hour out of Albuquerque, my motor cut out on me without warning. The ground was more or less level under me, so I set my ship down on the rim of a canyon. Devil's Canyon? Not the main canyon, but one of the many small ones leading into it. I'm sorry. Interrupted. Go ahead, please. I spent about 20 minutes trying to find out what was wrong with the motor. When a man comes up on horseback... On horseback? Yeah. He was dressed as a forest ranger. Did you learn his name? No, ma'am. Have you seen his man since? Only from a distance. Do you know who he is? Yes. He's the man you knew as Andy Andrews, the pilot whose body was found in that ship. I don't understand. Perhaps you'd better let me finish. This man came up and asked if he could help. I thanked him and went ahead with my work. He stood around a few minutes, and suddenly I saw him facing me with a gun. There was a flash of flame and a stinging pain through my left shoulder. That's all I remember. This man shot you without cause? He had a very good reason. What was that? He wanted my name and my position. Then Andrews was... I think I see a light. Please go ahead. When I regained consciousness, my pilot's license, identification papers, company transfer slips, and the ship were gone. I was badly hurt, but luckily the shot went high. I dug myself about two miles until I couldn't make it any further. When I woke up in a ranch house, there was a doctor attending me. And this man, whoever he was, thought you were dead? Yes, or he never would have left me. But I am back to run down the men responsible. I'm very glad that you decided to tell me this, Andrews. Well, that's the fingerprint, ma'am. Good fit. That fingerprint man, Burke, knows his business. He said he'd check them with his identification files and call us as soon as he finds out who they belong to. Very good, Sergeant. Oh, fit. Yes, ma'am. I want you to meet Andy Andrews. Oh, yeah. Huh? What's that, ma'am? This is Andy Andrews. Oh, what's the gag, Miss Delroy? You know Andrews is dead. That's what I thought, too, Sergeant. But it happens that we were both very wrong. Well, who was that guy then, ma'am? The one in the plane. If we knew the answer to that question, Sergeant, it would greatly assist us. Unfortunately, we don't know who he is. Of course, the identification's girl may be able to help us. Yeah, but where does this guy fit into the picture? Mr. Andrews? Yes, ma'am. It's a long story fit, but you'll probably be interested in knowing that Andrews was impersonated by someone who was connected in an important way with the mail robberies. Yeah? Yes, fit. And that is just one of the developments of the last five minutes. Well, how about Gifford, ma'am? You heard anything else? Nothing yet, Sergeant. I'd try again if I were you, Miss Delroy. You want me to call him on the radio again, ma'am? Yes, fit, by all means. How long has he been out? About 30 minutes, Sergeant. If he's still on his course, he'll just be nearing Devil's Canyon. Oh, the other switch, Sergeant? Yeah. Yes, yes. Metropolitan to Gifford. Metro to Gifford. How's everything, Jimmy? One thing's happened to him, I'm sure. I wish he'd never followed that other ship. Well, don't be alarmed, Miss Delroy. Try him again, Sergeant. Metropolitan Airport to Gifford. Metro to Gifford. Hello, Jimmy. Can you hear us? Go ahead. Hello, fit. Everything's still okay. I'm right on his tail about 1,000 feet up. Don't worry about me. Ask him where he is, fit. Yes, ma'am. Gifford. Miss Delroy wants to know where he are. Go ahead. How the heck should I know? Blanket's pitch up here. I'm keeping my eye on his navigation light. I'll follow him as far as he wants to go. Get his compass reading. I'll tell you where he is. Gifford, what's your compass reading? We'll give you your location. Still 41, fit. He's heading right out over that canyon unless a headwind is held him back. I know those past meals like a book. Yes, you've got to stop him. Tell him I won't let him do this. Tell him to turn back. That will be a ship leading him on. Yes, ma'am. Hey, Gifford. Miss Delroy says to turn back. You'd better take her advice, Gifford. That route is taking you right out over Devil's Canyon. Don't worry, sir. You're back. What's happened? Quick, fit. What's happened, Gifford? What's up, Jimmy?  It seems... There it goes again. It's gas tanks. He forgot to cut on his main tanks. He's run the emergency dry. Send him to cut on the main tanks, Sergeant. Quick before his motor quits cold. Cut in your main tanks, Gifford. Your main tanks aren't cut in. Oh, something else is wrong. I know what to call him, hit. Jimmy, are you okay? Answer us. Hurry. Are you okay, Jimmy? Something's up with short shooting. Fits, we've got to do something quick. Yeah, but what? We've got to get out there right away. But it's 90 miles, ma'am. Sergeant, get over to the other hangar quick. Get that new speed job out. I'll take you out to that canyon in 20 minutes. Hurry. Sure. I'll have it out for you. And Sergeant, check the gas and oil while you're warming it up. Do you think you can make it, Andrews? Sure, Miss. I'll get some parachute flares in case we need to land. And while I'm gone, you might scare up some artillery. I have a hunch we're going to need some. That's great, Andrews. Do hurry. Oh, where's the nearest phone? Over there, ma'am, on the desk. Oh, just a minute, Louis. Yeah? Hello? Is this the Forestry Service? Give me the station in Devil's Canyon. Say, Louis, get a couple of airshoots, too. Okay. Hello? This is Irene Delroy, Department of Justice. Listen, Jimmy Gifford is in a plane somewhere up there in your vicinity. If you see any parachute flares, get over there as quickly as you can. We'll be right out. All right. Just a second. I'm calling Chief Robertson. It'll just take a minute. Hello, Chief Robertson? This is Irene Delroy. Yes. Sergeant Fitzgerald and I are leaving for Devil's Canyon by air right now. What's that? Oh, yes. I know it's dangerous. Listen. I have all the information on the airmail case in the safety department. Here's the number. 3443 Security Branch. That's it. If we're not back by noon tomorrow, I want you to arrest... Uh, his name is on the John Doe warrant. Good. Thank you, Chief. Sounds like you're expecting some trouble, Miss Delroy. This business is all very uncertain. Are you ready? All set. This way. We'll take a shortcut through the shop. I hope the sergeant has the ship off. Just why are you so interested in this case, Andrews? I have a right to get revenge, haven't I? Here's the outside door. I see Fitzgerald's on the job. The ship is over here. Hurry, Miss Delroy. Fitzgerald is a very capable officer. The good-trying at times is very reliable. He's evidently going with us. I see he has helmets and goggles. Can you take him? I'd like to have him come along as possible. Sure, the ship's licensed before. It'll be easy. Nice work, Fitz. How's the gas and oil? Okay, I just finished checking them. Good. Climb in here and we're off. I'll give you a hand, Miss Delroy. All set. Get that belt for Miss Delroy, Fitz. All clear over there, Sergeant? All clear. Okay. Here we go. That's the hill beacon to your left. You hope nothing has happened to Jimmy? He'll be all right, ma'am. He's okay up there. Hey, what's wrong with the motor? It's stopping, Fitz. Hey, Andrews, what's wrong? Gas line broken. Gas is flying all over the ship. We'd have to try a landing. It's not here you won't. Look at those models. They're never making it.  They've got to try it. We're not down yet. Get back to that door, Miss Delroy. Fitz, come back here. What are you going to do? Get back in the ship, Fitz. None of the doors. We wouldn't have a source of a chance when we hit that mountain. Don't jump, Fitz. Don't jump. Jump. I'm going down under that wing and fix that feed line.


Brainard Police Chief Corky McQuiston says a social media threat made toward BHS students is not directed at Brainard High School students. The threat was circulating on Snapchat yesterday and created concern in the Brainard community. But McQuiston says the identical post has been received in other areas of the country with other schools that are also identified as BHS. McQuiston says law enforcement has no reason to think it's a credible threat on Brainard High School, but as a precaution there wasn't increased police presence at Brainard High today. If you enjoyed this segment of Lakeland News, please consider making a tax-deductible contribution to Lakeland PBS.


We're here at the ITU Plenary Potentialy Conference 2014 in Busan in the Republic of Korea and I'm very pleased to be joined by Musa Abdullah, who is from the Kingdom of Bahrain and is also chair of the Working Group plenary during PP14. Musa, thank you very much for the evening with us today. Thank you for having me, Max. It was a pleasure to be here. I'd like to start off by talking about your workers chairing the Working Group at the plenary. Can you explain briefly the main tasks and responsibilities of this working group within the context of the overall conference? The Plenary Potentialy Conference is obviously the highest decision-making body of the ITU and there are three substantive committees. Two of them deal with matters that are mostly internal to the ITU, whether administrative or legal or policy. My group, the Working Group of the plenary, deals with most policy and legal matters that are external to the ITU and are of relevance to the member states. So it would handle most of the sensitive topics that member states are trying to push and address. Now many people are remarking on a very positive atmosphere and a spirit of collaboration here at the Plain Potentialy Conference. Is this something that you also sensed in your workers chair of the Working Group of the plenary? Oh, most certainly. The attitude has been immensely supportive, immensely positive and I think it reflects it in the work of the Working Group of the plenary. We've managed to submit all our texts to the plenary for final approval and there have been very few comments which I think strongly shows that everyone's in agreement and everyone's happy with the outcomes. Now, I wanted to ask you, what did you find most challenging during your workers chair of the Working Group of the plenary? I know that consensus building is a hallmark of ITU conferences, but can you provide some insights into the consensus building that took place here at the Working Group of the plenary? And also I know that people, as an aside, people have remarked a bit about your age and how young you are, but I know there's also been a good push for young people's involvement here at the Plain Potentialy. I think what I'll do is I'll answer that in two parts. With regards to the first part and the toughest aspect of the work is the fact that we have over 190 member states, which means that you potentially have over 190 different positions, which you need to respect. These are of concern to the membership. So every position is valid and you need to find a way through the middle of all of that whereby everyone is comfortable with the outcomes. And even if you can't address some of the concerns raised, you need to at least convey that you respect, you understand and you empathize, but that the global community may or may not agree. So reaching that level of compromise and consensus is very difficult and a lot of it is based on communication, negotiation, mutual respect, and keeping the channels open. ITU, even though it looks very formal when you're watching the webcasts, a lot of the work is done based on personal relationships. Once you know the delegates, then you also know how to deal with them. It's not just a meeting of policies, it's also a meeting of personalities. So I think it's the most challenging thing is understanding them, understanding their concerns, then addressing them respectfully. And yes, there have been a few comments about my age. For the record, I'm 34. But I actually don't think that's unusual. What I do think is unusual is that others are finding it unusual, which it would seem to me that we don't have enough of the younger generation involved in the work of the ITU. And I note that there is a new resolution that is being approved in this conference on specifically youth and their engagement. And I'd also like to highlight that a lot of the delegations have people in the same age bracket and they are contributing immensely. I mean, their arguments are fantastic. The digital age has really pushed communication out there. So I think we need to re-examine how we perceive things and understand that age is not necessarily the biggest indicator of experience anymore. So I think there are a lot of young people in this conference who will surprise you. So Dilla, thank you very much in keeping with us today. Thank you very much for having me, Max. It's a pleasure.