Hello, my name is Mark Yarbrough. This is Prasant Anbalagan. I'm Balagan. Yes, sorry. And we're going to co-present today on a machine learning use case in the program management problem domain. So this is targeted not at developers or people who are advanced in machine learning, but more of a walkthrough of an initial use case, grabbing some data, preparing it, seeing how practical machine learning would be for our use case. So I'm with the program management team at Red Hat. And our primary mission is to guide multiple product releases through development and test and through ship. And so this means there are a lot of dependencies across a lot of diverse systems. Frequently, always, those systems work differently and they don't talk to each other. So the problem we have is how to anticipate and manage risk for heterogeneous systems like that. And that turns out to be complicated by the fact that most of the target dates in play are constantly in flux. So we have to do ongoing risk analysis against a set of changing target dates. So how could you deal with a problem like that? Well, if you have a lot of systems that don't work together well, maybe you just go to one system and get rid of the problem that way. That's what we call the dream on scenario. You could also assign dependency management guru to understand how all the systems work and be able to manage them kind of manually. You could write some rules-based code in the form of scripts or plugins or other applications or you could try to point some machine learning at the problem. So that's what we're going to explore today. For our use case, is machine learning a viable option? Do we have the data that we would need to do the training that the machine learning would require to be effective? If we do have that data, what steps would we need to undertake as non-developers and non-data scientists to get it ready for a process? And then once we have some training data prepared, how would we get that into a training algorithm? And that's where we leaned on the AI Center of Excellence with Dr. Unbeluggin. OK, once we answer that question, then we would make a determination. Do we want to add machine learning to our risk analysis process? So what we'll do today is I'll talk a little bit about the problem space we have. You may see that it resembles a problem space that you know, or possibly not. Then we'll go through preparing the training data for the machine learning process. I'll go through the first pass in a fair amount of detail, and it won't be super technical. It's just showing you the practical steps that we actually did to get the data ready. And then if we have time, we'll talk a little bit about some of the iterations. Once you do the initial data prep, what do you want to consider doing next? How many times will you iterate? Then Prasat is going to talk more about the actual training using the libraries that do the training model development, how you measure to know when you're done, and how that fits into the AI library strategy. So let's look at the problem space. Let's start out simply. Let's say that you've got an issue tracking system. We'll just pick a generic system and say that in the hundreds of thousands of issues that could be in this issue tracking system, maybe one of them represents a new capability. And that's a new capability that you care about for whatever reason. So when I talk about these systems, some examples of that would be JIRA, Bugzilla, GitHub, or GitLab, Trello, Rally, and there are lots of others as well. Now just to complicate things, there's probably not one system. There's probably at least two. And within those systems, what they're trying to achieve is pretty much identical in function and different in every detail. So for example, in Bugzilla, a new capability might be represented by something called a future feature that would have a state. What state is the development of that future feature in? And it would be complemented by custom flags and other attributes that would tell you more about the state. In JIRA, you might have something called an issue type of feature request. It would have a combination of status and resolution that are roughly equivalent to the Bugzilla state, but of course different. And then you would have custom workflows and other attributes wrapped around that issue to tell you how it's doing. You could probably have one or more schedules that want to incorporate both of these new capabilities. And I've drawn them as dotted lines here to illustrate that you have a schedule, and the schedule is the milestone. Successful that milestone depends on both of these new capabilities being ready. But it's a dotted line because it turns out tracking system A, tracking system B, and the scheduling repository don't talk to each other. So we know that there's a dependency, but there's no easy way to link the dependency in a way that's predictable. So let's extend the problem set a little bit further for this one case and say, what if we were to abstract into a logical table all of the dependencies, or at least all of the critical dependencies, on new capabilities associated with this schedule. Then we could have a table that, at the very least, would need to contain a pointer to the new capability issue in the native tracking system. So that's why the line's no longer dotted. And it would need to be conditioned by the target date. When do we need this thing to be ready? So we need to know at least those two pieces of information. And then what we want to understand is, based on those two pieces of information, what is our risk at this moment? Well, it turns out there's not a schedule in flight. Usually there's on the order in our domain of 80 to 100, depending on how many, how granularly you want to get, there could be hundreds of schedules. So there could be hundreds of tables. And then just to spice things up, there's usually more than two systems. So at Red Hat, we use bugzilla. We have several JIRA instances. A lot of these have been in production for years or decades, have hundreds of thousands or millions of issues relating to community development and also product development. So now let's take a look at, given that problem domain, how would we say, OK, maybe we have a use case. We've heard about machine learning. How do we go from having a use case to figuring out if we're a viable candidate for machine learning? So this is where we conferred with the AI Center of Excellence, understood a little bit about facilities that were available, and then made a few early decisions. Now we're going to go through some slides that are fairly detailed about just the mechanics. This is not theory. This is not advanced. This is just the mechanics that we followed to get this data ready. But in a nutshell, we're going to be looking at the case where we need to find or fabricate a large data set to feed the machine learning algorithms. We don't really know beforehand how large the data set needs to be. We're guessing that 2 million records is probably great. We're guessing that 10 records is probably not enough. But we don't really know until we do some training how many we actually need. And just to finish that thought, we're going to be munging this data. So I'd like it to be human readable. I'd like to be able to look at it and understand what I'm looking at. But at the same time, by the time we get it into a machine learning algorithm, it's going to have to be in a format that that algorithm understands. So we're going to do this following example using no programming at all, just munging things around in a Google Sheet. So if you remember the picture we looked at that had the fairly complex problem domain, let's zoom in on one of the dependencies and one of the tables associated with one of the schedules. Remember that had a pointer to the issue. It had a target date. When do we need this thing to be ready? And what we want is the risk. So if we look at the transformations we're going to need to do, to train, we're going to need to create training samples. And we're going to need to provide a value for the risk to check the training. Later on, when we're doing prediction, we're going to transform the information in the same format. But what we want is the output of the risk. So just consider that as we look at the next few pictures. So of all those different systems that we've got, let's choose one. First one we choose. In this case is the JBoss JIRA instance. This has been in production at Red Hat for over 10 years for both community and product. Has hundreds of thousands of issues in it. We're talking about new capabilities, not bugs, not tasks, but new capabilities, things like a new API, a new protocol, something that your product may want to leverage in that product as soon as it's ready. So in JIRA, the rough mapping of new capabilities to issue types would be feature requests, business requirements, and enhancements. And because there's so many of these issues in the system, we're going to try just grabbing the ones in 2018. They are the newest ones. And we'll see if we get enough data. So this will be anything that was opened after January 2018. And we took the snapshot in November 2018. So we're getting almost a full year. And then we're getting a variety of states. Some of them may be open. Some of them may be in test. Some of them may have been rejected. So hopefully we're going to get enough data. And we're going to get a spread of values that'll be representative for a training set. So the next step after choosing the data source for our issues would be to go grab the data. This is like getting the first raw data that we're going to start munging. So in this case, we just went to the JIRA system and issued a JIRA JQL query to get all the feature requests, business requirements, and enhancements since the start of the year. And recall, this was issued in November 18. So we get that bucket of issues. We definitely want the status and the resolution. Because between the two of them, they kind of tell you the state of a JIRA. And there are probably other fields of interest. We probably want to know the priority of the JIRA. There could be other things like target release, due date, et cetera. So we probably want to grab things that make sense. We don't want to grab everything in the JIRA because there's a lot. Export all of those to a CSV file. Import that into a Google Sheet. And that's where we can begin to prepare the data for the training process. So we executed that query. We got 5,600 issues. Is that enough data? We don't know. It's less than 2 million. It's more than 10. Once we run the training, we'll find out if it's enough or not. So here's a detailed slide of what we did. The JIRA query showing the different issues that we got in the fields. Dump that to a Google Sheet. And then you can start to see, at a glance, some of the parameters we're going to be dealing with. The priority appears to be available for most of the JIRA. It could be important. The status is present for all of the issues. It's definitely important. Resolution sometimes is a complement to status. And you need both of them to know the actual state of the JIRA. But now we're starting to see an interesting pattern. For a lot of the cases, the resolution is not there yet. It's too early. They haven't filled it in. So we've got some data that's sparse. Important that's sparse. Some things that might also be interesting would be the creation date. We haven't really figured out how to use that yet. But it may matter if a JIRA has been open a year versus a day, in terms of its state. And then things that might be interesting, like target release. A quick glance tells us that while that might be useful information, we can't rely on it. It's too sparse. So we kind of throw that out to get started. So let's do our first pass at preparing the data that we grabbed. And so what we're going to do is ask Prasant what kind of training we're going to be doing. He's going to say we'll be doing supervised learning, linear regression. And so we need to target that as our format. And we're going to grab just to start the status and resolution, just to kind of calibrate the process. If we get this data ready for a machine learning algorithm, will we get a result that's useful? And our original query, that 5,600 records we got, turned out there were 30 separate and distinct values of status and 15 values of resolution. So once we convert that to the one-hot format that we talked about, and again, on the top is just a screenshot of a Google Sheet. We're just doing this directly in a Google Sheet, creating these one-hot values. So the green 30 columns on the left are the status values. 15 columns on the right are the resolution value, including blank or not available. And then we have to assign the risk that last column. We export that to a CSV file, which is shown on the bottom, so 5,600 plus lines of the zeros and ones that you see. Give that to Prasant for training. And he'll talk more about that. All right. There are various iterations that are in the presentation that you can review, but we're going to turn it over to Prasant now. Thank you, Mark. Oh, I feel like a news anchor with all the wires going out. So I'm Prasant, and I'm from the AI Center of Excellence team at Red Hat. And we have my wonderful teammates out here, so any questions directed to them? So we had Mark walk you through the first half of the process, when you're trying to move from a use case to a machine learning solution to address that particular problem. So that's more like a user's perspective. Like you come up with a problem definition and then prepare the data that you feel like is relevant to address that solution to that problem. So the next half of the journey is more like you put on the head of data scientist and see what approach do I select by approach. In simple terms, I mean, what statistical algorithm do I use? And then how do I validate it? Say the algorithm works fine on my current data set. Will it work on future data set? How do I test that? And then kind of go in a loop and keep improving the model until you get a certain prediction accuracy that fits your process. And once the model is ready and it works fine, then it's like handing it over to the team or that's more like a model serving, like where they start integrating that into their existing process. So how do you pick an approach to solve a particular use case? Well, you first start by looking at the data and see is it well defined? Like can I label the data? And do I know there's some kind of relationship across the different features, or the input and the output columns? So if you have that, then it's more like supervised learning. But if you don't have a good knowledge about the data, it's more about turns to unsupervised, where you're applying techniques to learn more about the data. So in this case, say even if you choose the model, you decide, OK, I'm going to use random forest or deep neural networks. So then you have to go implement the code, pick up the infrastructure to deploy it, and then train it, and then go to the process of serving it. So this is where AI library comes in place. So that's an open source collection of AI components. So that has pre-packaged machine learning algorithms and solutions, solutions for common use cases we found at Red Hat. So it not only comes with the algorithms, but it also has supporting libraries that lets you handle the deployment or the storage or other sub tasks that revolve around a typical machine learning experiment. So the AI library is part of a much bigger project called Open Data Hub, which is a machine learning as a service platform that's built on top of OpenShift and Kubernetes. So I'll come to, I'll explain AI library in detail like time permits at the end of this talk. But this is like the structure of AI library. I thought it would be cool to point out, yeah. So let's focus on the list of algorithms that's present in AI library at the moment. So for the data set that Mark handed over, like that was definitely the supervised learning because he cleaned out the data to make my life easier. So anytime in supervised learning if you are like predicting like a number, a real numerical value, the easy way to start is like regression that kind of tells you if there's like a linear or a non-linear pattern within your data. So what we did here, we used correlation analysis to kind of study the data that Mark handed over. So we found that there was like a strong linear relation between the input and the output column. So we decided, OK, we had to move from correlation analysis to regression because correlation analysis just tells you that there is a strong relation, but it doesn't have the predictive capabilities. So we had to implement the linear regression model. Yeah, so here's the graph that shows like what's a linear regression is. So a very simple case is you have like one input column and the output. And you're trying to see can the input predict the output, say like if the input increases, can I say like the output increases as well, or if the input decreases, can I say the output is going to decrease too? So in a simple case, you would have like one input column and one output column. But in our case, it was a little bit more than one. That's like close to 50 or so. And we had like to predict the risk value. So that ended up being a multivariate regression model, meaning that I had multiple input columns and I had to predict the risk or the output. So note that like the days left until deadline. So initially we thought it could be like it's going to be a time factor like the number of days. But then we kind of like turned it into like a binary data. So kind of like classified as is the deadline within two days, or is it within a week, or is it within a month? So you could actually go with a logistic regression if you think like it's a binary data and you're not actually fitting it straight in, but more like a squiggly line. But in this case, we knew that there was a linear relationship. So we implemented using scikit-learn in Python, linear regression model. And so that was the output. So all the coefficients that you see there, that goes with each of the input columns that you use in the data. OK, so now you have the model. How do you know it's good? So to start with, we divided the data set into like two buckets, like 80% and 20%. So 80% is like the data you use to learn the line that best fits your data. And the next 20% is what you use as a validation or a prediction data set. Can I take this model, apply it to the rest 20% and see if it fits good? Does it fit good? So how do you validate that? So there are like measures of predictive accuracy. But the simplest one being like, you just look at the predicted value and see how close it is to the actual value. So that's called an absolute error. And it has to be as close to zero as possible. And in our case, it turned out to be 0.09. So that was fairly good. So now we decided the approach. We had the model. It works on our data. And what's left? So we had to just hand it over to Mark so that he can integrate that into his product, or into his process. And also, we wanted to take out the algorithm or the model and push it into AI library so that any user who wants to use it for their use cases, not necessarily a risk analysis, but a regression model, he can just leverage this, send data to the AI library deployment and get the result out back. So let me, do we have time? So let me explain the workflow with AI library. So this is the simple serving model that we have. You save the data to a S3 compatible storage, then run the model, and then you could get the results. So we support any S3 compatible back ends like SAF, AWS. And the model itself, you could either run them as Python modules, or you could make. So we provide REST APIs to just trigger the machine learning code. And they get executed as jobs on a container application platform. And once the training or the prediction is done, the results get pushed back to the storage. So this is more like a sequence diagram. So the user would just save the data, like invoke the action to open Visk. And if it's going to be a fairly simple thing, like polling the status of your job, it's going to just open Visk, it's going to do that by itself. If not, it's going to hand over the job to OpenShift and execute it and push the results back to your storage. So if you want to learn more about all the other algorithms that's out in the AI library, you could go to opendatahub.io. And you'll find all the documentation over there. So note that Mark also mentioned that you could, in this first slide, he mentioned that you could also define a set of rules to find out if one input column could dictate an output column. So you could use association rule learning, but that's for a well-defined set of associations. For things that are too randomized, you need a more machine learning process to go for. So finally, come to the conclusion. I'm on time. Do you want to go? Yeah, so the conclusion, did this approach work, or did machine learning work for the program management example? Yes, to start with. Where do I say to start with is for the data set that we have or the details that we have, it was more like a certain level of abstraction and it did work. So now the next approach is to keep expanding the data and include as more and more details that we want to capture the entire process that the program management team is trying to work on and keep improving on the model. So it's more like a iterative process that you prepare the data, the approach, then test the model, get the results, and keep going in a loop. So yeah, so in this journey, moving from a use case to a machine learning solution, we kind of learned a few things. So I'll probably explain on the modeling. So when it comes to the approach, always go from the simple to the more complex. Now if you know, okay, it's a supervised learning. I want to use like a neural networks, deep neural networks, but it's go from the simple to the complex, life will be easier. And the validation is like the most important part. Like once you implement the neural network, don't put it on your resume or just don't go to your boss and tell you I've done it. Validate your model because that's the most important part. Does it work or not? And then keep improving like until you get to your expected accuracy levels. Okay, yeah, thank you.


Hi everyone, Joe for jaspyscasebreaks.com coming at you with 2020 Topps Chrome Update Series Baseball Sapphire Edition Random Team Break Number 6, just one box, and giving away the final 10 spots in that 5-star break which is coming up in a separate video. But first, we'll do the box break itself first, firstly. Let's roll it in randomize, a lot of firsts here, let's roll it in randomize, 3-in-a-5, 8 times for names and teams, 1, 2, 3, 4, 5, 6, 7, and 8th and final time, 3-in-a-5, once again 8 times for the teams, 1, 2, 3, 4, 5, 6, 7, and 8th and final time. Alright, so there's no trade windows in these little filler breaks, but here's the first half of the list right here, and here's the second half of the list right there, good luck. Sapphire is pretty nice, alright let's sort by column B by team, and let's just print this baby out, let's rip this open, and let's see what happens. And then different dice roll for the 10 spot giveaways there, there's the final printout, let's see what we got here, good luck, that 5 star coming up after this too, which would also be oodles of fun. Man I can't believe the angels just doing a first ballot hall of fame or dirty, just so unceremonious, they're just like yep you're gone, there's the autograph right there, times, looking for the big rookie, there he is again, looking for the big rookies here, didn't see any yet, there's Aaron Judge, Thomas, is that the green parallel? It is, Jose Ramirez, Francisco Lindore, 32 out of 45, got a shower time going on here for Cleveland, Christopher with the Indians, alright so nothing too crazy here but all cards shipping that sapphire, it's too nice not to, the autograph is going to be nice, Rhys Hoskins, everyone see that Monday night, almost had a walk off home run against the Mets, which would have been huge but it just bounced off the railing which was not according to the grounds rules, not a home run, no yellow line there or anything like that but there you go and then we got Eduardo Nunez for the Mets, alright so nice Rhys Hoskins auto for Matt, now let's flip back here, let's grab everyone's names from Robert down to DJ and everyone in between, there's the new dice, there's the new list and it's going to be top ten after five, top ten after a four and a one five times, good luck everybody, one, two, three, four and a one, fifth and final time, alright so from eleven on down, sad times, my apologies, we'll get them next time though, we'll get them next time, happy time to the top ten though, Sean, Matt, Carlos, Christopher, Luke, Zach, Christopher, Christopher, Steve and Jonathan, there you go, you're in that five star break, coming up in a separate video, I'll see you for that, jaskiescasebrakes.com


We want to prove how it is, I mean gender is a speech act. Before we move forward, I would like to explain one thing more, that in case of conversation analysis and speech act theory, both background identity factors, they have no relevance. But a direct relevance is possible when the participant, the speaker, mention them openly, clearly. Okay, in case of speech act theory, how identity is known? Because the speaker never introduces like conversation analysis, his or her identity, who is talking and what is the age, what is the class, what is the location, etc. For example, here we have discussed in the last module, you are hired. This utterance itself shows that the person is hired and hiring happens when a person works in some organization. The person is professional. So this is how we reach identity of the speaker. Now, we want to prove that gender is a speech act. Aston helps here to know this. He says that whenever we perform a speech act, actually this single act is composition of three acts and we perform these three acts simultaneously. First, we say something. This is called locationary act. We say he involves other modes of talking as well. We say, we speak, are we right, are we signed? We talk through signed language, through gestures, instead of words or sometimes we just use visuals. We talk through paintings, we talk through other things like that. So whatever ways we use, because here our concern is only with speech, we focus on it. It should have some information, some content. It should have accurate form with reference to grammar. The statement, the utterance that carries that statement should be grammatically correct and then it must have meaning. This is locationary act. Then what you say is some act. It carries your intention. It carries illocation. Intention or illocation and sometimes we also call it force, illocationary force. What we have done in uttering something, this is called illocationary act. We might have promised, we might have invited, we might have warned somebody, we might have advised somebody. So promising, inviting, advising, these are illocationary act. The third thing is when you have uttered something, what is the effect of that utterance on your address? This is called collocation or collocationary act.  This is a collocationary act and then that gives some effect that shows some effect and influence on the address. This is collocationary effect. For example, after your utterance, the address may agree, may reply, may take some action, may bring something for you or simply may remain silent or there is another possibility that the address leaves you alone. All these are collocations. Aston says that we are concerned only with illocation. He says, collocation is matter of grammar and semantic. This is outside our concern. And he says collocation, we don't need it because this is not in control of the speaker. So that's why he says when we talk about speech act, that only means illocationary act. But unless collocationary act is not considered, how can we say that what we intended was performed? Whether our promise is understood as promise? Our order is understood as order. Our advice is understood as advice. How can we do that unless we don't consider collocation? That's why collocation is necessary, essential and especially in the context of discourse, language and gender, we do need this collocation. It is the Pollocation Act that is the response of the address which is important to reach speaker's intentions because this is the way that takes us to illocation. Otherwise, intention is in mind. Nobody can directly reach that. Nobody can, we just only approximate it and the only way is that we note its effect on the address. This is also the way we can understand relationship between intention of the speaker and gender, the language use and gender. How? Because this conveys what the speaker thinks about gender. For example, I advise you where trousers, this is a trend, we call it a locution, this is what we say. Now it is an advice, this is the intention, we call it a locution. Now if the address is equal and where trousers? So this is Pollocation. After that we can understand that this was an advice but our task won't finish here with reference to language and gender. Why does this advice was given to the girl? It would tell us the gender ideology of the speaker. This is how Pollocation helps us to know the relationship between language and gender. The word advice indicates that the address is advice and the address he takes this advice then she wears it. The advice is gender, how do we know that? Because in some cultures, parents and elders and teachers they advise girls that you want to wear pants or knickers, you should wear trousers. So we conclude from this module that success of Speech X depends on the intended Pollocation. From Pollocation we can judge, we can assess whether a speech act is performed or not. And from Pollocations we also know whether our talk or speech act was gendered or not.


ve a direct relationship with that provider. So you have made a decision to interact with Uber Eats or DoorDash or whoever. They have your personal financial information and so then they make a delivery and you can tip them as you wish. The company like FedEx, our relationship is not with the recipient. Our relationship is with the vendor. If you buy something from Antiglass or Bookshop or Wayfair, they then have a relationship, excuse me, have a relationship with FedEx. So we would have to then go get your personal financial information in order to allow you to provide a tip. So there's a lot of discussion about privacy before. It's another, you know, state of California has moved very aggressively on privacy issues. So we would have to then go out and try and somehow get that information. There's a lot of rhetoric been said or in the ordinance that it's so easy to implement. It's really difficult to implement.  So that just has to go through FedEx payroll processing, has to have applicable taxes and wages taken out. So it's a lot different. I don't, I'm trying to be, we've made these points to most of you. I'm not trying not to be duplicative. I guess I took too much time with my personal anecdotes about my family stuff. So, you know, I guess the big thing is, you know, we, as we look at this, it's very difficult to do on a single city basis. We have to put big systems in place. If this is something that is really good for the drivers who make a very, very fair wage working for companies like FedEx and UPS and others, then it should be done on a larger basis statewide, nationally, not for a small jurisdiction like California, or, I mean, like Santa Cruz. Thank you. Thank you. Anyone online, Ms. Bush? We do have two hands raised. Okay, let's take the first one. Good evening. Person online, good evening. Good evening. Jillian Greenside here. It's a very interesting discussion on this item, and I learned quite a bit. And so I'd just like to share a situation of a friend of mine, and I wouldn't have thought about this had he not shared when, more in COVID times, when he would be making, you know, just above minimum wage and making 30 burritos by seven a.m. And the drivers would come and pick it up. And right now, most people do tip drives. Well, people I know anyway, and I do, but my friend shared with me, well, yeah, we do all the cookie, we get nothing. There's no sharing of that tip. So whether you think, you know, that, and I agree the company should be paying a livable wage, but it sounds like at least some of the drivers are not, you know, they're doing a lot better than my friend, who has probably made a hundred burritos by lunchtime. So I just wanted to share that, which complicates it more. But if there were going to, if this were passed and there was tipping, somehow, and this would just make, as the previous speaker said, it's complicated enough, just make it more complicated, but some way of sharing with the producers of whatever it is that the drivers are delivering to you. Thank you. Thank you. Anyone with us in chambers wish to make comments on this? Good evening, sir. Yeah, good evening.  But the previous caller actually made a good point. The cooks and stuff, the people gathering the food, you know, maybe it's just their job, if it's like a grocery store, or let's say Home Depot or something, but the restaurant workers, they're losing out on tips. And so it really is up to the individual as to whether or not they're gonna tip. My current housemate calls out for stuff a lot. Yeah, and my understanding is she includes the tip as part of the thing. I don't know how that's split with the driver or with the company. But this whole squeeze play that happened when the ScamDemic started, and I'm absolutely calling it ScamDemic, when you look at all the small businesses that were gone, gone, but the pot stores and the liquor stores and the big box stores, they stayed open. So, you know, I think it's great that certain companies pay their employees a living wage. Amazon doesn't. Amazon has an agreement with the US Postal Service. Every package that gets delivered by Amazon through the US Postal Service loses money. What is right about that? So once again, this is really just a personal choice. I'm not quite sure what to say, and this is one of the many things that makes Santa Cruz special that we're even debating this. Thanks. Thank you. Anyone else online, Ms. Bush, did we have two? We still have two, all right. Next person online. Good evening. I'm gonna be brief because if I speak too long, my geeky nerdy friends who have something to say on this issue will be jealous. This is kind of turning the house with the key to require these corporations to change their corporate culture, which they have probably because it gives them greater control over their expectations of their employees. I'm for giving cash tips, Renee.  Anyway, just kidding. The California driver benefit was that was something that they put, I feel like this may be kind of a spearhead in a move to do something like that where the, I mean, all told me it's a bit of a tax on people who use these services. I would have maybe $20, $40 more in my pocket if they had not added that. I mean, why is this different than PayPal? Why is this different than a cash app or what's the other one, Venmo or I don't know, Zell is the chase man, I don't know. Anyway, I guess the point is, yeah, it kind of subverts the expectation that certain workers are not tipped workers, whereas you'll tax, politicians will tax some tips, which I find kind of absurd already. Just as I said, I'm gonna not talk too long, so I'm gonna, that's all I have to say on that, but I feel like it's kind of overstepping. I mean, in some way, it's, I mean, you're really, really, Renee is right about it. There'll be legal challenges, and it seems kind of like a really big pill to climb to make a system that works. Okay, thanks. Well, thank you very much. And one more person online, correct, Ms. Bush? Good evening, person online. Yeah, hi, this is Garrett. Hey, I wasn't gonna speak to this, but it'll be real quick, but I personally am a little taken back when the POS terminal, when I'm going for takeout, in other words, receiving really no service, wants a tip and you have to hit other just to put zero in, I don't know about that. And then have you seen the video where somebody went into a 100% self-service like a 111 kind of thing and no people in the whole place and at the checkout, it asks for a tip. You see that one? Anyway, that's all I'm gonna say. Well, thank you, Ms. Bush, anyone else online? Matters back before the council. Ms. Watkins is recognized. Thank you, Mayor, and thank you to those who commented and came to share their comments. I just wanna say how I appreciate that this requires the courage of a council that certainly some of these policies definitely have a place at the state and federal level and often in one of the things I love so much about local policy is that we can be innovative and we can be forward thinking and often a leader in pursuing these types of innovations and types of policies at the local level. And so I hope that that could be the case for this and hopefully that we can continue to work on some of the wrinkles that may come about but knowing that in the end, it's putting money in the pockets of mainly minority people who are often traveling from lower income communities to deliver our packages. And if you want, you would have the option. And so it's not required, it's an option. Anyways, so with that, I'm happy to move the recommendation and I know I think Council Member Calantar Johnson also had something to add. There is a motion. Is there a second? Council Member Calantar. Miss Calantar Johnson seconds and you may open on your motion. I pretty much did, I apologize. I did that pre-motion. Quite all right, Miss Calantar Johnson is recognized. Thank you, yeah. I'll echo Council Member Watkins sentiments that we as a city have been leaders in looking at new ways of doing things and looking at upholding our value of equity and I think this really speaks to that. We have a pretty long runway of six months for implementation so I invite continued partnership and working with you, Mr. Montgomery and others so that we can figure out those wrinkles. And I think with that six month runway, we can sort out and not get into a situation where we'll accrue any kind of litigation fees. We can't control other people's habits. I also am a strong supporter of small businesses in our local downtown and our midtown businesses but we can't control other people's behaviors but what we can do is provide options for folks to compensate even further those who are being of service to our community. So I'm happy to second this and support this. Thank you for the debate or discussion. Miss Brown. Let's make a quick comment here because I did hear members of the public speak about some of their concerns which I also share. I mean we are talking about a system where it's very selective so in terms of it's not equitably distributed it's based on voluntary decisions by individual consumers and I recognize that there are, I started envisioning all of the scenarios in which I get deliveries. People talked about food service and I agree that those workers who participate in the production of that food should be also fairly compensated and should be included in tip polls but we're talking here today about or tonight about delivery of manufactured goods that are coming from all over the country and there are supply chain issues and so we can't really get, we can't capture that in something like this and so I just wanted to say that I remain conflicted but mildly supportive so thank you for bringing this forward. I hope that we can find a way to make it work and in a positive way without it affecting folks negatively. Yeah. For the debate or discussion Miss Brunner. Thank you. I appreciate the input we received here today as we continue, if this passes today that is exactly why we built in a six month grace period and we hope to continue working through and to see how this could be one option to support last leg drivers and delivery drivers. Thank you. Thank you. Further debate or discussion? I'll say this, I'm gonna do the, wrong thing for the right reasons. I'm gonna support this and what I mean by the wrong thing is I don't disagree with you on what the need is what you're trying to do and so on. This just cries out for state legislation just screams out for state legislation. If this was going to go in effect in 30 days then I'd be making a pretty strong case against it but that isn't what you're suggesting as to use council member Collin Tar-Johnson. The runway here I think is sufficient to say this is coming, get ready for it. That's sufficient time that if this proves to be nearly impossible to do, we can revisit this. So I'm gonna do the wrong thing for the right reasons. I'm gonna go ahead and vote for this and I do appreciate you bringing this. This is who you are. You got the hardest, biggest all outdoors. If it isn't about kids, it's about low wage it's always about something good. So thank you for bringing this to us. Clerk will call the roll. Council members Newton? Aye. Brown? Aye. Watkins? Aye. Brunner? Aye. Calentari-Johnson? Aye. Vice Mayor Golder? No. And Mayor Cooley? Aye. Motion passed and disordered. We are on item 26. This is municipal code amendments relating to flexible density units responding to modifications requested by the California Coastal Commission. Is there any staff report on this? Nope, ready to go? All right, matters before the council adopt a resolution and introduce for publication the ordinance. Questions? Any member of the public wish to give public comment on this item? Ms. Bush, anyone online? Matters back before the council. I'll recognize someone for a motion on this. I move the recommendation. Motion by council member Watkins. Any second by Mr. Newsom? I'm sorry, by Mr. Newsom. If that's all right, by Mr. Newsom. Is there a debate or discussion? Seeing and hearing none, the clerk will call the roll. Council member Newsom? Aye. Brown? Aye. Watkins? Aye. Brunner? Aye. Calentari-Johnson? Aye. Vice Mayor Golder? Aye. And Mayor Cooley? Aye. Motion passes and disordered. We're on item number 27. This is an ordinance relating to the energy code and other related matters. We've had this before us on a previous occasion. This is the second reading of the ordinance. We created a bit of a window here for folks who had testified under first reading if they wanted to come in and provide input. Speaking only for myself, I received input from two of the speakers who were here on a previous occasion. They have, at least in their communication with me, dropped their objection to this. Is there a question or comment here? Do we have any public comment on this item? Good evening, sir. Miss Bush, do we have anyone online? We do have one. Thank you. Good evening, sir. Yeah. Thank you, Tony. My name is, we're not here. My name's Silas James Ewing Whitman. You know, I read through most of this. I don't think it's a bad idea to require when the work is being done before the walls are sealed up to be set up to do other things. It does mean that the electrical needs need to really be looked at because electricity is just not near as efficient as oil. Certainly the most plentiful fluid on the planet next to water is oil. Fossil fuels is a complete fabrication. I've spoken about this before. So this is gonna move forward. I don't really have any objections, but you know, my understanding is 2% of the vehicles in the United States are electrical now. Now in 2021, when there was that cold snap in Texas for whatever reason, there were wind farms that were designed by individuals that made their billions from running concentration camps in China. They didn't work. And some people said that the electricity bills increased by 9,000%. That's quite a bit. So we're trying to electrify our system when most of the electricity is created by natural fuel sources like oil and coal that can all be claimed to be mostly very pure. It's just not the way it is. There's a study done and stopped in 1987 in Santa Cruz where they purified the nasty exhaust from coal-fired power plants, created an algae where 60% of that material was oil. That could be diesel, jet fuel, or kerosene. So what is really unfortunate is, not that I like really any of the presidents the past 50 years, but we had a two-year strategic supply of oil in the United States. Yeah, at the beginning of 2020. Now we have less than 17 days. So you know, I'm glad people have sweaters because the electrical needs are, it's a total control grid. One more thing. I went in to find out about some information from the building department. I was very thankful that they were open at 7.30 in the morning or 7.35 when I got there. They were locked out of the room because their electronics wouldn't let them in their own door. Fortunately, that's a pretty big building right over there and they got in some other way, but all this infrastructure that's being done that is electronically controlled. Can malfunction on accident or can malfunction on purpose? Thanks. Thank you, sir. Ms. Bush, anyone online? We do have someone. Let me ask the person who's online to please proceed. Good evening. Okay, hi, this is Derek. Oh, hey, Mayor, I gave you a letter. I was still objected. Anyway, the State of California has been throwing the towel in on individual rights for some years now. First, with ineffective anti-capitalist socialist rent control, which I regard as similar now to this EV building codes and electric ready extra costs, making gas usage more expensive, even though gas is 40% cheaper for energy than electricity. This is similar, but based instead on mistaken theories of a climate change crisis used to impose coercive over the line laws which cannot address inflation or control the climate of the earth when the only real cost problem is government corruption and traffic at spending and debt creation was its abuses of individual liberty. That cruise went over the top of the state's rent control with the large rent and freeze ordinance ignoring reality inflation can top 7% annually for years. Here we go again, similar defects exist here with your and the state's impositions of PV system mandates in new housing based on that TV logic that relies on dozens of questionable assumptions of future cost effectiveness.  Those rich studies cited to justify even more costs are at best a snapshot look of not even the current new housing reality but creations of very EV friendly model housing assuming all of it will be reality for the next 30 plus years. Their projections have already been proven false in just one year. Those 2022 studies with already sky high energy price and due to Biden that were much higher than just in 2020. Assume for instance gas prices this November this month would be 4.6% higher per year per years and interest rates would be 4% for 30 years. We now know for a fact current natural gas rates for this month even with the anti gas bias Biden's energy policies don't face all according to PG and E are 20% lower than those projections 86 per third versus 71.2 cents and Morgan interest rates are not 4% but they're 100% off at 8.1%. Electric rate increases of 1.8% seem not to acknowledge rates got jacked by around 40% since 2018 peak prices basis change and could happen again easily. Add in all the other variables of real projects versus electric friendly models and all the justifications aren't really so in the bank justified factual reality at all. It's what I call baffling with BS. I'm not that easily baffled. The upfront extra finance costs are just one more incremental government increase in housing costs without any real proof they will materialize savings for the next 30 years. Only socialist central planning needs mandates to justify the unjustifiable. I hope you read my letter on this item which has a lot more very interesting information. One item there shows states with excessive climate change mandates have the most expensive energy in the nation and California tops the list of the lower 48 states in energy. And it's not. Santa Cruz has a laughably mild climate. This needs to be tabled and more data taken on real projects analyzing the corners using real historic variations of the important variables of which these bias studies never did. Thanks. Thank you, sir. Anyone else? Anyone else online? We do have one more. Good evening, person online. Good evening. Yeah, hi. I just, I guess I just want to say that this is really similar to the, it may seem obtuse to compare it but it's real similar to the tipping issue. It's like the choices people make for their energy in their homes and of course there are consequences politically who has access to heat, who has access to clean water and, I mean, who, it's political but I just, I feel like it's often overreach. You know, when I see the council trying to mandate things, people are often sort of left without choices and Lazlo Toth is the fictional, Guido Sarducci character. He wrote all these letters and one of them was, he was sending a letter to government official saying, oh, we should get our energy from rocks. We should get our energy from rocks. And it's funny because it's funny because you can get energy from wind, you can get energy from solar panels and you can get, I don't know, I feel like those solar panels up at the the Emeline complex would be a great place to just build within the confines of it, build a shelter for people because they're, I mean, they're essentially structural and the whole thing, I don't know, the more that gets invested by taxpayers, the more it's sort of a very, very gray area as to like what's appropriate use for it. I mean, that's maybe a society we're heading towards with Teslas powering homes and trying to get away from, I don't know, people having bonfires at the beach because it pollutes too much or the state of California banning solvents that get rid of graffiti as if the graffiti wasn't equally obnoxious. I don't know, I have all kinds of opinions about it and I'm sure everyone else does. So again, time for me to not talk more, thanks. Thank you, sir. Anyone else online, Ms. Bush? Matters back before the council. I'll move the second reading. Ms. Brown moves, is there a second? I'll second that. Ms. Brunner seconds. Second reading of the ordinance. Debate or discussion. Seeing none, the clerk will call the roll. Council members Newsome. Aye. Brown. Aye. Brunner. Aye. Calentary Johnson. All right, aye. Vice Mayor Golder. Aye. Mayor Cooley. Aye. Motion passes and so ordered. We're on item 28. This is the election of the Vice Mayor for calendar year 2024. I will recognize Council Member Watkins for a motion. Sure. I'd like to nominate current Vice Mayor Golder to see if she'd be willing to continue another year. Is there a second? I'll second. Motion and a second. Are there other nominations? Debate or discussion? Seeing and hearing none, the clerk will call the roll. Council member Newsome. Aye. Brown. Aye. Brunner. Aye. Calentary Johnson. I think she said aye. Oh, thank you. Vice Mayor Golder. Aye. And Mayor Cooley. Aye. Thank you for agreeing to do that again. Thank you. Thank you. You did a fine job on that. Ms. Bush, for the matters to come before us. Mr. Condadi, any further matters to come before us? No further business. Seeing and hearing none, just very, very briefly. Ms. Calentary Johnson, we hope you get better soon. We miss your presence here. And so get better in a hurry. You were letting me participate in this way. Very good, very good. Thank you. Without further business before us, the Vice Mayor with deep reluctance moves to adjourn and Ms. Watkins with equal reluctance seconds the motion not debatable. Those in favor signify by saying aye. Aye. Opposed motion carries and so ordered. We stand adjourned. Look at you catching up all that. You good? That's good. Bravo.


TheCube at EMC World 2014 is brought to you by EMC. Redefine VCE, innovating the world's first converged infrastructure solution for private cloud computing. Brocade, say goodbye to the status quo and hello to Brocade. Okay, welcome back everyone here, live in Las Vegas for EMC World 2014. This is TheCube, our flagship program. We go out to the events, extract a signal from the noise. I'm John Burrier, founder of SiliconANGLE. I'm joined by co-host Dave Vellante of wikibond.org. Our next guest is Miroslav Kavensky. Did I get that? You got it. All right. It's Jim Iogue. Hey, welcome to TheCube. Thank you. Thanks, I had to be here. So one of the things about all Flash arrays is I don't believe you. The doubting Thomas is the people who are saying that's all BS, show me the numbers. Dave and I were just talking last segment about that. Flash is in prime time right now. People are loving it. And you guys have a million dollar giveaway, not giveaway contest, if someone does something. Tell us about, how do you test this stuff? I mean, like, if it's a new animal, I've never seen this animal before. How do I evaluate its success? If I got apples over here, I got orange over here, or I got a dog and cats, I mean, what? Well, you know, doing apples to apples comparisons for storage arrays is always a little tricky. But when it comes to testing Flash, it's probably even more different from how you would test a spinning disk array than most people are used to. And Flash is a different beast. You know, I'm sure this was kind of covered in a bunch of different segments over the years, but it behaves differently when it's new versus when it's been in production for a while. The latency tends to be really low. It's a random access media. And all of that has different implications for how you would go about testing that array. So what is the biggest thing that you focus in on right now? Do you build a comprehensive suite? How do you guys build it from the ground up? How do I trust it? All the above are the questions I would want to ask you. Great, so there's a lot of different tools out there for evaluating storage. What we've noticed is over the last few months, since we've been generally available, we've had increasing requests from customers to help them figure out how to evaluate an all-flash array that they're looking to purchase, whether it's from Extreme.io or evaluating different vendors. And what we've done is come up with a suite of tools. And that suite of tools is just using free software. It's freely available. And we built things around VD Bench. I'm not sure how familiar you are with that, but it's a tool that was actually developed at Sun, is now owned by Oracle. It's part of the Storage Performance Council suite and used in SNEA. So it's a very neutral tool to use for evaluating storage. And we liked it because it has a number of great criteria. One of the most important ones is it's actually very savvy around generating data that can work with storage efficiency algorithms, whether you need to dial in a certain D-dupe and compression factor or you want to avoid D-dupe and compression by accident. So that's why we selected VD Bench as the load generator, but we've done a bunch more around VD Bench. So we've written a number of scripts that can implement the IDC methodology for evaluating all flash arrays. What's the biggest difference, bottom line between flash arrays and the old way? What are the big issues to worry about? Well, one is how the performance looks out of the box. If you test a spinning disk array out of the box, it's going to look pretty much like how the array is going to look. But when you test a flash array brand new out of the box, the performance is going to be artificially high. And it's important to precondition that all flash array, which basically means making sure that you've written to all of the flash cells, making sure that the rate stripes or whatever the data protection scheme is has been aged so that it represents what the space will look like after it's been in production for a few months. So it's a false image, if you will, if you want to go right out of the box because you got to do a lot of preparation to kind of understand the media. Is that what you're saying or capabilities? I think the capabilities of the media change over time. And if you evaluated when it's brand new, you're going to get a very false impression of the capabilities of that box. When the media ages in, the performance of flash can drop to a third of its brand new out of the box performance. And the preconditioning is really important, but another factor is flash is fast. You can throw a bunch of flash into a box and there are solutions out there that do that. But just because the flash is fast doesn't mean it's going to be fast in all different conditions, under all different circumstances, under heavy load as well as light load. So if all you're doing is throwing a couple of VMs on there and kind of lightly tickling the box, you're not going to get a real impression of what this array can do. Can we go back to the objectives of the testing? So from a customer standpoint, what are the objectives? You're trying to just understand the performance, understand the performance in specific workloads, understand the durability, all of the above? I think all of the above, but where we seem to get a lot of different questions is around characterizing the performance of the array and comparing them, looking at the consistency of the behavior and performance. People buy flash arrays and pay kind of premium dollars for those flash arrays because they really care about low latency and they want that low latency to be predictable. That low latency needs to keep showing up no matter what you throw at that array, if there are spikes in the demand or if there are changing workloads, that latency still needs to be relatively low and the performance needs to be predictable across a broad range of different workloads. Okay, so the objective was not a hero benchmark, okay, we set that right away. You were setting out to develop something that a customer could actually use to predict the performance of the array and their environment. Yes, and the toolkit that we developed around VDBench tries all sorts of different workloads with an emphasis on random access because that's how people tend to deploy flash arrays and one of the things that's really cool about the toolkit is that it will automatically analyze the data and generate the graphs for you. So you get graphs and tables that show how the array will perform and it makes it real easy to compare one platform versus another. And so is that my, let's talk about the platform comparison. Am I trying to compare different all flash arrays? Am I trying to compare an all flash array relative to an old disk subsystem or a modern disk subsystem or a hybrid disk subsystem or any of those? Well, you know, it's a toolkit. You can point it pretty much at any storage array. It was really meant for testing and comparing different all flash arrays. So it emphasizes random performance. If you were going to compare, let's say a spinning disk array for streaming media, you'd probably test it in a slightly different way. So even though it has an emphasis on all flash, you could use it for a spinning disk array evaluation. You could look at how, let's say if you characterize your existing array and then characterize the all flash array that you're considering, you can compare the two and see how it'll do. So am I right, Mirself? When I look at such comparisons, I'm going to zero in on writes, right? Writes are always the problem. It's that we're the high old bottlenecks exist. So I presume with your testing you can vary the right intensity, is that right? Well, yeah, and it's actually programmed to automatically try different read-write ratios, different IO sizes. 10, 20, 30, 40, 50, if I want to go nuts. Exactly, and we built this to be kind of a enterprise reliable information tool. So honestly, it's not like the ZDNet benchmark that you can kind of download and run on your laptop. It's meant to be set up and run for over like two, three days of heavy IO intensive load on the array. And it will characterize a lot of different combinations and give you information about how the thing we will perform under stress in enterprise environment. So I'm envision, so in the metrics, I'm sure there's a gazillion metrics, but I'm simplifying it, right? I want to see IOPS maybe and I want to see latency. I want to see how that changes. He showed some graphs. I mean, Joe, I guess it was more David was showing some graphs today. And so what have you been able to find in terms of applying it? I couldn't agree with you more. Customers want consistent performance from a latency standpoint and they want, I guess sub two milliseconds. I think Joe even said sub one millisecond. Is that what you're sort of prescribing in the field? That's what we find tends to be a good match for all flash arrays. When someone actually has an application need for consistent sub millisecond, sub two millisecond latencies, we are a good fit. And along with that raw performance, we're bringing all sorts of data services around thin provisioning, deduplication, really awesome VAI enabled performance for virtualization. All of those are data services that come along with the great consistent performance. We have a question from the crowd. I want to ask you here. This is Dave Vellante from the crowd father. Ask about native flash architectures versus caching controllers. Well, so that's a good question, right? Because if I have a caching controller, I'm going to asynchronously trickle to disk and signal, use a mainframe term, device end when I hit the disk. Flash is different, right? How so, talk about that. It is different and I think probably one of the biggest differences is flash as a random access media is great for your entire data set, right? With caching controllers, part of what happens is you have a working set and when your working set is relatively small, it fits in cache and everything's golden. But working sets change, they shift over time. As you add and remove applications, the working set is going to be different. So when you're experiencing problems because the working set no longer fits into cache, you can either replace your array with something that has a larger cache or you can consider an all flash array. So many of the people that are coming to us or are coming and looking at all flash arrays have applications where the working set is very large and when you're evaluating an all flash array, you also want to be testing across a very large data set where the working set might not fit into a cache. I know we got to leave it there but where do I get this benchmark? How do I get it? It's up on the EMC community network. We have the URL and we can share it with you. It's publicly accessible right now. We've created some how-to videos to let everyone use it and we have pre-built VMs that makes it really easy to get started. Fantastic. What you guys are doing, we love data. We actually geek out on our data for our data science operation. We know what this is all. We need an engine to run it. Thank you for coming on. I really appreciate your commentary. Pre-conditioning that flash, understanding the capabilities, big part of it. You got to know the engine, the car you're driving, know the machinery before you push the envelope. This is theCUBE, we'll be right back after this short break. Great.


And Mendoza here with coach Johnson after the Stevenson Mustangs defeat the Newman Knights 4-1 and coach What do you attribute the quick start to I mean three goals in the first period one in the first minute of the game What did you tell your team before the game month-long layoff and they came out with their hair on fire? Yeah I think anytime that you get an opportunity to take a step back and refresh and reset after the first half that we had You know they came back ready to go and ready to start off a new half of the season a new year on the right On the right foot and so I think our focus was just playing like we can and playing like we wanted to and that's exactly what they did They were flying. They were keeping their feet moving. We were winning races to the puck. We were staying strong on our sticks It was just all-around great effort for 60 minutes and Chelsea Blackburn two goals She was everywhere offense defense Obviously you you come to kind of expect that from her, but what can you say about her performance tonight? Yeah, I mean I think that you can pretty much predict what you're gonna get from Chelsea Blackburn She's gonna give everything she has when she's out there and just a fun player to watch and she gives it her all and You know she finds her way on the score sheet one or the other and just a fun player all around. Thanks so much coach Congrats on the big one. Thank you


So, carbon dioxide. Why is it important to learn about it? Well, it's a product of one of the most important chemical reactions in the world, combustion, which is how a lot of people in the world heat their homes and power their cars. It also unfortunately contributes to the greenhouse effect and global warming. If you want to learn more, here's a video from our channel just for you. The carbon dioxide molecule is formed from one carbon atom and two oxygens. Now both these elements are in row two of the periodic table and have two electron shells. They are happiest when they have a full outer shell or octet of electrons. As an element, carbon only has four outer shell electrons and oxygen six. We get double covalent bonds between the atoms where two electrons from each atom are shared, making four bonding electrons in total. We draw this with a double straight line, like so. The two groups of bonding electrons in carbon dioxide repel each other and this keeps the oxygen atoms as far away from each other as possible. Because of this, we can say that the carbon dioxide bond angle is 180 degrees. Knowing what you know about the bonding in a water molecule and a carbon dioxide molecule, which do you think is going to be more reactive? Again, pause the video and resume once you've had a think. Well, the answer is that carbon dioxide is the less reactive molecule. Because it has two bonds with each oxygen, you need a lot more energy to break the atoms apart. Think of the covalent bonds as thin wires. To cut a bond between carbon and oxygen in carbon dioxide, you'd need to cut through two wires, which would take more effort than just cutting through one. Its strong double bonds make it very stable. And so whenever there are stray carbon and oxygen atoms flying about, they love to get together and form carbon dioxide. But in one way, though, carbon dioxide bonds are similar to water bonds. So here's a final little challenge. Can you think what this similarity might be? Here's a hint. Oxygen is a very electronegative element, but carbon much less so. Pause and resume once you've thought about it. The answer is that, like water, the bonds are polar covalent, making the carbon atom delta positive and the oxygen delta negative. Although, unlike water, carbon dioxide is not a polar molecule overall. Its linear geometry means that the two polar bonds effectively cancel each other out. So did you work that one out? Let's sum up. Carbon dioxide is made from one carbon atom forming polar covalent bonds with two oxygens. These bonds are at 180 degrees to each other and are stronger than the bonds of a water molecule, making carbon dioxide more stable and unreactive than water.


Welcome to the online module on air pollution and the world of work. In the first module, we aim to introduce how social and gender inequalities shape people and communities and even exposures to air pollution. To explore this, it will begin by introducing the parallels between social economic marginalization and higher levels of exposure to air pollution. It explains how those who are socio-economically marginalized are disproportionately exposed to air pollution. Then, the module will zoom into how gender and socio-economic identities shape exposure. It will introduce the concept of intersectionality and explain how an intersectional lens can help us understand differences in air pollution exposure. Lastly, the module will outline the structural drivers of air pollution. It will delve into the political economy of air pollution, which includes understanding how aspects such as existing urban development trajectories and air pollution governance systems intersect with pre-assisting structural inequalities that influence exposure. The module will include case studies from Southeast Asia as examples. In this section, let's discuss about inequitable exposure to air pollution. While air pollution impacts all of us, the cost of air pollution are not even. Different people are more exposed or vulnerable due to existing social and gender inequalities. Exposure to air pollution mirrors existing inequalities and vulnerability to air pollution varies based on socio-economic factors. In general, populations that are socio-economically disadvantaged tend to bear the brunt of air pollution. Poor populations are unequally exposed to air pollution based on where they're living, their occupation, and access to protective equipment. Those who are disproportionately exposed to chronic air pollution are often low-income households and outdoor workers, such as within the construction, transportation, and agriculture sectors, or in certain informal occupations who cannot insulate themselves from exposure. Furthermore, poor and or racially and ethnically marginalized populations are more likely to be concentrated near population sources due to a lack of choice and other systemic discriminations, such as occupational or housing discrimination. In addition, pre-existing poverty, gender, and energy inequities can lead to high levels of prolonged exposure of certain groups of women to household pollutants, such as from indoor cooking stoves. This phenomenon can be summarized by the concept of environmental inequality. This concept sheds light on the fact that more marginalized individuals, communities, and population groups are more likely to be exposed to environmental pollution. Environmental inequality is the outcome of multiple, multi-scale, and interacting factors, including socioeconomic inequalities, racial, ethnic, and gender discriminations, as well as the policies, systems, and norms and values that drive these. We now look at a case study on occupational air pollution exposure and how informal workers may be disproportionately exposed and vulnerable to its impacts. In Lao PDR, the grilled food sector is a rapidly growing part of the informal food economy. Grilled food restaurants, markets, and streetcarts in Vientiane uses open charcoal fire, which generates high levels of particulate matter, or PM2.5 pollution. In this part, we'll look more closely into how do gender and socioeconomic identities shape exposure. Specifically, how does gender inequalities create uneven exposures to air pollution? First of all, please consider the definition of gender and gender identities in this slide. Gender differentiated exposures to air pollution can be the outcomes of the gender division of labor and responsibilities, as well as an equal influence over decision-making around air pollution control. The gender division of labor refers to the allocation of different types of jobs or work to men and women, often based on pre-existing gender norms or stereotypes. Gender divisions of work and responsibilities, both within the household and in the workplace, can shape individual exposure to air pollution. For example, women tend to shoulder disproportionate responsibilities for domestic care work. This may increase the exposure to indoor air pollution, such as from cooking fuels. When family members get sick from air pollution, women also tend to be the ones taking time off work to stay home and take care of them. This can negatively impact their livelihoods, strategies, and income. Men can also be disproportionately impacted by air pollution in many contexts. For instance, men tend to be overrepresented in industries with high occupational air pollution, such as construction and mining. However, it is important to understand that along with gender, socioeconomic dimensions play a key role in mediating exposure to air pollution, as it influences aspects such as location, occupation, and ability to protect against air pollution. Therefore, intersectionality is an important concept that ties these aspects together. Intersectionality highlights how gender intersects with other social inequalities, such as ethnicity, class, race, age, disability, and geography legal status, to create different privileges or disadvantages. An intersectional gender lens is a frame of analysis in which to examine this phenomenon. Applying this concept to understanding air pollution exposure, we can see how gender in this intersection with other factors shapes people's exposure to and experiences with air pollution. To illustrate how gender and intersectional considerations play out in air pollution exposure, we now turn to a case study on Vietnam's craft villages. The study explores the socioeconomic disparities and gendered exposures to occupational air pollution. The last section will take a deep dive into the structural drivers of air pollution and the political economy of air pollution, which influence unequal exposures. In order to understand the root causes of unequal exposures to air pollution, it is important to understand the political economy of air pollution. This entails understanding the structural drivers of air pollution, which are a range of factors at both the macro and community level that perpetuates air pollution production. This includes but are not limited to aspects such as air pollution policy and regulations, urban planning trajectories and the growth of certain industries. The outcomes of these drivers have unequal impacts on different people groups based on pre-existing structural inequalities, which is when different people are attributed unequal statuses, roles, rights and opportunities based on biases in social institutions. Furthermore, when considering the political economy of air pollution, there is also the need to address the transboundary impacts of air pollution and its related governance frameworks. Oftentimes, air pollution is not confined within a specific territory and is transboundary in nature. For example, in Southeast Asia, Thailand's severe air pollution problem, particularly from industrial and agricultural emissions, is transboundary in nature, which extends beyond its borders and affects neighboring countries such as Cambodia, Laos, Myanmar, and Malaysia and vice versa. Transboundary air pollution challenges are likely to worsen in a context with unregulated air pollution policies, or policies that favor the expansion of certain industries, which are often driven by political or business elites. Historical and current urban planning decisions influences air pollution exposure. In many contexts, wealthier and or more powerful residents settle in areas that are far from pollution sources or less geographically hazardous. This is typically the outcome of hysterical and current urban development policies. For example, in Nairobi, Kenya, European colonists segregated the city based on race, where the colonists settled in high-altitude locations that are more sheltered from air pollution, as pollution particles tend to settle in low-lying areas. Today, upper and middle classes live in former European quarters that experience much lower air pollution than other parts of the city, whereas many poor residents live in informal settlements that are highly exposed. Furthermore, urban development plans that are geared towards attracting corporate investments often fail to refer to the lives of local residents. For instance, those living in informal settlements. This further reinforces disparities in environmental exposures and stratifies health and wealth outcomes. Secondly, the lack of certain essential services, such as waste collection systems and affordable electricity, can lead residents to have little choice but burn accumulated waste and use solid fuels to meet energy needs or to get rid of trash. This not only results in the rise of air pollution that endangers the health of families and communities, but is also environmentally hazardous. Finally, it is important to look at air pollution governance frameworks. The complexity of air quality governance, with multiple institutions and actors involved, can lead to challenges in coordination and effective policy enforcement. There can also be a lack of funding or expertise to make effective decision-making around air pollution. Furthermore, some policies can also have vested corporate and private interests. For example, policies that give high priorities to private cars but neglect the development of public transport systems can exacerbate congestion and increase vehicular emissions. Here are some key messages that you've learned from this module. Thank you for attending.