So let's talk now about low power modes and let's see what support for low power modes free Arto's has Normally, it's not a very friendly environment because it gives you a periodic interrupt every millisecond So even if you would sleep you would still wake up quite often which means Setting up the oscillator running at some frequency may be setting up another wake-up period and This all eats Especially from battery So free Arto's in its default configuration isn't really battery friendly However, it doesn't prevent you from configuring it into much nicer state What we can do we can enable a tick less idle mode What it does When we enter the idle we don't have any other task to do its job So we know that during this time We don't have anything to do and we can spare the energy You know that the microcontrollers can spare the energy by shutting down unused peripherals clocks flash memory and so on and Among that because the wake-up time can be significantly long Compared to the runtime We will benefit if we are able to disable the periodic wake-up So if we enable the tick less idle The kernel will call any time it gets into the idle mode. It will call the function suppress ticks and sleep so we have to implement this macro and It's our responsibility to set up the microcontroller in the mode appropriate to the required current consumption So it can be a sleep Or it can be even a stop mode If you are able to wake up the RTOS After a specific time based for example on a real-time clock or Based on a communication from some peripheral like a UART a Ternet SPI or something else we can disable the microcontrollers clocks and We can drop down from milliamps To just couple of microamps or even lower within this macro We can enter in the sleep or stop modes But we have to as well be careful about activating our wake-up sources Especially if you enter a stop mode You will need something as in her nose to wake you up because without internal clock no timer runs No communication peripherals if it doesn't have extra support for a stop mode So we must be able to set up the appropriate external interrupt or wake up We can for example take care about the wake up using things like a low-power timers LP timers or RTC This one helps us to Get down to the stop mode these still run from auxiliary oscillator that has a consumption of half of micro amp or less and The rest of the microcontroller can sleep then these two can give you a periodic or Once in a time a pulse and this will bring up the whole microcontroller How to achieve the tick less mode and how to achieve the peripheral? Functionality as we require it for low-power mode so we know that the idle task is created automatically and Whenever the idle task is executed it cleans up the deleted tasks it yields if we don't use preemption and If no other task is waiting it gets the yield it executes the application idle hook and It uses the macro That jumps to the low-power mode if we use the tick less mode So how to enter into the low-power mode? We first need to calculate the expected idle time. There is a macro for that So we calculate the expected idle time and then We can continue we suspend all tasks then again we check the expected idle time if Some other task has got updated it then we execute the macros to suppress ticks and sleep and We put the expected idle time in these macros If it's a bigger than the default one we should continue otherwise We enter the suppress tick and sleep function and we enter a low power mode So this way we have obtained for from the Friartos The time after which we need to wake up all the blocked tasks This is a very important thing because now if you have a task delay or If you have for example a timeout in the queue waiting This function the get expected idle time will calculate The minimum time for wake up of some of the tasks or objects so if for example use OS delay one second and OS message get from the queue with a timeout of four seconds What would be the expected idle time in this case? one second delay in task one and Getting message from the queue in task two with a timeout of four seconds What is my expected idle time one second the minimum one? Because you need to wake up earlier for the first task So the function will give you one second and then you can program this one second wake up within an RTC or low-power timer Then you enter low-power mode. You can completely stop the clocks of the micro So you drop the consumption to one two micro amps And then the RTC or low-power timer that you preprogrammed gives you a wake-up call this restores the clocks for the Friartos and Then when you wake up you can continue with the rescue When you resume you have to restore the functionality of the scheduler so you should resume all tasks and This will return the functionality of the scheduler and the scheduler Will wake up of course you have to as well Increment the time manually in the time base and then the scheduler will call the task because it elapsed already and It will manage proper timing for the rest So you can see that it's a little bit of job to enter into a low-power mode, but it's worth it and Especially for battery-operated applications. It's a must Do you know what is the difference between the interrupt and the event? First they have a different input to the core The interrupts come from the NVIC And they are typically registered from different peripherals When you activate the interrupt in the peripheral interrupts have as well associated interrupt vector and priority So when an interrupt is registered The core will stop a current function and It will jump to the interrupt handler Proceed the interrupt handler and return So interrupts have kind of private Handlers private functions to serve them However event is a different thing Event can be generated first by external Interrupt and event unit xt. It doesn't cause calling the interrupt handlers So when you wait for an event and you get an event The difference is that the microcontroller will wake up But it will continue linearly with the code from the moment where it stopped So it has a drawback and the benefit the drawback is you don't know what source of the event caused it And the benefit is that you don't need to care and you don't need to call the interrupt routine So the resume from low-power mode is faster Additionally, events can be generated if you have an interrupt that is registered but not enabled in NVIC So if you enable this in the system control block, you can as well generate events from disabled interrupts That's just a little off topic to tell you how you can activate the entry into low-power modes in the sleep mode Do you know what is the difference between sleep and stop mode in a sleep mode? Only the core is stopped and if you set up the Peripheral bus registers, you can as well stop some peripherals selected once But the clocks run the system bus runs DMAs run and All activated peripherals run as well. So the consumption can be for example, still 15 10 milliamps In stop mode the situation is different Because in stop mode you deactivate the oscillators Which means the crystal oscillator is off high speed oscillators are off PLL is off System bus is off core and flash are not clocked. So the consumption drops to the static consumption of the chip and If we don't optimize too much it can be around three five micro amps maximum So in stop mode most peripherals are frozen because they don't have any clock and If they are frozen they can't generate wake-up events So a big difference between sleep and stop is who can wake you up? Because in sleep mode any peripheral that's alive can give you an interrupt or even in stop mode only the ones that have some external connection or Some low-power oscillator still active Like a low-power timer or real-time clock you can as well get from the stop mode with the buttons Up to 16 pins connected on the package So if you have a power on button like on a mobile this can as well wake you up from a stop There is one another difference if you were running at a PLL clock and you resume from the stop mode The PLL was off and that the high frequency oscillator was off So you need to configure them again. So the startup time is a little bit longer But very often it's worth The current consumption use pair so consider which of the two modes is suitable for your low-power applications still from both of these low-power modes you can get back and And continue in the free RTOS and your application operations


So good morning, good morning, everyone. My name is Laura Schmarmann. I'm a PhD student in the Department of City and Regional Planning here at UC Berkeley. And I'm also a graduate student researcher within the Center for Community Innovation. And I'll talk more about the Center and the Urban Displacement Project shortly. So today I'm presenting along with undergraduate students Hannah and Rachel on a research project which we are undertaking which maps the vulnerability of BIPOC owned businesses across the Bay Area in the wake of the COVID-19 pandemic. We have been working on this project since March of this year. The project is ongoing and I would like to just emphasize that we're presenting the preliminary findings. The outputs of the project are not yet finalized or publicly available. So I'll talk through the Center for Community Innovation and UDP and provide an overview of the project we are working on. Rachel will talk through the Neighborhood Vulnerability Map. Hannah will talk about business database and associated map and we'll wrap up with some findings and plenty of time for questions and answers. So if you have any questions, please hold them till the end. You're welcome to post them in the Zoom chat and we'll get to them at the end and we'll also welcome questions from the audience here as well. So the Center for Community Innovation at UC Berkeley has a mission to nurture effective solutions that expand economic opportunity, diversify housing options and strengthen connection to place. The Center houses three projects, one of which is the Urban Displacement Project. So the Urban Displacement Project conducts community centered data driven applied research towards more equitable and inclusive futures for cities. So our research aims to understand and describe the nature of gentrification, displacement and exclusion and also generate knowledge on how policy interventions and investment can respond and support more equitable development. So I'd encourage you at a later date to have a look at UDP's website and explore more of the kind of data and mapping tools that we've produced. So now turn to this specific project. So many small BIPOC owned businesses are already vulnerable and are at risk of closing permanently due to the ongoing impacts of the COVID-19 pandemic. While research has been undertaken at a national level, there has been limited research on BIPOC owned businesses in the Bay Area. Various stakeholders we have spoken to including business advocacy organizations, chambers of commerce and cities have undertaken surveys of businesses to understand how the COVID-19 pandemic has been impacting their business. However, the information is rarely publicly available and it's not consolidated. So we are working with the Asset Funders Network to undertake the study of BIPOC owned businesses across the Bay Area. The Asset Funders Network is a national grantmaker membership organization and they focus on advancing equitable wealth building and economic mobility. So the study which we are undertaking aligns closely with the mission of that organization. The project will bring together a range of data sources to create a database on minority owned businesses across six cities in the Bay Area and we'll explain more about those specific locations shortly. So now I'm just going to provide a bit of context in terms of the study. So in March of 2020, as I'm sure you're aware, stay-at-home orders were introduced across the Bay Area in response to the increasing number of COVID-19 cases. As a result, restaurants, bars, salons, gyms and other what are termed like non-essential businesses were required to close or alter their operations. So 18 months later or over 18 months later now, many businesses have either temporary or permanently closed and others have reopened with various restrictions or reduced capacities. So while restrictions have not been the sole driver of operational change, they have had a significant impact. A survey done by the Federal Reserve called the Small Business Credit Survey has identified various other reasons for impacting operations of businesses and reasons for closure. So one of these is the nature of people's lives during the pandemic and probably impacted change in demand for products and services. So you can imagine that people would have had reduced income and that would impact their ability to purchase products or services. Government mandates affected various businesses and their clients businesses compared to previously. This includes restrictions on operations such as restaurants. Other reasons include the need to adapt to health and safety guidelines, which can be quite costly for companies, businesses, worker availability, supply chain disruptions, global supply chains in particular have been impacted. And the final one is owner's personal or family obligations. So an example of this would be that many people had to childcare responsibilities with homeschooling that would have impacted their ability to run their business as well or staff their business. So the drivers of operational change we expect would vary depending on the location of the businesses as well. Businesses operating in downtown commercial districts, for example, would have experienced reduced demand associated with the shift to working from home. Office workers were probably a significant source of demand and with businesses closing up that demand would have reduced. So an example of this would be in San Francisco where there's a lot of offices. So the types of financial challenges faced by businesses have also varied including paying operating expenses such as labor and rent, making payments on debt, purchasing inventory or supplies to fulfill contracts. And this can be difficult when there's uncertainty about demand and also accessing credit. So reports have suggested that various forms of government support have helped to prevent dramatically higher business closures. However, this may not be a solution for businesses in the long term as they may have only been able to defer payments and they would eventually become due. Businesses who are provided with the paycheck protection program loans across the US were required to retain employees in order to meet the requirements of these loans, although some of these requirements were eventually relaxed. The first round of PPP loans only provided for up to two and a half months of payroll costs and the pandemic has obviously continued beyond two and a half months. Businesses with high labor costs or fixed capital costs would also be particularly vulnerable as they might not be able to shift these costs as well. So the US Census Bureau previously undertook a survey of business owners which included questions regarding race and ethnicity. The last survey unfortunately was back in 2012, so there hasn't been one more recent than that. This chart on the screen here shows the results of that survey. So in 2012, 29% of businesses across the US were minority owned. And that's shown by the black line across the chart. The sectors where there is an overrepresentation of minority ownership include transport and warehousing, other services, healthcare, admin and support services and accommodation and food services, which have about between 39 and 44% minority ownership. We've also prepared updated statistics based on the current population survey. The current population survey is undertaken monthly by the US Census Bureau and the Bureau of Labor Statistics, and it's the primary source of labor for statistics across the US. The survey uses a representative sample so we can extrapolate that out as an indication for minority ownership across the whole United States. We reviewed the industry of employment for self-employed respondents, so those who own a business. And according to that overall 26% of businesses are BIPOC owned, which is a slightly different statistic compared to that 2012 source. The industries with the highest BIPOC ownership are transport and warehousing, accommodation, administration, personal services and food services, and that's a consistent pattern with the 2012 survey. So McKinsey's produced this chart which identifies the most vulnerable industries based on both financial and COVID related challenges. So smaller businesses and minority owned businesses are more vulnerable. The industries which are particularly vulnerable, which are shown in the top right quadrant, accommodation and food services, arts, entertainment, other services, mining, transport and warehousing, wholesale trade. And we can see that these are the industries here with higher minority or BIPOC ownership as well. So you can start to see how there's an kind of cumulative impact of vulnerability among these groups. So for our study we are looking at how different characteristics of businesses impact their vulnerability across kind of three areas. The first is business ownership characteristics. So minority owned businesses have reported greater financial strains during the COVID-19 pandemic. Financial impacts vary depending on the minority group as well. So various reports have indicated that black owned firms credit availability is a top expected challenge. While Asian owned firms have cited weak demand and some of this is due to discrimination associated with the COVID-19 pandemic. In terms of industry sectors, accommodation and food services and personal services have been hit hardest due to the restrictions placed on these operations and the time it took for them to be able to open up. Businesses where workers were able to work from home have obviously not been impacted as severely. We are also looking at geographies, so the spatial distribution of these impacts. Businesses operating in lower income neighborhoods or in commercial districts or locations which have had more restrictive guidelines about business operation are expected to have experienced much greater impacts on reduced demand as well. So if you think about California's had more restrictions compared to other states, so businesses in California might have been more severely impacted from a purely economic perspective. So our study focuses on the industries which are most vulnerable to displacement or closure and those which have higher minority ownership. So we're looking at retail, personal services, food and beverage services, accommodation, arts and childcare. And we're also focusing in on black owned, Latinx owned and Asian owned businesses. And so the analysis which we are undertaking, particularly the business database, it has the potential to be expanded to incorporate other industries in the future, but we have to constrain it just to kind of limit the amount of analysis we could undertake in the short time frame. But we see the benefit of expanding it in the future. So our study has three kind of stages to the data component. The first is a neighborhood vulnerability layer and for this analysis we used the national level data on BIPOC ownership to identify minority ownership by industry and clusters of this across the nine County Bay Area region. And so this is a top down approach using data at the national level and identifying concentrations at the local level. And Rachel will talk about this shortly. The second component is the business vulnerability layer. And this covers six Bay Area cities so it covers Oakland, Richmond, Redwood City, San Francisco, San Jose and South San Francisco. So we've identified businesses which fall within the industry categories we're looking at and the minority ownership groups which I mentioned. And the map provides a more comprehensive picture at the local level because we're using local local level data. We've also undertaken some site visits to certain areas to kind of sense check what we have found as well. And Hannah will talk through this in more detail a bit later. The final component is the property ownership layer and we're using tax assessor data on property ownership to determine the share of businesses that own their own property. This is based on the assumption that businesses who own their own property are less vulnerable or at less risk of displacement than those who rent their property. We're still in the process of undertaking this analysis so I'm not able to present on this specifically today. In addition to the data analysis and mapping, we've also been undertaking outreach with various stakeholder groups across the Bay Area and we'll be developing a policy brief which brings together the findings of both the quantitative and qualitative analysis. So I'm now going to hand over to Rachel who's going to talk through the neighborhood vulnerability mapping. Hello. My name is Rachel McCarty. I am a junior studying data science at UC Berkeley. And this past semester and summer I was working on this project helping to create the database and helping to create the map that you see here. So perfect. So basically what I'm going to talk about today is we basically identified concentrations of BIPOC owned businesses across all zip codes in the nine county Bay Area region. So we made this database based on the national level estimates. I'm going to talk about this a little bit later. A BIPOC ownership from the current population survey and we did the number of businesses in each zip code from the zip code business patterns data. So one of the conclusions we got was that across the Bay Area there are about 61,000 businesses that are BIPOC owned across all industries. Okay. So now I'm going to talk a little bit about the different surveys, different census surveys that we use to actually create the database. So the first thing we use is a zip code business database from the Bay Area. So this is a survey conducted by the census. It's annual and it has a bunch of data about businesses by the industry and it's only businesses with paid employees. So basically this we use the survey to get the counts of total businesses in the Bay Area. So for example, we could look at some zip code, some zip code in like Berkeley and see the number of hair salons like in that industry. So we just got that using this database we got the total number of businesses in that industry. Then we also use the current population survey. It's a monthly US wide survey and it's a representative sample as Laura was saying. It's also conducted by the census and this is how we found the actual percentages that we applied to our database. So basically this would be like, oh, there are X percent of hair care like at a national level. And we applied those percentages to our original zip code business database to get the counts like estimated counts of BIPOC businesses in the Bay Area. So like I said, we combine these two databases and then this is how we actually created a map that shows the proportion of BIPOC businesses in the Bay Area. Okay. So here's an example of one of the maps we made. We can see here that we have a map and there are different sections in the different colored sections are each zip code that we actually looked at. So we can see like a little bit of a range. We see like lighter colors representing that there are less BIPOC businesses and darker colors representing their more BIPOC businesses. So we can see I just like this is interactive maps. So for the screenshot, I just like scrolled over one of our zip codes. So nine, four, five, 10. And you can see we have the percentage of BIPOC businesses on there. The total number of BIPOC owned businesses, the BIPOC owned businesses per 1000 people and the BIPOC owned businesses per acre. So we did this for everyone in this map is actually representing the total number of BIPOC owned businesses, not the percentage. So all the different colors are representing how many BIPOC owned businesses are there. Okay. So here's another data table that we created. So this is a table representing each county in the Bay Area, the number of BIPOC owned businesses. This is calculated by using both the percentage we got from the CPS survey and the total number of businesses using the zip code business patterns data. The total number of businesses is just from the zip code business population survey and then the percentage BIPOC owned is just a ratio. So we can see here that Solano, San Mateo and Alameda have the highest concentration of BIPOC owned businesses with 30.9, 30.4 and 30.3 respectively. And we see that surprisingly San Francisco and Marin have less BIPOC owned businesses with 29.7 and 28.7%. So you might be thinking these percentages are really similar, like the biggest difference is 30.9 to 28.7, which is not that big of a difference. But you have to keep in mind we applied these percentages from the CPS survey. So it's a uniformly, we uniformly applied them to every business or every, sorry, every county. So these percentages are very similar, but it does make the differences very significant because of the difference between Solano and Marin is actually very different considering that we applied these uniform percentages. So yeah. So I wanted to compare two maps side by side and it's a little bit hard to see. Sorry about that. But so on the left we have the total number of BIPOC owned businesses. This is the same map I showed a little bit earlier. And we see that in like Northern San Francisco we see some concentrations. We see some concentration in the San Jose. These are again places where there's a lot of higher quantity of BIPOC owned businesses. And then on the right we see a definitely more uniform distribution of percentage of BIPOC owned businesses. This map is the colored based on the proportion of BIPOC owned businesses. So like I said, we do have a very uniform spread because we applied those uniform percentages. But yeah, we don't see that. I think it's interesting that we don't see that concentration really in San Francisco anymore. And we kind of theorize that's because San Francisco has a lot of businesses, but the proportion of BIPOC owned businesses is not as high. So here are like two concentrations I wanted to look at a little bit. So we can see that even though it's really hard to see the coloring on the screen on the screenshot, but in North Northern San Francisco we don't see any like higher concentrations. But in South San Francisco we see a little pocket that has a higher concentration of BIPOC owned businesses. Oh, and I'm sorry, these two maps are about the percentage. And then on the right we see Oakland and we I just highlighted that one zip code in Oakland that has a higher percentage than a lot of its like neighbors. That is 32.66% of BIPOC owned businesses. So that's just how we identified like concentrations both by looking at the table and both by looking at the maps. So kind of summarizing what I talked about, we saw a high number of BIPOC owned businesses in San Francisco. But when we actually looked at the percentage of BIPOC owned businesses, it was actually very uniform and very standard to what we saw everywhere else. The percentages of minority owned businesses in our categories are pretty nor pretty uniform, like I said, because we applied those uniform percentages, but because of that are the differences were very significant. And we saw higher concentrations of BIPOC owned businesses in Solano, San Mateo and Alameda. So I want to talk a little bit about like what I learned about doing this because I'm an undergrad student and I like I learned a lot of things for the first time. So it was kind of difficult to create this database I've never worked with census data before. So I had to learn how it worked and I didn't realize how much census data there would be. I think when we actually filtered by the zip codes we wanted there about 150,000 to 200,000 like records in the database. So it was really interesting getting to work with them seeing like what each column which each record actually represented because it was pretty. It was not as clear as I thought it would be. But it was really interesting and it was lots of trial and error with queries and trying to figure out how exactly we get just the data that we want because these surveys have a lot a lot of data like not even just records a lot of just like different things that they keep track of. And it was actually really cool though and I'm really glad I got to work with that. And maps actually were really interesting too I've never worked with maps like creating maps before. It was actually pretty easy to learn and I really liked working with maps because it was a lot of information that we put in. Because first we have the layer that's just like the Bay Area that we just have like this back layer. Then you have to add a layer for each zip code or each county like depending what we're looking at. Then we have to add a layer for each metric, like for example our map kept track of the total number of BIPOC owned businesses, the percentage of BIPOC owned businesses. A thousand people on the BIPOC owned businesses per acre. So it was a lot of information that we encoded those maps. And part of the reason I liked them so much was looking at the tables it was harder to see like what was happening. It's definitely hard like you can sort things but it's harder to actually get a clear picture of what's happening. But using the maps it's really intuitive and really easy to understand and we're going to show the maps like how they actually are in a little bit. But it's really helpful to actually see and get to play around with the maps a lot. And after the project I feel a lot more comfortable working at census data, like working with maps, creating maps and driving databases. I'm really excited I got to work on this project. It was really interesting and I'm hoping it has positive impacts. But thank you. Hello. My name is Hannah. I'm a senior studying data science. I worked on this project starting from this June till now. I worked in with a group of other undergraduates including Rachel. We went through like minority owned businesses throughout the six cities. I primarily worked on Richmond and Redwood City. And then when all that work was done I compiled it into the database that I'm going to talk about today. So the data sources that we used Orbus is through available through the UC Berkeley library. It's a massive, massive database that has information on like over 200 million public and private privately owned businesses. There is an indicator for minority ownership information. It does not have information about the group. So we use that as sort of a way to flag a business that we might want to look at later. The payment protection program data, which was mentioned earlier is data that was it's a publicly available data set. And it has information on business owner names, addresses, race and ethnicity category, which we also use to identify minority owned businesses. The Caltrans DBE SMBE data is another publicly available data set. Unfortunately, it doesn't have as many industries that are we're looking at in the study, but it is. It was like one of the most complete sources. So that was nice to work with. We also used publicly available lists. So pre aggregated lists of minority owned businesses in the Bay Area, and we went through them one by one and added them as we went along. We also had lists provided by ethnic business chambers, primarily from Oakland and San Francisco. Again, we went through those out of them one by one. And then we also used ArcGIS Geocoder and Census Bureau shape files to do the mapping. Okay. So this is an example of some of the websites that we used. There's do the Bay, which had lists for like black owned businesses, AAPI owned businesses, Latin X owned businesses. And we went through these one by one by the city that we were working on and like search to see if they were still open operating and then added them to the database. This is another pre compiled source of black owned businesses across the entire Bay Area. Again, we went through this one by one to confirm that they were still open and operating and like which industry that they're a part of. This is another pre compiled source for black owned businesses that we went through business by business and added it to our database. This one. So these lists were nice because they were already sort of like separated by industry so we didn't have to like go through and see like what industry they were a part of like they were restaurants we knew that they would be in that category. So this is the street survey that I went to with Tara, who was a graduate student that was also in the group. We did this as a way to kind of like sense check the data that we had and to, because the Orbis data is also very. There were some businesses that were like out of date or like no longer in operation.  And we also found like some signage on businesses like storefronts that identified them as like black owned businesses or Latinx owned businesses, which was really helpful because almost all of the businesses that we found this way we didn't find online. So that was like a really nice source that we never would have had had this project just strictly been like online. Okay, and then so putting all of these sources together. So once all of the work was done and we had compiled the information across the six cities. The, we had to identify what was a unique business. So the fields that we use were name address NAICS which is the four digit code that can be mapped to the industries that we were working on. And of course minority ownership group. So the database was assembled by doing an outer merge on the Orbis database, the payment protection program data, Keltrans. And of course what we have manually added through like what we have found online or found on the street. And then so once everything had been merged together into like one big data frame. I went through it and looked at duplicates for everything except for one category. And the reason for doing this is because the NAICS code, so a business would have the same information. Same address, same minority ownership group, but they would have different NAICS codes. And so it's still the same business, but maybe Orbis has them listed as like 7223 a restaurant, but they're listed as 7225 catering and PPP data, but it's still the same business and we don't want to over report that information. So I went through that and then pick those out and this was also how I was able to find like businesses where the owner was part of two or more minority groups, because there wasn't a way to indicate that in the PPP application, but the owner might belong to more than one of the groups that were studying. And so I wanted to make sure that we were able to like capture that information. And then once all of those exact matches have been filtered out, then I did geocoding with the 2020 census data. And I did this so that we could do the mapping and ultimately these were all going to be aggregated by like census tract minority ownership group and industry group. So along the way, I had noticed that there were a ton of like partial duplicates. So for instance, a business would have an ampersand in one source, but they would be spelled out A and D in another source. And if I was trying to look for an exact match, those would never come up as being an exact match, even though in reality they are. But anyway, once everything was geocoded, I could group them by their census tract industry and minority ownership group. And then within those groups, I could search for like partial matches and exact matches by name. The exact matches could be dropped immediately, the partial matches I exported and we looked over them to manually review them. So that way, like, we wouldn't drop any false positives or like lose any information. Oh, okay. So the rest of the Bay Area is almost entirely a pale blue. This is primarily because there's so many like large census tracts with few businesses in them. The only areas that really did have any variation in like the business density was San Francisco and Oakland. And it sort of lines up with what Laura had mentioned earlier about businesses being primarily located in like commercial districts, which you can kind of see are the census tracts on the map that are bold blue. And so like I said before, there is a very, very large number of low density business tracts. So I wanted to kind of explore what the data looked like by city. And when I had just the raw numbers, it was just a bunch of like very, very concentrated columns around zero with like a rapid descent and then a few outliers at the very end. And to make it more presentable I did a log transformation so there is not actually like negative to minority owned businesses per squared mile. Those are the census tracts where there's like between zero and one. So those are the very, very low density tracts. But you can kind of see like San Francisco is and Oakland and San Jose are the cities that tend to have like, they're really the only ones that have anything going on unlike the right side of the graph which is where the high density tracts would be. And you have places like Richmond and South San Francisco and Redwood City, which have like larger, lower density tracts that are kind of concentrated towards the middle and the left side. And then, okay, so I wanted to share this as well of like the same screenshot, because it, I think that it kind of shows like an interesting difference between like the number of businesses in a tract versus like the density and how many are like concentrated in a small space. So there's a triangle there in San Francisco that has something like 80 businesses in that tract. But if you look at the density. It's in one of the like lower categories because it's a much larger tract compared to the smaller census tracts that are like in like, I guess, more to the west of it. So the same case with Oakland as well how there's like a very attract that has a lot of businesses, sort of like south and to the east of it, but the higher density tracts that were lighting up on the graph before were like more towards the west. But you see in reality they don't actually have anywhere near as many businesses as that larger tract does towards the east. And so, because there wasn't as many like extreme outliers in this case I did not do a log transformation. So the numbers that you see here are the actual number of minority owned businesses per tract. You can kind of see like the far outliers that I had mentioned before with Oakland and San Francisco. And again, redwood city Richmond and south San Francisco are have like a more uniform distribution. One thing that I did think that was kind of interesting that I haven't really figured out what this is yet is why redwood city has like such a empty part in the middle and why there's like such a concentration of towards the left and then that one single spike around like 50 or so. But okay. So now, these are the results by industry. You can see that the most popular of the industries are the ones that we were originally looking at which are retail food and beverage services and personal services, arts and accommodation and child gate daycare services were added afterwards but they still, they're still like a pretty decent number of them. And then again by minority ownership group. You can see that almost over half the businesses that we found our Asian owned, followed by about a quarter or black owned and about a fifth or Latin X owned, and the remainders are either still in specified or in the other minority group category. And so again you can kind of see the issue of the extreme outliers with most businesses hovering around like the median of like eight when it comes to the count or four when it comes to the density. And then there are two tracks that kind of pull the mean up farther than away from the median. And that's what we saw in Oakland in San Francisco. So what I learned on working on this project. I learned like a direct application of like time complexity which is something that I had learned back in like my data structures class, but it's, it's the issue of when you have to do a lot of computations over and over and over again on the same thing. So for instance, when we were looking for duplicates to do that you have to compare a business name to all the other business names in the city. And over and over again, if I had done this early on before geocoding, this would have required I think something like 11 million comparisons, which is like, it took a while to run and it's not the best way to do things. But, and there's really no way, like, using like Python libraries to make that easier. But if you have things geocoded first, you can group them by census tract industry group and minority ownership group. And then that turns. So now you have like, about 600 different census tracks in the Bay Area. And you can see at most there's 89 businesses per tract. So even in the worst case when you're doing 89 comparisons 600 times, that's like 55,000. And that is far, far, far less than 11 million. And like we already know that the 89 is an extreme outlier so you're really only making closer to like eight comparisons 600 times, which is it was a lot faster than doing it the original way where it was grouped by city. The last thing that I learned is that not having any skeleton code or any like pre existing project or database to like go off of meant that like, I had to create this just myself, and that opened a lot of problems when it came to the fact that like we had to expand the study or add new data sources so I spent a day of just like rewriting all of my code and made it to where anybody else could run this if you know they had like the Python libraries installed, but it's now it's just like six scripts so the output of one is the input the next one. And just kind of in the interest of like looking forward and like making this easier and making this that some thing that somebody else could use and like make use out of. So yeah. It's interesting to look at all the technical complexities of study because it emphasizes why this probably hasn't been done before and how kind of time consuming it has been but we've learned a lot through this process. So I, I'm kind of going to talk through some of our kind of emerging findings because this study is still ongoing. So we've identified concentrations of BIPOC owned businesses in Oakland and San Francisco, and this reflects the concentrations of businesses generally within these locations. And also the known cost us so we know that there are black owned businesses concentrated in Oakland. We know there's Asian owned businesses concentrated in the China towns of San Francisco and Oakland. And we know that there are Latinx businesses in the mission and also around Fruitvale and Oakland. So the mapping has kind of confirmed those things that we already knew. Through some of the qualitative work businesses have identified the need for small business support systems. They've also identified the need to improve access to capital and also mechanisms to prevent property displacement. We're currently looking at policy solutions to address these needs and we'll be working with stakeholders in the coming weeks to have sort of focus group type webinars to develop up these policy recommendations. This is a substantial amount of data data out there, but it's often difficult to access, or there are a large number of gaps. So some organizations we have spoken to have access to data but they do not necessarily have the resources to manage it so they might only be one person who's employed. And that's not their sole purpose, but they have this database. So accessing it and keeping it updated is quite complicated. And that has been a constraint that we've come across. Also data availability and quality is varied across different types of businesses and locations. So we found that some locations we were able to access a much larger quantity of data than some of the others. So an example is Oakland. We found that we were able to access a lot more data sources for Oakland. Outreach has been very critical in accessing data and understanding more about the local context. And that's been an invaluable step throughout the process and it continues to help refine the analysis and plug gaps in the database. The mapping reinforces what we already know, and this is important in telling the story and using this to kind of promote policy interventions, but it's not the only story and qualitative research is important to this process too. And so this project is only the beginning and this potential for the database to be expanded in the future. And we're also looking at ways that the database can be kind of handed over to a key stakeholder to be kind of regularly updated and made available for public policy purposes. So we have time for questions. I thought maybe before that I might just show the actual maps so you can see how they work. So I'm just going to, so this is the business database map. And this shows the concentrations in terms of numbers of businesses. And at the moment you can kind of click on to a census track, see the total minority on businesses and number per square mile. You can see the breakdown by type of minority groups. So there's a concentration of Asian-owned businesses. This is around Chinatown in San Francisco. So that makes sense. There's also some black owned and Latinx owned as well. Can move over to Oakland and see concentrations as well. I believe that's also around Chinatown. And then the other layer we have on here is the density. So you can also see the different illustrations there. The other map, which is the one that Rachel spoke through. This is the early neighborhood concentrations map. It's the same sort of thing you can scroll through and click on a zip code and find out more about it. So I'll leave it on this one. Are there any questions here? Yes. So I have a question on, at the end you talked about adding qualitative data to the map. I'm just curious like what kind of, if you know what kind of qualitative data you're looking for or how you care exactly in terms of this project. I'm curious to see what kind of information or how you would just incorporate it into this database. So one way that you can kind of feed through is through the street surveys, which I guess is somewhat of a qualitative research method that when teams went out, then we filtered that information back through to say like these businesses are closed or operational. The more qualitative policy recommendations will be, we anticipate will be kind of a policy brief type report that will be used in conjunction with the database, but it won't necessarily be mapped physically onto the database, but it'd be that we would have hopefully a website where they're both there together so that people can read the policy recommendations in conjunction with viewing the map. Thank you. This is an amazing project and we have done a ton of work. And so I just want to commend you on this. This is a really important tool that you're creating. And I just thought I asked a little bit more about what you have planned for it. In watching your presentation, it reminded me of another mapping tool that I'm familiar with that is designed by one of our research centers here, Berkeley Interdisciplinary Migration Initiative, and they designed a tool to identify where immigrant populations are and where the services for those populations are. And what they showed was a spatial mismatch in terms of if the resources are here, the populations are here. Is that possible for your tool as well? Is that something you're thinking about in terms of identifying maybe a spatial mismatch or an opportunity for if we had a small business association, you know, if we're going to add one, it makes sense that it goes here where there's the highest concentration. I mean, are those things that you're thinking about? So that's one question. Then another opportunity here is, and I just want to clarify, did you identify businesses that received PPP paycheck protection plan? Okay. Do you have that data? Could you show it to us? Because that is something that our students have been also working on. You're talking about qualitative research. A lot of our projects involve students talking to business owners and getting their stories about why they did or did not receive PPP. And it would be really important, I think, to add another layer, which is the PPP layer. Okay, here are the concentrations. Who got the PPP and who didn't get the PPP or it could be California Relief Grants or something else. Is that also in the works as well? And I guess I'll just stop there. Thank you. So the first question about mapping kind of spatial mismatch, so that's definitely something we can look at. And we have various sources or different layers we can kind of produce around where vulnerable populations are located and where these businesses are as well. So that's something we can definitely look into as well. In terms of the PPP data, we have that. We haven't mapped it specifically, but we do have that in the database. That's something we could interrogate a bit more to look at that variation and whether there's particular types of businesses in terms of industries, types of minority groups or types of physical locations so we can kind of break it down and compare. So that that will definitely be an additional insight that that will be important. So it's a good suggestion. Thank you. We do have. Oh, yes. Sorry. Hello. It's fun to have the mic. This is super interesting. Last year we spent eight weeks talking about vulnerable populations and COVID-19 and consulting like a lot of the same databases and there's a lot of work around that and like imagining what a vulnerable person would look like. And then I kind of took that same research and started looking at a more qualitative approach to like certain businesses in the Bay Area. And one thing that I found particularly interesting was the story of Chinatown. I'm from the Bay Area and like I've lived across the East Bay and a lot of the cities that you guys are studying. And I think what's really interesting about San Francisco and the Bay Area in general is that we have two really prominent Chinatowns. And I'm wondering what it was like researching what happened to those areas specifically. I know I understand that there was some qualitative research done there. I'm wondering if like the influx of hate crimes and just also like a lot of negative media and how that translates to a business owner or just an individual living in that area, but particularly a business owner, how that may show up or like any further insight like those businesses in Chinatown and Oakland and San Francisco. Yes. So we haven't spent too much time in looking at that specific issue yet. But I think through the policy discussions that we're going to be having with key stakeholders who represent some of those locations we'll be able to understand more about that and make sure that's integrated in the policy brief. And we're very aware of that issue, which is why we made sure we incorporated the Asian-owned businesses in the study because we thought that they've been particularly impacted by COVID-19 and associated discrimination. So a very, very important point and something we're very conscious of looking at. Yes. I wonder if it's possible for you to follow the businesses that exit your data set. So we often create arguments about what helps a successful business by surveying existing businesses, but we know very little about those that die. Yes. And you have this amazing opportunity to, as they leave your data set with all this information, who's more likely to die, where they're more likely to die, what sort of areas. And then if you could continue that conversation, you can perhaps even survey them about why, et cetera, what were the challenges. That would also, instead of just cleaning the data, so allow you to get a nice data set, but also give you just this invaluable piece of information of folks that sort of exit. Yes. Yes. So through what we're developing at the moment, we do have some indication of business closures based on some of the information provided through the kind of Google Maps and Yelp and some of those places, but it's not consistent. And that's definitely something moving forward that we could make sure is a kind of key outcome for when the database is updated. It's something that I'm looking at in my PhD research, which is focused on something different, but it's very difficult to track businesses once they get physically displaced because you don't know where they end up, let alone what they've closed altogether because then it's hard to contact them. So databases such as this that can kind of start to track them in a longitudinal way are important. So that's something we can definitely look to incorporate into how this is managed moving forward. And that's something we're trying to work out is who takes ownership of this database and continues to update it in the future, because it takes a lot of time and resources to do that. Yes. I have one more question if that's okay. So how did you decide on, because as you were scrolling over the map, you had different minority groups.  Because then there was a section for other minority groups. Did you go off like census categories? Because I know like with census, there's a lot of controversy around, you know, certain Middle Eastern groups being counted as white, even though they're not just like historically being counted as white, even though the experience different, they have different experiences, especially even in business than like white business owners. So I'm just kind of curious like how you decided which ones to categorize and then which ones to put under other. And like if that was related to the, I guess, census groups, because then that would provide context. So other often includes whether it's two or more minority groups or that's the main source. We didn't specifically include indigenous yet because there's very limited data available. So we didn't want to suggest that we were capturing all of that because we know we're not capturing all of that yet. Asian, I believe, includes like all of the continent of Asia, those sorts of groups. So it would include kind of Middle Eastern, I believe, but that's something we can definitely kind of make clear when we're reporting on this. And we had to kind of constrain the study and recognize, we do recognize we're not capturing all. And that's a significant constraint from doing the studies. There's a lot of businesses that don't identify as minority even though they are for various reasons. So even in the PPP data, there's a lot of businesses that are unclassified. So they're not necessarily because they're not minority and they just choose not to identify. And there's various reasons for that. And that's something that we're aware of. And we're trying to capture as many businesses as we can, but some businesses we know will not be captured in the study. Yes, so there's a question in the Q&A thing. What is the total number of black land businesses in baby hunters 0.94124? We won't. This is not at the zip code level. This is at the census tract level. I do not know where Bayview is. San Francisco? Is it around here? I'm not sure if anyone knows the exact census tract number for Bayview.  Here's a large costar. I'm not sure if this is a specific place. But we've identified 32 in this specific census tract. If we go to the zip code level map, I might be able to find that's the zip code there. This is not the actual number. This is our estimated number based on the national level statistics for that zip code. So we think that there would be 31%. Something we can do is aggregate our business database to the zip code level and we can compare. We just haven't gotten to that stage yet. But that would be useful in comparing if there are zip codes where we've actually found more than what the national data would suggest and some that is a lower as well. Any final questions before we wrap up? Thank you very much for your time and also for the great questions because this study is still ongoing. That helps with forming some of the ideas of how we finalize this study. So I appreciate all those questions. Thank you.


Prime Minister the Honourable Alan Chastney has fulfilled his promise to resolve the issues confronting the Israelong laboratory due to the increase of COVID-19 cases here. The lab has been weighed by the volume of samples for testing and processing. During his address Tuesday on the new protocols, Honourable Chastney informed that the forensic lab will now receive samples from two of the largest facilities, Viewfort and Grossly Polyclinic, for processing. This is to reduce the backlog and burden on the Israelong laboratory. Antigen tests will be used for screening in high-risk settings and workspaces. Training on antigen testing began today. Exit tests for travels will be facilitated at private laboratories. Mass testing will be introduced to increase population coverage. In light of increasing cases and community spread, two additional wellness centres will be used as testing sites, one in the south and one in the north. Nationals will also have the option of testing at private laboratories.


Can I smoke? This time the receptionist narrows her eyes at Marion. No, she says. You...  Just because it's an administration building doesn't mean we don't have lungs, or labor law. Marion notices the exasperation on the young man's face. I've asked you that before, haven't I? Twice in the last quarter hour, the receptionist says, you must really need a smoke. She's genuinely puzzled at the repeated question, and she's doing a bad job of concealing her puzzlement. You think this is like Memento, don't you? Marion offers, charitably. You think I have no long-term memory, and if I stay in one place for too long, I forget why I'm there. The receptionist is only just old enough to remember that film. I... Guess? Marion smiles sympathetically, and shakes her head. It's nothing so simple. Minutes pass. She toys obsessively with her lighter. She is turning 50 this year, and slowly graying, well on her way out of petite, towards little old lady. In her bag, her phone beeps because it's time for a pill, but she tells it to remind her later. There's a slight tremble in her fingers, but that's not age-based infirmity. That's just ordinary nerves. She's nervous because she's here to meet an O5, and O5s are scary. O5s never want to see you for a small thing. It's the end of the world, or nothing. Finally, 14 minutes late, the door to the inner office opens. Four or five high-ranked foundationers spill out, carrying laptops or briefcases. As a group, they head straight past reception and out to cars which are waiting. Marion recognizes a few of the faces. The Site-19 Site Director, the head recruiter for Western Europe. None of them glance in her direction.  He's 20-something, improperly youthful, like a teenager stuffed into one of his dad's business shirts. His haircut is barely regulation. In one hand, he holds a tablet computer, showing his boss's day planner. It's packed. The man evidently does not sleep. Marion, you can come through now. The office door closes behind them with an unusually heavy mechanical clunk, as if the whole thing is part of a machine built into the office walls. While Marion takes the indicated chair and sets her back down, the assistant turns and does some confusing additional things to the door, causing it to make several further strange noises. O5s have non-trivial privacy and security requirements. The office is spacious, but somehow it contrives to be dark, despite two big corners of a window and broad daylight outside. The walls are all bookshelves and dark wood paneling, perfectly stylish. But a style from the 90s, a little worn, and not yet old enough to be fashionable again. As for the fellow behind the desk, well, an O5 never looks like you imagine. Marion takes a deep breath. So, what's the topic? All I got was the meeting invitation, no agenda or subject. I mean, an O5 says jump, you jump, but looking to her right, she notices that the assistant, without saying anything or making any undue noise, has set his tablet down on the table, produced a gun, and aimed it at her head. Marion slips talking. She sits still in a chair for a little while, absorbing the change of pace, letting her heart rate rise to a hummingbird's, and then start to flatten again. Okay, she hazards. She licks her lips and grips the armrests, otherwise staying perfectly still, waiting for another prompt. The assistant's face is totally neutral now, like, this is just how meetings go. Maybe it is, for people up here. Who are you? O5-8 asks her. Marion blinks. What? Oh god. Let me rephrase, O5-8 says. Marion Wheeler, 49, with loving husband and two boys in tow. Likes camping, hiking, and ornithology. Mother with perfect airtight background and financials, as far back as we can examine. And you've got full foundation credentials, which we've never issued, including access to a list of installations and rooms which, some of these locations don't exist, or torn down decades ago. At least one hasn't been built yet, yet you've got the front door key to it. That's before we get to your SCP access control lists, which I can only term as egregious.  But I was able to bring him around. I talked him into a face-to-face. I thought there was a slim chance that if we got you locked in a bomb-proof room, and us politely, you'd have the good sense to spare yourself the rest. Everyone has long since stopped listening. You dullard, she says, now she can finally speak. I'm your chief of anti-mimetics. We don't have an anti-mimetics division, Clay says. Yes, you do. We do. 058 says. We have a mimetics division, a telecontainment division, fire services, ops A, ops B, personnel, and two dozen others. We don't have an anti-mimetics division. Do we have an irony division? Marin asks. She hesitates, hopefully. No. All right, well, try this. What do you think the anti-mimetics division would show up in the listing? This is just a cover story, Clay says to 058, not taking his eyes of Marion. It's a good one, but she's had it worked out in advance. Clay, lose the piece, says the 05. Grudgingly, Clay does so. Marion relaxes fractionally. There are SCPs with dangerous mimetic properties, she says. There are contagious concepts which require containment, just like any physical threat.  Right, 058 says. He could name a score of SCPs fitting this description without even thinking. There are SCPs with anti-mimetic properties, Marion goes on. There are ideas which cannot be spread. There are entities and phenomena which harvest and consume information, particularly information about themselves. You take a Polaroid photo of one, it'll never develop. You write a description down with a pen and paper and hand it to someone. But what you've written turns out to be hieroglyphs and nobody can understand them, not even you. You look directly at one and it won't even be invisible, but you'll still perceive nothing there. Dreams you can't hold onto and secrets you can never share and lies and living conspiracies. It's a conceptual subculture of ideas consuming other ideas and sometimes segments of reality. Sometimes people. Which makes them a threat, that's all there is to it really. These memes are dangerous and we don't understand them, therefore they are part of the problem. Hence my division. We can do the sideways thinking that's needed to combat something which can literally eat your combat training. 058 stares back at her for a long moment. Clay fidgets, disliking and distrust in the story, but the 05 seems more open to the concept. Name one, he says. Name an anti-mimetic SCP. SCP-055, Marion says promptly. There is no SCP-055, Clay retorts. Again, yes there is, Marion says. There isn't, Clay asserts. SCP numbers aren't assigned sequentially, there are gaps. That number hasn't been assigned.  We have SCP-666 and SCP-13, but there's no SCP-1 and there's no SCP-55. Clay, 058 says, you should look at this. He turned his monitor so Clay can see the file that he has just retrieved. Clay bends over and reads it from top to bottom. Stunned, he scrolls back and reads it all a second time. But the files dated from 2008, 058 says. It's got all the right flags and signatures. It's keyed and coded. It's real. You've seen this before? Clay asks him. Never in my life, 058 says. As far as I can remember anyway. On the other hand, if the content is accurate, both of us have probably seen it dozens of times. Clay glares at Marion. This isn't possible. Marion nearly spits. For Christ's sake, Clay. How long have you been working here? But if this SCP is this powerful, he begins. Yes? Who wrote the file? The 058 finishes. And for that matter, how was the interview conducted? And who is Bartholomew Hughes? And most importantly, how do you, Mrs. Wheeler, retain any knowledge of this? Bart Hughes wrote the file. He's dead, Marion says. What happened to him? You don't want to know. There is a very long pause while both 058 and his assistant react to this. In fact, they pass through a long, discreet sequence of reactions. Indignation at the seeming rudeness, confusion at Wheeler's in caution in front of sinister superiors, surprise at the magnitude of the claim, pure disbelief, comprehension, and finally horror. What 058 asks carefully would happen if we did know. It would happen to you as well, Marion says, levelly. As for the rest of your questions, we managed that pharmaceutical. You know we have class A amnestics for people who very badly need to forget things. Of course you do. Cooker forget about class A amnestics. Well in anti-mimetics, we have a different pill for people who need to remember things that would otherwise be impossible to remember. Nestics, class W, X, Y, and Z. Same Greek root as the word mnemonic, the M is silent. In her bag, her phone beeps again. With a nod of approval from the 05, Marion reaches into her bag and turns her phone off, acknowledging the prompt this time instead of postponing it. She pulls a blister pack from another pocket and pops a pill out. It's hexagonal and green. She holds it up and is satisfied to see a flicker of recognition on the 058's face. He's beginning to put it back together. Marion says, these are class W nestics, the weakest, suitable for continual use. Two pills per day. Go down to the side pharmacy and ask. The pharmacist will claim they don't stock any such thing. They're misremembering. Tell them to double check. 058 size. And now I think I get it. I see why we're having this conversation at all. Yes, Marion says, popping a second pill out and handing it over to him. It's because you missed the dose. You're supposed to be on these, the same as me and everybody on my staff. It's the only way we can work. You forgot to take a pill, and then you forgot all the information that the pills were helping you retain. You forgot why you were taking them, who gave them to you, where to get more. You forgot about me and my entire department. And now I have to bring you up to speed. And if I take this, the 058 says, I'll remember this whole conversation and we won't have to have it again. Hopefully not, Marion says. Clay pipes up. Uh, should I be taking those? Ciriculo, 058 says. Need to know. Maybe when you're an 05 yourself. He swallows the pill. Marion swallows hers too. So what is SCP-055? 058 asks. SCP-55 is nothing, Marion says, now relaxing entirely. SCP-55 is, as described in the file, a powerful information auto suppressor. As far as experimentation hasn't covered, it can only be defined in negative terms. We can only record what it isn't. We know it isn't safe or euclid. We know it isn't round or square or green or silver.  And we know it isn't alone. But what we do know is that it's weak. It's weak because it's the only anti-mimetic agent in our possession which has a physical entry in the files. We have paper records of the thing. We have containment procedures. It's not safe. Which means it's dangerous. But it's contained. 058 blinks. You have procedures? Where? Marion points at her head. Then how many other anti-mims are there? How much more dangerous do they get? 10 that I know of, Marion says. Statistically, probably at least five more that I don't know of. This does not count the anti-mimetic entities freely roaming the halls, not under containment. There are at least two in this room with us right now. Don't look. I said don't look. Pointless. 058 does an impressive job of controlling himself, keeping his attention focused on Marion.  He finds nothing. He looks baffled.  Marion explains patiently. SCP-4987. Don't look it up. I've learned to match with it. It's like a demanding pet. I produce tasty memories on purpose so it doesn't need something important, like my passwords or how to make coffee. And what's the other one? Clay asks. With another nod from 058, Marion goes to her bag again. This time, she pulls out a gun and shoots Clay twice in the heart. More aghast than in pain, Clay collapses sharply against the bookcase behind him. Rolling his head around evades Marion. He manages. How did you... MMMM. Marion stands, aims more carefully, and shoots him a third time, this time in the head. 058, again, does an impressive job of not reacting. That's Clay's gun. He deadfans. You stole it from him. It's tricky to steal a firearm this heavy from someone without them noticing. Marion explains, unloading it and carefully setting it down. Stealing a firearm and then stealing their memory of the theft is a little easier. Like I said, a pet.  Yes, 058 says evenly. That much I'd guessed. But why? Because you were supposed to be taking Class W Nestix, Marion says. You can't skip a dose of Class W Nestix. I've tried. You can postpone a dose, but you can't forget unless someone actively prevents you from taking it. There's only one person who could get close enough to you to do that, and that's your assistant. And remember when I asked him how long he'd been working here? He didn't answer. 058 says, I thought you were being rhetorical. He doesn't work here, Marion says. He's an anti-meme. Since when do you have an assistant? You don't have an assistant, Brent. Look at this office. It's got one desk. You've got a receptionist outside. He's the one who screens your calls and schedules your meetings. Where does Clay even sit? Where does he fit? Don't blame yourself, you're human, and these things are redaction incarnate. You need to think like a space alien to get around them. 058 asks a question which, in any other workplace, would be absurd. Is he dead? Maybe, Marion says. I can put his corpse in our research queue and we'll see what we can see when we open him up. There's a duality here, though. They're like parallel universes sharing the same space. It's conceptual versus concrete, figurative versus physical. It's very unusual for things to cross over. I don't know what Clay was, but he had a human body, which instantly makes him weird, even by our standards. As ever, the search for stalemate continues. I will let you know if we get any closer. Any side effect of these pills? 058 asks. Noja and dramatically increased risk of pancreatic cancer, Marion says. And very bad dreams. End of file. To learn more about the SCP Foundation, subscribe to SCP Orientation Today and turn the notification bell on so you don't miss any of our videos.


My name is Bill K. Wood. I live in Greenwood, Indiana and I'm an Army veteran of Vietnam. I was walking point with my infantry platoon and I had just stepped out of a rice paddy when I set off a explosive device and that resulted in the amputation of my right leg. With all these events you get, I think as the young guys call it, you get stoked and then the more you get stoked, the smaller your disabilities get. They become more diminished. These type of activities just really bond you together with other veterans and it helps everybody in the group to try to aspire to new levels of places they may never have reached before. Bill was cheering everybody on. He was always telling everybody, great shot. He was always encouraging each person that they did a wonderful job. No matter where that ball went, how far it went, what direction it went, Bill always had a positive attitude at everything he was saying to everyone around him and he was just a great mentor and great motivator to people around him. Nice job Leonard. It seems to be an amazing partnership between the VA and the AAV in the way they have created these events and they continue to work together to make them so great. Thank you to the AAV for everything they've done for me personally and everything they've done for all these other veterans in the VA. I very much appreciate the treatment they've given me for the past 50 years. It's just again a wonderful partnership.


This video is part of a series. Be sure to check out the description of this video for a link to the full playlist. And today we're just gonna quickly look at the uname command. Uname command, if I just type it, it tells me what kernel I'm running, but it gives you a lot of different input depending on what arguments that you pass it. So I'm gonna say uname dash A, and that'll give me all the information that it can display, which is my kernel, the host's name, my processor type, and so forth, so on, that I'm running Debian, and that my operating system is GNU Linux, and that I'm running a 64-bit processor. And so when my kernel was compiled, I believe is what this is. If we type in uname dash help, it will give me a full list of the commands. We already did A, which is all. We can do uname dash S, if I just wanted to get the kernel. I can do N for node name. I can do R for my kernel release, the version machine, which is the processor type, I for hardware, which in this case is unknown, and dash O for operating system. So if I wanted to break that down, if I wanted to see what kernel I'm running, what version or release of that kernel, I can get that information all using uname. It's a great little command. You can also look at the man file, which is probably gonna give you the same information in this case that help does, because it's a fairly simple program. But it's definitely very useful to determine things like architecture. What type of system is this running? What kernel are they running? So I thank you for watching this. Again, this one was just a quick little short one, but a very useful command I use quite often. And I thank you for watching. Please visit my website, filmsbychrist.com. That's Chris at the K. There should be a link in the description, as well as a link to my Patreon page, patreon.com, forward slash mail x1000, where you can become a supporter of mine a dollar a month or more. It would be so appreciated. If you can't support me financially, think about commenting, liking, subscribing and sharing. Let me know what you think of these videos in the comments below. I thank you for watching. And as always, I hope that you have a great day.


I've been expecting you it's time now come on Disney y'all can do better than this y'all really follow it off my name is brandy Keith Avery and this is just my opinion hey what's going on everyone thank you so much for tuning in to my opinion slash review for the nutcracker and the four realms better late than never 2018 edition I really do appreciate it now I remember when I first reacted to this trailer a number of months ago it looked good it looked very colorful I thought Disney was putting their money behind this and they really did and I thought that you know hey you know this may be something that if it's great I can buy it on blu-ray or 4k and just have a beautiful display at home unfortunately that is not the case now when it comes to the nutcracker of course we've all heard of that before coming up I just know it has to do with gingerbread soldiers and toy soldiers and things like that and remember the song and all that good stuff but as far as this movie I mean it's directed by Lasse Hallstrom and Joe Johnson I have no idea who she oh excuse me excuse me this is a I don't know okay this is a guy I'm not trying to be funny I really could not tell by looking at that picture but Joe Johnson I mean I'm a fan of his October sky rocketeer Jurassic Park 3 Captain America the Winter Soldier I like all of those films but I do not like this film at all for the most part only thing I liked about this movie is the main actor her name was Clara play by Mackenzie Foy I mean she's cool she was an intelligent character she was bright she made smart decisions you know you know she had a lot of things going for her it seemed like you know you look at her and he's like okay you know this person has a positive future and they're gonna do something with themselves they're gonna be somebody do something worth their life but other than that I really didn't care for it you know especially like Morgan Freeman I don't even know why he was in the movie he just kind of looks silly with an eyepatch and really none of the characters none of the other characters I liked in this movie either this kind of film reminds me of a wrinkle in time another bomb that isn't made early this year you had like a Oprah Winfrey and then those other two people I forgot their names but y'all know how I'm talking about there were three other similar characters like this in this film and it was just really a distraction I really don't like over-exaggerated characters with voices that are not realistic at all to what like I mean that that's how some of the characters are acting and so that just kind of tells me they're like okay hey this film right here is not tailored for adults it is tailored to those children that's fine but then another grab about the movies I don't even think that the film is tailored to those children I mean this film just does not know because there is a few sequences where there's like some ballet and I'm look I'm not a fan I mean excuse me I'm not a hater on ballet I'm a fan of ballet I'm not this one oh ballet is on I'm with DVR you know I'm saying but at the same time I have seen some performances before and I was like damn that is beautiful that's what's up I like Black Swan one of the best movies of that year but in this movie it sucked I don't know why I mean ballet is such a beautiful elegant dance and to me it's really not that hard to capture but for some reason they could not do that in this film with some of the angles that they were delivering to you on screen I'm just really did not understand and then you got somebody like Joe Johnson first of all the movie is boring as hell and it's switching tones left and right like draws and then all of a sudden we have this ballet scene out of nowhere you can't even get the right angles it's just I'm really just not feeling it then you have these over-the-top characters and then all of a sudden the film turns into like nightmare on the M Street to where you have these characters that are rolling around that look like walking talking pillows that can switch heads this is freaking terrifying I mean it's not funny and it's scared the hell out of a five-year-old so I really don't understand what they're trying to do there and then and then I will give the film credit as far as like the costume design because there is a lot of detail in the costume design but at the same time there's just so much going on that you can't really appreciate the detail I mean is this like it's just a bunch of colors on screen that don't blend well together I mean and for so many colors the film felt flat it was bland the cinematography was trash and but then then again you have all this CGI on blue screen and green screen galore sometimes it was good sometimes I was like okay what happened here is this green a blue screen one time million dollar film was this water color I don't know and these so you have all these elements bouncing back and forth not mixing together all with a story in a pot that's just like okay dang the woman's mother was gone and Clara is looking for a key and the message is like okay you looking for some special but everything was in you the whole time like we've seen that have been times before I don't get it I mean I get it but it's just not a good story it's not a good film it's not a good character development in my opinion so and then you have this this rat monster like Jesus like it was it was not good I really didn't enjoy this film if you can tell if I had to rate the nutcracker and the four rams out of a one out of ten I will give it a three out of ten yes a three out of ten I gave a wrinkle in time or two out of ten I'm giving this one a three out of ten Disney y'all need to step it up with these magical fairy movies and things like that but guys that is just my opinion have you seen the nutcracker and the four rams and the four rams like why the hell do you want to go to stay away you got three other realms you can kick it in and you want to go to the devil in anyway did I have a turn you on have a turn you off do you agree with me or do you disagree with me let me know down in the comment section below let's get this conversation going to keep it flowing if you like this video go ahead and give me the thumbs up if you don't that's fine but you can still subscribe to my channel you can also look me up on social media Facebook Instagram and Twitter all that good stuff it's right there the bottom of the screen and I made it very easy by providing a link to all that good stuff there in the description box below but guys I just want to thank you again for tuning into my opinion last review for the nutcracker and the four rams better late than never 2018 edition before you go don't forget that my name is Brandon Heath Avery and that's just my pee peace


Welcome to DataDoctor's video Suggestion 863, Missing References Tag. Reference tags come in two parts. The first, the inline citation, which goes in the biography section, is a pair of multiple open and closed ref tags. The second part, which goes in the sources section, is the references tag. Note that there should be a space between references and the forward slash. Suggestion 863 is reporting that this tag is missing. That could be because it isn't there, or because it has been mistyped by being misspelled, or having the angled brackets or forward slash wrong. Here is a profile in its public view. The biography section shows a numbered reference used twice, which indicates a named reference, and yet it is not listed in the sources section. That is because there is no references tag. Before proceeding, check if there are more suggestions. On the top right, click on the person's wikichui ID. From the drop-down menu, scroll to the end and click on Suggestions. The next screen shows how many suggestions are for this profile. There is only one. Return to the profile and click Edit. See the named reference in the biography and its second use? Note that below the sources heading, the references tag is missing. Add the tag just beneath the sources heading, where it should always be. Remember to add a space between the word references and the forward slash. Now, click on the preview button to check your work. The numbered reference in the biography is now properly showing in the sources section. Go to the middle of the screen and explain your changes. This is required for all data doctors. I identify myself as a data doctor, copy the suggestion number and name, then explain what I did. In this case, I added the references tag. Save your changes. Return to the suggestion status page. From here, choose the status. In this case, corrected. Then leave a comment. You can pick one from the comment hints or make one of your own. The last step is to click the update status button. The next screen will show the update in the status history. And you're done.