Our presentation this morning is based on a brief that Danielle and I co-authored a few months ago. It's informed by Danielle's involvement in several Ethica SNR research studies about changing behavior of scholars, which we will cite later. And it's also inspired by my experience when I was at Cornell University Library in designing and implementing digital scholarship and research data management services. And maybe also, again, in the university environment, witnessing the challenges scholars face in their daily practices, especially in engaging and finding supporting services. Building and creating collections is such a central stewardship role for cultural heritage organizations such as libraries. So what we want to do today is to kind of flip the coin and position collecting as a central activity in scholars' daily workflows. So what we will do is I'll start with a discussion of collecting as a part of scholars' daily workflows and then I'll turn it over to Danielle for her to discuss how institutions can effectively provide research data management support services. We aim to allow ample time at the end as we look forward to hearing your feedback and your ideas. What do we mean when we emphasize that scholars are collectors? So let me start with a story, which is inspired by a recent Center for Research Libraries research brief. You might know that during the last 10 years, the internet use and content have boomed in the Latin America region across disciplinary spectrum. So if you were a group of interdisciplinary group of scholars studying political movements and their impact on environmental change, you'll be really interacting mainly with the open web. Even commercial publishing has started moving to open web in this region. So you will be finding scholarly articles, research papers, climate data, you'll be finding social media interactions, interviews about maybe political movements and knowing the highly fluid nature of online content and knowing the fact that you might not find the same page even the next day as a scholar, what you'll be doing is you'll be using various tools, whether they're open access, open source or whether they are commercial to extract, to scrape, to download these pages. And actually we are hearing often now these tools being characterized as research workflow support tools or also we are hearing as productivity tools. As I said, they could be open source or they could also be different commercial. Actually, the irony is that media libraries and archives are very active in harvesting content, from the web, maybe on major matters interested, my matters that are of interest to this group of scholars. However, collecting their own evidence data through building their own collections allow scholars more control and greater accuracy in means of knowing, for instance, when a page was captured. This story really captures the findings of ethic SNR research on scholarly information practices. You might know that ethic SNR has an ongoing series of qualitative projects that explore how research support needs vary by discipline. So far, actually, we covered 13 different disciplines. And in addition, there is a tri-annual faculty survey to examine the evolving attitudes and behavior. And this actually, in a nutshell, very well captures the findings, the empirical findings. There's strong evidence that the vast majority of scholars organize resources on their own computers. Actually, interestingly, we will be releasing the 2018 data in a few days, so stay tuned. It will be kind of nice to see how the behavior are evolving. So based on, again, as I said, our experience and empirical data that we have been gathering for almost 10 years, let me go over some of the characteristics of scholars as collectors. Just to illustrate further what we mean. First of all, scholars create personal collections over the course of their careers. And these compilations vary widely, depending on the discipline, and they might take several different forms. They may have digitized archival materials, they might have recordings of interviews, maybe numeric data sets from experiments, visual materials. And another important thing to note is the terminology. Although we very often refer to as research data, when you talk with scholars, what you will find is that they will use different terminologies. They would call information, they would call their research materials, they will call evidence, but data is definitely still continues to be maybe closer to probably the conceptual frameworks of scientists than humanists and social scientists. During the last 10 years, we have witnessed the development of research, data support services in many libraries, and that I think one of the motivating factors was the open science movement, especially open access, replicability of research studies, and also probably funding requirements, or funder requirements, I should say, in means of whether it's providing research data management plans with project proposals or public access requirements after a project, after scholars start implementing a project. So as Kamensky articulates in this code, there's quite a bit of attention on big data, shareable data, open data, replicability of science, so on and so forth. However, a vast majority of scholarly work generates what people refer to as small data. Now this long tail curve is very familiar to you, and as you know, there's this kind of intense, steep beginning where data have reused value, they lend themselves for standardization, whether it's metadata standards or interoperability, but then there's a very long tail that where scholars collect information themselves or in small groups, and what we want to highlight is that if you look at this long tail, not everything is shareable or should be discoverable or archived for posterity, there is definitely that kind of transient, transient aspect to data. That is created during various stages of daily workflows. It's not a linear process. It's actually non-linear and quite iterative, and that again, it's not only research data that is, that's a product of implementing a research project, but writing a grant, maybe doing a public science presentation or even a fundraising initiative. Scholars are creating their own collections, they face a number of challenges. In our brief, we categorize them into four groups, organizing, storing, sharing, and preserving, and that just to keep our discussion more succinct today, I will just give three examples, not go through all the information we presented in the brief. So, my first example is a very common one that you hear often. Scholars, as they collect their own content, they need guidance for selecting storage and backup configurations, and I think it's very nuanced, nuanced in the sense that sometimes they need to store just like a backup, almost like a dark archive that their storage needs are different. Sometimes they need to store in such a way that they will be using some software applications to analyze data, so therefore the storage need to lend itself for that sort of storage. The second example I will give is related to the heterogeneous aspects of data that they are collecting, which means that they will have JPEG files, TIFs, they will have audio-visual files, they might have PDFs, they might have 3D images, and one of the challenges that they face in regard to heterogeneous aspect of their data is they research workflow support tools or productivity tools that they will be using. Sometimes they don't necessarily work well each other, some of them are commercial, they lock the data, some of them are open source, and the project sometimes dies down and there's no support for the open source tool that they are used to using. And a third quick example is that of course, scholars have legions to their home institutions, but on the other hand, if you look at it, again, empirical data over and over illustrates us that scholars also have commitment to their own scholar networks, their communities of practice, and during their careers actually they move. They rarely stay in one institution. So as they are creating these collections, as they are storing these collections, one of the challenges they have is how they move these collections from one organization to the next. And also, of course, if they are engaged in a collaboration that includes multiple organizations, again, it pauses a very similar question. So they express need for further support in terms of storage, organizing, managing, discoveries, and now I'm going to turn it over to my colleague, Danielle, who will actually share with you some of our findings and recommendations. So up until now, we've been talking about what the scholars are up to and really putting them at the center of discussion around collecting. And to put that back into dialogue with the kinds of support services that are out there, I start with this visualization. So in essence, Oya and I have identified four stakeholders that are doing some work to support scholars as collectors. Perhaps an exaggeration, but I hope that the visualization illustrates that we have different groups doing tiny little parts or helping out with tiny little parts of the scholars process, but of course, no one of them is particularly comprehensive. Perhaps if I was a better graphic designer and wasn't relying on smart art, I would maybe have figured out a way to also recognize that sometimes some of these other smaller circles also intersect a bit. So just to walk you through these four main stakeholders and what they're currently doing to support scholars as collectors, I start with the funders. So we have the growing trend among funders to require those who are funded by them to make some form of their research outputs openly available. Sometimes that's the published content that could include data, but of course, adherence to these requirements is very uneven and is unenforced and quite difficult to enforce. And another common critique of this approach to moving the needle on openness is the reality that there is much less support for supporting for the infrastructures that would be need to make the content available and preservable. Then we have the advocacy groups, particularly open data advocacy groups. And similar to the funders, the focus is typically on what I would say is a very narrow component of what scholars are working on, which is the emphasis being on data. As Oya already highlighted, the reality is that there's so much other forms of content that scholars are working with, creating and collecting over their life cycle. So this is a challenge with this advocacy angle. Of course, there's also a very strong emphasis on the value of making things open to society at large or research communities as opposed to scholars, individualistic needs and practices on the ground. Now that's not to say that this emphasis isn't important, it's incredibly important, but I highlight this here in particular because there are trade-offs. One approach and the approach, I would argue, of most advocacy groups is very top-down and thinking about the very broad landscape when there is also the reality of the experiences of the scholars on the ground. And as they are the ones that need to be, our partners in this work, it is important to be thinking about their day-to-day experiences and what we're actually asking them to do. And then finally, I want to acknowledge that this kind of advocacy work has been taken up in some geographic areas much more than others with Western Europe and the UK and somebody can explain to me afterwards if the UK is Western Europe, but it has much more traction there. Then we have the tool and service providers and this is some, on one end, there's a lot of vendors doing this work and then we have the more not-for-profit angle, but in essence, the goal of these stakeholders is to serve scholars through tools and services. And I would say that the emphasis here is on a broader array of content. They're probably the group that is doing the best job at this. The reality is, is that a lot of these tools are framed around optimizing scholarly activities or even more neoliberally or nefariously around assessing scholarly activities and their value to the institution. And of course, there is an aim of many of these groups to monetize research content and or the usage analytics that would come out of when the scholars are engaging with them. And finally, we have the academic institutions or perhaps it would be more accurate to say some portion of some academic institutions. Here we find an orientation very strongly in the direction of research data management. So that's going back to the earlier thread that is typically focusing on one particular form of information that scholars are working with. One could argue that they're largely reactionary. This isn't necessarily the locus of innovation in this space. And as I think the folks at Penn State did an excellent job talking through yesterday at their CNI talk, there is the issue of centralized coordination. And if anybody missed that talk, I would highly recommend connecting with the Penn State folks because they just did such a beautiful job of explaining the hard work they've been doing to centralize the various groups on their campus who are interested in research data management. It is really hard work. It often is under-recognized. It's not the kind of thing that you can boast about in the same way you can boast about other shiny new things on your campus. So my hat's off to them, really. And then ultimately, even when we do come up with solutions that are very in-house or campus-oriented, they're just too resource-intensive to build them out, even when they're successful. I mean, it's a situation where they're typically a victim of their own success because it's really hard to scale any of these kinds of services when they're created in-house. So Oya and I are particularly interested in the challenges that are faced by academic institutions when it comes to supporting scholars as collectors. So I highlight here just a few of the risks that are the reality of not, you know, thinking about these issues seriously and particularly through the paradigm of being holistic about the approach as opposed to scattershot. So first and foremost is the reality that there's the potential for a decline in research productivity. And I don't even mean necessarily just for the individual scholar, although you could say that as well, but I'm also thinking about the institution as a whole and the fact that research productivity is a metric that we are often evaluated by. Then there's the reality of simply there being a lot of work duplication, service gaps, the fact that, you know, there's risks to IT, particularly through issues pertaining to privacy. And then finally is the reality that there's a real risk of ceding a lot of control to external vendors. That there's, you know, the potential for a loss of ownership and control if institutions don't take a centralized approach. We are spending so much of our energy on talking through journal subscriptions, UC, Elsevier, but this is the future of vendors taking the control away from institutions. So I would recommend that this is a big reason why we should care about this. So we, Oya and I have thought of some ways forward. They're recommendations and part of the reason why we really like to come to places like this to talk about this work is because we want to hear what people actually think is viable as a way forward, what makes the most sense. So these are our recommendations that we pulled out for our report, you know, first and foremost is designing and promoting services that really think about scholars, not only as collectors, but particularly as curators. This idea that particularly in the humanities and the social, humanistic social scientists, people are collecting primary content in a lot of, you know, really novel and important ways. Any kind of effort to harness that activity would be really exciting. We strongly recommend reframing research data management services to be more inclusive. The reality is, is that they just alienate the whole component of the academic population and are not accurate in framing how people are for the most part doing their work. You could argue that across the entire academic community that there's a lot of, you know, digital information fluencies that need to be fostered. Number four is something that we're particularly interested in, it's about what kind of opportunities there are to actually collaborate with tool providers, either to ensure that institutions, you know, don't seed too much control in this area, but also to make sure that tools are built in a way that are actually useful. We definitely recommend developing more university-wide approaches and policies, and once again, I think that, you know, I'd love to hear more narratives by groups like the group at Penn State about what they've been doing. And then finally, of course, there is still a physical media component to what people are collecting, and so any sort of work around thinking about scholars of collectors really needs to think about the diverse information content types, including the analog. So we, of course, welcome any questions you may have for us. We have two questions in particular we've highlighted, which we love people's reactions to. The first is around who on campus should really be taking ownership of a more holistic approach to supporting scholars' workflows, and whether our paradigm resonates. Do you think that thinking about scholars as collectors could be a good rallying call across the institution? Who should be taking up this work? Is this really the role of libraries, or should it be someone else? You know, Oya and I both come from a very strong library background, so we're always really interested to hear if this is the kind of thing that libraries in particular want to take the mantle up on, or if it just doesn't make sense. And then our second question has to do with how people can actually work with vendors productively and not-for-profit groups around workflow support tools. What would actually make sense here? How far away are we from this? It makes me think a lot of Kathleen Fitzpatrick's talk yesterday. She has such a rallying call around collaboration and not just between institutions or within institutions, but also about these other external groups that we work with. What is possible here? So now we open it up, and we apologize that this is not the most conducive space for conversation, which the video recording will never know. Thank you.


Hello everyone. Today I'm giving a presentation on how to successfully integrate a cover glass onto our new product, the VL53L5CX. So when you put a cover glass on top of a time-of-flight device, the issue that occurs is crosstalk. So you might ask, what is crosstalk? Well below is a diagram showing exactly how we want the device to operate and what we don't want to operate. So the green line is showing exactly how the time-of-flight works. We have a laser emitter, the photon goes out, hits a target, comes back, and we get a nice reading.  We don't want that to happen. But the nice thing about our device is we can characterize what the crosstalk is, measure it, and then remove it when doing final measurements. So in this presentation, what we're trying to show is the best properties of the cover glass to give us the least amount of crosstalk. But on top of that, at the very end, we're going to do a calibration. So whatever crosstalk is remaining, we can remove it. The first step for successfully integrating a cover glass is to obtain one of our application eval boards with the time-of-flight sensor that you're going to be using for your application. And from this, you can actually do a full calibration on a cover glass. So this slide is showing the eval oation kit for the VL53L5CX. When you get this, it's going to have the expansion board, which is the big blue board. On top of that, you need an STM32 nuclear board, which is going to actually give the communication to our sensor to configure it and operate it. Also included in our eval kit is we get some cover spacers. These spacers basically let you adjust the height of the cover glass from the top of our device. Obviously, it is impossible to have a cover glass touching our device because in a manufacturing environment, you can't get those precise of tolerances. So we give some different thicknesses of spacers so you can put the cover glass to match whatever your product will be. We also give an example cover glass that has an IR encoding on it. But obviously, the perfect test is if you can use the actual cover glass of your final product with the spacers to match the space in your final product, then you can actually measure the crosstalk for this device. The next step is to pick the cover glass that fits your application. In this, many different criteria are needed and we're going to go over the criteria that will give you the best performance with as little crosstalk as possible. Before picking a cover glass, many people ask, why do we need a cover glass in the first place? The main reason for this is to protect our device. Our device is not hermetically sealed. So if you want to ensure that no dust gets inside our part or water or any sort of contaminants that could interfere with the long-term performance of our board, a cover glass is needed to ensure protection of our device. In choosing a cover glass, the main things we want to do is minimize the haze. A haze on a cover glass will actually take the photons hitting it and spread it out in all directions. What we want is that the transmitter of our device goes cleanly through the cover glass and hits the target and comes back with no interference. To do this, you want the cover glass to have no structural defects. We want it as flat as possible. No anti-reflectance coatings on there and be very careful on choosing the anti-fingerprint coating that you put on the cover glass. Some anti-fingerprint materials can actually increase the crosstalk dramatically. So we want it to be as low a crosstalk as possible. One thing to note is if you put an IR ring that basically lets all the 940 nanometer laser to go through the glass and blocks out the visible light, you can see two pictures here, one where we show a rough ink and one where we show a smooth ink. Essentially, we do not want the surface to look rough when looking through a microscope. You want it to be as smooth as possible and this will reduce the crosstalk as much as possible. One other thing is the cover glass transmission. We want as much of the IR to go through as possible. So in our guidelines, we recommend that your transmittance of 940 nanometer is greater than 90 percent. Sometimes that can't be helped that sometimes for your application, you might have a lower reflectance or I mean lower transmittance. But just note that the lower the transmittance of the glass, the more the performance of the part will be reduced. For mechanical requirements, the closer the cover glass is to our part, the less crosstalk it will have. So we recommend having an air gap of 0.5 millimeters or less. Obviously, if you can have it touching, it's going to give you the best performance, but obviously in a manufacturing environment, that is not always possible. The second thing is the actual thickness of the cover glass. The photons can bounce within the cover glass, so the thinner it is, the less crosstalk you're going to have.  This is showing basically a side view of how the cover glass looks on our BL5305 device. The IR coating, we want it to be on the bottom side of the glass, not the top, to help reduce the crosstalk as much as possible. And another thing to consider is tilt. On the bottom, we're showing how the device might be tilted with the cover glass. What we recommend is that the cover glass is as parallel to our device as possible. Anytime you put a slight slant on it, it will definitely increase the crosstalk in the final application. Another thing to consider to reduce crosstalk is a gasket. So a gasket can be made of foam or rubber, and it's placed on top of our device. You put hole openings in the gasket. So essentially the gasket is touching the top of our part and it's touching the bottom of the glass. And essentially the photons are then guided through these holes to leave the device and come back only through these holes, by which we're reducing crosstalk as crosstalk can be caused from the photons coming out of our device, hitting the glass and bouncing back to our device. By having the gasket in between the transmitter and the receiver, this can reduce crosstalk dramatically. This also helps in situations if you need to have an air gap more than 0.5 millimeters. The last step is to actually do a crosstalk calibration. ST provides a software that will do the calibration, so this can be done in a production environment, or in a laboratory environment, whatever is needed for your typical application. This slide is showing the actual results of crosstalk calibration on our VL53L5. There's actually two components that we have in our calibration. The first is the signature or the shape of how the photons are coming back to our device. And the second one is the actual amount of crosstalk in each of the 64 zones of our part. With this you can see that the crosstalk is as high as 45 kilofotons per second and as low as 10 kilofotons per second. So with knowing both of these information, we can then know that this crosstalk is always coming because of the cover glass, and we can subtract this out from each individual histogram on the part, giving you the perfect results every time you do arranging. So in conclusion, when selecting the cover glass for your device, the main considerations that we want you to choose from is that the cover glass is very flat, has a very smooth surface, especially when looking under a microscope. This will ensure that your haze is as low as possible. We want an optical transmission to be greater than 90%. We want the transparency of this 90% to be at 940 nanometers, which is the transmission of the laser on our VL53L5CX. Some good examples is a glass and acrylic, a PMMA or a polycarbonate material. The cover glass material from glass or plastic, we don't want to have an anti-reflectance coating at all. We want the clean cover glass, so no dirt on the cover glass. If dirt is added afterwards, such as dust from sitting from a long period of time, this will add crosstalk over time and a new calibration can be needed, or you could apply a higher crosstalk value for each zone to compensate for any dust or dirt that is accumulating over time. For the mechanical constraints, we want the air gap to be as small as possible. We want the glass to be parallel with our device, and the thinner the glass, the better, where we recommend a 1mm cover glass thickness. If you have any questions, please do not hesitate to go to our website at www.st.com.timeoflight, or contact your local ST sales representative.


Welcome back to my channel and welcome if you're new here. My name is Jen. I'm a certified weight loss and nutrition coach and I'm on the WW or Weight Watchers blue plan. Today I have another copycat recipe series video for you. I did my first one not too long ago where I shared three fast food copycat recipes that were WW friendly and amazing. I will link that video down below for you. Today I am back with two more copycat recipes. This time we're talking comfort food, soup chili recipes from some of your favorite restaurants. Not only are they less expensive to make, they're calorie friendly and point friendly and equally if not even more delicious than the fast food or food restaurant version. So I am so incredibly excited about today's video. If you are new, I'd love for you to stick around and subscribe, hit the little bell next to it so you're notified whenever new videos are uploaded. You certainly don't wanna miss out. Also make sure you thumbs up this video if you enjoy recipes and copycat recipes and check out that description box down below where you're going to find my website where you can find and recreate these recipes. They will be on my website, genswwwjourney.com. You're also going to find my nutrition coaching website. I would love to at least figure your macros and calories out for you. I offer one-on-one coaching and group coaching, lots of options, so check that out. And lastly, you're going to find links and discount codes to all of my favorite things and the link to head over and join me on Facebook. We'd love to have you join that community over there. So definitely check out the description box down below. So without further ado, let's make two delicious comfort food copycat recipes. For tonight's copycat recipe, we're going to be making Panera Bread Broccoli Cheddar Soup. We are going to make this WW friendly, calorie friendly and we're even going to pair it with a baguette so we really, really copycat simulate Panera. So let me show you what's in our recipe. First, you're going to need some salt and pepper, low sodium chicken broth, whole milk, butter or butter alternative of your choice. I'm using melt plant-based butter. You could also use, I can't believe it's not butter light and just recalculate your points. It very well could make them a little bit lower. You'll also need flour, light shredded cheese. I'm going to do the Trader Joe's. You'll also need some Belvita Shreds. You could do Mexican style cheddar, really any type of the Belvita Shreds will work.  And for bread, I'm going to be making this essential baking take and bake sourdough loaf. I picked this up from Imperfect Foods. You guys know I love them. It is linked down below. You get $10 worth of free groceries, no minimum order. So you can go pick your $10 out, pay the shipping and have it sent to your doorstep. So we're going to bake this up so we have that nice crusty baguette to dip in our soup. So let's get started on dinner. So in my big stock pot here, I have two tablespoons of butter. I'm going to go ahead and get this melted and then we're going to add in some flour to kind of make a paste before we start with our broccoli, milk and cheese. Once the butter is melted, we're going to go ahead and add in two tablespoons of all-purpose flour. And then we are going to stir until the flour and butter is combined and a paste is formed. Then I have one cup of whole milk. I'm going to add it in slowly and stir as I go. We wanna make sure that the milk gets combined really well with that flour and butter mixture. We wanna make sure that we don't have any big chunks. Look how nice and thick that is.  So again, I'm using the cheddar. So we're actually going to add the entire bag, give it a stir, make sure it gets nice and coated in the flour and milk mixture. We're going to let the cheese melt completely before we add in our next ingredient, which is the chicken broth. Once your soup is fully combined, our cheese is completely melted now, we are going to add in the cup of chopped carrots. Again, you can really chop them however, julienne, shredded, whatever you can get your hands on, but I just went ahead and chopped mine fairly small and we are going to let this cook for about 10 minutes. We wanna make sure that those carrots start to soften. We have our crusty bread in the oven. Ooh, you guys are getting steamy. Okay, so bread is in the oven. It's going to cook for about 15 minutes. This came to a boil, which is completely fine. And you know what else I was thinking? I haven't had Panera's soup for a long time. Are the broccoli pieces this big or are they smaller? Like what you would find in like a store-bought broccoli and cheddar. So I'm thinking once this is all done, I'm going to pop mine in the blender to kind of puree the carrots, onions, and broccoli down. So let me know if you are Panera connoisseurs if the broccoli is big chunks like this. And the last step is to add one cup of the light shredded cheese. I'm going to stir this in. And then again, I'm going to pop this in my blender to puree it down. So we just have little teeny pieces of broccoli and carrots. So I went ahead and added the soup here to my ninja blender. And again, I'm gonna puree this down. Okay, this looks really good. So there are no more big chunks of broccoli. So I think this is more how the soup is supposed to be. Again, let me know down in the comments. Of course, you could leave your broccoli chunks whole if you wanted. I'm pretty excited. This smells really good. So the last step is, is we are just going to add in a little bit of salt and pepper to taste. Our bread is just about done. And I'll be back to show you dinner and give you smart points and calories. All right, so here is dinner. Oh my gosh, this is in this bread. Look amazing. So that big pot of soup only makes six servings. And it is seven smart points per serving for the soup.  So this, you guys, is a 10 smart point dinner. And this soup, by the way I tasted it, is amazing. So I can't wait to dip in that crusty bread. So dinner, 10 smart points. Tonight's copycat recipe, we're making Skyline Chili. Now, I don't have a Skyline where I live, but I have heard rave rave reviews about their chili. We love chili in this house. So I'm pretty excited about this. It has chocolate in it. So that makes me a bit nervous. My husband hates chocolate. So I'm hoping that that flavor doesn't come through. But yeah, we're gonna make through a chili and we're gonna see how we like it. Everything shown here is what you need for the recipe, minus five cups of water. I just didn't show the water, but you'll need five cups. So here are the rest of the ingredients. Sugar or sugar alternative of your choice. I'm just gonna use this monk fruit sweetener. I did not have any bakers chocolate. I thought I did and I don't. So I'm just going to use cacao powder. This is unsweetened. So it's gonna be very similar to baking chocolate. I also have Lily's baking chocolate, but it's sweetened, I believe. So I'm just gonna go with this. Apple cider vinegar, salt and pepper, tomato paste, a six ounce can of tomato paste. And the recipe calls for a pound and a quarter of ground beef. I'm just doing a pound to make it easy. And then we need a lot of seasonings. So red chili pepper flakes, chili powder, cinnamon. And I'm gonna sub the Dax pumpkin spice in place of cloves and nutmeg and all spice and all those pumpkin-y seasonings, because all of those are in this. And it just makes it a lot easier. I love Dax, no MSG, no salt, all natural, clean, clean ingredients. So if you are a big fan of no MSG flavorings with lots of deliciousness, check out Dax. I do have 10% off and free shipping. And I'll put that down below for you guys. I have every one of their seasonings. You can't go wrong. But this pumpkin spice is thebomb.com. So I'm gonna use that. And then we need cumin.  Let's make some Skyline chili. You're going to need quite a large pot for this recipe because we're adding five cups of water. So make sure you're using a Dutch oven or a large stock pot. So I'm going to go ahead and add my five cups of water. So there is four in this glass measuring cup. And then I went ahead and added an additional cup of water to the smaller one. And then to the water, we are going to add our chocolate. So this is half of an ounce of cacao powder or you would do half of an ounce of baking chocolate. And then I'm going to add my entire can of tomato paste, whisk it together until combined. Once the tomato paste is combined really well with the water and the cacao powder, we're going to add about a quarter of a teaspoon of chili powder. Now I'm not measuring, I'm just going to kind of wing it. We're also going to add some ground cinnamon, same about a quarter of a teaspoon. We want one full teaspoon of garlic powder. And then we want one full teaspoon of cumin. And cumin is gonna give it such a delicious flavor. And then we are going to add our pumpkin pie spice about a teaspoon of that. And again, that's in place of the all spice and the cloves that are in this recipe. And then one eighth of a teaspoon of red pepper flakes. Again, we do not like our food spicy. So I'm being very mindful of that. I'm going to put in a good pinch of salt and a good size pinch of pepper and stir it together. And lastly, two tablespoons of apple cider vinegar and one half of a teaspoon of sugar, which I just put in with the apple cider vinegar. And again, give that a quick stir. So you can see that I went ahead and added the raw. I know it sounds weird. Ground beef to the pot here. Go ahead and break it up the best that you can. I did, you can see I kind of crumbled it as I popped it into the stock pot. And then we are going to bring this to a boil. Once your chili has come to a boil, we're going to reduce our heat down to medium low. And then we are going to let this simmer at a very, very low boil for about one to one and a half hours. And that way it'll become nice and thick. The flavors will really start to meld together. And then periodically you just want to give it a quick whisk to make sure that everything is stirred. So we have been simmering for about an hour and a half. It's nice and thick. So this entire pot makes six servings and it is only three points per serving. Now the recipe says to serve this over spaghetti noodles, which is super weird to me, or to serve over hot dogs and make like a chili dog. So I'm opting to do neither of those things. And we're just going to have it in a bowl with some cheese. So I'm going to serve up one sixth of this for three points and I'll be back to show you my dinner. All right, so here's my dinner. This is one sixth of the chili.  So this is six points.  It smells really, really delicious. So by the time you see this video, obviously I would have tried it.  Thank you for joining me on today's copycat recipe video. I hope you enjoyed both the broccoli cheddar soup and the skyline chili. Who'd have thought that chili was so delicious? These are much more affordable, calorie friendly, low point options. So definite, definite must makes for both of these recipes. You will find these recipes on my website, which is linked down in the description box along with my nutrition coaching website. Links and discounts to my favorite things. And of course, the link to head over and join us on Facebook. Come over and join us there. Give this video a thumbs up if you enjoyed it and please take a moment and hit that little red subscribe button and the bell next to it so you're notified whenever new videos are uploaded. You certainly don't wanna miss any. Thank you guys again so much for hanging out with me and I'll see you next time. Bye.


four tips to successful find a job in Kathmandu and why you should start now I guys welcome back to this channel if you need here please consider subscribing don't forget to press the bell icon it's the only way you'll be alerted when the video is out thank you so much for subscribing the returning subscriber thank you so much for the support and really appreciate it but the new subscriber please consider subscribing it's the only way you can support this channel support this channel we are looking at a road 1000 subscribers thank you so much in today's video we want to look at the four tips that you can successfully use to find a job in Kathmandu and why you should start now or why you should start right away remember despite all the favorable factors finding a job in Kathmandu is it, isn't it it will impose here and there up and here, up and here but remember it's only the perseverance that we are talking about we are talking about how you persevere or how you get so much focus and remember we talked about the first document or the item that you should have and one of them is the first one secondly we talked about the mind the mind set which is also very important when you are going to start so it may not be that as a silver plate as someone will do but finally the whole the question is that have you got somewhere where you can start and that's what we talked about you may go through a lot of hassles here hassles here, hassles here, hassles here hassles here, hassles here hassles here, hassles but still you have to look away of how to go there it is only going where you are supposed to go it is going to begin so we are going to look at the four tips that you can successfully use let's start with tip number four that is go to the network levels we have what we call the network levels there is what we call you find that seminars in some countries and some seminars recruitment agencies in some areas please call them try to ask them about be bored enough go ask them about available jobs it does not mean you need to go there with money but go ask them find out what are the available jobs it is a good job I have to let you know guys it is a good job for people who are looking for start up jobs if you want to start up your dream if you want to start up your dream it is the right time we have FIFA World Cup FIFA World Cup 2022 in Qatar this small harrow of countries hosting the FIFA World Cup 2022 there is a lot of opportunities I talked about the opportunities the cleaning opportunities the construction opportunities the technicians opportunities and also delivery opportunities so it is about networking those recruitment agencies wherever they have some seminars call them you have a network you have whatsapp groups talk about talk to people in those groups ask them about people you have Facebook ask them just put a person say anyone who knows any company that is recruiting right now people will give you comments people will comment people will get back to you they may not be all but one or two will give you the right information start basing on yourself build up the networks talk to people that are outside your country ask them they give you connection some of them even know the recruitment companies that are doing the good jobs or doing the good work in your company especially in Africa we may not have too many recruitment the manpower agencies they have those good company they have those good companies they can directly to those companies they can directly to those companies agencies go there and ask ask them what they have and how much you have to put in in case you are to be recruited from outside the country but if you are within the country you can as well move to these cities we have very many cities here in Qatar you can try to move try to move in those cities there are a lot of seminars try to talk about them you can find the right job for you but remember it's all about networking that you have to put first we look at number three is from what you call connections is very important yes we have very many whatsapp groups very many group pages facebook pages but what we talk about yes, put a post up I am looking for this and this please anyone to help me there is one post recently I was just reading actually someone had to get back to that person he say I have a passport for anyone who is willing to help me with import then when I get to the country I pay up there is one gentleman who is coming from India he actually was asking what is a good company how can I come, how can I start but I am just surprised he has said few months he is just here he is in a security company so meaning that the information I gave he was right at that time I helped him that is what I am talking about with connections look around in this group whatsapp groups talk about it don't feel so much proud at one moment in time but we are looking at the way how we can start how we can move it it is all about coming then the rest will fall after remember some of these jobs you given a contract making of that two code one in one year then you can set up something but remember it is all about you having those connections how those connections people that can guide you people that will not make you down people that can give you some bit of something if it is something people can alert you look at the website look at internet also use that look at some of these companies just go and say companies in Qatar security companies in Qatar they will come try to go through one of them give yourself a try and that is what we talk about with information we look at number two number two is make your CV it is very important remember the CV says here how you make your CV it says here get that job look at the description of that job let it match with your CV make that CV so much juicy make that CV you may know that you are powerful and show that that person is going to the company that he really has someone that is going to put them someone who is going to make a contribution to the company and that is going to be by your CV so make that CV make that CV post that CV on the websites there is a lot of government there is a website where you can post your CV make it juicy give it out put the career pages post your CV send that CV tell them to be post some of your CV to this company tell them what come you might be there what company is calling you up and you get that opportunity at one moment of time which is my point number two we look at point number one is all about start early remember I say there is no opportunity that is going to look for you but you are going to look for that opportunity how do you tend to look for that opportunity it is by starting start looking for that opportunity start early that opportunity why start before you begin even starting make sure you have the two important documents your mindset is prepared for that job you prepare to take on the opportunity you prepare to take on your potential to persevere to take on anything that can feel in between you prepare to face the realities don't put too much expectations but no you will face some kind of reality remember when we are talking about our journeys you only must be in the journey the people around you the people next to you they will only come to celebrate with us remember starting early is very important start early start up the look out talk to people around talk to your friends talk to them go to the internet start most of these companies such about the people world cup kind of opportunity it can be the hospitality it can be the cleaning it can be the security guards try to search that try to get your CV drop it in one of the career pages of those companies try it on the journey it's not all about losing the power but it's getting more energetic don't forget to sub don't forget subscribe don't forget to put a comment in a sub section tell me what you feel about the view tell me what you like about the view if there's anything that you want us to know but in the comment section our role is to be more than happy because we are looking at helping one another we help one another మ్మ్న్న్ness మ్వ్ద్న్నార్ందార్క్దార్ంద్డిధరందినిసిందార్త్క్గ్ఱంది. ఇవంన్తాయు. పంఆపఔన్ందారి. మ్త్టకిత్కిందింద్. now it's right now where you need to go for that opportunity, look at those who want to make companies, it's better right now to start it up such that you can also have, thank you so much, see you again in the next video, don't forget this makes from the makes creation team


Who is your ballast? Who anchors you?  Well, I'll tell you who's been for me. It's been great friends. The problem with that is that great friends are rare and hard to find. I'm going to show you how to find people that bring this amazing gift to our lives and the most needed of being a ballast to our lives and anchoring, a grounding to our lives. Join me next Monday for my mentorship, Zoom teaching conversation when I'm going to be speaking on the forensics of friendship. Listen, on Sunday the opportunity expires for you to get three months for the price of two. Jump in and join us for three months, but for the price of two that offer expires this Sunday. Until then, I'll see you on Monday.


In this video we will see how to send messages on WhatsApp without saving a number using the app MessageToUnknownNumber on your iPhone. Let's start with opening the app. Here select the country code. Enter a mobile number which you don't want to save on your phone. Type your message and tap send message. You will be asked to open in WhatsApp. Just tap on it and it will redirect you to WhatsApp chat. Tap send and it's done. With this you can also send an unignorable message. Enter the country code, mobile number, your message and tap on send unignorable message. Tap on open in WhatsApp and it will redirect you to WhatsApp chat. Tap send and this will add urgent before your message which can only be read by tapping read more.


Sometimes you have missing data in the dependent variable and the missingness depends on the actual missing value. For example, if you study the wages of people, whether you actually observe a wage for a person might depend on the wage that the person was offered and then the person declined the job. In this case, selection models can be useful. The most commonly used and well-known selection model is the one introduced by James Heckman in 1976 and 1979 papers that won him the Nobel Prize in Economics in 2000. How does the Heckman selection work and what kind of assumptions does it make? Let's take a look. So, Heckman's selection model and selection models more generally address the missing not-at-random case. So, the worst case scenario in missing data is when you have missingness in the dependent variable and the actual, whether the observation is missing or not depends on the value that is missing. So, if the missingness depends on the value that is missing, then of course we can't test what is the missing data mechanism. So, this must be assumed based on theory. For example, if why here is the wage that the person was offered and then we observe only those people who decided to accept the job, we could infer based on theory that if a person is offered a very low wage, then that person would rather stay at home, for example, to take care of the kids or stay at home and enjoy unemployment benefits or some other reason. So, this is the scenario that the Heckman selection model addresses. When Heckman's selection model is used, you can see a couple of terms appear in papers. So, this is not a good example of how the Heckman selection model is applied, but it shows you the key terms. You can see citations to 1979 paper, you can see the word Heckman, and you can see the word inverse mills ratio. So, what is, who is mills, why is the ratio, and why do we take an inverse? Normally, in a selection model, you run this kind of equation. So, you have two equations in the selection model. So, you have the selection equation. This example comes from Ender's book, and then you have the main model. So, in Ender's book, we have IQ explaining job performance, and then we have well-being predicting on whether that person stays in the job or not. So, we have one equation that tells us or tries to explain if we observe the value of the dependent variable for a person, and another model that tries to explain the differences in the dependent variable, controlling for the fact that some of those values were not observed. And how this works is that we must make the assumption that the error terms of these two models are normally distributed, and we assume that they are correlated. So, whatever causes job performance also causes the missingness. So, this is basically that an unobserved variable, unobserved part, or the error term of job performance also correlates with the selection. And this selection model is a probit model, and the idea of a probit model is that the job performance data is observed if this predicted value plus the error term zeta is greater than zero. So, at this point it's a good idea to recap what the probit model was about, and the idea of a probit model was that we estimate the linear prediction first. So, here's our linear prediction, we call it y star, and this is like a normal regression model. And then we add a normally distributed error term to the linear prediction like we do in the recursion analysis, and instead of observing that y star directly, we observe one if y star is greater than zero, and we observe zero otherwise. And here one is that the person was observed, and zero indicates that there is missing data for that person. So, this is briefly the probit model, and if you don't know probit model, then use the probit look at another video where I explain this model in detail. Why we use the probit model will become clear pretty soon. So, let's get back to the selection model, and let's use an example. And this example comes from status user manual. This is a way to implement the selection model using status or generalized structural ecosystem modeling. We will not be using it structurally because I'm modeling an example, but this is a nice path diagram, a nice data set that shows what we're going to do. So, we are explaining the wages of people, women in this case, and how the wages depend on age, education and age. The problem is that if a person was offered a low wage, they would choose not to work. And this is the problem that Heckman addresses when he was developing the selection model. We also have a selection equation, and we think that whether a person decides to work or not depends on whether they're married or not, and whether they have children or not. We could, for example, theorize that a person who has many children needs more money, and therefore is more likely to work. We could also theorize that a person who has many children would favor staying at home with the kids instead of going to work, depending on what kind of benefits you get for having kids from the government in the society where you live. But anyway, we think that whether you're married, whether you have kids affects whether you go to work or not, and we also think that the marriage or the number of kids will not affect how much money you are offered for a job. And it would be illegal for that to matter, at least in Finland where I live. So we run a regular model, or Heckman's selection model, using this data. So these are artificially generated data by this data. And we get two sets of estimates. We get the wage equation. This is the equation of interest. And we get the selection equation. And this selection equation is simply a probit model. So we can see that if you're married or if you have kids, you're more likely to work. And age has an effect. If you're older, you're more likely to work. If you're higher education, you're more likely to work. And then now this wage education here controls for the selection effect. So how does this actually work? And let's take a look how it works. And here's the selection model again. So this is from Ender's book. So we have job performance, a dependent variable. We think well-being explains whether you actually stay at job or not. And we think that well-being does not affect job performance. And we think that IQ does not affect selection. Normally, we would regress selection on IQ because that's how the thing is done. I will get to that a bit later, but let's take a look at the basic idea. So the idea here is that we try to control for zeta. So if we could control for zeta somehow, then this epsilon would be uncorrelated with the selection. So that's the key inside here. This selection is kind of like an omitted variable problem. If we can come up with the right control variable, then the epsilon after controlling for the right control variable will be uncorrelated with the selection. So the key problem here is that we need to estimate the zeta here. And how do we go about doing it? How do we estimate zeta? And this is where the probit model and the normal distribution assumption becomes important. So the idea of a probit model was that we observe a one if this equation here is a y star is greater than zero, and we observe a zero or we don't observe the dependent variable that is missing as if this equation is less than zero. And how does it work? So we have linear prediction. So we have our betas times the observed predictors in the selection model. We call it XB. And if the linear prediction is A, then we know that the zeta must have a value of at least minus A. So for example, in this case, the fitted value of the linear prediction is minus one. We know that then zeta must receive at least the value of one for that case to be observed. And then the area on the right hand side of the normal curve gives the probability of actually observing the case. Also, if we know that zeta is at least minus A, then we need to calculate what is the expected value of zeta. So if we know that zeta must be at least one, so this is the normal distribution zero is here centered at minus one, and this is plus one standard deviation about the mean. So if we know that zeta is a plus one standard deviation, then what is the expected value of zeta? So we basically know that if we observe a case, then zeta did not receive any of these values. So it didn't get any of the values that are below one. It must have gotten a value above one. So assuming that the value is above one for zeta in this case, what is the expected value of zeta? So we basically would have to integrate over this normal distribution, this part, and that would give us the mean. So we don't actually have to do the integration because the expected value or the mean of this data is given by this kind of equation. So basically what this quantifies is the ratio of this probability density, so the height of the normal curve at the cutoff point divided by the area under the curve. And A is the predictive value, so A is the value that we need, and one minus A is this area on the right-hand side of the curve. The reason for that is that the cumulative distribution gives us the area on the left-hand side of the cutoff, so one minus the cumulative distribution is the right-hand side. There are different ways of calculating or expressing this quantity known as inverse mills ratio. So why is it an inverse mills ratio? There is a statistic called mills who worked on the ratio of probability density, probability, cumulative probability, and divided by probability density, so that's mills ratio, and this is just the opposite of the same ratio. So mills was working on the ratios of probability, cumulative probability divided by probability density, and we are dividing probability density by cumulative probability, and there is not much more to that. So if you want to know where this comes from, you need to study statistics, but I don't think that understanding where this comes from is very useful for an applied researcher. It's a bit unfortunate that the name inverse mills ratio is used in applied papers. I would think that, for example, expected zeta would be a lot more descriptive of what the inverse mills ratio actually quantifies. Let's take a look at how this works in a bit more detail to try to understand what the Hecron Selection Model is doing. So this is the probate model using the same data set, so we are running a probate model selection on married children, education, and age, and this is the probate coefficients. They are, of course, the same that we have in the Selection Model because we are estimating the same probate model. And we will then take the predicted values, the fitted values, they are called xb, so x is the observed data, b is the regression coefficients, so observed predictors times regression coefficients is the predicted values, and here is the predicted value for a particular case. So, for example, the first case, the predicted value is minus 0.69 and this case was not observed and so on. And then we have min zeta, so I generated the value of min zeta, and this is the value that the zeta must receive at least for this case to be observed. And I only calculated it for the selected observed cases. We could of course calculate it for all cases, but I chose to just calculate it for those cases that we observed because those are the ones that we use in the main equation. So if the predicted value is minus 0.2, then zeta must receive a value of at least 0.2 for that case to be selected. So zeta is normally distributed with the mean of 0 and standard deviation of 1. And so if we require a positive value for zeta, we know that the probability for being selected is less than 50%. And then the inverse mills ratio tells what is the expected value of zeta, given that we know that zeta must be at least 0.2. So this is the idea. And let's take a look at the inverse mills ratio a bit more. So here is a hypothetical zeta. So this is the distribution of zeta. And the gray area is those values that would cause the case to be selected. And if the predicted value is very large, then the case will be selected no matter what. So if the predicted value is, let's say, the linear prediction is 5, then pretty much any value of zeta that we add to 5, because zeta is standard normal distributed, will lead to a value that is greater than 0 and therefore the case being selected. This red line gives us the average or the expected value of zeta, assuming that zeta is greater than whatever cutoff we have. So we can see how the inverse mills ratio, the expected value, depends on the minimum required zeta. And this animation shows it that when we start increasing the required zeta a bit, then we can see that the mean or the expected value of this remaining zeta goes to the right, so it increases. And this basically just demonstrates what the inverse mills ratio quantifies. It quantifies what is the expected value of those values of zeta that would cause the case to be selected. And we can see that as the required zeta increases, so does the inverse mills ratio. In fact, if we take a look at the relationship between the linear prediction and the inverse mills ratio, we can see that this is a decreasing function. So if the linear prediction is very large, if we say that, okay, the linear prediction is 4, then pretty much any value of normal distribution would qualify and the expected value from normal distribution is 0, so the inverse mills ratio is 0 as well. When the predicted value goes to, it's highly negative, for example, minus 4, then we need a very large value from zeta, only the right-hand tail of the zeta qualifies and produces y star that is greater than 0. So the relationship between the linear prediction and the probit model inverse mills ratio is here and we can observe a couple of things. For the xb, the linear prediction is, of course, a linear function of the observed variables. We can also see that for the most part, inverse mills ratio is approximately linear function of the prediction. So if we take, for example, from minus 2 to 2, then a line would be a pretty good approximation and that is where most of the cases fall between plus and minus 2 standard deviations from the mean. So the inverse mills ratio is close to a linear function of a weighted sum of the independent variables and this causes a bit of a problem. So if we include the inverse mills ratio from a probit model, if we calculate it using the same set of predictors that we use in the main model, it might work but it would produce an extremely collinear model. So we would need a large number of observations and also the identification of our model would depend on the fact that this curves a little bit and justifying this kind of functional form would be very difficult based on theory. In practice, we need to have instrumental variables to avoid this collinearity issue. So the collinearity issue is avoided here in Ender's paper by having well-being being a predictor of Selexon but not a predictor of a job performance. So in practice, you need to have these instruments in the Hikma-Selexon model and the reason why we need to have them is that otherwise the inverse mills ratios will be perfectly or nearly perfectly correlated with these predictors in the main equations. So if we have a very large sample size, we could in theory estimate the IQ's effect, only IQ in the Selexon model and get this to actually run but we would rely on the differences in the functional form linear here and probit here for identification and that's a bit tedious because we know that probit model is roughly linear for a large part of the observations so we would rely on the ends of the distribution for identification and that's challenging. In the status example, we have two instruments so we think that whether you're married, whether you have kids, they affect whether you go to work or not, that's reasonable and these are not supposed to affect how much wages you get. In fact, in many cases, if you apply for a job, you wouldn't tell the employer whether you're married or how many kids you have and therefore there's no way that the wage that the employer offers you would depend on marriage and children because that's not something that the employer would know and these are instruments because they are uncorrelated with the wage determinants. So are these models useful? They are certainly widely used but there are some big caveats and these caveats are that the Hekman Selection Model is highly sensitive to the assumption that the Selection follows the probit model. So you saw that when we calculate the inverse mills ratio, we use the normal density and the cumulative normal distribution for calculating the quantity. If those distributions actually don't characterize the selection process then the inverse mills ratios or the expected values of zeta will be incorrect and the results will be widely mislead. There is some research that shows that the Hekman models assumptions are actually very important if the assumptions don't hold then it might be better to just estimate the selection equation estimate the main equation with other missing data techniques that assume missing at random instead of trying the Selection models. Nevertheless these are very commonly used, people generally don't justify the assumptions and justifying the assumptions would be very difficult to do based on theory because it is difficult to justify the functional form of things that we don't observe. How do we justify that there is a strict cut-off for y star that we don't observe? y star, how do we justify that zeta is normally distributed? We pretty much can't. So because of that some methodology or sources say that these should be used more as a sensitivity analysis. So we could do a normal model with missing at random techniques such as multiple implementation or maximum likelihood for missing data and then do a Selection model then compare the results and say that okay so the first set of results shows there are estimated effects assuming that missing data mechanism is MAR and the second set assuming that there is a normally distributed Selection process. We of course don't know if idea of these assumptions are true but that would give us some kind of ballpark estimates of where the effects might be. So HECMA Selection model while it's a very useful conceptually there are some empirical challenges that make it less useful than what it seems and that's one of the reasons why for example Ender's book takes a rather dim view on the Selection models.


In this video, we're going to look at the basics of algebra, focusing on the notation that is used. Whilst we will quickly look at why we use algebra, I am going to assume that you have met it a little bit before. In algebra, we use symbols to represent numbers. These are usually letters. The symbols are called variables. Calculating symbols instead of numbers is great because they may sometimes represent long numbers and so it saves us having to write them out every time. Like pi, which is 3.14159 and so on, which makes calculating much easier. Instead of writing out the whole numbers every time, we can just say a multiplied by b instead, much easier. A big reason for using symbols instead of numbers is that the symbols or variables can represent unknown quantities and for showing formulae like the area of a circle. So let's have a look at some of the key notation. Adding and subtracting stays the same but instead of writing the multiplication sign, we can remove it and just write a b instead. So if you ever see two letters next to each other or a number and a letter, then it just means to multiply those things together. Notice how we always use a curly x for the letter x because otherwise it would look like a multiplication sign so make sure your x is a curly. So that's adding, subtracting and multiplying. Instead of the division sign, in algebra, we usually write it as a fraction and then we still use everything else that you've probably come across in arithmetic. Another great habit to get into is to leave numbers as fractions rather than decimals, so 3 eighths a rather than 0.375a. From that note, these mean the exact same thing. You can write it whichever way you prefer. So there's an introduction to basic algebraic practises. Remember to always use a curly x that we don't really use a multiplication sign and that we usually write things as fractions rather than using the division sign.