So, let us take a question from your NCRT, write down y is equal to 0.005 sin of this, this is in meters 80 radians per second, 80 radians per minute, so they are all in the SI units. You need to calculate these things amplitude, wavelength, time period and frequency, you need to calculate displacement at x equal to 30 centimeter and t equal to 20 seconds. Solve it, step forward numerical, you must be getting it right, you step forward 0.005 meters 5 by 40, which is how much, 0.078 meters, find out the time period and then tell me the frequency. So, it is time period is 2 by 30 and t equal to 3.14, can you take rest 3 by 30, 3.13. So, it is time period is 2 by 30 and t equal to 3.14, can you take rest 3 by 30, 3.13. 2.09 is the time period, frequency, inverse of this 0.46, 0.48, the unit is hertz. So, it is root 5 plus 1 by 4. No, it is 36 and 88. Solve it, solve it, no one is getting answer, no one is capable to get the answer. So, how much comes inside sign of this 36 inside minus 36, what do you get, now I have to understand that this is beyond 2 pi or minus 2 pi. So, I will first bring it between 0 and 2 pi, alright. So, how many times I have to add pi to make it, add 2 pi to make it come between 0 and 2 pi? 5. 5 pi is how much? 5 times 2. 5 pi is how much? 15. 15. So, why are you adding 5 pi? 5 times 2 pi. 11 times. 11 times. 5 times of 2 pi, so that is 10 pi, which is 31.4. So, you can go further in, I can do it 12 and then subtract. Let us see. So, you can add 31.4 without affecting its value, yes or no? Yeah. Right? So, let us add it. So, what will come inside? Minus of? 4.6. 3.6. 4.6. Ok. Now, I can add 2 pi. 2 pi is how much? Roughly? 6.28. 6.28. So, add 6.28. How much it will come? 4.6, this is 8. 1.6. 1.6. So, this is 0.005, sign of 1.68. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 1.6. 0 and t equal to 0, the value of y is a by root 2, a is the amplitude. You can assume omega and k to be present. k omega are given. Oh, okay then done. You should not say that only one question sir has done in this topic sir. Two questions. So, value of phi is? Pi by 4 or 3 pi by 4. Pi by 4. Or it can be the other. So, y is equal to k x minus omega t plus pi by 4. Okay, right now, speed of travelling wave, speed. Have you played that game Need for Speed? Yes sir. Right on speed of travelling wave. So, we are going to talk about the transverse wave first. Now, what we have just learned, speed of it is equal to omega by k. Right, it is omega by k. Similarly, acceleration is equal to dv by dt. But still, we use force equal to mass m acceleration to find out the acceleration. Why can't we get acceleration as dv by dt? See, it is different actually when you write down the, so velocity is omega by k is the formula. Now, that is assuming that you know the equation of wave. But what if you don't know the equation of wave? Equation of wave is not good to you. Then you need to find out the velocity of wave by applying some physics. You need to understand what kind of medium it is. Okay, what is the force applied on it. And then depending on that, you determine the velocity of the wave. Okay, so velocity of the wave that is what we are finding out. It is like finding out velocity of a person when the person is inside the car. If you know the displacement of the person after certain time, you can find out the displacement by time as velocity. Okay, at the same time, if you know what kind of force is applied on the car and using that you can find the acceleration of the car and then velocity after some time. Okay, so why I am taking this example of a person inside the car because the velocity of the person inside the car depends on the property of the car, not on the person. Similarly over here, write down the velocity of the wave velocity of the wave depends on the property of medium only. It does not matter what kind of wave it is as in whether it is sinusoidal or triangular. We are talking about transverse wave though. Velocity of the wave depends on what kind of wave as in transverse or longitudinal but what is the shape of the wave that is immaterial. With how fast you are moving one end of the string up and down like that, it doesn't matter. How big a stone you throw in the water, it doesn't matter on that. The velocity of the wave depends only on the property of medium, whether you are throwing stone on the water or on the oil. Okay, whether the string which is tied, whether that string is very light or it is very heavy, whether it is very stiff or very elastic. So velocity of wave only depends on the property of the matter. And let's take example of a string transverse wave in a string. Now suppose I am plucking it from here and I am moving this end up and down. How should I move this end up and down so that sine wave get generated? It should be in SHM. Every point should be doing SHM. So you force this point to SHM from your hand, every other point will automatically start doing SHM. Got it? Now the disturbance will travel from this point forward like this. Now what kind of property the string will have? It will have inertial property and elastic property. Understood? So property of the medium is defined as, please write down, it is inertial and elastic. When I talk about inertial, what I mean to say is that inertial represents mass. Okay, it represents mass in a way. Now if I take a bigger string, will my mass increase? No. Mass will increase, inertia will increase but will velocity get affected if I take a bigger string or a shorter string? No. But it should depend on some sort of inertia. So basically the only thing that is constant between the bigger and the smaller string is mass per unit length. So inertial property is quantified as mass per unit length. This is represented by letter mu. Mu is mass per unit length. Okay? This is inertial property. Now elastic property, how should I quantify the elastic property? By the way, what is elastic means? Elastic means what? The amount of force or amount of resistance it can generate to come back to its original shape. Yes or no? That is how you can define the elasticity. So if it generates more amount of force for smallest of the change in shape, it is more elastic because it is trying very hard to go back to its original shape. So that is why steel is more elastic than rubber. Okay? So elasticity can be quantified as the resistive force that gets generated. And in a string that resistive force is tension. Okay? So this is the force with which the string tries to regain its original shape and size. Okay? Now the thing is that velocity will be 0 if either of inertial or elastic property becomes 0. If it is not inertial then what will happen? But the particles have 0 mass so when particles are doing SHM, when it comes to the mean position, force is 0. So why it will come down? It will stop as soon as force becomes 0. It does not have any inertia. Okay? So it will not perform an SHM. So inertia is required. Okay? And tension is required because the thing is that this particle which is there has to stretch the adjacent one also. If one is getting stretched it has to pull the adjacent one also then only the shape will get transferred from one point to the other point. Are you getting what I am trying to say? Okay? So if this point is moving along with it the adjacent point will also move because of elasticity. Okay? Now tell me velocity should be directly proportional to or velocity should increase when tension increases or decrease when tension increases? Increase. So velocity should be proportional to tension raise to power A let us say, T raise to power A. I don't know whether it is directly proportional to T or T raise to power 2 or T raise to power 3 or whatever it is. Okay? So I am writing T raise to power A and what about inertia? There could be some doubt whether inversely or directly so let's take it as mu to the power B. Now it could be inversely if B comes out to be negative. I don't know. Okay? So combining these two I can say that velocity is some constant K times mu to the power B and T to the power A. Now using dimensional analysis get me the value of A and B. A is half B is minus half. So basically velocity comes out to be K under root T by mu. K is a dimensionless constant and the value of K comes out to be equal to 1. So V is equal to under root of T by mu. So velocity of the transverse wave on a string is given by this equation. It has nothing to do with the shape and size of the wave. It is fixed depending on what is the medium property. Understood? No doubts.


on disruptive technologies. And if you think about the disruption that's happening in business with IoT, with OT, and with big data, you can't get anything more disruptive to the whole of the business chain as this particular area. It's an area that I focused on myself asking the question, should everything go to the cloud? Is that the new future? Sure, is 90% of the computing going to go to the cloud with just little mobile devices route on the edge? Felt wrong when I did the math on it. I did some examples of real world environments, wind farms, et cetera. It clearly was not the right answer. Things need to be near the edge. And I think one of the areas to me that solidified it was when you looked at an area like video, huge amounts of data, real important decisions being made on the content of that video. For example, recognizing a face, a white hat or black hat. If you look at the technology, sending that data somewhere to do that recognition just does not make sense. Where is it going? It's going actually into the camera itself right next to the data because that's where you have the raw data. That's where you have the maximum granularity of data. That's where you need to do the processing of which faces are which right close to the edge itself. And then you can send the other data back up to the cloud, for example, to improve those algorithms within that camera to do all that sort of work on a batch basis over time. That's what I was looking at and looking at the cost justification for doing that sort of work. So today we've got a set of people here on the panel and we want to talk about coming down one level two where IoT and IT are going to have to connect together. So on the panel, I've got, I've got to get these names really wrong. Sanji Kumar from Foghorn. Could you introduce yourself and what you're doing where the data is meeting the people and the machines? Sure, sure. So my name is Sanji Kumar. I actually run engineering for a company called Foghorn Systems. We are actually bringing analytics and machine learning to the edge. And so our goal and motto is to take computing to where the data is than the other way around. So it's a two-year-old company that started, was incubated in the hive and we are in the process of getting our second release of the product out shortly. Excellent. So let me start with the other hand. Rohan, can you talk about your company and what contribution you're focusing on? Sure. I head product marketing for Mana. Mana is a startup about three years old. What we're doing is we're offering an enterprise platform for large enterprises. We're helping the likes of Shell and MERSC and Chevron digitally transform. And that simply means putting the focus on subject matter experts, putting the focus on the people. Data is definitely an important part of it, but allowing them to bring their expertise into the decision flows so that ultimately the key decisions that are driving the revenue for these behemoths are made at a higher quality and faster. Excellent. Well, two software companies, we have a practitioner here who is actually doing fog computing, doing it for real, has been doing it for some time. So could you like Janet George from Western Digital? Can you introduce yourself? Say something from the trenches, so what's really going on? Okay, very good. Thank you. I actually build infrastructure for the edge to deal with fog computing. And so for Western Digital, we're very lucky because we are the largest storage manufacturer and we have what we call Internet of Things and Internet of Test Equipment. And I process petabytes of data that comes out of the Internet of Things, which is basically our factories. And then I take these petabytes of data, I process them both on the cloud and then on the edge, but primarily to be able to consume that data. And the way we consume that data is by building very high profile models through artificial intelligence and machine learning. And I'll talk a lot more about that. But at the end of the day, it's all about consuming the data that you collect from anywhere. Internet of Things, computer equipment, data that's being produced through products, you have to figure out a way to compute that. And the cloud has many advantages and many trade-offs. And so we're going to talk about the trade-offs. That's where the gap of fog computing comes into play. Excellent. Thanks very much.  Berkevici. Thank you. You are in the midst of a transition yourself. So talk about where you have been and where you're going. For the better part of this century, I've been with NetApp, working in various functions, obviously enterprise storage. And around 2008, my developer instinct kind of fired up and this thing called cloud became really interesting to me. So I became the self-oriented cloud czar at NetApp and I ended up initiating a lot of our projects, which we know today as the NetApp data fabric that culminated about 18 months ago in an acquisition of SolidFire. And I'm now the acting CTO of SolidFire, but I plan to retire from the storage industry at the end of our fiscal year at the end of April. And I'm spending a lot of time with particularly the Cloud Native Compute Foundation, that is the open source home of Google's Kubernetes technology and about seven other related projects. We keep adding some almost every month. I'm starting to lose track. And spending a lot of time on the data gravity challenge. It's a challenge in the cloud. It's a particularly new and interesting challenge at the edge. And I look forward to talking about that. Okay, and data gravity is absolutely key, isn't it? Yeah. It's extremely expensive and extremely heavy to move around. And the best analogy is workloads are like electricity. They move fairly easily and lightly. Data is like water. It's really hard to move particularly large quantities around. Great. I want to start with one question though. Just in the problem, the core problem, particularly in established industries, of how do we get change to work? In an IT shop, we have enough problems dealing with operations and development. In the industrial world, we have the IT and the OT who look at each other with less than pleasure and mainly disdain. How do we solve the people problem in trying to put together solutions? You're must be right in the middle of it. Would you like to start with that question? Absolutely. So we are 26 years old, probably more than that. But we have very old and new mix of manufacturing equipment. It's a storage industry. And in our storage industry, we are used to doing things a certain way. We have existing data. We have historical data. We have trend data. You can't get rid of what you already have. The goal is to make connectors such that you can move from where you're at to where you're going. And so you have to be able to take care of the shift that is happening in the market. So at the end of the day, if you look at five years from now, it's all going to be machine learning and AI, right? Agent technology is already here. It's proven. We can see Siri is out here. We can see Alexa. We can see these agent technologies out there, right? So machine learning is getting a lot of momentum, deep learning and neural networks and things like that. So we got to be able to look at our data and tap into our data near realistically, very different. And the way to do that is really making these connections happen, tapping into all versus new. Like for example, if you look at storage, you have file storage, you have block storage, and then you have object storage, right? We've not really tapped into the field of object storage, really. And the reason is because if you are going to process one trillion objects like Amazon is doing right now with S3, you can't do it with the file system level storage or with the block system level storage. You have to go to objects. Think internet of things. How many trillions of objects are going to come out of these internet of things? So one, you have to be positioned from an infrastructure standpoint. Two, you have to be positioned from a use case prototyping perspective. And three, you got to be able to scale that very rapidly, very quickly. And that's how change happens. Change does not happen because you ask somebody to change their behavior. Change happens when you show value. And people are so eager to get that value out of what you've shown them in real life that they are so quick to adapt. That's an excellent comment on that as well.  And two analogies that actually work very well. One is the operational people are very familiar with circuit diagrams and flow of things through essentially black boxes.  So that's one thing that worked very well. The second thing that works very well is the PLC model. And there are direct analogies between PLCs and analytics, which people on the floor can actually relate to. So if you have software that's basically based on data streams and time as a first class citizen, the PLC model, again, works very well in terms of explaining the new software to the OT people. Excellent. OK. And would you want to come in on that as well? I think a couple of points to add to what Janet said. I couldn't agree more in terms of the results. I think Manna did a few projects, a few pilots to convince customers of their value. And we typically focus very heavily on operationalizing the output. So we are very focused on making sure that there is some measurable value that comes out of it. And it's not until the end user started seeing that value that they were willing and open to adopt the newer methodologies. A second point to that is a lot of the more recent techniques available to solve certain challenges that are deep learning neural nets. There's all sorts of sophisticated AI and machine learning algorithms that are out there. A lot of these are very sophisticated in their ability to deliver results, but not necessarily in their transparency of how you got there. And I think that's another thing that Manna is learning is, yes, we have this arsenal of fantastic algorithms to throw our problems, but we try to start with the simplest approach first. We don't unnecessarily try to brute force, because I think an enterprise, they are more than willing to have that transparency in how they are solving something. So if they're able to see how the software was able to get to a certain conclusion, then they are a lot happier with that approach. Could you maybe just give one example, you're a real world example, make it a little bit more real? Right, absolutely. So we did a project for a very large organization for collections. They had a lot of outstanding capital locked up in customers not paying. It's a standard problem. You're going to find it in pretty much any industry. And so for that outstanding invoice, what we did was we went ahead and we worked with the subject matter experts. We looked at all the historical accounts receivable data. We took data from a lot of other sources and we were able to come up with models to predict when certain customers are likely to pay and when they should be contacted. Ultimately, what we wanted to give the collection agents were a list of customers to call. It was fairly straightforward. Of course, the approach was not, I mean, the solution was not very, very easy, but at least on a holistic level, it made a lot of sense to us. When we went to the collection agents, many of them actually refused to use that approach. This is part of change management in some sense. They were so used to doing things their way. They were so used to trying to target the customers with the most largest outstanding invoice or the ones that hadn't paid for the longest amount of time that it actually took us a while. Because initially, what the feedback we got was that your approach is not working. We're not seeing the results. And when we dug into it, it was because it wasn't being used. So that would be one example. So again, proof points that you will actually get results from. Absolutely, and the transparency. I think we actually sent some of our engineers to work with the collections agents to help them understand what approach is it that we're taking, and we showed them that this is not magic. We're actually, instead of looking at the final dollar value, we're calculating time value lost. So we're coming up with a metric that allows us to incorporate not just the outstanding amount or the time that they haven't paid for, but a lot of other factors as well. Excellent. Val. When you ask that question, I immediately went to more of a non-technical business side of my brain to answer it. So my experience over the years has been particularly doing major industry transitions. I'm old enough to remember the mainframe, the client-server transition, and now client-server to virtualization and cloud. And really, sales reps have that well-earned reputation of being coin-operated. Well, it's remarkable how much you can adjust compensation plans to pretty much anyone in a capitalist environment. And the ITOT divide, if you will, is pretty easy to solve from a business perspective when you take someone with an IT supporting the business mentality, and you compensate them on new revenue streams, new business, all of a sudden, their world perspective changes sometimes overnight or certainly when that contract is signed. That's probably the number one thing you can do from a people perspective is incent them and motivate them to focus on these new things. The technology, particularly nowadays, is evolving to support them for these new initiatives, but nothing motivates the right compensation plan. Excellent, a great series of different viewpoints. So the second question I have, again, coming down a bit to this level, is how do we architect a solution? We heard you've got to architect it and you've got layers like this. It seems to me that that's pretty difficult to do ahead of where you're going. That in general, you take smaller steps, one step at a time, you solve one problem, you go on to the next. Am I right in that? If I am, how would you suggest that people go about this decision making of putting architectures together and if you think I'm wrong and you have a great new way of doing it? I'd love to hear about it. I can take a shot at that. So we have a number of customers that are trying to go through a phased way of adopting our technology and products. And so it begins with the first gathering of the data and replaying it back to build the first level of confidence in the sense that the product is actually doing what you're expecting it to do. So that's more from, you know, monitoring administration standpoint. The second stage is you begin to capture analytical logic into the product where it can start doing, you know, prediction for you. So you go into, so from operational, you go into a predictive maintenance, could be predictive mentors, predictive models standpoint. The third part is prescriptive where you actually help create a machine learning model. Now, it's still in flux in terms of where that model gets created, whether it's on the cloud in a central fashion or some sort of a, you know, the right place at the right context in a multi-level hierarchical fog layer. And then you sort of operationalize that as close to the data again as possible. So you go through this operational to predictive to prescriptive adoption of the technology. And that's how people actually build confidence in terms of, you know, adopting something new into let's say a manufacturing environment or things that are pretty expensive. So I'll give you another example where, you know, you have the case of, you know, capacitors being built on an assembly line, manufacturing, right, and so how do you, can you look at data across different stations in manufacturing on an assembly line and can you predict on the second station that it's gonna fail on the eighth one, right? By that, what you're doing is you're actually reducing the scrap that's coming off of the assembly line. So that's the kind of usage that you go into in the second and the third stage. Excellent. Janet, do you want to come in? I agree and I have a slightly different point of view also. I think architecture is very difficult, right? It's like Thomas Edison. He spent a lot of time creating negative knowledge to get to that positive knowledge, right? And so that's kind of the way it is in the trenches. We spend a lot of time trying to think through the keyword that comes to mind is abstraction layers because where we came from, everything was tightly coupled and tightly coupled, computing storage are tightly coupled, structured and unstructured data are tightly coupled, they're tightly coupled with the database, schema is tightly coupled. So now we are going into this world of everything being decoupled. In that, multiple things, multiple operating systems should be able to use your storage. Multiple models should be able to use your data. You cannot structure your data in any kind of way that is customized to one particular model. Many models have to run on that data on the fly, retrain itself and then run again. So when you think about that, you think about what suits best to stay in the cloud? Maybe large amounts of training data, schema that's already processed can stay on the cloud. Schema that is very dynamic, schema that is on the fly that you need to read and data that's coming at you from the internet of things that's changing, I call it heteroscedastic data, which is very statistical in nature and highly variable in nature. You don't have time to sit there and create rows and columns and structure this data and put it into some sort of a structured set. You need to have a data lake, you need to have a stack on top of that data lake that can then adapt, create metadata, process that data and make it available for your models. So, and then over time, like I totally believe that now we're running into near real-time compute bottleneck processing all this parallel processing for the different models and for training sets. So we need a stack that we can quickly replace with GPUs, which is where the future is going with parallel processing and machine learning. So your architecture has to be extremely flexible, high layers of abstraction, ability to train and grow and iterate. Excellent. Do you want to go next? So I'll be a broken record, back to data gravity I think in an edge context, you've really got to look at the cost of processing data is orders of magnitude less than moving it or even storing it. So I think the real urgency, I don't know, there's 90% I think of data at the edge is kind of wasted, you can filter through it and find that signal through the noise. So processing data to make sure that you're dealing with really good data at the edge first, figuring out what's worth retaining for future steps. I love the manufacturing example. We have lots of customer examples ourselves where for quality control and a high moving assembly line, you want to take thousands, if not millions of images and compare frame by frame exactly according to the schematics, where the device is compared to where it should be or where the components and that device are compared to where they should be processing all that data locally and making sure you extract the maximum value before you move data to a central data lake to correlate it against other anomalies or other similarities. That's really key. So really focus on that cost of moving and storing data. Yeah, that's one of the last words. Sure, Mana takes an interesting approach. I'm gonna up level a little bit. Whenever we are faced with a customer or a particular problem for a customer, we try to go with the question answer approach. So we start with giving a very, taking a very specific business question. We don't look at what data sources are available. We don't ask them whether they have a data lake or we literally get their business leaders, their subject matter experts. We literally lock them up in a room and say you have to define a very specific problem statement from which we start working backwards. Each problem statement can be then broken down into questions. And what we believe is any question can be answered by a series of models. And you talked about models. We go beyond just data models. We believe anything in the real world in the case of let's say manufacturing since we were talking about it, any smallest component of a machine should be represented in the form of a concept. Relationships between people operating that machinery should be represented in the form of models. And even physics equations that are going into predicting behavior should be able to represent in the form of model. So ultimately what that allows us is that granularity, that abstraction that you were talking about that it shouldn't matter what the data source is. Any model should be able to plug into any data source or any more sophisticated bigger model. I'll give you an example of that. We started solving a problem of predictive maintenance for a very large customer. And while we were solving that predictive maintenance problem, we came up with a number of models to go ahead and solve that problem. We soon realized that within that enterprise there are several related problems. For example, replacement, apart inventory management, right? So now that you've figured out which machine is gonna fail at roughly what instance of time from now, we can also figure out what parts are likely to fail. So now you don't have to go ahead and order a ton of replacement parts because you know what parts are gonna likely fail. And then you can take that a step further by figuring out which equipment engineer has the skill set to go ahead and solve that particular issue. Now all of that in today's world is in somewhat happening in some companies, but it is actually a series of point solutions that are not talking to each other. That's where our patented knowledge graph is coming into play, where each and every model is actually a node on the graph, including computational models. So once you build, say, 10 models to solve that first problem, you can reuse some of them to solve the second and third. So it's a time-to-value advantage. Well, you've been a fantastic panel. I think these guys would like to get to a drink at the bar and there's an opportunity to talk to you people. I think this conversation could go on for a long, long time. There's so much to learn and so much to share in this piece of information. So with that, over to you. I'll just wrap it up real quick. Thanks, everyone. Give the panel a hand. Great job. Thanks for coming out. We have drinks for the next hour or two here. So feel free to network and mingle. Great questions to ask them privately one-on-one or just have a great conversation. And thanks for coming. I really appreciate it for our big data SV event live streamed out. It'll be on demand on youtube.com slash SiliconANGLE all the video. If you want to go back, look at the presentations. Go to youtube.com slash SiliconANGLE and of course siliconangle.com and wikibond.com for the research and content coverage. So thanks for coming one more time. Big round of applause for the panel. Enjoy your evening. Thanks so much.


They've gained financially from hurting people in our community that I think that separates this these charges from other drug charges in most situations we arrest people that have Been using and they they sell enough to support their habit. That's not the case these individuals were selling to support a lifestyle and They need to be held at a higher accountability It's a black eye on the community really And we're just we're hoping that those people that are involved in any situation will come forward before we have to seek them out and In voluntary information, this is going to be a lengthy investigation And we're going to work it as far as we can go


So, Fumiko, when people approach you and say I want to become a better service designer, what is the tip you give them? I think I would like to say not just the service designers, but whoever is trying to make difference or change, whether in your organization or in different parts of society. The most crucial thing is to stay in touch with what's happening in the world. Now, it seems so obvious and it's so simple, but when you start to pursue a certain discipline, you forget that you should also know about the rest of the world. To have this peripheral view, why you need to do this job and what's the ultimate goal? Most of the time, the hints are not in your practice, but outside of it. Nowadays, I feel like that's almost the biggest crucial element that we often forget as soon as you pursue your career. Does that make sense? Yeah, get out of the bubble. That's what you're saying. Get out of the bubble. Great tip. Thanks. What do you think? Was this a helpful tip? Let me know by clicking that like button down below. And if you're interested to learn how to explain service design in plain English, I've got a free training for you which you can find over here. Thanks again for watching.


Hey everybody, I'm Lance Goyke and for some people the shoulder is the sketchiest joint in the body. It might seem like no matter what you do, your shoulders never feel good. They range from okay to garbage. It's a complicated joint for sure, but broad shoulders are one of the key features of an impressive physique, so we still need to train them. In this video, I'm going to give you a comprehensive breakdown of lateral raises, the best way we know to train the shoulders. We'll discuss the anatomy of the shoulder, how to execute the lateral raise, how to mess it up, and all the ways you can modify it. I'll be sure to leave you with what I believe to be the best exercises for training shoulders and as a bonus, we'll talk about proper ways to force a few reps at the end of a set. Let's get at it. A brief overview of the shoulder joint, shoulder movement, and shoulder muscles will help you understand how to best perform lateral raises. The shoulder is comprised of three bones. The clavicle, or collarbone, the scapula, or shoulder blade, and the humerus, or the arm bone. The shoulder is likened to a golf ball on a tee. The humerus, the ball, sits inside the glenoid of the scapula, the tee. Normal shoulder function requires this ball on tee spinning, but it also requires rotation of the shoulder blade. As the hand lifts up, the scapula upwardly rotates. This requires activation of the trapezius and serratus anterior muscles. Approximately one-third of shoulder abduction or flexion is attributed to this scapular movement. Without this, shoulder impingement and injury are likely to occur. The rotator cuff is comprised of four muscles. The supraspinatus, infraspinatus, teres minor, and subscapularis. The two most superiorly positioned muscles, the supraspinatus and infraspinatus, are the most likely to be injured. When the arm bone rises up, the rotator cuff is pushed into the acromion process of the scapula. We classify this bony projection based on its shape. Type 1 is flat and safe. Type 2 has a mild curve and is most common. Type 3 has a sharp hook and is most problematic, while type 4 is convex and safe. This shoulder impingement results in friction that can tear these rotator cuff muscles, especially with a type 3 acromion. Contrary to their name, lateral raises do not work the lats. The deltoid muscle gives mass to the shoulder. It's comprised of three sections, anterior or front, middle or lateral, and posterior or rear. The anterior deltoid brings the arm up and forward. It assists the chest during a bench press and looks a lot like the upper pectoralis major. The middle deltoid brings the arm purely up and out to the side. This is our main target during lateral raises. And the rear deltoid brings the arm up and backward. The force factor of the weights in a lateral raise can confuse the body. It's obvious to the lifter that we are targeting the deltoids, but the upper trapezius muscle is highly involved for two reasons. First, the upper trap helps upwardly rotate the shoulder blade. Kind of not directly. And second, the upper trap helps elevate the shoulder blade. Both of these can create momentum that helps perform the lateral raise, but at a cost. Excess upper trap activity during the lateral raise stiffens the neck. It's best to keep tension on the deltoid instead. To perform the lateral raise, stand tall holding two dumbbells. Keeping the arms straight, slowly lift both weights out and up to shoulder level. Pause briefly and lower slowly under control. Lateral raises can be modified in any of the following ways. Hand orientation, plane of movement, range of motion, set up position, load, implement, and symmetry. Hand orientation changes the degree of internal or external rotation at the shoulder. Turning the thumb down internally rotates the shoulder, placing more emphasis on the middle deltoid. This is desirable, but not without consequences. When the thumb points down while the arm raises up, the humerus in the shoulder joint rises up, pushing the rotator cuff muscles into the acromion process we described earlier. This is called a shoulder impingement and can contribute to rotator cuff tears. In general, you shouldn't point the thumb down during lateral raises. The risk is not worth the reward. Lateral raises are traditionally done in the frontal plane, meaning both arms stay in line with the torso. The frontal plane, however, is not a neutral position for the shoulder. The scapular plane keeps the arm in line with the shoulder blade and provides the most freedom of movement in the joint. This reduces wear and tear on the rotator cuff, but shifts muscle emphasis from the middle deltoid to the front deltoid. Initiating the movement, bringing the weights from the sides of the body to 15 degrees away from the body, is done by the supraspinatus rotator cuff muscle alone. Developing large shoulders does not require this 15 degrees of movement. If lateral raises are performed at roughly 105 degrees of abduction, mechanical load placed on the deltoid increases. This extra movement, however, also pushes the rotator cuff toward the acromion and can create an impingement. The weights we train with create a torque around the joint we're training. When doing lateral raises with dumbbells, the lift gets really easy when the weight is close to the body. This is because the moment arm of the weight and therefore the torque required to oppose it is small. There are three ways we can challenge the muscle through a longer range of motion. First, we could use a machine designed to train this particular muscle group. Second, we could change how the external force is applied, like using a cable or a band, instead of a weight and gravity. Third, we could change the orientation of the body during the lift. Most lifts that promote muscle gain are done bilaterally, where we're moving both sides at the same time. Unilateral movements can also be used to demolish the muscles, but they offer the added benefit of shoulder freedom. Slight twisting, turning, and bending at the torso promote proper shoulder and shoulder blade alignment. Okay, we've outlined that there are seemingly infinite ways to switch up the lateral raise, but some are better than others. Instead of showing you every possible variation, here are the three best ones. Best free weight option, chest supported one arm lateral raise. We will combine our variations to compose the most useful exercise for building the middle deltoids with only free weights. Set an incline bench to 80 degrees. Grab a light dumbbell and place the chest on the bench with the head just above the bench. Keeping the arms straight and palm facing down, raise the dumbbell through the scapular plane roughly 30 degrees in front of the torso. Pause briefly once the arm hits parallel with the ground and lower the weight back down under control. The incline bench here supports the body, replicating the stability you'd get from a machine. The incline bench also allows a slight forward torso lean, which intensifies the load placed on the deltoid through a longer range of motion. Moving through the scapular plane helps our shoulders stay healthy and allows us to keep the palm facing down to still hit the middle deltoid rather than the front deltoid. Best cable option, side lean, low cable, one arm lateral raise. Set the cable as low as it will go and select a light weight. Grab the cable with the working arm and then grab the machine with the free arm and lean the body 15 degrees away from the machine. Keeping the arms straight and palm facing down, raise the dumbbell through the scapular plane roughly 30 degrees in front of the torso. Pause briefly once the arm hits parallel with the ground and lower the weight back down under control. The deltoid has poor leverage at the top of a lateral raise. This is because the muscle fibers are too short to generate appreciable force. This lean away cable variation puts more load on the deltoid when it's strongest, that is at the bottom of the movement, and removes the challenge when it's weakest at the top. Best full gym option, bent elbow lateral raise machine. All machines will do fine here, but finding one that places the pad at the elbow reduces the forearm and upper arm involvement, better isolating the deltoid. Now that we know the best variations, let's talk about how people mess them up. It takes discipline to perform lateral raises correctly. It's important to keep tension on the deltoid even when it gets tired. This maximizes the growth response while minimizing the risk of injury. Shrugging during the lateral raise removes tension from the deltoid while promoting neck stiffness. It is best to be avoided. Shrugging will commonly occur in one of two places. When starting the lateral raise in an effort to build momentum or when finishing the lateral raise in an effort to feel a stronger contraction at the top. Back arching during the lateral raise shifts the tension from the middle deltoid to the front deltoid. Arching the back also promotes back and neck stiffness while reinforcing bad movement patterns. It's best to be avoided. Rushing through the lateral raise means less tension is placed on the muscle aimed to pause briefly at the top of each rep to kill momentum. Momentum and bent elbows are often used to lift weights that are too heavy. If you can't do the lift without momentum or you need to keep the elbow bent, lower the weight. It's worth noting that the lateral raise is not an exercise where we routinely use heavier weights. This is for two reasons. First, most of the torque comes from the moment arm so slight changes in load have a large impact. And two, this deltoid isolation exercise is generally performed near the end of a workout when the muscles are already fatigued. Next, we'll talk about how to change your technique at the end of a set to force out a few more reps and really challenge the muscle. When the deltoid is fatigued to nearly its limit, slow and perfect technique might be unattainable. The muscle, however, might not be fully fatigued. Forcing out fractions of a rep can provide an extra growth stimulus. To force extra reps and therefore more fatigue into the deltoid, you can start the movement with the legs to assist the raising of the weights. Once the weight reaches the top, attempt to stop it briefly and lower it slowly. This uses momentum to assist the difficult part of the rep, the raising, while forcing pure technique on the easier part, the lowering. You can also instantly lower the torque required to lift the weights by bending the elbows. This can change where the emphasis on the muscle is placed, so be sure to target the middle deltoid. All right, that's it. There's everything you need to know about lateral raises. To recap, lateral raises are the best isolation exercise we have for the middle deltoid, but we can modify them to be safer and more useful. We outlined how to perform them, mistakes to avoid, and ways to safely force out a few more reps at the end of a set. That's going to be it for me. If you learned something, hit the like button and subscribe to be notified when I release new videos. If you need something else to watch, maybe it's a good time to go back to the basics. Check out my video on the four must-dos for muscle gain. There's a lot of training information out there, and there are many ways to be successful, but there are a few things you cannot avoid doing. I think you'll like that one.


I'm going to go over just a few of the new treatments in cornea and refractive surgery here. So the first thing that we've started doing at the Moran also is corneal cross-linking. And so I think Julie is going to talk a little bit more about this, but I'll just be brief on this one. So this uses UV light and photo sensitizer to strengthen chemical bonds in the cornea. And it was FDA approved this year for using keratoconus with the VEDRO system. And the approved treatment is an epithelium off treatment. So we stripped epithelium off and then applied riboflavin for 30 minutes and then do a UV application for 30 minutes for keratoconus. There are a few things that this has been done for quite a while outside of the US. And so some of the future applications that we may be doing are for a polluted marginal generation, for ectasia after refractive surgery, taryons. And after cross-linking, it's possible that we may be able to do refractive surgery, like PRK on keratoconus patients. And some people are doing this already even in the US. A couple other interesting treatments that are done outside of the US are PIXEL, which is a refractive procedure that uses these apertures. These apertures, like you can see over here, to selectively cross-link parts of the cornea as a primary refractive procedure. And then there's also LASIK extra, which has been done pretty extensively outside of the US, where after lifting the stromal flap or lifting the flap in LASIK surgery, that cross-linking is applied briefly to the stromal bed in order to provide better stability in patients that may be at risk for regression after refractive surgery. Before the ablation, yes, but after lifting of the flap. Yeah, actually, I think the people who are most cautious are actually, I mean, there are people doing LASIK, but most people are doing it. Yeah, yeah, you can have LASIK or PRK extra. There have been about 100,000 cases done outside of the US. So I move around. I want to send you guys to patients for point of cross-link treatment. What papers do you have? 12 to 50. OK, yeah, we started recently, but we have a list of people that were going to operate, too. You were in the FDA trial, or one of the trials for the rapid cross-linking. What was approved by the FDA is a 30-minute standard kind of resident protocol, which is what we have to do now. But we treated about, I think, four-year, 50 patients. And we treated as young as 12, and as old as, you know, infant 50s. But I don't think it works very well in all the people, but honestly, I mean, it doesn't work very well. Most LASIK, at least in our experience, but it works quite well. It's a good adjunctive treatment for young care of those patients who are discussing it. We don't want to still do this kind of thing. OK. What's the utility of cross-linking in an ulcer patient if you wanted to use the ultraviolet light to hopefully kill the bug and also strengthen the form of the test and all? Well, yeah. There are some case reports of this that were, there are a couple of case reports from Mexico. And I think there was one for Spain, also, where they showed afterward that they were able to eradicate even a canthamoeba from the cornea using cross-linking. But, you know, it hasn't really, we don't have any controlled studies of that yet. Is that approved? Not approved.  You mean, especially for, you know, We have some results from the symphonies that have been done here from Dr. Crandall too. So I'll be quick on this one, also. but the symphony, the symphony TORC lens is a new lens that's available recently and it's different than the other AMO multi-focals. It's an extended range of vision IOL. Both TORC and non-TORC versions are available and it uses this diffractive echelette to provide an elongated focal area. So it basically has a larger defocus curve. You can see that in the graph down here. So it supposedly is providing quality vision for up to the intermediate range. And you would expect that patients would need to use reading glasses still with this lens, but down in the bottom right here, you can see the mono focal has a single focal point. Of course, multi-focal has two, and then this has the elongated focal point. And we can talk a little bit later about the results from the Moran so far with this lens. So another of the big drugs that is out now for corneas is Zidra, the fitagrass 5% ophthalmic solution. They have a not so modest advertising campaign, so probably a lot of people have heard about this. So it's the first drug in 13 years that's approved for dry eye, both for signs and symptoms of dry eye. It's a two time a day drop. And the mechanism, or at least in vitro mechanism is it binds LFA1 receptor on T cells and blocks the ICAM1 LFA1 binding. And in vitro studies, this prevented T cell adhesion in pro-inflammatory cytokine release. And there's a thought that the ICAM is upregulated in patients that have dry eye on their ocular surface. So they did, in the clinical trials for Zidra, they did four separate studies, both looking at the signs and looking at the symptoms. When they were looking at the symptoms, the results looked a little bit better with these studies when they compared the vehicle to the drug. And they showed improvement on this 100 point dryness score in all four studies over a period of several months. The, for the signs of dry eye, those results were pretty close together actually, I think for the drug compared to the vehicle, but there was improvement in the four studies. And the side effects are pretty similar with Zidra compared to rastasis, burning or insulation, side irritation, some decreased vision and eye redness. But probably the most prominent difference is that a lot of people have bad taste with this drug and it's not always a mild side effect either. And some patients are saying that this, even though the company says it lasts for 15 minutes, some patients say that it can last for hours that they have this bad taste in their mouth. But Dr. Lin and I have used it in a number of patients so far and we've noticed that some people that don't get any benefit from rastasis that they have said that this has been pretty helpful for them. And also it seems like it may be taking effect more quickly than the typical three to four months that it takes to get the full benefit from rastasis. But those are just kind of anecdotal evidence so far from what we've done. And then the last thing I wanted to talk about is topography-guided LASIK and PRK, which we've also started doing here with the Wavelight, Allegretto, eczema laser. So the topography-guided LASIK, T-CAT, was approved in 2013 and has become more widespread early this year. And then just a few weeks ago, topography-guided PRK was also approved with this laser. So the difference compared to Wavefront Optimized, the prior treatment was based primarily on refraction and treating the sphere and cylinder. And Alkan's kind of changed the way that they think about this recently, but they're saying that now the treatment, which is based on refraction and corneal topography, is more thinking about treating sphere and higher order aberrations because they say that the higher order aberrations affect the amount of cylinder that patients are taking in their refraction. And so in a number of patients, and I'll show an example in a minute here, they're, the amount of cylinder is quite different from what they have in their refraction that we're actually treating. So this is how the procedure works. There's a topolizer, which is a placidodisk topographer, basically. And we take a number of pictures on a single day for the treatment eye. And using these pictures, which it also has iris registration, so when you're applying the treatment, it stays on the correct axis. We create a composite image using at least four separate images of the cornea that are similar. And then that's sent to a planning software and it's quite a bit more complex process for planning the treatment compared to wait for an optimized. And then that's sent to the Axmer laser after the treatment is planned. So who might benefit from this treatment? So the definitely normalized with higher order aberrations, especially coma, can benefit from this. And it's approved for LASIC for up to eight diopters of myopia or up to nine diopters, which with three or less diopters of astigmatism. And you can't actually program more than this into the laser, it won't let you do the treatment. But it can be useful potentially for a regular astigmatism, eyes that have already had a refractive procedure or patients who had a previous de-centered ablation or small optical zones. And then like we talked about post-cross-linking, this is gonna likely be a good option. So here's an example of one of the treatments that we planned recently here. So you can see this patient has kind of asymmetric astigmatism and some changes superiorly here. Down in the left, this is the way front optimized ablation pattern. And with this treatment, we're treating a little bit of sphere and just a half diopter of cylinder. When we planned this with the topography guided treatment though, the amount of cylinder went up pretty dramatically from a half diopter to 1.75 diopters. And because this is inducing significant coma. And then the thought is because of that, the patient's manifestor fraction is different because of the higher aberrations. So these are some results from the FDA trials for topography guided LASIK. And so you can see the results are pretty impressive here, better than 90% of the year getting 2020 vision. And probably the most interesting thing about this is at one year, 30% of patients had an uncorrected visual acuity that was better than their pre-operative, best spectacle corrected visual acuity. And more than 10% even gained greater than two, well at least two lines more than that. So that's a pretty significant difference. It just got for more than one day. Yeah, we're actually gonna do a study for our fellow study for probably next year or two is gonna be a comparison of one eye that receives a way front optimized treatment and then the other eye, we're gonna do a topography guided treatment. And it's hopefully gonna be both for PRK and LASIK working on getting the final approval for that right now. Since everybody's these patients with dry eyes, you may want to expound a little about where you should use the fitting graphs for the site of the scoring, for the site of the scoring. Where do you introduce it? It's what, because a lot of patients are coming, as you say, with advertising in their hands, say, can I have a big Zidra, please? Well, so finding, a lot of this is financial, I think at this point, because they're doing a 30 day free trial for Zidra. We've been using it mostly in patients that have tried or stasis and didn't have good result from their stasis. I don't think that we've tried doing both in the same patient, but some people are doing that since they work by different mechanisms. I think sometimes people that can't perform, they can't perform for stasis, or at least doing a trial of the filth of grass, but the insurance coverage for this variant.  That may change at the beginning, but I think we have been doing it fairly early on for patients after artificial tears, and maybe we've been doing it for four blocks in some cases, but I don't think it depends on them. Yeah, so I guess the protocol that I use is for usually trying artificial tears, artificial oil furs, fail that, try to stasis, then do plugs, later on maybe consider Zidra. Zidra though is, I think it's gonna be, it is gonna be financially driven because it'll depend on their insurance, and the other thing about Zidra is that for most insurances that aren't covering it very well, it's still very expensive, and the stasis is still very expensive. And the thing about stasis is that there's a lot of drops in each little vial that each unit dose is actually good for multiple doses, like about three doses of restases. So one month supply of restases can last at three months, or maybe even more. So that really kind of cuts down on the cost, but the makers of Zidra were very savvy, so they cut down the number of drops in each vial. There's literally only maybe two and a half drops in each vial, so you cannot reuse it. What that means is that the one month supply of Zidra is gonna be a one month supply of Zidra. So again, if you have someone who maybe has the same, if their insurance has the same price for both restases and Zidra, in reality, and in kind of practical usage, the restases is gonna be a third of the price of Zidra. So Zidra is provided, reserved to free single-body deliveries by understanding? It is, yeah, just like restases is. Right, it is reserved for free, so all these parts of the company say, it's reserved for free in single vial, it is single use to free vial, so that's what the expense comes in. In my clinic, we keep an average cost of the drugs that I respect. People are asking, how much do these cost for me? It's, we should know, do we know what a month supply of Zidra costs? It's really very good. Again, it's from a zero to like $500 a month. So that means for the residents, if you're practicing medicine today, you don't know the cost of the drug you prescribe in, do not prescribe it, because there's an antibiotic that I can prescribe for a simple infection that'll cost you $500 with insurance, and an equally effective one that'll cost you $4. I think it's not in my job to really know these things. If you ever come to my clinic, in the cupboard where the technician sits, open it, and there are costs of all the top of the drops, the steroid drops, anti-bacterial drops, a few more or less, perhaps it's there. And I'll urge you to carry that to your pocket. I think that should be the pretty 16th where we position these days. So I think it's not zero to $500. We need a better idea for our pharmacy, for what we say. Yeah, but the problem is that the insurance companies are so variable that that really is the price, zero to $500, I mean, depending on the insurance.  Unfortunately, it's kind of, the onus is now in the patient to look into it themselves. I'm not even sure that you can. Yeah, it's still possible. And the number of insurance companies, when you get some that are, you know, common, then we can kind of figure it out. I think a lot of the insurances are still figuring out how they're gonna cover this drug. It's only a couple of months. Sometimes the patients are the first one to try to get near enough.


Hello, welcome back here to Stevenson. My name is Adam Pold, joined here by coach Rayleigh. Coach, obviously it's a defeat in the end, but I think that this was a really valuable experience for your young team. You were in a tight game down the stretch and it was a great fourth quarter comeback. I think we did some wonderful things today for such a young group. I think the youth is what hurt us in the end. We got a little less confident when the pressure was on, but we'll grow from that. And I think Rochester was a good team. They did some really nice things. And we can walk away today saying it wasn't like we didn't have the opportunities to score. We had great opportunities to score. We just couldn't finish like we wanted to today. We played a lot of people today. Pretty much the whole lineup almost. And it was good to see some young people get out there and play in their first game or have their first goal. That's always so exciting and so fun to watch. You guys are up 5-4 late in the second quarter. That was maybe a turning point, their ability to score late and then have a strong start to the third period. Well, I just think that we do have some down moments and that we have to kind of time out and regroup. And that's just youth, I think. This team that we're playing is a little bit more experienced. And I think that show today, the experience show today, not the desire, not the will to win, not the X's and O's. It was purely just a little bit of we were a little young to handle some of that pressure, I think. But don't get better from that. And you've got to tell me about that. Kara, you're just about to join us as well. Kara, have you seen somebody who have so much confidence on the free position shot? She has worked harder than any player I think I have ever coached to take her game to the next level. Kara is an unbelievable workhorse in every single practice, every single meeting. She watches more film than anybody. She works harder in the summer. She works harder in the non-traditional season. She, I just don't know how, when we're out there recruiting, we want 12 Kara Yaroosos. Because she was a good player when she came, she's going to leave here one of the greatest, if not the greatest. And her teammates look up to her. And it's just for her to have a game today, there's only one thing on the planet that I wished for. And that's I wish we could have gotten the win. She could celebrate this great performance and the win. We'll get there this season. I told the girls six games. So I don't want them to make me a liar out of me, but I think that every game we make the steps in the right direction to do that. But Kara is just unbelievable. I mean, she plays 60 minutes. She plays both ends of the field. That just doesn't exist anymore. And sometimes we call a timeout just to give Kara a rest. And Isabel, who is sophomore, who's taking right after Kara. And Kara is just such a great captain and great player by example of, you just don't ever give up. And when it's on the line, give me the ball. Well, Coach Bradley, we wish you all the best of luck. And good luck as you continue to build towards conference play. Thank you very much.