Pharynxes. Pharynxes are fabulous. So we did look at this image already, but I just blew it up a little bit bigger so that we could really see the distinguishing parts. Now, the fact is we already did the oral cavity, right? And we sort of colored it in. I actually can't remember what color we colored it, but let's just color it. This was my oral cavity. And the oral cavity was inferior to the hard palate and the soft palate. So you notice those guys write about her. Hard palate, okay, and then uvula slash soft palate. We need to do a different color of greenish right here. Okay, so that was the uvula. Okay, so a pharynx, let's define this puppy. A pharynx is a space. Ah, spaces are usually defined by structures. They don't just float in nothingness. So there's anatomical structure surrounding the space and defining it, but it's still a space that food and air passes through. So think about that for a minute. Food involves what system? The digestive system. And air, what system are we talking about? Talking about the respiratory system. And we are going to spend one more day on the digestive system, but then we're going to do the respiratory system. And so we're going to have structures when we're talking about digestion that are also respiratory. Those are our pharynxes. So the nasal cavity, I think we already defined that. Yes, this was the nasal cavity. I actually can't remember if we defined this or not. The nasal cavity is superior to the hard palate. The nasal pharynx is superior to the soft palate. So if you look at this right here, and the nasal pharynx goes from the... I just want to make sure, yeah. The uvula is the end of my nasal pharynx marker, because really the uvula is the end of the soft palate, and the nasal pharynx is associated with the soft palate. If we took the uvula and stood it up straight, we could actually mark off the nasal pharynx. But this whole space back here is the nasal pharynx. Does anybody want to fight with me about my definition of pharynxes when you look at or think about the nasal pharynx? How about the fact that do you put food in your nasal pharynx? Don't. I don't recommend it. It isn't going to feel good if you put food in there. And I'm sure many of you have before that food go in there, or at least soda pop or milk. And dude, it's just embarrassing and don't. So food can go through the nasal pharynx, but it's definitely not a first route of choice. Inferior to the nasal pharynx is the oral pharynx. The oral pharynx goes from the end of the uvula or the end of the soft palate to the hyoid bone. And so take a look at this. This right here is my oral pharynx. And again, let's talk about what is the hyoid bone here. So maybe that color orange. The hyoid bone is this fabulous bone. Dude, there's so many fabulous structures in your body. You're just fabulous. The hyoid bone floats. It does attach to muscles. Like all of this stuff, just for perspective, is muscly. These are all skeletal muscles, all this is skeletal muscle, whatever. But it floats. Like it's not actually forming a joint with any other bones in your body. And you know, I can't ever feel my hyoid bone, but I'm kind of a wimp when it comes to like palpating my throat. It just doesn't feel right. So I tend to not like get in there and like try and grab my hyoid bone. We can definitely identify the hyoid bone in our half head cadaver preps and our cadavers. Like we can definitely identify it. And it marks in the half head, it marks that place where the oral pharynx ends. Inferior to the oral pharynx is a structure called the laryngeal pharynx. And the laryngeal pharynx goes to the thyroid cartilage. Inferior border of the cricoid cartilage, which is probably going to be right about her. So this whole thing is going to be laryngeal pharynx. And the laryngeal pharynx is that shared space. It's the last shared space. And then look what happens. Because you share space for food and water, this is the trachea. Trachea is anterior. You can feel that thing. It's super cartilagy. And posterior to that is the esophagus. And the esophagus is the next structure that we're going to talk about. So I just tried to color in the esophagus. And let's talk about that esophagus next. Is there anything else I need to tell you? I don't think so.


Welcome to the original gangsters podcast. I'm Scott Bernstein your host quick hater edition. We're going to go out to the windy city in Chicago Maybe more specifically out to Springfield, Missouri in a prison medical hospital. We're talking about that Mike Sarno big Mike Sarno the large guy You know acting boss of the Chicago mafia for for you mob washers out there ran the family from 2005 to 2010 on the street has been locked up doing a 25 year sentence on a extortion racketeering conviction where he was found guilty at trial in 2010 I've reported that in his five years as acting boss he Made a batch of at that time really young guys from Cicero some from Elmwood Park I believe that were just only loyal to him maybe Two dozen guys maybe these guys are now in their You know 40s and 50s and they are the future of the outfit and from what I'm hearing from sources. We kind of thought that Mike Sarno was maybe a thing of the past in the outfit and I'm hearing that he is very much a thing of the present and the future that Sarno from behind bars is Counseling these protégés of his in Cicero His boots on the ground as everybody knows as long time underboss right hand man Salvatore Sammy cards Caught at Catadela is the underboss right now to Sally D. Who's the who's the Don Sally is You know I would guess is either going to be Retired or possibly dead in eight years from now when Mike Sarno comes out but Mike Sarno himself will only be in his early 70s. I think as of right now it's being planned that Mike Sarno if he comes out And is of health Will become the overall Don of the Chicago ma Chicago ma I know his attorneys push back on the notion that he is Even in good enough health to continue serving his prison time. They've been trying to get a medical compassionate release or sentence Shortening over the last couple years claim that he's in a wheelchair. He's down to like hundred and eighty pounds or something And is you know has a variety of ailments that that make him unfit to serve prison sentence. So I'm guessing if you talk to them They would say there's no way that he is a chiming in on anything mob related from prison Nor has any plans on on taking over the crime family when he comes out in right now his out date is two thousand thirty two My sources are saying the opposite that he has been advising and counseling the Cicero guys Almost from jump since he went in there 15 years ago and that it's it's even become more prevalent in terms of this Advisory role since Jimmy and Dino died last year and these new Cicero guys it's like they were on deck and now it's kind of their time to take over the family We know that there were some court filings a couple years ago related to the Cicero crew where Sarno was named as someone that was receiving a portion of gambling profits And what I'm reporting now is something that's happened in the last couple weeks is that he's now having to or I don't I shouldn't say now he is This is good and a role he's been playing all along but I know there's been some minor internal dust ups within Cicero Some different factions setting up other factions to be robbed and beaten or whatever and I'm told that just in the last week or two Sarno had to get all these guys kind of on a phone call and he squashed the beef from his prison prison phone So just something to keep an eye out for Mike Sarno seems to be back in play in a major way in the Chicago outfit right now Sally D and Albie are still running the show Rudy fratto and then some of the younger guys over on the south side Nikki Ferriola little Frankie Caruso are kind of being groomed by the Tootsie Babe and his guys That's kind of the Southside younger group and then you got a Cicero and Elwood Park younger group So you know I would say that reports of the outfits demise or predictions that they don't exist anymore they don't wheel power anymore There's only 10 members that's just false there's a lot of new blood in this organization and a lot of positive momentum towards the future Now I will say just this is my analysis these guys that Sarno was brought in that are loyal to him these let's say just roughly two dozen guys maybe more I would say that they tend to be more gang banger types than buttoned up wise guy types or maybe a hybrid of the two You got guys that are earners kind of white collar guys but then you got a lot of street guys that come from more of a street gang background 12th street players Cicero feeder club And a lot of these guys just are I would say more gangster than racketeer So we'll keep an eye out for it, but I'm being told Mike Sarno is settling beef from prison with the Cicero crew in the winter of 2024 OG pod Scott Bernstein out


Okay, in case you don't know, I'm a retired professor of economics in Trinity, but I still teach an economics course on European integration. So I want to welcome everyone to the lecture, especially following the unexpected topic earlier, which is close to my heart, labour markets in the EU. We want in particular to thank Mr Draghi, of course, president of the European Central Bank, and Professor Philip Lane, governor of the Central Bank of Ireland. It's a great honour for me and all of us to be part of this very special occasion. So did you know who you're mixing with? The audience is overwhelmingly economic students in Trinity, undergraduate, MSc and postgraduate. We also have some staff here, and we have the committee of the Philosophical Society, but more about that later. Philip is an economics graduate of this college, and he's a professor, the Whitney Professor, on the Department of Economics. The first work I got from Philip as a student was in Mickey Mouse term in the senior first year, and he handed me an essay then that has had and had all the hallmarks of Philip Lane. First of all, it was extremely intelligent. Secondly, he wrote in an incredibly tightly argued structure. And thirdly, he was very sparing with words, both written and oral ever since. In fact, I often said to students when they're reading the chapter in the book, make sure you have to read Philip Lane's paragraphs five times compared to everybody else's once. I just wanted to say in the presence of Philip and Mr. Draghi, we're immensely proud of Philip's achievements as an academic and also immensely proud of his achievements in the world of central banking. Now to Mr. Draghi. I often wonder, any of you who do the European economy course, why Italy as a nation doesn't play a bigger role in European affairs and Eurozone affairs? I've never been able to work that out because it's the same size as France, but yet it has much less prominence. Yet, there are some individual Italians who have made a huge impact at a European level. And one of those, of course, is Mr. Draghi. According to the Financial Times about a week ago, I had a major review about the turnaround of the Eurozone economy and then the Euro. And they accredited almost solely the reason for that turnaround, what they called Super Mario, what all of us will call Mr. Draghi. Philip is going to moderate the question and answer session now for maybe 25 minutes or so. Then after that, Conor McCormack, president of the field, will make a presentation to Mr. Draghi. Then there will be some closing remarks by myself. So I hand you over to Philip. Thank you, John. So let me welcome you all to this second ECB youth dialogue. There's one, a similar event was held in Lisbon back in June, which I think went quite well. Of course, with the modern social media and so on, there can be a lot of people that are across Europe following the dialogue this morning through the ECB website. So I think that's great to combine the importance of having the physical interaction here at a local level with the students here today, but also the amplitude of that through the web and so on. So I look forward to the dialogue. I'll be calling on a list of students to ask their questions. Time is limited, but I think we can have quite an interesting exchange. I would also say that Trinity, I think I always say this wherever I would go, Trinity College has always been somewhere where the students have had a high level of participation in the economics debate through the student economic review, through various debating societies and so on, and through a lot of interaction with the staff. I think Professor Hagen has been very much the leader of that over many decades, John, and so it's very nice to have this event here today. So let's get going, and first of all, Colin Aditya Garg to ask a question. Good morning, President Jagi and Governor Lane. So some five years ago, five and a half years ago, you delivered what is often cited as the most influential speech by any economist in the history, and during the course of your whatever it takes speech, you mentioned some short-term challenges and particularly pertaining to the, how the inter-banking system was broken and needed some recalibration. So since then, a lot has changed, especially with Brexit now playing out. So looking back, my question is looking back, do you think you've achieved the challenges that you set out back then, and looking forward, what are some of the challenges that you see that the euro faces today? You asked me to go back with memory to that year, 2012, and what has happened two years before, starting in 2010, was a progressive decrease in trust of the euro as an institutional construction. The financial crisis started at the end of 2007, actually left in Europe, left countries, governments with very high debt, low growth, and that put in question the sustainability of government budgets. It created a very significant increase in interest rates, started the recession, and this morphed into a banking crisis because banks, some of the banks at least, especially in the periphery, actually had a substantial part of their portfolios invested in government bonds that have lost value in the meantime. That in turn became what we call fragmentation of the banking and financial system in the euro area, which basically means that some parts of the euro area had no problem about funding the real economy, the banks were working, and other parts were not. In other parts, banks were not working. It was a credit crunch. It meant that when we changed the interest rates at that time, we still changed interest rates. I remember that I started in November 2011, and we changed the first time exactly two days after my starting, and then the second time was a month later in December, so it was pretty clear that it was a drop in credit flows all throughout, but these movements, these changes in interest rates were transmitted into lower lending rates in the core countries where banks were actually functioning, the banking system, with positive effects on the real economy and were not in other parts of the euro area. So what was at risk was basically the existence of the euro, at least what we call re-denomination risk. And what was at risk was the unicity of our monetary policy, whereby when we change rates, we expect that this being transmitted in the whole of the euro area. And so the response was, first of all, response to the crisis time, reaffirming, deciding, and the governing council did exactly that through what was called OMT, Outright Monetary Transactions, which was the implementation of whatever it takes, and addressed the crisis time, and the re-denomination risk was fought and the challenge was won by the end of 2012. And second, and in a sense longer undertaking, was to rebuild the banking system and the financial system, basically fighting against this fragmentation we had. And that took longer, that took longer. Now we can say fragmentation is gone, now it's gone has been, I think it basically went about two years ago, and since then we see credit flows growing continuously, though not exceptionally, because in the meantime, and that's the other part of the answer to the crisis, many actions being taken to make the banking system more resilient. I would say that since the crisis, the whole regulatory and supervisory framework has been overhauled. And so now we can safely say that the banking system, and to some extent also the financial system at large, is much more resilient against future crisis, which by the way was quite important from a monetary policy viewpoint because counting on a stronger financial and banking system allowed an extraordinarily accommodative monetary policy to continue for much longer than it would have been possible 20 years ago. Very good. Let me turn to the next student on my list, which is Annabelle Rourke. Good morning President Draghi. My name is Annabelle Rourke. I'm a senior officer, maths and economics student, and I'm also an honorary member of the College Historical Society. It's such an honour to have you here today and to have been provided with such an excellent forum, directly to my question those. Firstly, a fundamental element of the Euro and indeed its success is that of the harmonisation of the Eurozone members and their response to economic shocks. This harmonisation however has been stunted by lack of labour flows from areas with high unemployment to areas with greater employment opportunities. Do you think the EU needs to move closer to becoming United States of Europe with its own more prominent European identity in order for the Euro area to be truly successful? Thoughts on language, culture and other social elements are also of interest. Thank you. In a sense I partly touched on this during my speech, but we know that the optimal currency area as concept, as was defined by Mandel many, many years ago, I think it was what, 50 years ago maybe, was calling for free movement of capital and free movement of labour. Now we are not there yet of course, but if you compare the current situation with 30, 40 years ago, significant progress would be made. There are barriers. There are language barriers, cultural barriers, policy barriers. Just think about a measure that would increase mobility, the portability of pensions. And in principle, at least it shouldn't be that difficult to construct a system, but there are political obstacles to moving that direction as well as on other directions in the labour market and so on. But still in spite of these barriers, the European construct has, as in a sense it gave us peace and probably the longest period of peace we ever had since the war, it has shown to be able to gradually, perhaps slowly, sometimes more, sometimes more fastly, overcome these barriers. And to some extent what we are seeing, in a sense I'm pretty confident that as we move forward, as the recovery continues strengthening, as employment will go down, and as curiosity of the young generation towards exploring job opportunities also beyond the borders of the countries will continue, will go up. And Europe provides exactly that framework where this exploration, this curiosity can be satisfied. Very good. Let me call on Rory McState. I'm honored to have you here as a student. Thank you very much. Much of the motivation from the last ECB Youth Dialogue was from innovation. Do you think that new technologies, specifically blockchain, have a role in monetary policy in the future? And if so, how do you think they'll change the monetary policy mechanisms? Thank you. Thank you. I have to confess I'm not an expert, but I can tell you that we at the ECB are looking into this and now have been looking into this for some time. One conclusion is that at this point in time the technology is not mature yet to become, to be considered in either central bank policymaking or in the payment system. So we have to look at what progress this technology will do in the future years. But let me also point out to another dimension of this. If I were asked what is the, in this realm of digitalization, what is the dominant theme today? It's cyber risk. And so we have, I would expect that any innovation, like the one you mentioned, any innovation will be screened from this viewpoint. How resilient is this? Or how much our exposure to cyber risk is going to go up because we embrace a new technology? Maybe I just have one line on that, because the history of money, of course, money, all central banks always try to protect the money by fighting fraud, fighting counterfeit activity and so on. So of course with new technologies, that spirit of money, every state, every system, has a unique state-backed money and the protecting the integrity of that is paramount. So let me turn to Stefano Minnetto. Morning, President Draghi. Thank you for being here and the insightful talk you gave before. I want to ask, given your personal, academic and professional life in the field of economics, where do you feel the teaching of the subject could be improved and if so, how? Thank you. Again, I must confess that I left university about 30 years ago. So if you ask me where I'm up to date with teaching, the type of teaching that's being done today, I should confess my ignorance. However, what I can sort of pick up from talking to my colleagues who are still students and still teaching or have been doing so until recently is that things have changed since the time I was in university and the emphasis on methods, mathematics, statistics, decrease in the early years of learning and much more attention is being given to, using simple words, understanding the world, understanding reality, the functioning of institutions, the interactions between agents and institutions. Some of you are more into finance, the working on markets. I think that's probably the change that's taking place and I think it's something that I would agree with. Thank you. Next on my list is Jonathan Rice. Morning, President Draghi. Welcome to Trinity and thank you very much for talking to us here today. My question, living in Ireland, we are painfully aware of volatility in house prices and while the central bank's macroprudential policies seem to have been somewhat effective, we are still seeing house price growth in double digits. So given the present prolonged period of low interest rates, I'm just wondering whether the potential for price bubbles is a growing concern of the governing council. Let me tell you that I get the very same question in each press conference I do in Germany. Which actually shows that house prices are going up, not only in Ireland, but in some other parts of the euro area. So we asked ourselves how systemic is this phenomenon because if it's local, then it's another, and I will come to this, then it's another issue. And the answer so far is it's not systemic. It's actually affecting certain large cities in certain countries. Then you ask yourself how far from historical averages are these valuations? And with few exceptions, I don't know about Ireland, but with few exceptions, we find out that house prices in these large cities have recovered from troughs where they were for several years. Then we asked ourselves how much is it due to low interest rates, to easy mortgages, to our monetary policy. And there the answer is that quite often factors like supply shortages, building permits, lack of social housing are the main causes of these increases in house prices. And the other question we're asking ourselves is because to become a bubble, you have to see, you have to observe an increase in prices, but also an increase in indebtness, in other words, in leverage. And how much this phenomenon of increasing house prices is accompanied by an increase in leverage? And the answer is not much. As a matter of fact, credit as I was saying before has recovered in the last four years now, but it's still pretty subdued if we compare it, not with the period immediately before the crisis because we are clearly having peaks that were unjustified, but even 10, 15 years ago, no, 15 years ago. And so historically is not excessive, what we would judge excessive. So if there are problems and where there are problems, they should be tackled with local measures, not through monetary policy. And so what are these measures? Some of them have to do with what we call macroprudential measures. So you have things like loan-to-value ratios for borrowers and other types of measures of the same kind. And there the competencies is shared between the ECB and the National Central Banks. And many countries, several countries amongst which Ireland have actually implemented these measures. And they've been, I think I said before, it's 2015, it started in 2016, it was reviewed. So I think the same is happening naturally in other countries as well in the Netherlands, I can remember, and I don't know about France as well. Thank you. Next on my list is Conor Parle. Thanks for coming today, Draghi. Draghi, so I just wanted to ask a question about how markets reacted quite sensitively to your speech in Cintra recently, and it kind of shows the importance of clear central bank communications and the further impacts they can have. So my question is, to what extent do you see four guidance and clear communications as a key component of modern central banks? Well, to some extent communication has become itself a tool of monetary policy. And it really goes with being independent. Independence of central banks is a big institutional change that took place in say the late 80s and through the 90s. Of course, in Germany, the central bank has always been independent. It's enshrined in the constitution. But in most other countries, central banks were not independent. So they became independent, and with independence you have to have accountability. In other words, central banks are independent within a mandate. We have a mandate of price stability. The Federal Reserve have a dual mandate, price stability and full employment. And then the legislators would ask ourselves, how well are you complying with your mandate? And this is one part of accountability. The other part is vis-à-vis the public opinion at large. So communication becomes a very important element of how we comply with our mandate. And together with communication, the next step is transparency. Communication has to be transparent. Give you an idea of how things have changed. Until 1994, the Federal Reserve would change its interest rates without any communication to markets. So markets would have to infer from the fact that things were changing actually in front of their screens that the Fed had changed the interest rates. Now this sounds unthinkable today. And so this is the new world. And so the new instruments like forward guidance, for example, forward guidance is essentially based on communication. And that's one of the reasons why credibility is so important. Credibility means that markets, that the rest of the economic agents at large believe that things are going to go like you are saying they will. And therefore they behave accordingly. And that's why communication is important, but also continuous compliance with your mandate and especially independence. If a central bank is being viewed as independent is also viewed as credible. Or at least is one of the necessary conditions for credibility. If a central bank is viewed as non-independent then is certainly viewed as non-credible. And therefore market expectations will not react according to the monetary policy indications we give. So for the final question let me turn to Townsend Greenbarker. Good morning Mr Draghi. Many of us here have just graduated or are in our final year studying economics here at Trinity. I wanted to ask is there any advice you would like to give to us students who are at the very start of our career in economics? And also what words of wisdom would you have liked to have known when you were at the very start of your career Mr Draghi? That's a very difficult question. It's... Well the advice I would give you is just stay curious. Stay curious. Think...learn from the world as I was... when I was asked about how teaching should be... should change. I am only saying what I understand now the emphasis is more on learning from experience from the world, from institutions and stay curious. Curiosity is what drives you to be... to explore job opportunities to explore different environments and also to be creative. So that's the advice I would feel like giving you. And never lose your courage of course. Very good. I think that's been a very interesting exchange. With all due respect to the journalists in the room it's great to have questions from students as opposed to... they're very important to have the media to do in communicating ECB policies. Let me turn the floor back to John O'Hagan. So I'm going to invite Con McCarrick the president of the Phil who's going to make a presentation to Mr Draghi. I want to tell you there is a connection between the Phil and economics. Maybe five, six years ago the Phil and the Hiss, these are two big debating societies, compete fiercely every year to host the student economic review debates and in Macomb's term it's against Oxford or Cambridge and in Hillary's term it's against Yale or Harvard. The Phil people won't like to hear me saying this in this competition to get these debates but the battle up to date has been pretty equal. So Con. Good morning. I'd like to welcome you all to the University Philosophical Society here in Trinity College and my name is Con McCarrick and I'm president of the society. So on behalf of the society I'm President and Provost Patrick Prendergas and Trinity College Dublin thank you all for joining us here this morning to welcome the society's newest recipient of the Goa Medal of Honorary Patronage, Mario Draghi. Every found in 1683 the world's most preeminent thinkers, doers and change makers have addressed the society. Every year the council and members of Phil elect a number of exceptional individuals to the honour of honorary patronage in recognition of their outstanding and significant contributions to their given fields. We are delighted to welcome the president of the European Central Bank, Mario Draghi to the ranks of our honorary patrons in recognition of his outstanding contribution to the field of economics. Recent recipients of this award include Vice President Joe Biden, Chancellor Angela Merkel and Martin Scorsese. Mr Draghi has served as president of the ECB since 2011. His appointment to this prestigious position was the culmination of a hugely successful academic and professional career. After earning a PhD in economics in 1935, he was a professor at the University of Florence and a fellow at the Institute of Politics at the JFK School of Government at Harvard University. He has served as Italian Executive Director at the World Bank and as a General Director of the Italian Treasury. Perhaps most famously he has served as Vice Chairman and Managing Director at Goa Man Sachs International. In 2005, Draghi became the Governor of the Bank of Italy and finally in 2011 he took his place as President of the European Central Bank. Mr Draghi was listed as the eighth most powerful person in the world by Forbes in 2014 and ranks as the world's second-greatest leader by Fortune in 2015. However, we're sure that the acceptance of the failed Goa Medal will be his greatest honor today. So without any further ado I invite you to please welcome our society's newest Goa Medalist, Mr Mario Draghi. Thank you very much. I'm especially honored to receive this this medal. You told some of the recipients but there are many others really and so I'm especially honored to be in this list. Thank you so much. It's really moving. Thank you.


Welcome to CTM member highlights. I'm Leslie McVane and today my guest is Will Atheridge from the Portland downtown district. Hi. Hi, thanks for having me on. Well I'm excited because we're going to talk about music today and the Alive at Five series. Let's talk a little bit about why this August 7th concert that's coming up is special. Sure. Well this summer we partnered with the State Theater right here in Portland, they're the premier concert venue at St. Paul State of Maine and they're bringing the Spirit Family Reunion which is a nationally touring act from New York to play for Alive at Five so I'm really excited to have this band perform. Well and they're really roots music and really exciting. They used to play in the subways in New York. Yeah, they really have that classic folk sound. They still gather all around a single microphone, a lot of harmonies, a lot of banjo and it should be a really great experience. Well it should get Monument Square really hopping and dancing and all ages. Yeah, this is a really great event. It starts at five p.m. so you got to work, you picked up your kids, bring them down to the square. It's of course a free event. We also will have the State Theater sponsored beer garden set up there so if you want to get a cold drink you can do that and of course all the Monument Square businesses so it's a great opportunity to come down, enjoy some music, enjoy the great summer weather. And remember to bring your I.D. if you want a beer. Yes, exactly. We do have our security personnel check-ins. No matter what your age.  The opening act will be the Ghost of Paul Revere. Yeah, so it's been really important for us to showcase our local music that we have here since you have such a great local music scene. I think that's one of the nice things about the 11 at 5 series. We're bringing people not only from out of Portland but also showing you know we have great music going on right here. They also, Ghost of Paul Revere, played at the Oldport Festival as well. Well and they've got a huge following. Yeah, I think for their CD release I think they just sold out Port City Music Hall. And people are traveling distances to hear them. Yeah, I mean it's great that we have a band literally right down the street that has such a great audience. And coupling them with the other band, it's just going to be a mob scene that night. We certainly hope so. I hope you have an in with the weather man or whoever's in charge. I've been pretty lucky so far. We had such great weather when we did the Oldport Festival weekend that hopefully haven't used to pull my luck at this point. Yeah, I don't think so. Now this collaboration with the State Theatre is new this year? Yes, the first time we've done it before 11 at 5 was essentially just a PDD one event but we figured we have this great resource here, this great talent that really knows how to bring great bands up here. So we said why not make this into a partnership. They're really excited to do it. So we're hoping to see great success this year and something that we can build and even add more shows next summer is what we'd really like to see. And the series that you collaborate with the State is called something different from Alive. Well Alive at 5 is still with the State Theatre but we've also started an Arts Alive series which is just PDD sponsored. And this is, it also takes place on Thursdays in Monument Square. So on this Thursday, July 24th, we have a Rosa Noreen and Doc Barbie and Rosa is a local belly dance performer and instructor. So she'll be not only belly dancing but maybe even instructing a few people if you want to get in on the accident. I know Rosa and she's fabulous. And she'll be performing with Doc Barbie which is a Middle Eastern music group. And then on July 31st we have the Portland Symphony Orchestra is bringing their tune-up main quartet, so just a four-piece quartet. And they'll be performing again for free right in Monument Square. It's the Portland Symphony Orchestra's 90th anniversary this year. So they're doing different sort of mini concerts all over. And to introduce an audience that might not necessarily go to the symphony. I think it's great and I think the variety you're offering the city is terrific. Now if people want to find out more they can contact you. Yeah, the www.portlandmain.com is the source of information for all these events and for everything we do at PDD. And if there's a cancellation due to rain or something that would be posted on that site. And I encourage people to check out our Facebook page since we do a lot of information there as well. So they can friend you on Facebook. And in the case of the Alive at 5 event, if there is a rain it will be moved to Port City Music Hall. So it will still go on. Terrific. Good to know. And it'll still be free. It'll still be free. It's still going to drink. Alright, well I'm looking forward to it and I hope our viewing audience will all be there with their dancing shoes on. Thank you Will. Thank you.


Hi, my name is Sam Boyd and I'm the lead athletic trainer here at Mid-Michigan Health in Midland, Michigan, and this is my fellow athletic trainer, Heidi Smith and Gull. Today we are going to demonstrate and critique the form of four specific ACL dynamic warm-up exercises. We're going to go through these four that have been diagnosed as evidence-based practice for preventing ACL injuries in the future. So while ACL prevention is important for all athletes, it's especially important for female athletes. So today we have a female to demonstrate the form of these ACL-specific exercises. The first one we're going to show is a squat jump. So with the squat jump it's very important for her to be in the correct position to begin with. The correct position for this exercise is going to be toes pointed forward, feet shoulder with the part, and then before entering the squat position, hinging the hips backwards with the knees going no further than directly over the toes. From here she's going to take off and land in this correct squat position with soft knees which means she's going to try to land softly with knees not fully bent and not fully extended. Good. So as she lands she's trying to land softly with the feet and she landed in a good position with her knees no further than over her toes. Her chest should also be up the entire time when she takes off and when she lands. Let's look at this from a side view. So the important things to watch here are the position of the knee not going more forward than the toe and the hips setting backwards as she takes off and lands. So that would be proper position for a squat jump. The next exercise we'll be demonstrating is a walking forward lunge. So the athlete will start in a position of toes forward and she's going to start in that split stance with her knee no farther over than her toe, her hips back and her weight centered and she's just going to drop her center of mass down into the lunge. Excellent. And what we're looking for from the side here is that her knee doesn't pass her toe and that she keeps her center of weight centered. Next we will be demonstrating the lateral hop.  She's going to hop to the other side landing with soft knees and coming back. Now if the athlete needs to touch down that is perfectly fine as they advance they will no longer need to do the slight touch down. This is the final and most advanced exercise that we're going to demonstrate for this ACL preventative dynamic warm up. This exercise is a squat jump so it's going to encompass the mechanics of a jump squat and a forward lunge. So to begin this exercise your athlete is going to stay in a position similar to a squat where the hips are dropped down, the knee is directly over the toe with the hip square and pointing forward. Center of gravity here drops straight down not forward or back because the chest and torso are up. From here she's going to jump and land with the same soft landing of a squat mechanic. The feet switch here but she still lands on the opposite foot with the knee directly over the toe and the center of gravity going straight up and down. When we look at this from the side we're going to be looking specifically at knee and toe position as well as the hips because there will direct the center of gravity up and down. So again she'll line up with the hip square toes pointing forward and then dropping down so that the knee is directly over the toe. She'll jump and land with soft mechanics.  That exercise should not be performed until the mechanics of a squat jump and a walking forward lunge have been mastered. There are plenty of other exercises that you can choose to do to encompass a full muscular warm up for your athletes. These exercises you may be more familiar with such as butt kicks, karaoke, high knees or skipping for height. If you'd like more information on these exercises or any others please visit our website at www.midmichigan.org backslashwellsport.


Six people were killed and only one person survived after a plane crashed near Canada's Fort Smith, Northwest Territories, according to the Chief Coroner for the Territory. The plane crash occurred near the boundary between the territory and Alberta.I.N.A. news release, the coroner's office said four passengers were among the dead, as well as two Northwestern Air Lease crew members, the lone survivor was taken to Fort Smith Health Centre before being airlifted to Stanton Territorial Hospital in Yellowknife. The plane was en route from Fort Smith to the Diavik Diamond Mine when the crash happened, the coroner's office said. The global mining company Rio Tinto said it had employees on the aircraft, we have been informed by authorities that a plane on its way to our Diavik Mine, carrying a number of our people, crashed near Fort Smith, Northwest Territories, Canada, resulting in fatalities, the company said in a statement Wednesday, Fort Smith is on the border with Alberta. The Northwest Territories Coroner Service said its representatives are working with local authorities to access the crash site and start the recovery process.


In this class, we will sort of revise and wrap up what all we have been doing in random vibrations. We will go through what all we did, some of them at least and then move to the complete use of these results of random vibrations for determining the tolerance of a human being for the vibrations that exist in a car. Maybe you have already done a course on ergonomics, I am sure that you know human factors, vibration, how what can be tolerated and so on, okay. So we will not go into those details as it is covered in another course, but we will understand this random vibration a bit more and see how we actually calculate or what is that calculations that we do in order that we determine the levels that the human beings can tolerate. So we will go through this, the whole of these things in one go. So we were looking at what is called as an observation, so in any probability sense, observation is an experiment and the result of this observation is the outcome. In our case, we are looking at the roughness of the road as we had seen. The set of all possible outcomes is what we called as sample space. So subset of the sample space is an event, we explained it with simple example and then the function or a map from an event to the real line is what we call as a random variable. So there is an event, there is a mapping to the real line which is called as a random variable and the map from this random variable to a probability measure can be done or we can directly give this probability measure to the event. So this in a nutshell is or the definitions that we need to know. We already saw that the random process is a family of random variables. Remember that we did this in the case of the roughness of the road as a random variable. So we said that we can take a number of measurements. So this family of measurements is now going to be used and we can call this as the ensemble. Remember that the random variables have two indices, I have put one on the bracket, in other words this r can vary from 1, 2, 3, 4 and so on and s is the distance and this indices 1 of s then zeta 2 of s and so on are the independent realizations as they are called or sample functions. So we have a number of such sample functions may be s and that we can call as zeta s and so on. So all these things, these roughnesses form what we call as the ensemble of roughness values. Now there are two ways in which we are going to look at this. We can look at it at a particular value of s n. Of course this s n can be or s can be replaced by time and you can define the whole thing in terms of time. Remember that we can look at this at s 1 and look at another one s 2 and so on. So we have now at s 1 a number of what are called as random variables or the measurements that we do at s 1 and we do similar measurements at s 2 and so on. Now this one for example I can say zeta 1 or zeta 2, zeta 3 and so on has a probability density function the most common amongst them being a Gaussian or a normal distribution. The Gaussian or the normal distribution function all of you know and that is the Gaussian or the normal distribution function. Remember that the difference between the probability density function and the distribution function, probability distribution comes out of integration of this probability density function or the probability density function comes from the differentiation of the probability distribution function. So for example if I have to calculate the probability of this zeta what is the probability that zeta is between a and b what I need to do is to just integrate this between the limits a and b, p, zeta gives the probability density function and if that is integrated you get what is the probability that this variable is between a and b. We are also interested in joint probability of two variables x 1, x 2, s 1, s 2 and so on. The joint probability is given by a Gaussian joint normal distribution function. Joint probability between two variables x and y is that look at that closely probability that x lies between or x of k lies between x and x plus delta x and y lies in a range. So in other words it is the second derivative or px, y can be written as dou by dou i of dou p by dou x or dou squared p by dou x dou y where p is the probability distribution function. The joint probability can be written as shown here and rho is what is called as correlation we will see that in a minute. We talk about when we talk about this example, we talk about what are called as expectations. So in other words it is the ensemble average is written in terms of expectation. Note that from a sheer definition point of view there is a difference between the time averaging and the averaging across or averaging of the whole ensemble. Time averaging simply means that I am averaging across here. So expected value or in other words ensemble gives us what is called as the ensemble mean or ensemble average and that is given by, this we had seen it couple of classes back that it is given by mean is equal to the value zeta 1 multiplied by the probability into d zeta. Now remember that we had seen, so the key factor here is this equation which gives you the expected value of say a function y is given by the expected value of y, so function of x or y is equal to g of s or function of s is given by g of alpha p of alpha into d alpha. So now I can replace g of alpha by any function. So I can replace that for example if I am looking at the expected value of the, you know what is called as the variance then we can determine the expected value of the variance by substituting that difference zeta s 1 minus the mean that can be substituted, that squared can be substituted instead of g and calculate what is called as the variance. So expected value of what is in this bracket that square of this term is called as the variance. You can operate the integral as you know it, you can substitute it, expand it and that is the right hand side is what you get. Let us now, so the two important parameters, the first two important parameters are what are called as the mean and the variance. They belong to one set say calculated at s 1 or calculated at s 2 and so on. So when I say it is mean or the expected value it is across this line vertical line. So if I talk about horizontal line though it is distance I would talk about time averages loosely talking it is a time average. So average can be taken along the vertical line or the average can be taken along the horizontal line. So those are the two things. The first is respect to one variable s 1. You can then look at how two guys who are separated by a distance say tau or time okay, how they are related. There are two things or two definitions which now become or characterizes this connection between the two random, I should not call two random variable but random variable at s 1 and s 2 okay because we have to be very careful in using these terms. This whole thing is one random process, please note that this whole thing is one random process. is characterized as one random process okay. So I can say in that way this is not a different random process belongs to the same random process and so they are the random variable. For example if I look at the acceleration levels in the seat okay then I can call that as a another random process. As we go along in these roads what is the acceleration level at the seat if I calculate okay that is called as another you know random process. So I have another set of these values there as well. Then I will combine both we will see that a bit later. So here we are looking at the same random process. So autocorrelation is the connection between those measurements that are done at s 1 and the measurements that are done at s 2. So that is the expected value okay it is expected value of zeta 1 zeta 2. So you can see what is that what is that the definition is there okay zeta 1 zeta 2 we are now replacing zeta 1 zeta 2 instead of g and so the probability now we see that there is a joint probability of zeta 1 zeta 2 multiplied by okay d zeta 1 d zeta 2. So the same thing yeah yeah that is the sorry that the second one is auto covariance you know there is a small I mean there is a mistake there it is actually auto then we have the what is called as variance okay auto covariance. So that is subtracting the mean and I think there is a small mistake in that as it is not cross correlation okay it is again the auto covariance between the two. So the second line that is the one okay. Now we can actually calculate what is called as the so what is the difference between the two the difference between this same as the difference between a mean and the variance one talks about how much the variable the variable is connected okay the other talks about how much the distance away from the mean okay how is that connected so both of them you look at one without mean and one with mean usually in most of these processes we would make sure that the mean value that is by adjusting the measurement that is being taken we can make the mean value to be 0 in which case both these cases both these things would become 0 or in the words that M would become 0 okay so that is the so the second one is actually the auto covariance so that not cross correlation in auto covariance and auto correlation and auto covariance is given by these two equations okay. We define what is called as a correlation coefficient what is what is the what is the connection you know why are we doing this what is the relationship between the two once we finish this once we finish a few more definition we will look at what this actually means okay. So I define another one I just normalize it normalize the variance okay this auto covariance by means of the mean and I get what is called as a correlation coefficient we will come back to this correlation coefficient in a minute we will let us look at we will now narrow down this process. Now if it so happens now look at this I define I am defining S1 and S2 if it so happens that that the statistics what we are defining defining okay does not depend on the particular value of S1 and S2 but depends only on say for example the difference in distance or in other words time okay does not and depends only on the time okay then we call this process as a stationary process. So the stationary process in fact can be classified into weakly stationary processes or we can call them as strongly stationary okay so but we will not go into that detail because we will then it depends upon whether all of them all the statistical measures or in other words all the moments higher order moments all of them they are the same okay or they do not depend upon where I put this my line does not depend upon where I put the line but depends only upon the separating distance or separating time okay all the higher order moments okay that is very strongly stationary if the first two moments depend upon this time tau then we call this as a weakly stationary process okay we will just go to ergodding then we will understand this whole thing you know what we are talking about please note again I am looking at it like this the vertical segment okay so it is independent of time in other words that vertical segment if I take and determine and do all my expected values and so on or I take this vertical segment they are the same or I take two other vertical segments and then separated by that same distance tau then all the statistical parameters that I calculate they are the same okay so this distance does not come into picture and that is what we call as a stationary process okay as far as we are concerned we are one interested in the first two and so that is the first definition of stationary process but a more interesting process because of the limitations and other things is called as an ergodic process it is a special case of stationary process where one sample represents the whole of the random process so here I have to go to a different roads and determine this okay so one sample determines the whole of this process so I take a sample if that sample determines all the statistics then we call this okay as an ergodic process more importantly the ergodic process we will see how it is whatever time averaging I do along this the regular time averaging I do along this that reduces to the end sampler expected values so the time averaging gives me the complete statistical parameter of the whole of the random process yeah we will come to that so one of the conditions of course is that the length of this sample we say that the length of the sample has to be big we will come to that in a minute what should be the length of the sample so we call this we already said that if the mean and the covariance you know function or independent of time and we also defined what is called as a strongly stationary process so we said that if the ensemble averages are determined from the time averages of a single process we said that is ergodic let us write down all the equations corresponding to that what am I doing I am only okay defining you can also write this as an expected value does not matter but when you write this in terms of time the calculation becomes quite simple so the first is the the mean definition look at that x tends to infinity okay so I integrate over a very large length it is very important that the lengths are large okay so that is the mean across that is the horizontal side so the mean variance and the third how look at how it reduces so we will we look at this from the probability distribution density function will be Gaussian and the third is what we called as much that autocorrelation okay autocorrelation and the fourth is mean square value and lastly we look at auto covariance okay so what essentially has happened is that that d psi d d psi 1 d zeta 1 d zeta 2 which we had put earlier okay that is now we have replaced it okay that time averaging and so it becomes very simple now it is possible to get the estimate of all these things we will not cover that in this course what are called as estimators we can do that estimators by looking at discrete values that are taken okay and then summing up the discrete values we will not look at what are called estimators for these things okay we will leave this as a continuous expression and maybe we will cover that in one of the later courses so these are a bunch of definitions which I am sure you understand if there is any questions we will take it okay now what does this these things mean you know what is the what does autocorrelation mean what does the autocorvariance mean and so on it simply tells you how far as a correlation you know the as a term indicates how far the signal that is happening at s1 okay the road roughness is how far does it affect something that happens at s2 in other words what is the memory of a signal this is not only for a roughness but it is also for any signal so it looks at what is the memory of the signal if this is white noise which means that say the we will talk about the spectrum or power spectral density which is distributed throughout then in other words it is purely a signal which is such that even the next guy or in other words delta t okay t if this happens at time t what happens at t plus delta t he also is not related to t in other words a white noise is where there is a stick standing at tau is equal to 0 okay you can if you go and substitute tau is equal to 0 in the expert in the third expression you would notice that that is nothing but that is nothing but if I if I substitute tau is equal to 0 in the third expression this is nothing but the fourth one or of zeta of 0 okay it is nothing but the mean square value of this variable okay which means that there will just be a stick and there won't it won't be connected at all if I mean of course this varies depending upon what are the frequency contents and so on okay what is actually ergodicity it is a very big questions can't be covered in this course there are what are called ergodic theorems and so on we will just follow a very simple method to just understand what this ergodicity is so what we said is that we have a time or a space average let's stick to that word time because that's what is used in most textbooks okay what we are saying is that the ensemble average that is the expected value that's the expected value is equal to the time average how does this come about okay let's look at this carefully now the expected value of the ensemble or the ensemble average given by that expected value okay we will substitute that okay expected value of you know in terms of what we had measured here what we substituted here this expression switch the expectation after all expectation is the integration now you can shift it okay and ultimately you would see that okay when I substitute that the ensemble average reduces to what is called as the line average or the time average right so what is important to understand is that these for a stationary process the ergodic process is also a stationary process so this very I mean this expression very simple expression you can see that the simple expression where I just took the expected value inside shows that the ensemble average can be calculated in terms of the time average or in terms of the distance yes now so these are the five things so which are important to us that's what we had defined okay let's define what is called as a Fourier transform we know that already what Fourier transform is not that 1 by 2 pi can be interchanged it can be root of that and so on okay depending upon the textbook so it is only a scaling quantity so let us look at the two values okay the what is called as the Fourier transform on the inverse Fourier transform okay these are well known this comes from your Fourier series okay you know you know that already we will spend some more time in one of the later courses on all these things so we define what is called as the power spectral density ultimately we come to a quantity which is called power spectral this is the sixth right sixth important quantity look at mean variance auto correlation auto covariance correlation coefficient which is nothing but normalized by means of the variance a square of the variance okay and then we come to what is called as the power spectral density power spectral density is defined as the Fourier transform of the auto correlation function so Fourier in other words what is that we are trying to do we are moving from the time domain to the frequency domain okay we are moving from the time domain to the frequency domain the auto correlation function is in terms of time domain and now we are taking the Fourier transform of it so we have now moved to the frequency domain right so the these are the two the Fourier transform on the inverse Fourier transform do an inverse Fourier transform of the power spectral density we get what is called as the auto correlation function okay substitute tau in that second expression you would get the meaning of what is power spectral density you know to understand what this power spectral density is what is this power or spectral density as sometimes called okay spectrum as it is loosely called okay all these what does this really mean the expressions which I had written there they are very straightforward okay they are just that the mean square value okay which is the auto correlation at tau is equal to 0 is what is defined in the third line that is this one we had seen this before if I now substitute tau is equal to 0 here it becomes that the mean square value is nothing but s omega d omega so in other words the mean square value or the power okay is now determined in terms of the power spectral density now let us understand this more carefully let us for a moment take that second expression for granted let us say that I have a process okay where I send in input and I get an output okay let us say that the frequency response function of this process is h of j omega and the impulse response of this is say h of t right let us consider that this process as a bond band pass filter okay with a narrow band in other words this has this is a filter which has the frequency response function such a function that it allows only a small band of frequency to pass through going to filter off the rest of them. So if I now other words let us say that centered amount around omega not okay usually it is practiced to do both so it is going to allow only a small narrow band okay now this pass band this is what is passed okay this narrow band of frequency is what is passed okay which I would also call as pass band if you do not understand this filter okay let us now calculate the expected value of the square of this output okay the expected value of the square of the output in other words mean square value of y usually the square of these any of these quantities the electrical engineers are formed to call them as power it comes from I squared r so usually the square of a quantity is called as the power okay so the mean square value which we saw in the last slide is now in other words this slide we saw it there as the last equation now since this narrow band is only passed this band pass only a narrow band is passed okay let us say that this is delta omega that integral from minus infinity to plus infinity is now reduced okay reduced because the rest of the places it is 0 so it becomes this is the integral limits now okay so that pass band is the power spectral density multiplied by d omega okay and you can as the band becomes smaller you can understand that the power that is passed okay in a very small range is nothing but is given by the power spectral density sometimes you know some of the softwares which are used in mechanical engineering they call that s y omega into delta omega as auto power okay so power spectral density because the word density is used because when you multiplied by by delta omega so it gives you the power so we that is why we that is why the density term is added many of the envy edge softwares okay you would see that they use the term auto power in what they simply mean is that they multiply this with a band of delta omega okay so that multiplication in other words what is inside the integral is what they call as auto power okay so in other words the spectral density or the spectrum gives you the energy content of the single signal and we are now looking at it in the frequency domain the energy content at various frequencies is what is determined okay by the spectrum okay one of the key factors though we will not be deriving it completely in this course take that is granted is the relationship between what comes out here and what goes in the relationship is given by the first equation in other words the power spectral density is a very key equation the power spectral density of a of an output okay output determined from a linear time invariant system okay which is characterized by a frequency response function okay that is given by the square of the magnitude of of this function multiplied by the input power spectral density so in other words in other words if I have a vehicle say for example we looked at a quarter car model and we had looked at various frequency response functions at various places now if I want to say that I want to find out what is the power spectral density say at the at the automobile what we call as the sprung mass say the base of the car okay the sprung mass the floor of the car okay if I want to find out that we already know what is the frequency response function we had written that okay now that frequency response function the magnitude square of the frequency response function if I now take note that it can be complex so we are looking at the magnitude of the frequency response function square of that multiplied by the input power spectral density gives me the output power spectral density so if I want to find out at the seat I put one more okay what is the frequency response function between the road and the seat I write it and then I can find out what is the power spectral density at the seat location okay that brings us to a very important thing or the properties of these of these functions the autocorrelation function by the shear definition remember that there was a xt and xt minus tau so from its shear definition okay is such that the it is a even function or in other words autocorrelation function of minus tau is equal to the function of tau okay from the shear definition so that is the first one. The second equation again comes from the definition go back and look at the definition okay you would notice that in e power minus j omega t substitute minus omega okay by I mean omega by minus omega so you would notice that the power spectral density in other words what is this nothing but the complex conjugate becomes the power spectral density of omega in other words the power spectral density is a real valued function okay power spectral density is a real valued function and that is what is okay important here that the output power spectral density and the input power spectral density both of them are related by the power spectral density which is a real valued function okay we can also look at cross correlation functions these are the cross correlation functions between two different random processes this is very important to understand okay what we are looking at this whole thing is a random process though we call this as one random variable and another random variable they belong to the same okay this is that is why we use the term here when we calculate it is auto okay so auto is what we used because it belongs to the same random processes so we can connect two different okay the cross correlation between them what happens when you give an input here how is this correlated with another random process which is at a time so in other words if I have another random process see I have a random process and I have two random processes okay the other random processes I let me call that as eta it can be it can be anything you know whatever it can be maybe what happens at the seat location okay or something else now the cross correlation is the connection the statistical connection between say for example that road input at S1 okay to what happens the statistical connection between that and at S1 plus say tau okay because of the fact that the distances do not now matter because we are looking at something else then so S has to be okay though we define the road in terms of distance it is proper that we come back to time rather than stick to only distances so please note that though I used it interchangeably note that it is always better to look at that in terms of time so we define what is called as a cross correlation cross covariance and cross spectral density okay so cross spectral density note the difference between okay yes the power spectral density eta eta to s zeta eta okay so note that difference the difference is at the same we are looking at when we look at the y y you know that is at this place what happens now there is a delay factor okay a tau which connects the cross correlation factors okay again through the definition we can see that the cross spectral density is a complex valued function obviously it is it is a complex valued function we also have to look at the phase what because we are looking at two different points okay and this is the frequency domain so we look at the phase as well and the cross correlation function is neither odd or even and again go back and put that in the definition you would see that that is the expression you would see one of the quantities see we saw the cross correlation coefficient okay the correlation coefficient rather sorry correlation coefficient one of the quantities that is in the time domain okay and one of the quantities of interest to us in the frequency domain a corresponding in the quantity in the frequency domain is called as the coherence is called as the coherence. So all of you know what is the correlation coefficient okay if the correlation coefficient all of you know varies from minus 1 to plus 1 okay rho is between minus 1 to plus 1 that we had normalized it by means of that variance okay so it is simple probability so which means that if there is a perfect correlation then you know that you can plot that the two variables and maybe all the points will draw will fall in a straight line and if there is a negative correlation you would also know that okay when one is positive the other one is negative and so on okay so one so the negative correlation means that one something increases the other factor decreases and so on. So that is the correlation which we defined okay in the time domain what is the correlation in the frequency domain you should be able to now imagine time and frequency in a very very similar fashion the x axis is time now the x axis becomes frequency it does not matter no x axis just becomes frequency okay so frequency domain where we look at this whole thing in terms of that f after we do the Fourier transform in fact today there are techniques that are available in order to look at say fatigue fatigue life in frequency domain so you do that in time domain or you can do that in frequency domain so coherence which is extensively used to look at how I mean look at the input and the output connection in many of the experiments say for example you would do some experiments in the vehicle dynamics lab in which case you would look at coherence all the time and look at how whether output what you measure is pure noise or whether it is correlated English word correlated with respect to the input okay that you look at it in the frequency domain if the coherence is not near 1 then okay suppose it is say 0.9 to 1 then you know that there is a good coherence between what you gave or in other words what you are measuring is due to what you gave as an input if there is no coherence then you would see that what you are measuring is noise there is nothing to do with what you have given as an input so coherence is an important quantity especially you would be using that in the experimental work one of the quantities of interest is what is called as the mean square value of the acceleration so why are we doing all these things okay ultimately I measure or I calculate what is the power spectral density at the seat okay we know that now we have defined quantities we know the connection between power spectral density at the road power spectral density at the seat we know that it is everything is done now okay why am I doing this what is it that I am doing this I mean what is it that I am going to get out of it one of the simplest thing you would notice is that the acceleration levels okay what you say for example feel when you do a drilling or something where you are subjected to accelerations are very important input of vibration to our body okay in other words in other words our tolerance levels depend upon the mean square value of the acceleration okay now what is the mean square value of an acceleration remember y squared the same thing okay mean square value of this now instead of why we say that it is the mean square value of the acceleration we know that this is nothing but the integral of the power spectral density this is exactly what we did in this problem so power spectral density in a band omega 1 to omega 2 okay in other words we have a central frequency like this and we take this band from omega 1 to omega 2 okay these band omega 1 and omega 2 is called as an one-third octave band is called as a one-third there are one-sixth octave band and so on one-third octave band if the upper frequency or upper bound this point is given by root 2 one-third of I mean power of root 2 or the lower frequency is given by as you see divided by third in a cube root of 2 and so on when you calculate that that becomes 0.89 to 1.2 1.12 omega 0 octave has a very important parameter and you will see that okay again next goes on noise noise and vibrations so the RMS acceleration is now calculated okay based on the power spectral density this RMS acceleration is related to our ability to withstand vibrations okay now we have various so I can calculate for various frequencies what is the RMS acceleration from that equation okay so in other words I can have a plot of frequency versus the one minute versus the RMS acceleration no it does not matter I am looking at it anywhere power spectral density so you can take that power it is a general expression so we are talking about power spectral density at the seat so yes okay yes no no no this expression here this expression here is now substituted in terms of the seat okay if you want to call this as eta eta again call this yes eta eta okay so suppose this is the road input okay and this is the seat why is the seat output at the seat and then if I calculate the power spectral density at the seat and call that as say yes eta eta or yes eta whatever it is okay which is nothing but remember that h squared value right so I am talking about this value so take it at the seat okay that is what goes inside that integral okay and take one minute let me explain this process and take one omega okay and then put that as a band and calculate one third octave band at that value so I get one value for a mid frequency of omega by integrating that value that is the RMS acceleration okay for a frequency that frequency so like that I will have RMS acceleration at various frequencies acceleration with function of frequency yes root mean square value of the seat position but note that I am calculating or I am measuring what I am measuring as an output okay that power spectral density of the acceleration say for example if I measure the power spectral density of this output okay is what I measure is yes so I can measure anything I can do I can measure whatever I want suppose I put an accelerometer usually what I do is to put an accelerometer and measure it and so what I get out is the power spectral density of this acceleration measured from the accelerometer and that is what I am going to use okay it does not matter the simplest and the best method is to put only an accelerometer and measure it okay now that is the so this is called as the RMS acceleration right so usually you can put an accelerometer at the seat you can put an accelerometer at the back okay and you can put an accelerometer at the floor and so on. Now a lot of tests have been done from the point of view of human tolerance human vibration tolerance okay and it has been found that there is a difference about you know the tolerance level of a human being whether we are subjected to say a seat vertical acceleration or an horizontal acceleration also I am sure you have been I mean this has been done to you and talked about a very famous standard 2631 okay in your earlier course on ergonomics because that is what essentially the whole of ergonomics is all about that standard 2631 it is a huge standard when we do not have time here to cover it but nevertheless once I know how to calculate that RMS acceleration you have what are called as weightage factors and these weightage factors are applied which are different whether it is a vertical vibration or whether it is a horizontal vibration and so on so with these weightage factors we will understand what is the what is the tolerable limit whether we are going to be comfortable or not okay we will talk about that in the next class the last of the topics that we need to cover which we will do in the next 10 minutes is what is called as the road roughness of course you had already seen what is power spectral density you already saw that power spectral density is very important input to this whole problem and that road roughness is actually the input remember that many of the equations you can either solve it in the frequency domain or in time domain okay but nevertheless if I have to give the road as an input as a power spectral density it is in the spatial domain in the words when you look at when you go and measure what is called as road profile okay this would be in terms of distance x and the actual power spectral density okay from which you can get the correlation order correlation and so on is actually measured in terms of time so we have to have a relationship between the spatial frequency and the temporal frequency in other words when the velocity of of the vehicle is V obviously the frequency with which they the vehicle gets excited would be a function of P so in other words if omega is the what is called as we will call this as a spatial frequency which means that this is expressed in terms of radians per meter okay and for the problems that you do you require the same thing in terms of say radians per second so I have to convert radians per meter which is a road characteristics that has to be converted into a characteristics as an input into the system into the vehicle and that obviously comes from V the velocity of the vehicle which is expressed in meters per second so obviously omega which is radians per second is given by capital omega spatial frequency multiplied by V okay so this becomes omega radians per meter meter per second so this becomes radians per second okay so this is the first thing that we need to know understand when we convert the spatial frequency or spatial power spectral density into a power spectral density which goes as an input I do not want to call this as temporal and confuse you just that as an input when at power spectral density when it goes inside all our calculations then it should be in terms of the omega should be in terms of what is called as the temporal frequency there is meters per second okay now so this is a very good nice way of distinguishing between the road and the vehicle so that brings us to the topic of how do you specify a road roughness in other words how do you characterize the road there are a number of ways in which you can write down a equation for the power spectral density of the road okay let us let me call that as the power spectral density of the road the power spectral density of the road is written in terms of capital omega which means that it is written in terms of radians per meter is given by a characteristic power spectral density phi naught multiplied by omega naught by omega whole power w the w actually indicates the waveiness and w varies from okay of course you have the wavelength to be 2 pi by omega obviously you know that and w actually varies from 1.75 to 2.25 practice is to put w is equal to 2 omega naught specifies a characterized you know frequency of your standardized spatial frequency okay it is a standardized spatial frequency we will see in the next you know equation how we are going to characterize the standardized spatial frequency this is one of the first equations that were written the equations were further modified later okay and there is an ISO specified power spectral density of the road so you want to calculate the power spectral density of for in terms of the temporal frequencies meters sorry radians per second we will do some small manipulation to that now the ISO came up with a small modification to this and they said that if you plot it in a log log plot then there are actually two different you know slopes in a log log plot of the frequency versus the power spectral density. So they gave this actually this is a log good to plot in the log log plot because of the type of equation you have so the equation that you get is something like this and for omega greater than omega not the equation is equal to the same similar type only thing is that omega to the power w2. So in other words there are two slopes okay and they are characterized by two equations or two w's w1 and w2 now the good roads and the bad roads are characterized by these w1 and w2 so if you really look at that for example for a very good road the standards are something like this for a very good road the range of this w1 w2 is that is less than 8 which means at 0 to 8 the geometric mean is 4 and this called a class road and for a b class road which is a good road this range is 8 to 32 okay and that is the the finite value the finite value okay and then C which is an average road the values vary from 32 to 128 and so on okay there is a poor road which is 128 to 552 that is 128 to sorry 512 and so on. The usual practice is to have w1 to be 2 and w2 to be 1.5 so in other words the lower bound for an average road if you want to do an analysis the lower bound for an average road you replace this finite by 32 okay that gives you the lower bound okay of course omega 0 is 2 pi okay and finite is 32 and for a upper bound you replace that finite by 128 okay and put that here right so you can have for an average road you can't I can give you one figure okay it varies from 32 into 1 by 2 pi omega whole power 2 and then again same thing whole power 1.5 when omega 0 changes this is a very standard so standard but people in recent times have issues with this the issue is that when omega the frequency okay if you really look at a graph the graph would be something like that with respect to omega versus this phi there have been issues with this equation the issue with this equation is that when that omega the omega value when it goes as it tends towards 0 the power spectral density now shoots up okay and goes to infinity the variance reaches infinity at omega is equal to 0 when omega is equal to 0 so that gives us a very unrealistic value when you reach omega okay when you move omega to be closer to 0 in other words in other words this would start this part the error is very large okay because of that fact that as omega tends to 0 okay this one tends to infinity so in order to avoid this in fact just one statement is very important that essentially what is that you do you put down an equation so if you want to like for example our road if you really want to model your vehicle in this road then you have to do a profile measurement and then you have to fit a curve okay which is given by an equation which you are going to write down which you have written down so it is very important that you identify a road with an equation of this form or an equation which we are going to put down because of this difficulty okay there are a number of issues for example this gives what is called white noise but the roads are colored noise where when there are a number of parameters we are not going to cover all that in this course but there will be a course on NVH if any of you want to take it may be next semester is after this two semesters romance is in the January session where the first part of the course will cover a lot more on random vibration as well as on signal processing and there we will be covering more detailed analysis of the road as well as how you know what is white noise what is colored noise what is shape filter what is shape filter which converts white noise into colored noise all those things will be covered there. So the only thing I am going to write down before I close this topic is that there has been changes in the way the power spectral density is written because of this difficulty that omega makes this omega as omega tends to 0 the power spectral density goes to infinity so in order to avoid that there have been other this kind of unrealistic behavior is avoided by using other types of equations or that is one type of equation the other type of equation that is written and so on. So these are other types of equation this which gives you a more realistic picture of the actual road measurements especially as you move towards you know omega to be small you give you get much better in a picture of this and of course when you want to convert it into omega substituting for V and so on so you would notice that we can write down V phi 0 into omega 0 say for example if w is equal to 2 you can write down that is the equation. There are lot more issues in random vibration because of lack of time in this being the last class I do not want to go into further details. People who are interested in some more details can slightly more information again look at Gillespie others if you want more details on shape filter white noise colored noise can look at the paper by Shilan in the journal Sadhana okay we will stop here and this course will end with this and we will continue that in the next course okay thank you.


Yeah, so shabbat shalom everyone. It's a mitzvah to commemorate a good life and to console the bereaved. A lot of Bill's family is here, so if you see them give them a hug on the way out. To my mind, Bill had three qualities that defined his life. Brian Dan talked about some of them. I summed them up as follows.  I used to say intolerance, but I think impatience is a little better. But integrity formed the bedrock of his unshakable values. He was loyal first to Susan and to the kids, then to his students and his friends, and his patience. One of my favorite phrases that Bill would drum into the residents and fellows as they were getting ready to take on the job of being an attending physician was, you are an attending. That means you are there. Stay there. Do not hide. Be there. Be prepared to speak about hard stuff. Stay there. He used to say ethics is easy. Life is hard by which he meant that whether you're raising children, coaching soccer, taking care of a dying child, or trying to be a good friend, rules and theories don't help very much except that one rule. Stay there. Never abandon your friends. Never abandon your patients. He would promise to his parents whatever happens, I will be there with you. One sign of his integrity and this may be a good sign of a well-lived life. Both his friends and his enemies agreed about one thing. Both always knew exactly where he stood and they either hated him or loved him for that thing. Second quality generosity, Bill and the whole family have always been famous for their extraordinary generosity. I was remembering last Thanksgiving which we spent together at their house in Michigan. If you've never been to a Meadow family celebration, let me see if I can paint the scene for you. There will be maybe 50 or 75 people milling around in that special golden meadow ambiance of controlled chaos.  A half-finished jigsaw puzzle, maybe 12 things bubbling over on the stove and 10 appetizers, half eaten on the counter. The dining room table will have been extended with every table in the house so it bends around into the living room. Every chair will be used and still somebody will be sitting on somebody else's lap but somewhere between an Indian wedding and the pilgrimage holiday of Sukkot. There would be passionate debates about politics, feminism, careers, kids. There would be games on the TV. There was always hope for the bears and disdain for the cubs. It was like that for bar mitzvahs, graduations, holidays, and often they had catering brought in from places in Hyde Park and Bill was generous to the caterers as well so whenever you'd go out for lunch with Bill in Hyde Park they'd say, oh Dr. Meadow, and just start bringing more food than your no bill, no check was ever brought to the table. Dr. Meadow got the best in the house.  He loved data but he also recognized that just because something was an empirical claim, something was not an empirical claim did not mean that it was useless. He once wrote to our friend and colleague Annie Jean Vie and he said quote, to make an article that will change things you have to realize that there are empiric facts and there's bullshit. Look at what you're good at. These require different skills. I do the empiric facts.  BS without facts will not change practice very much. Facts without BS will never be read. So you need both. Let John help you with the BS and I'll help you with the facts. So for Bill BS had two meanings.   Bill had been pretty sick for his last four years and Susan through those tough years you were his friend his advocate his inspiration his coach you kept giving him hope and without you by his side every minute calling in friends nudging his doctors cheering him on he never would have lived as long or as well as he did so thank you from all of us for giving us Bill for another few years sad that he can't be with us anymore but the warmth and the love that so many people felt for him reflects his enduring legacy of compassion integrity and selfless loyalty his was a gloriously well-lived life