Now we will discuss nutrition, the role of nutrition, what it is, why it is important for students and what we can do on the basis of nutritious factors. So the term nutrition refers to the science of food, its chemical components which are called nutrients and their relationship to health and disease. So it includes all the processes from the ingestion and digestion of food to the absorption, transportation, and utilization of nutrients. So see, whatever the children eat, drink, intake, they all have some good or bad symptoms that affect their bodies. So now it is like our diseases, our health, everyone and our children's growth, the well-being of our children, their body, their society and their emotional state, all of them are based on their nutrition and the nutrients that are actually inside them. For example, if an egg eats a child, it has protein, it has magnesium, there are many things that the organs of the child's body have to go and support them from somewhere. Similarly, if you are giving them some fried food, or if you are giving them a lot of musnoi, then they will go and go and affect them somewhere. So environmental and family factors such as financial resources, transportation, geographical location, cultural preferences, convenience, and nutrition knowledge can also influence a child's dietary quality. Now see, all the children in the world cannot get a good, complete and balanced nutrition. Obviously, there are different types of situations. We are a developing country, so we have many parents who do not have the resources to give their children a complete and balanced nutrition. In fact, many times it happens that they do not have the resources to give their children a complete and balanced nutrition. So now see, it is important for our community and for our parents and for all the caregivers, it is important that they have basic knowledge about nutrition, that what food is better for the children, and what kind of food is better for the children, which we are obviously doing for them, but it is causing them harm. Then they play critical roles in a variety of vital body functions including, now see, as I said, those nutrients that we are giving to the children, they are playing different functions in their body, like supplying energy, for the child to play, even for the child to sleep. So the activity that the child is doing, these nutrients are giving energy. Then promoting growth and development. Growth and development are both different phenomena. Growth comes to an end when all the development is going on all the time. But growth is specific to genes. It is specific to how genes will be. It will be the same growth. And the development is mostly related to the environment. The way you will give resources to the children, the way they will handle the situation, according to that, it will be their development. Then improving resistance to illness and infection. Now see, it is all based on the rules that how the children's strength and sacrifice is made. If they keep good strength and sacrifice, then obviously they will suffer less diseases. And if they get sick, then they will recover very soon. Otherwise, there are many rules that help the children instead of getting sick. Meaning, they are giving a lot of oil, fried things, they are giving a lot of carbohydrates. And sugar is a very harmful thing. Children are very sad about sugar. And parents are giving them a lot of sugar. So these kinds of things affect their liver. Later on, they can have diabetes, they can have problems with their liver. Then the fact that children should use water properly, especially in the field. Even though parents do not even try many times to see if the children have drank water or not, then at least 6-8 glasses of water or any kind of Maya cheese like juices or some kind of Sikhanjabeen or if you are giving them some other tea, then that is also Maya. But at least 6-8 glasses should be given at least 24 hours. So we see that all these things are playing a complete role in the body. It is not like the children do not eat for food, but their entire body needs it. And then you see that building and repair of body tissues. Every day we have so many cells that die and end and then we take new births. Now those who take new births, obviously they also need food, ingredients and nutrients. So if we do not give child-friendly food to children, then they will have a lot of deficiencies and then we will go ahead and talk about lifestyle, that our running is actually better for our muscles and a good sleep is also necessary for that. But at least before you get good food, then your ingredients will be made. So children's nutrition has effect on their behavior, learning and illness. Okay children who behave in different ways, some are more angry, some are very slow, they listen to the people. You have told them to do something and they will remain angry. The state of their body and the dullness and the slow learning, you are telling them that they are not able to understand. So these behaviors and the problems of learning are also related to food. If it is healthy food, healthy body, then all these functions will be correct and accelerated. And the stages of the children's life will be according to them. But if they have a lot of deficiencies, then all their learning behaviors are bad and emotional behaviors can also be bad. So well-nourished children are typically more alert, attentive, physically active and better able to benefit from learning experiences. You see, the same child can learn better, the same child can be alert. It is a healthy food that has good nutrients, good nutrition and healthy body. So for that, we have to consider a lot of things. We have to understand the children, understand their genetic inbuilt. We have to give them adequate nutrition. We have to take them to a proper lifestyle.  The child who is weak will be weak in every matter. And the healthy one, then all its activities are better.


The last time we were discussing about the singular value decomposition and recall that the singular values of a matrix are sigma 1 through sigma n where these sigma 1 through sigma n are ordered with sigma 1 being the largest and sigma n being the smallest singular value they are all real numbers and non-negative and such that sigma 1 squared sigma 2 squared up to sigma n squared are the eigenvalues of a Hermitian a now we also saw the singular value decomposition theorem which says that if a is an m by n matrix and sigma 1 through sigma r are the non-zero singular values of a where r equals r is the rank of this matrix a and if we define d to be a diagonal matrix containing sigma 1 through sigma r as its diagonal entries and zeros everywhere else and sigma to be a matrix with d as its top left r cross r block and zeros everywhere else and of size m cross n then there exists a unitary u of size m by m and a unitary v of size n cross n such that u Hermitian a v equals this matrix sigma so this is a very important theorem in the sense that I like it for its generality it applies to any matrix a absolutely no structural assumption made on the matrix a and you can construct a singular value decomposition for any matrix we also saw that we can partition the singular value decomposition like this we can write it as u 1 u 2 where u 1 contains the first r columns of the matrix u u 2 contains the remaining m minus r columns of the matrix u this is sigma tilde which is the r cross r matrix is the same as the d written in the statement of the theorem containing the non-zero singular values in fact this is yeah these are the non-zero singular values and v is also partition row wise with the first r rows of v being denoted by of v transpose denoted by v 1 transpose and the remaining n minus r rows of v transpose being denoted by v 2 transpose so this sigma 1 is therefore a diagonal matrix containing sigma 1 through sigma r so we were looking at several properties of the singular value decomposition the first was that the rank of the matrix a equals the number of non-zero singular values and we saw this because this is true because if we write it like this then we can multiply these together and we see that we can write a to b so we can write this as u 1 sigma tilde v 1 transpose and we also recall that this is called the economy singular value decomposition so basically the columns of a are linear combinations of the columns of the matrix u 1 with the coefficients given by the columns of this matrix b and this sigma this sigma tilde has linearly independent columns and v 1 tilde has v 1 transpose has linearly independent rows so basically the columns of this matrix are linearly independent so there are exactly r linearly independent columns in this matrix a okay so in fact the converse is also true that is if the rank of a equals r then it has r non-zero singular values this comes from the svd theorem itself if you look back at the proof of the theorem other properties are that the null space of a is the same at the span of the columns of v 2 this matrix here and v 2 is actually an orthonormal basis for the null space of a the range space of a is the span of the columns of u 1 u 1 is this matrix here and u 1 is an orthonormal basis for the range space of a and the range space of a transpose is the span of the columns in v 1 and the orthogonal complement of the range space of a which is the same as the null space of a transpose is the span of the columns in u 2 so basically the svd reveals the four fundamental subspaces associated with any matrix it gives you an orthonormal basis for all of the four subspaces also the the the the the spectral norm of a is equal to sigma 1 which is the largest singular value and if a is square and full rank we can easily determine a inverse once we know the singular value decomposition a inverse is simply v sigma inverse and u times u hermitian and since sigma is the diagonal matrix inverting it is trivial you just have to invert the each of the diagonal entries another interesting property of the svd is that it diagonalizes any system of linear equations so for example if you're given ax equals b this is a system of linear equations then substituting a equals u sigma v transpose we get u sigma v transpose x equals b and if we define c to be the matrix u transpose times the vector u transpose times v so i'm pre multiplying this by u transpose and what i get on the right side would be u transpose b and i call that my c and i partition c as like this with c 1 containing the first r entries in c and c 2 containing the remaining m minus r entries in c this is the same as representing this vector b in the basis defined by u this is a big change of basis because u is an orthonormal matrix multiplying by u transpose corresponds to a change of basis and c is basically the same as b but in a new basis represented in a new basis similarly if i define d to be this v transpose x and i partition it as d 1 d 2 where d 1 contains the first r entries and d 2 contains the remaining n minus r entries this is a representation of x in the basis v so if i do these substitutions then the system of linear equations reduces to sigma times d equals c which is which is which looks like this it's sigma tilde 000 times d 1 d 2 so this is r cross r and this has r entries this has n minus r entries this has r entries and this has n minus r entries so this is now a very simple system of linear equations and in fact we can visually solve the system of linear equations in particular if m is greater than n that is the number of rows is more than the number of columns then the matrix a is tall and if you look back at the system of linear equations this means you have more equations than you have variables that you need to solve for and in in in particular if you assume a is full rank that is the rank of this matrix is equal to the number of columns in a which is n then then there won't be any right block here because r is equal to n there is no such thing as d 2 and there's no right block over here so it's just sigma tilde times d equals c 1 c 2 and of course these equations because d is only only multiplying zeros in these rows these equations can only be satisfied if c 2 equals 0 and c 2 equals 0 in turn means that it means that u 2 transpose times b equals 0 because c 2 is just exactly equal to u 2 transpose times b and if u 2 transpose b equals 0 it means that b must belong to the range space of u 1 or in other words the range space of a we already saw that the range space of a is this the span of u 1 so that is clear right if you have more equations than if you have more yeah more equations than you have unknowns there exists a solution only if b lies in the range space of a similarly if m is less than n that is the matrix a is fat then you have more more variables than you have equations and if a is full rank that is r equal to m then there are no bottom blocks here so you'll have sigma tilde d 1 d 2 is equal to c there is no bottom there's no c 2 here then if we define d 1 to be sigma tilde inverse time c this is allowed because we've assumed a is full rank so sigma tilde has all non-zero entries and so if we so in in that case we can d 1 the solution d 1 is just sigma in tilde inverse time c and d 2 can be anything because it's anyway just going to multiply 0 here okay and so the solution is not unique the and in general we can write the solution to be c v 1 times sigma tilde inverse time c plus v 2 d 2 where d 2 is arbitrary so this is the complete set of solutions of this system of equations so in in this case there's always a solution in fact there are always infinitely many solutions if a is not full rank then both of these apply so yeah you have to you have to use both these conditions so there may or may not exist solutions another another property is that v it rotates the rows of a to get orthogonal columns so in particular since a equals u sigma v transpose if we write multiply by v a times v becomes equal to u times sigma now the columns of u are ortho orthonormal and then this is a diagonal matrix so this matrix u times sigma has orthogonal orthogonal columns with each column norm being equal to one of the singular values of this matrix a similarly if i take u transpose a a is u sigma v transpose so u transpose u becomes the identity matrix so what i'm left with is sigma v transpose which in turn has orthogonal rows with the row norms being equal to the singular values of this matrix a so the last time somebody asked me a question about connections between the eigenvalue decomposition and the singular value decomposition the explicit connection is exactly this that if i if i consider the matrix a transpose a again a is equal to u sigma v transpose then a transpose will be v sigma transpose u transpose so a transpose a is v times the sigma is sigma tilde 000 and since i'm writing it as sigma tilde i don't need a transpose here this is just a diagonal matrix its transpose is itself but the matrix overall is of size n by m not m by n whereas this is the sigma matrix which is sigma tilde 000 which is of size m by n times v transpose now if i carry out this multiplication i'll get the matrix sigma squared as the top left r cross r matrix and then zeros everywhere else so that the overall size of the matrix is n cross n and i have v v transpose so from this we see that the eigenvectors so basically this is the eigenvalue decomposition of a transpose a so the eigenvectors v of a transpose a are the right singular vectors of a and of course this also shows that if a is fat um then a transpose a will have and if it's actually full rank then all these entries will be non-zero so there will be m non-zero eigenvalues for a transpose a and the remaining n minus m eigenvalues will be zeros similarly if you write out what a transpose is that is u times sigma tilde squared 000 but now this is of size m by m so the zeros are of appropriate dimensions so that the overall dimension is f by m times u transpose and so the eigenvectors of a transpose at this u which are equal to the left singular vectors of a and of course the singular values of a square are the eigenvalues of a transpose so basically the price paid for being able to diagonalize an arbitrary matrix is that we need two matrices to diagonalize the matrix instead of one if it was a if a was square and symmetric we can find the eigenvalue decomposition like this q lambda q transpose where lambda can is a diagonal matrix containing the eigenvalues and q is an orthonormal matrix containing the eigenvectors so basically a q is equal to q lambda or a times the ith column of q is equal to lambda i times q i i equal to 1 to n so this is the eigenvalue decomposition so you need only one matrix to diagonalize this a but for a single for a for a for an arbitrary matrix which is not necessarily square we can find an svd a singular value decomposition like this a equal to u sigma v transpose and of course this means that a times v equals u times sigma or a v i equals sigma i u i so these are v i's as unit norm vectors and u i's are unit norm vectors so these are kind of like eigenvectors of the matrix but not really because this u i is not equal to this v i but they are unit norm vectors they're in fact of different dimensions v i's are of dimension n by 1 whereas u i's are of dimension m by 1 similarly a transpose equals v sigma u transpose taking the transpose of this and this this matrix i should actually write it as sigma transpose u transpose so then if i write multiply by u i have a transpose u equals v sigma transpose then a transpose u i is equal to sigma i times v i and so again so this is the kind of relation you have a v i equals sigma i u i and a transpose u i equals sigma i v i that is the relation you have for the singular value decomposition what you should do on your own is to compare these two compare the eigenvalue value decomposition and the singular value decomposition when the matrix is square symmetric okay and positive definite or in the case where it's indefinite that it has some that is it has some positive and some negative eigenvalues okay there is another geometric view or the ellipsoidal view of the singular value decomposition so again take a matrix which is of size m by n let's look at this space defined by the vectors y where y is equal to ax for some x which is of unit l2 norm okay and let's look at what this what the space of y looks like it turns out that the space e okay so that is you take any vector which is of unit l2 norm you compute ax and you look at you plot that point in the m dimensional space and look at all the points y that you can reach by by this operation it turns out that that's that set of y's describe an ellipse are actually in in m dimension it's called a hyper ellipsoid and these singular values of the matrix a are the lengths of the semi axis of these hyper ellipsoids what I mean by that is for example if I take a two dimensional ellipse then these lengths the lengths of the semi axis actually just to be a little more clear let me draw it in a different way so suppose there's an ellipse that goes like this then this is called the semi major axis and this is called the semi minor axis and this length will be sigma 2 and this length will be sigma 1 okay so let's see that so suppose y if I consider y equals ax and now I use the singular value decomposition it's u sigma v transpose x then once again if I define u transpose y to be c and v transpose x this part to be d then this this equation reduces to c equal to sigma times d and further if x is of unit l2 norm because v is an orthonormal matrix it means that instead of searching over or instead of going over all points such that x2 equals 1 I can as well go over all points such that d2 equals 1 so we can look at the set of c vectors that I get corresponding to d satisfying norm d2 equals 1 now because c equals sigma times d norm d2 equals 1 simply means that summation i equal to 1 to p where p is the number of nonzero singular values in the matrix a c i over sigma i squared which is the same as sigma i equal to 1 to p di square this is diagonal that's why I'm able to write this is equal to 1 now the set of points c is actually exactly described by the c that satisfies this equation and this equation sigma i equal to 1 to p c i over sigma i squared equals 1 is an ellipse or a hyper ellipsoid so basically e and this going from c to y is just all it takes is multiplying by u which is an orthonormal matrix it's just a orthonormal change of basis so e is also an ellipse when or a hyper ellipsoid when expressed in the basis u and the principal axis of this when you write an ellipse equation of ellipse in this elementary form like this then sigma i is at the semi major axis of this hyper ellipsoid so the principal axis are along aligned along the columns ui and have lengths sigma i which are the singular values of the matrix a there's one more very very interesting and useful theorem associated with the singular values a singular value decomposition so suppose a equals u sigma v transpose which we can if we expand this product out we can write it as sigma i equal to 1 to r sigma i ui vi transpose so in fact we see that the matrix a can be written as the sum of r matrices of rank one ui vi transpose is a matrix of size m by n and it has rank one and so a can be written like this and the question we ask is given this matrix a with rank r what is the matrix b with rank equal to k which could be less than r which is in fact less than r that is closest to a in the two norm sense so that the answer to this is given by the following theorem define ak to be the summation i equal to 1 to k sigma i ui vi transpose that is I am I am I am stopping this summation okay from i equal to 1 to r instead of going all the way up to r I am only going up to k and that matrix I define as ak then the minimum over all b matrices b of rank at most k rank equal to k of a minus b l2 norm is equal to the norm of a minus this particular matrix ak which is in turn equal to sigma k plus 1 in other words ak is the rank it is the matrix of rank k which is as close to a as possible in the two norm sense and how far is it from a it is it is sigma k plus 1 away from a recall that for all this to I mean the assumption here is that sigma 1 is greater than or equal to sigma 2 is greater than or equal to etc up to sigma r is greater than 0 okay these are ordered so it means it means what this means is that the in words what what this theorem is saying is that the closest rank k matrix to a in the two norm sense is given by ak this matrix here okay and this matrix is formed by excluding the the contribution of the smallest singular values from in the in the expansion of this matrix a given here it also means that any mate any m by n matrix of rank k less than or equal to r is at least sigma k plus 1 away from a in the two norm sense so you can't find a matrix of rank k which is strictly less than r that is arbitrarily close to a there it will be at least sigma k plus 1 away from a now the proof of this theorem is actually very interesting but before I walk you through the proof note that what this is saying is that the solution to this optimization problem is ak this by itself is actually the fairly you know it's it's not obvious how you would show something like this it's saying that yeah you need to show that when you solve this optimization problem you will get ak as a solution so the way you actually prove theorems like this often is that you show a lower bound on this quantity if you look at all matrices of rank equal to k what is the smallest value that a minus b2 can achieve and then you show that this ak as defined here actually attains this lower bound so that basically establishes the theorem so this kind of tricks can be used when you somehow have a guess of what the answer to the optimization problem is so I show that kind of problem is actually always almost always easier than find the solution to this optimization problem so to the proof goes like this first of all rank of ak as defined here is equal to k okay that's simply because if I do u transpose ak times v I get this diagonal matrix containing sigma 1 through sigma k along the diagonal and now this clearly has rank equal to k so rank of ak equals k also if you look at the l2 norm of a minus ak this l2 norm is invariant to left or right multiplication by unitary matrix so I can consider u transpose a minus ak times v which is substituting for a to be u sigma v transpose and ak to be u some sigma dash v transpose you can see that the first k singular values will just cancel off and what you will be left with is a diagonal matrix containing sigma k plus 1 through sigma r and of course the largest eigen value of this diagonal matrix is just sigma k plus 1 so this norm is exactly equal to sigma k plus 1 okay so that shows that that shows this part of the theorem a minus ak is sigma k plus 1 now what we need to show is that there is no other matrix that can have an l2 norm of the difference being bigger than sigma k plus 1 sorry being smaller than sigma k plus 1 in other words ak itself is the matrix that is closest to a among all rank k matrices in this l2 norm sense so suppose b is a matrix such that rank of b equals k which in turn means that by the rank nullity theorem the dimension of the null space of b is equal to n minus k define w to be the span of v1 through vk plus 1 the first k plus 1 columns of v okay so a is equal to u sigma v transpose and v is v1 v2 vn okay take these first k plus 1 columns here and the span of this is the subspace w now the dimension of this subspace w is k plus 1 the dimension of the null space of b is n minus k so the sum of the two dimensions is more than n okay and therefore by dimensionality considerations we see that the null space of b intersection w cannot be the null set there has to be some nonzero vectors in it so suppose we pick one such vector x which belongs to the intersection of these two sets subspaces which is of unit norm x2 equals 1 now this x of course belongs to the null space of b and it belongs to w so since it x belongs to w we can write x to be a linear combination of these k plus 1 columns here so we will call that i equal to 1 to k plus 1 alpha i vi and these vi's are orthonormal and so we can actually directly find these alpha i's to be vi hermitian times x okay i equal to 1 to k plus 1 now since the l2 norm of x equals 1 it means that if I take vi hermitian x square these when you square and add them that should also be equal to 1 you can simply take this hermitian times itself all the cross terms drop off because these vi's are orthogonal to each other and you're left with sigma alpha i mod of alpha i square which is the same as sigma vi hermitian x square i equal to 1 to k plus 1 and that should be equal to 1 on the other hand since x also belongs to the null space of b it means that b times x equals 0 which means that a minus b times x is equal to a x because b x is equal to 0 and if I write out what a x is so all I'm doing here is I'm just substituting sigma i equal to 1 to k plus 1 alpha i vi for x so I get sigma i equal to 1 to k plus 1 alpha i is just a scalar so I can bring it out front and it's equal to vi hermitian x times avi but we already saw that avi is the same as sigma i ui and so I can write it as i equal to 1 to k plus 1 vi hermitian x times sigma i times ui and so basically if I look at norm square of a minus b x this is equal to sigma i equal to 1 to k plus 1 vi hermitian x square times these vectors are orthonormal so once again if I take this thing squared all I'll all the cross terms will drop off and all the direct terms will have ui hermitian ui which is equal to 1 there and so I have vi hermitian x square times sigma i square and so and further by the sub multiplicativity property a minus b x l2 norm is less than or equal to the l2 norm of a minus b times the l2 norm of x but the l2 norm of x equals 1 we started out by saying that that x be a vector such that l2 norm equals 1 so this is less than or equal to the l2 norm of a minus b and so this is bigger than or equal to this which is in turn equal to this and so a minus or rather the square of this so it's equal to the square root of this so a minus b l2 norm squared is greater than or equal to the square of this which is equal to this sigma i equal to 1 to k plus 1 vi hermitian x square times sigma i square now these sigma i's are in decreasing order and these vi hermitian x squares add up to 1 so if I replace all of these sigma i squares with sigma k plus 1 the smallest value here then sigma k plus 1 can come out but this summation i equal to 1 to k plus 1 vi hermitian x square is equal to 1 that's what we wrote over here and so this is greater than or equal to sigma squared k plus 1 okay so that means that if I had taken any other b which is of rank k then a minus b squared is is at least equal to sigma k plus 1 square and we found the matrix ak which is exactly at sigma k plus 1 square distance from a and so that proves the result any questions so far sir yes you have not proved the first part right the the matrix b which minimizes this problem is ak now what I'm saying is this this these two points together you made two points okay the first point is that let me go back here the first point is that a minus ak l2 norm is equal to sigma k plus 1 okay so ak is exactly at it's a matrix of rank k which is exactly at sigma k plus 1 distance from a and then we said let b be any other matrix okay such that it has rank equal to k then what we showed is that this matrix b is at least equal to sigma k plus 1 distance yes sir okay I understand so now basically I can challenge you to say find me a matrix which is of rank k which is less than sigma k plus 1 distance away you won't be able to because I already proved a lower bound that any other matrix any matrix b of rank k is at least sigma k plus 1 away from a and we found the matrix ak which is at sigma k plus 1 distance away and therefore this matrix ak that we found is a solution to the optimization problem of finding a matrix b of rank equal to k which is as close as possible to a in the l2 norm sense of course there will be other solutions but this is at least this is certainly one solution yes sir okay thank you okay so if there are no other questions I move on to the next topic which is generalized inverses of matrices sir yeah sir a minus b into norm for fixed a and where it will be if it's a convex function then it's the only solution we can say yes okay so you need two things to prove that this uniqueness based on convexity okay you need that the cost function is convex and if I take a minus b l2 norm squared that is indeed convex in b however you also need that the constraint set is a convex set and clearly you can see that if I look at the space of all matrices of rank matrices b of rank k if I take a convex combination of two such matrices that need not be ranking and so this space is a complicated space it's not a convex space okay and because of that you cannot use convexity based arguments to say that this is a unique solution okay very understood thank you welcome


this meant to be playing all these nodes that currently we cannot hear because in the browser we also get this buffer not found make this window smaller just so we do not load that many nodes including the 10 second at a time I cannot hear the sound is not being played the button does work so I have this main js that is important the html is not where the problem is hopefully picks up on the prompt at the end of the code yeah it's something to do with the nodes not being loaded properly yeah github is giving generic responses whereas gpt4 is actually giving more specific trying to solve the problem yeah it's something with the node the names of those nodes essentially have to look at the documentation so that's where the JavaScript is this was built just recently it's like a JavaScript where we're loading piano so to understand why do we need to load these nodes manually is it possible to load all the nodes yes it's actually playing all the test nodes just select a different channel just for the heck of it things already should be playing the nodes but still skipping stuff that's not cool yeah this is from the console it's something to do with the syntax of this being just try these two variations one will have the basic ones that suspect all of this one will be played okay right so this for playing fine it's something with the syntax yeah now I suspect only a5 will be played right so all those are being missed yeah so three of them do not work is there a conversion happening play sequence convert the eg to music or the eg data update data load piano yeah we need to verify the names so the problem is these nodes the one that have all the sharp nodes not work these nodes will work so it's a problem with the syntax issue if this node's not playing while this one's playing fine could be due to naming convention this is a common issue is there is no strict standard for naming sharps and flats in midi libraries and cell phones did I just try it manually what are the other options how do I get this character on comment that's safe no none of them plays yeah but the other ones do not work a sensitive something ah it's these little things get me stuck forever okay so I should just replace them in numbers but how do I know which number is which so we're loading a grand piano it's working okay as you can hear then there is a function translating this e g signal in to those musical nodes so when you scroll through it that should play the notes that's playing them okay it seemed to be two separate problems because the skipping business is coming from a different part of the code there's this play sequence and let's go with this suggestion I don't have anything better play sequence is this complete need to modify the back end as well for this to work this bit is done in the back end isn't it we haven't shared the python code yet time to do it now yes we have midi numbers instead of notes but these notes generated by the back end we'll have to change quite a bit yeah we'll just go with this but that's not it we need to modify the back end as well let's start from the bottom so in load piano we just load the all the notes so we don't need those names anymore this is the one that's actually generating the notes we updated the java script to use midi numbers instead of notes so we need to update the back end to produce midi numbers instead of notes as well so we have this extra function there's a lot of magic number a couple of magic numbers in it it's supposed to convert midi frequency to midi it's not good enough what is the conversion yeah convert the hg previous code okay so we're still using interpolation just trying to make sure there's no any placeholders convert the hg to music and it's the same code okay there's a placeholder there it's this range and scaling the midi should have been doing this already it probably wasn't doing it correctly converting the frequency didn't we already have that code and note that should be okay and this is a flask application right let's pull this one in try integrating ourselves that's the regular test when we scroll through the hg nothing the piano was loaded with all the notes still thinking about it isn't there was this something wrong let's just reload this quickly no piano is loading quickly let's make the window size 10 any other placeholders or something scaled frequencies return midi numbers come here anything ah come on GPT 4 fix this for me need to start troubleshooting again just wondering these notes and how play sequence it's the function let's say midi it's the pattern play midi is played when scrolling through eeg small bars move yeah that's right thank you github co-pilot okay i'm not sure how much progress we made today if any matches continue this next time it appears that your JavaScript code is structured to handle eeg data visualization and music generation there's an issue with the play sequence function not being triggered when scroll buys moved to resolve this you need to ensure that play sequence is called with the correct midi numbers whenever the scroll buys adjusted let's make some adjustments to your code to ensure this happens check data handling in convert eeg to music function ensure that the data received from the convert e to music endpoint is corrected processed looks like you're expecting data notes but based on the python backend changes it should be data ready


Welcome to a new week of the rundown. On this episode, more chiefs are heading to sea. Pupers Online is being updated. CNP welcomes the Royal Australian Navy and then messes to sailors from Secretary of the Navy. The Navy announced a new policy this past week that aims to improve fleet manning and warfighting readiness. The Navy anticipates that more than 1,100 senior enlisted supervisors could be returning to sea to fill critical leadership billets in the next several months. The policy change will place a greater emphasis on career management for chief petty officers. Assignments for enlisted leaders, including those selected for chief petty officer, will be reviewed to maximize fleet manning readiness. CNP said that chiefs are the Navy's critical leadership element and it is essential that we have our deck plate leaders, including newly selected chiefs, where it counts. On ships, submarines, and aviation squadrons, and other operational or fleet production units on the leading edge of our combat teams. Read more on the policy in our story on navy.mil. Pupers Online will be offline for two weeks, starting August 25th for system updates. Many of the applications sailors use will be affected, including prims, officer data card, electronic service record, OMPF, and more. NPC has established a number of workarounds that will allow sailors to continue to access certain systems during the outage. Pupers is expected to be fully operational again on October 3rd. Read the article on navy.mil for additional information and links. CNP welcomed Royal Australian Navy, Rear Admiral Michael Noonan, and his staff during a visit to NSF Arlington last week. During the two-day visit, they discussed personal issues affecting both navies. CNP also lauded the Royal Australian Navy and thanked Noonan for being a great ally and friend of the United States Navy. Read more from the visit in our story on navy.mil. Our new Secretary of the Navy, Richard V. Spencer, sent a message outlining his expectations for sailors, and he said, we have an opportunity to make a contribution towards a more effective, versatile, resilient, and lethal organization. Resect and ask message by clicking the banner on navy.mil. That's all for this week. For any questions, send us a message at usnpeople.fct and navy.mil. For the Chief Naval Personnel, I'm MC2 L.J. Burleson. Thanks for watching.


In this video I want to walk through an example problem of doing a design using the more rigorous analysis for gears. So in the problem we have a setup such that there are two gears, a gear and a pinion coming together, and I've kind of already prescribed out some of the setup stuff here. The pinion has 20 teeth. It's rotating at 1100 rpm. The gear has 40 teeth. We want to design for a safety factor of 1.5 with 99% reliability. The gears are made out of steel with a Bernal hardness of 350. There's accurate mounting, a dimensional pitch of 8. The gears are 1 inches wide with a 20 degree pressure angle and they're manufactured by a top quality hobbing operation. So then the intended outcome is to determine a horsepower rating for this gear set if we want it to last five years running 60 hours a week for 50 weeks each year. Okay so that's all of our setup. So we're going to start by saying we have our base equation that looks like this and we have to start filling in the gaps. P we have, B we have, J the geometry factor we can look up, Ft we don't have, but if we think about what the question is actually asking it's asking for horsepower rating and remember before we talked about how power can be determined by the the force transmitted. So this is actually what we're trying to solve for so keep that in mind and then we need each of these factors. So let's go ahead and start with the factors. The first is the velocity factor Kv but in order to know the velocity factor we need to get the pitch line velocity. Pitch line velocity is equal to pi dn over 12 and we actually weren't given the diameter but we can go ahead and specify that or to calculate that because we know the pitch and we know the n value. So if we rearranged our pitch equation we'd see that diameter equals n over p so we can actually plug that in so we have n of 20 over p of 8 and we're working with the pinion in this case usually we start we actually analyze the pinion because it's the gear of the two in a pair that's most likely to fail so usually we'll start with that. So we have this plugged in for diameter 1100 rpm over 12 and this gives us 720 and if we carried this out carried out our units this would be in feet per minute. So now we need to go ahead and come up with Kv. If we go to our charts in the book we can actually find Kv here this is figure 15.24 and we said we had a top quality hobbing operation so there's a Hobbs operation here and we'll assume we're on the best line for that which would be the top quality so line c and just below the figure there's an equation given for for line c which is 50 plus square root of the pitch line velocity over 50 so we'll go ahead and use that then 50 plus square root v over 50 and that comes out to be 1.54 for this particular setup. All right next we need or we can get a value for j since we had that figure up so if I go back here I'm looking at a geometry factor let's see we said so there's two charts here a and b the first one is for a 20 degree and the second one is for a 25 degree we have a 20 degree in this case and again we're going to use the the pinion as our matching option the number of teeth we said was 20 so I have a x-axis here of 20 I'm going to go up my mating pair had 40 so the lines all get really close together here and it gets kind of hard to read but what we could do is if we're going to assume worst-case scenario we might say that there's no load sharing which is kind of a you know less likely but also just a worst-case scenario situation so if we do that we could pick this line here which is 0.24 approximately it looks like just before 0.25 so we'll go ahead and say 0.24 and yeah we can move on from there all right next might work on mounting factor mounting factor we can pull from a table in the textbook first on face width we said it was one inch wide so that falls into this first column and then depending on what side of style of mountings it says accurate mountings so probably somewhere between here and here arbitrarily we might pick a middle this this middle value which gives us a little bit more slot built into it kind of more conservative I guess as a as a choice so that's all kind of personal opinion I guess on that and overload factor ko we don't have anything in the problem that specifies whether we should expect any shock loading so we might go ahead and pick a km value of one or excuse me a ko value of one so putting this all together we get ft times 8 over 1 times 0.24 times 1.54 times 1 times 1.6 and this gives us that the stress in our part is 82.1 times the tangential force so again we're trying to find a horsepower rating and this tells us the stress in terms of the force transmitted and force transmitted remember is related to that horsepower that we're passing through our gear set so in that case we don't actually know the numerical solution here instead we need to find something that we can compare this against and our comparison point is you know failure what what the stress would be at failure so we need to go ahead and apply a fatigue analysis to this in order to determine that failure point so separating this out a little bit remember we have the equation that looks something like this let's see if I can get all my correct factors in here something like that so we need to start plugging things in um sn prime we were given that this is uh steel with a 350 hardness and if we look back at our fatigue chapter we'd actually see there's an equation for sn prime based on that which is 250 times that hardness which comes out to be in psi so we can make use of that load factor we'll pick a load factor of actually I'm just going to write it in of one because the the dimensional pitch is greater than five that means that our cg is also one cs surface factor I would read this off of figure 8.13 and we'd get 0.66 reliability factor from table 15.3 99 reliability gives us 0.814 and we don't have anything about high temperature so that gives us a temperature correction of one and this is not given as an idler gear which gives us an kms of 1.4 great so we've got this uh spelled out and we can go ahead and calculate that through so if we do this I think it comes out to be 65812 psi and this we're going to go ahead and set equal to now our sigma value from up there so we have 82.1 ft is equal to 65812 great and what this is doing is it's basically saying here's the stress in our gear here's the stress limit based on fatigue what do we expect to happen now the one thing that I haven't included yet is we said we wanted a safety factor of 1.5 um two ways we can think about this we can think about it as reducing the fatigue limit or increasing the stress in order to factor in that 1.5 so I would go ahead and just pull this out and say I need to multiply this times 1.5 to build in my safety factor there and if I do this then I calculate that ft with a safety factor of 1.5 would be 534.4 pounds so that's the load that I can transmit at the gear tooth and presumably not shear that tooth off under this fatigue loading condition so I can from that calculate my power which is equal to ftv over 33 000 and I get 534.4 times my pitch line velocity that we calculated before and 33 000 and this comes out to be 11.66 horsepower so what this is saying is I can transmit up to 11.66 horsepower through my gear train gear my gear set I should say um and presumably not fail in fatigue and I have built in a safety factor of uh 1.5 into this now you may ask one thing uh that you might notice is I didn't really take into account uh where did it go I didn't really take into account this life here and I probably should have written it down but basically what the only thing I'm really having to check here is how many cycles this would be so if I took five years multiplied it by 50 weeks per year multiplied it by 60 hours per week multiplied that by 60 minutes per hour and multiplied that by 1100 rpm I'd get 9.9 times 10 to the eighth cycles so where this comes into play is that this value is larger than one times 10 to the sixth which is what we usually consider to be um that infinite life condition so I really already did factor that in down here where I said that the sn prime was equal to 250 times 350 because that's an infinite life condition um specification um and therefore I've kind of already built that in I should have made that more clear but I didn't realize till the end that I had skipped over it so we've we're expecting it to last a whole a whole bunch of cycles and to do that with a 1.5 safety factor we have to limit our power to 11.6 horsepower all right thank you


Everybody forced here ripping 20 23 touch chrome update hobby six box case number four sec There we go. There are the names Bigger team style here Anthony are Astros Christian L. Cardinals Christopher J with the Mets Darren G. Royals Dave K Cubs David S. Rangers Don B. A's raised Wayne with the White Sox Phillies and Rockies Gary Pirates and Nats Isaac Angels Jeffrey Dimebacks Braves Orioles John W. Tigers Kevin L. Guardians Logan M. Reds Michael P twins Nate S. Padres Pojen with the Baroners Ray G. Marlins Robert L. Giants Shigunori Red Sox Tim W. Brewer's Wayne P with the Dodgers and J's and William F with the Yankees No job prospects. There you go mark. All right, take center drop you guys link first. I'm just coming up next Museum I'm I'm yeah, the game was not okay, but that's great. That's why why would we try to win against the Giants? You don't want to be bad. You want to be really bad. You got to get that top pick, yo Top two taught like top two pick this year Then they should honestly trade back and get some alignment and stuff and then find a quarterback another way Or like next year, maybe Because you can't you can't draft one of these rookies and be like all right come play with the Patriots offense You don't want that you don't want that You gotta ruin somebody immediately Yeah, that's odd mark. You just have another David Carr on your hands. That's odd man. I Don't know why I did that but uh Yeah, careful out there Do not condone that Trust me That's not where I missed the freaking can Sorry, I didn't I didn't account for the wind Oh Kelly greens. Well the Eagles are in this game. Yeah, dude bills are screwed I'm eagle should win anyway, they're at home and they're better than the bills Yeah, when rich told me that he was like you're talking to the phone earlier He told me the other was 30 over under 33 and a half. I was like, oh easy under easy under Like that's a layup If the under was in the 20s, I probably would have taken it to I would have been right And they don't watch either those teams recently Do they not know that the defenses are like kind of decent and their offenses are just Non-existence. I Didn't see the question mill. Sorry You mean if I had to choose one You choose me I'll probably choose you I'm not good at top loading but I Am the I am the funniest person. I would choose I would choose for I am the forest. I Am the funniest person though Definitely top four before Sam Kyle Shawn.  They work here a factor Bobby Miller Dodgers Prism gray rod debut for the Orioles Freddie Freeman all-star rookie refractor Ben Joyce angels noise Blue-green lava lamp or Aldous Chapman to 125 for the Royals It's lava ink. It's lava ink just in time prism of JP Sears A's Fractured Dustin May I'm glad that Lane's lava lamp works Appreciate the donation lane. Sorry. It's our lava lamp now. Thank you for the donation Some of them are this way some of them are that way Panthers owner drops f-bomb furious after falling to one in ten. Well Run a good organization, dude refractor debut is Volpe for the Yankees Prism Garrett Stubbs Phillies Kodai Senga all-star game Board of Mets for key refractor action a's Crush Petal Anzo SP for the Mets. That's sick. Congrats to the Mets Pete crushed he does crush Prism rookie Xavier Edwards for the Marlins Natalie Arietta Take my checker Jake McCarthy. Dude's got two Jake Myers. Who's got? No, just one sign Jake Myers the center fielder for the Astros Or what? He was an outfielder for the Astros. Yeah, I'll go Arietta Jake McCarthy is All right guys just the Mets in a delights case after this just the Mets Metropolitan New York Metropolitan Metropolitan New York Metropolitan Joe's boys They're on sale They own sale. They're on sale 20 doll hairs off. He was all right when he first came up. They just kind of fell off Makes sense. We got out Thomas Corbin Carroll. I was wondering I was like I know I've heard that name before I pulled that person's cards. I couldn't picture it You guys ready to start seeing Christmas music holiday music. Oh my god Dominic the donkey Like that one June Dominic the donkey never heard that one. No, what? No It's time Christmas donkey. I've never never heard of that in my life God The rock you're living under what's that from? Holiday song, what do you what do they what any of them come from? Somebody starts feeling festive and they need to write a song to start ranking the holiday songs now Best one is definitely the n-sync Christmas song green action stars Come on now. That is a refractor debut terrain for the Brewers Prism Pablo Lopez twins all-star seager Garrett Hill rookie refractor for the Tigers Famous Tigers pitcher left thumbnail tonight very famous magenta purple laval amb to 299 Royals Jordan Liles Spencer was here We refractor lugo Padres the 99 Prism debut Jordan Walker for the Cardinals set lugo future See some orange wave coming up here in a minute Air your dawn Kelly Jensen gonna Titans. There we go Orange wave Orange wave rookie auto Jonathan Aranda. Let's go Number 17 of 25 for Tampa Bay fire Gotta show you I got a Taj Bradley rookie auto refractor auto in a top scrum break No, I did I got Taj Bradley rookie auto refractor. Let's go refractor debut case. You should make giants Prism rookie Hicklin for the Royals. I might grade it and hang on to it And sell to Jake for 200 acts next year. Hmm. I'll start J rod Refractor Soriano for the Marlins the rookie do we need a debut patch we do man Purple rookie Perkins for the Brewers Action stars Adley Prism Nikki Lopez Royals Ricky refractor Brito Yankees The bull to butt Kyle is talking about how much you you you you think you like that word a lot Good one though, I want to see to buy though. Oh, man. Here he goes.  Oh, man Mmm. What's better to buy her songs? I sing that are stuck in my head. He can't stop.  What song can we get stuck in Jake's head guys?  I hope to God Are we doing thermo thermo? All right so far nothing nothing crazy crazy so far out of the packs tonight, but There's only an eight boxes stature and two boxes of update chrome hobby, so We will pull some heat tonight Friday was insane Friday.  God for Friday was literally shroud stained glass We couldn't be stopped I'm gonna get a pinata and put empty pack wrappers in the shop Wow, what's in this? Busted to find out Refractor debut is Gorman for the Cardinals So far about picked out up prison rookie of Maya for the Cubs Nardi rookie refractor for the Marlins Nardi-nardi Narvaez Nardi-narvaez photo negative Mets, Nardi-cosas party Nardi Prism Dom Smith the Nationals probably future Red Sox Plain Wendell classic Red Sox signing Yandy Yandy be the 40 man, but he'll hit like ten homers in like a month with him or something refractor La Stella Rookie refractor auto Orioles Josh Lester at a 499. That's that that's that refractor debut Paraz a Yankees John Lester Nicholas, what's up? How you doing Nicholas?  I'm Sam was here. He was singing that one Eagles get it together One Eagles fourth down the bill's got the sneak on them. I thought all the Eagles could do that Why I'll sleep in on me Yeah stained glass straw was a monster. There were some other monsters too. Oh and Nebula of Devon white We were pulling just heat everywhere Every product One-on-one yard on auto Yep piles got almanac on fanatic slide gold-pack giveaways really really well price breaks Like stuff is going super cheap over there earlier, okay, Magenta 399 jewelry angels He lives on jewelry lane Got corporate Carol action stars Prism rookie Caballero for the Mariners. How was Lane saying that guy's name on release day? Cava, I don't even remember. It was not it wasn't even like Cabal Caballero It wasn't even like that refractor rookie. Hopey Harris Nationals Caballario photo negative a rookie is Jose Hernandez pirates action starts again Carol prism fire eyes and Dodgers We're here a factor Jose Quas Royals all-star Adley-Ruchman Orioles Fracture debut Michael Bush Dodgers Jesse winker Alvarez and Walker on the inserts Walker Titans teen Titans Teen Titans go Prism Kyle Lewis Diamondbacks. You remember the original Teen Titans. I don't just show They only know Teen Titans go There's Teen Titans and there's Teen Titans go rookie refractor is Cole Reagan's Rangers Purple refractor Clevenger White Sox the 250 Knux dude Knux Canoes Prism pro far How else can you say that Knux? Refractor debut Bryce Miller Mariners my saying it right Knux like now I'm questioning like how can Yes That's right rookie auto Brian servant for the Rockies He's serving rookie refractor Luis Medina sure is A's all-star Nato Belly Fractor why y'all sleeping on me? Well, we found it. We found the song We found it he did say Knux. That's right. You have video evidence. That's right You have video evidence Just the Mets still y'all said canoes like he's paddling a canoe just the Mets Here goes this Sunday afternoon every Sunday afternoon some car playing a bunch of bass out there They like line up out there. Yeah, it was like six of them listen to my bass Your bass sounds like bass listen to my bass. I knew I knew you'd like it Spencer. It was funny I was telling Jake I was like this is we're gonna put on a thumbnail Just for you No, no coat Yeah, why are you doing thumbs down? I think you clicked the wrong Come on man This isn't April Spencer's running around for it. He's getting into fighting any excellent emotes and dies This isn't April fools man. Come on. We're in November. Can't get me Jake is so lit right now. Your life is so boring. It's my JP Ryan rushing touchdown. Oh my god My fancy legend last year. It wouldn't be an NFL November without smart JP Ryan touchdown folk Coke was a not that All right, next time I got Jabba Chamberlain But tigers are Yankees Jabba. That's the question. Got any tigers for Spencer Tigers Austin meadows Come on, let's get a debut patch here right here. That's a that's a pretty good deal Devin There they go. We're here factor JP France Astros Rookie SP image variation Diamondbacks Gabriel Moreno Moreno Bless yes more. No, we're prison refractor fire eyes and Dodgers Christian Vasquez refractor Two-time World Series winner Claims sunny gray all-star future brave probably Pete Titans Aqua wave rookie auto Cal Stevens and Philly's number two one ninety nine Grissom refractor rookie debut for the Braves Yeah, at least he had a good mustache news in Detroit Prism Fairchild Reds Whoa Why are you doing this rookie refractor a J. J. Bladay? Green refractor Brewers and 99 Devin Williams Michael Harris action stars for the Braves, which Disney Park should I go to on Tuesday? Which one have you not been to prison rookie nice one Reds Matt McClain Fracture debut Michael Bush for the Dodgers. I'm thinking I should go to Epcot try to get on Get on our guardians. Have you been on it? Yep It's freaking awesome debut refractor Edward Julian for the twins either that would have as you can then we try to get on Tron I'm gonna be getting passes soon Because my dad got a job there Rookie refractor Medina for the A's Very nice J. Prism rookie debut Gunner Henderson Orioles Gonna righty then the last box Dang Spencer Gotta be careful we might get hurt doing all that stretching Boston is gonna be Boston's gonna be the place It's a cliff. How we doing cliff on days, right?  Let's get some breaks filled a week 12 mixer Week 12 We're almost in December December's next week weekend Cannot believe it man. That's are still there y'all 129 129 for the Mets on sale 129 arena breaks next The New York Like the stream everybody we appreciate it we do appreciate it 28 to go on Sunday fun day There's the finest flashbacks coming up as well division's case still has six to go Sean Murphy Adley Refractor debut Mervis for the Cubs Blue ray wave Michael Grove rookie out over the Dodgers with a color match Number to 150 rookie refractor Jose Quas Royals Prism Pasha Phillies Strider all-star game Fractor rookie Giants Blake sable Magenta purple lava lamp Danny Mendic Number to 299 for the Mets Prism Narvae as Mets Refractor debut Mariners Bryce Miller All-star game Austin Hayes Allard refractor Titan's a mookie Fractor rookie Ryan no duh no duh Hayes Prism debut Masataki Yoshida Red Sox All-star Kunya Kunajuna Kunajuna Mason England rookie refractor Tigers Sean Murphy photo negative for the Braves and prism rookie Bryce Miller for the Mariners That'll do it. Give me one second. We'll do a quick recap hunt for a debut at LSE continue. Come on All right, you've got a image variation of Moreno crushed SP Peter Lonzo Number to cards now Drury Mendic Lyles Clevenger Perkins aloo Chapman Devin Williams Seth Lugo Autos of Brian Serven Randy Vasquez To 499 Josh Lester to 199 Cal Stevenson to 150 Michael Grove and a 25 Jonathan Aranda Okay, moving on to the delight case. Thanks everybody


in the aircraft so you would walk all the way back there and then you take the rope so when you go make sure you turn away from the master not towards him because you don't want to hit him


So a brand new statement just came out from the Jaclyn Hill camp and a lot of people aren't very happy because they feel like Jaclyn Hill is using her mental health as an excuse for everything that's going down with her lipsticks. So that's exactly what we're going to be talking about in this video, but do me a favor and make sure that you stay tuned to this entire video because we're going to be talking about some very important mental health subjects. What is up everybody? This is Chris from the Rewired Soul where we talk about the problem but focus on the solution. And if you're new to my channel, what I like to do is take different topics going on in the YouTube community and try to see what lessons we can pull from them to improve our own mental and emotional well-being. So if you're into that stuff, make sure you subscribe and ring that notification bell. And it is a beautiful beautiful day. It is the first day of July, but even more importantly, my brand new book, Rewire Your Anxiety, is out and available now. So check out the description as well as in the pinned comment down below. The ebook version and audio book version are both out. So go grab yourself a copy. Also for anybody who entered the contest on Twitter or Instagram, I have announced the winners. So go check that out. And everybody, if you are a loyal patron, make sure you go check out the Patreon page because I've uploaded the files for all of you who get free copies. All right. Anyways, Rewire Your Anxiety available now. Check out the description as well as the pinned comment. All right. But anyways, yeah, let's talk about what's going on with Jacqueline Hill and those of you who are new to my channel. This video isn't necessarily about Jacqueline Hill. I'm using this story as context, but the goal of my videos is to help all of you out because mental health is something that we all need to care about. And whenever topics like this come up, we need to really look at it and see, okay, well, let's, let's maybe look at this in a different angle, but there are definitely some issues with the way Jacqueline Hill is handling this situation. So just keep in mind, like I want you to look at this story and reflect on it and see how it affects you personally. All right. So anyways, this is an Instagram post from Jacqueline Hill's mom, which she put out yesterday. And it says this, I want to thank each and every one of you for your wonderful messages, thoughts and prayers for Jacqueline during this time. As her mom, it has been heartbreaking in so many ways. It was difficult and very disappointing enough that some of her lipsticks had issues, but the onslaught of hate, lies and even betrayal has by far been the worst part. It has been shocking. No one deserves to be beaten when they are already sad and down. But this post is not intended to focus on the negative, but rather the positivity that has come from this. From the bottom of my heart, I want to thank those of you who are true and loyal followers and friends of Jacqueline. I've always said that she has the very best followers of all.  And Jacqueline truly loves you. I love you. It has always been an honor to meet you, whether it's in person or through a written message. I'm so very grateful for each and every one of you, and I wish I could personally give each one a hug. Thank you for having faith in her and for giving her. Please know that Jacqueline needs some time, but she will be back stronger than ever. She is surrounded by love and support, and she will get through this. She will dust herself off, learn from her mistakes, and try again. After all, she has a dream to fulfill. As long as I have breath in my body, I will always be her cheerleader. Please join me in continuing to cheer her on as true friends do, and thank you for having courage and being kind. So, yeah, after that post, there is a lot that has been said about this. Now, one thing I do want to mention, because I saw some other people saying like, oh my god, this post is getting so much hate. Like, I was scrolling through the comments to put in this video because I'm about to read a few, and like, there's a lot of love and support, and that's really good to see, all right? Like, people do need to be held accountable, and that's what we're going to be talking about, especially with the aspect of mental health and how that affects you and all of that, but it does look like Jaclyn does have a lot of loyal, loving supporters, all right? But anyways, here's just a few comments that I saw from the aspect of her saying she needs a break for mental health. Bullying and attacking are not okay, but I don't think all the negative feedback comes from hate. It's a public safety issue at this point, but she decided to run and hide. She is the CEO of her company. It's completely unprofessional. I know mental health is important, but how about public health and safety? Why not just recall them if she actually loves her fans? I'm really sad and disappointed on how she handled the situation. Next one says, quote, unquote, she needs time. She's a brand owner. It's incredible that poor Jaclyn needs a break, but every other company who's ever had a bump in their road of success has had to take the loss, the hit, the backlash and move forward and work their butt off. But Jaclyn has another failed launch and she gets to delete her stuff from social media after disrespecting her customer slash fan base. Wow, just wow. Jaclyn sounds like the last person who should be running a company. And last one, I think this is one of the best comments and it's professional and it makes sense, but it says, I think people can take the toxic hate too far. But I have to say, while I haven't been a CEO, I was part of the senior management team in my company for many years. If I made an error, of course I did as everyone does. What I didn't do was expect my staff or family to tell my clients it was okay because I was a nice person and had to take some time away from my mental health. My clients would have left and I probably would have been fired. Instead, I would own my mistakes and ensure I fix them for my clients so their bottom line wasn't impacted. That is how you run a business. Most social media influencers, particularly the young ones or those who started young and know no different, do not seem to understand good business practice. Perhaps, as Jaclyn said, she needs to get back to what she's good at, tutorials, even if her heart isn't really in it. Maybe she can find the passion in that again. Not everyone is cut out for running slash owning a business as much as they may like the idea of it. I want to talk about a couple different angles of this. Like I said, that last comment right there, I think that's really, really true. The first thing that we need to understand is that mental health is impacted in everything you do, whether it's good or bad. If Jaclyn Hill had the most successful launch, if you had the most successful project that you were working on, that could affect your mental health. That can make your ego blow up if you have imposter syndrome. Maybe you think that you're really not that good enough and this is all fake and da da da da.  But here's the thing, like we have to be proactive about our mental health. We have to be resilient. And this is something I had to learn a long, long time ago because you never know when life is just going to slap you right in the face and you're going to have to trudge forward. Because here's the thing and this is why I feel a lot of people are very upset that mental health is being brought up in Jaclyn Hill's case. Because when you are the brand owner, when you are the face, right? When you are the head honcho, like you don't have that luxury anymore. Like that luxury went, it went bye bye. It's kind of like being a parent.  Like some people do do that. But we don't really think too well of them because our kids still need us. So in Jaclyn Hill's case, like there are people out there who are still dissatisfied and they need answers, they need a recall, you know, I know they released the refund, but a lot of people want the recall. And I see a lot of people are confused about that and just real quick to touch on that. A recall gets the contaminated items out of people's hands. All right, it takes them out of people's hands. Like I don't get why that's hard to comprehend. Like if there's something potentially dangerous out there, it's not about just giving the money back. We have to make sure that anybody who might become physically ill or hurt by the lipsticks, that product needs to be removed from their hands. That is why you do a recall. All right, but anyways, again, like when there's people expecting something from you or who are counting on you, you have to be there. So like, I worked in a drug and alcohol treatment center for over three years.  And one of the one of the worst parts about it was was like I followed up with all the clients after they left. I talked to clients, I talked with their family members, everything like that. And I experienced a lot of death, a lot of death. All right.  And that is one of the reasons why I have to work so hard on my mental health.  Right. And like, don't get me wrong, like I do think that more companies and businesses do need to allow for, you know, mental health days and things like that. But like part of a lot of our jobs is that we have to work through it.  Like now don't get me wrong. If I was sick, I took a day off because I didn't want to get everybody else sick.  And that I think is what people are really upset about. So in Jacqueline Hill's case or some of your case, like you have to trudge through it, you have to work through it, you have to get through that, even though you're not mentally well during that time, you still have to show up, you have to suit up and handle that business. And once everything is taken care of, now you can take your mental health break. But kind of like that commenter said, like not everybody has cut out for this. Like there's a lot of things that people want to do, but they're not cut out for it. Like when I look at Jacqueline Hill's case, but it's not just Jacqueline Hill, it's a lot of us, right? Like we don't like asking for help. And I see this a lot with entrepreneurs and business owners, where they don't hire certain teams or whatever to save money and all this other stuff. But then all of the pressure goes on them, right? Because CEOs of bigger companies, like if they were in Jacqueline Hill's position, they have teams of people who are designed to handle this stuff, right? So it's not just one person managing all this.  Okay. But in the case of Jacqueline Hill, here's something that I want you all to think about, which kind of bothers me when I see people saying, Oh, she's just using mental health as an excuse. We see this all the time. We see so many people saying that. And I really hope that people understand the difference between mental health being an excuse or being a reason, all right? Because there's a big difference. Now, don't get me wrong. Some people do use it as an excuse. But what happens is my worry is the stigma. My worry is that other people will be afraid to talk about their mental health in fear that other people are going to say, you're just making an excuse. You see what I mean? Because just people watching this and reading these comments, they might be like, Oh, well, I can't open up about my mental health or people will just think that I'm making an excuse for this. And here's the thing, we need more people talking about mental health, not less. All right. But the last thing I want to say is therapy, therapy, therapy, therapy.  I have my therapy appointment this morning and I was able to talk to my therapist about being anxious about my launch of my book and everything like that. And we were able to talk through it. I was able to break down like, you know, these fears and these belief systems and everything like that. Like if you have the resources, see a dang therapist, like there is nothing, there's nothing good about taking a break. If you're not working to improve and strengthen your mental health. So I do hope the best for someone like Jacqueline Hill, but I hope she's doing something with this break and not just trying to put out the fires, but also like seeking a therapist and improving our mental health. Maybe it'll help her like the last video I did, retrace her steps and help her take in the criticism and see what other people are seeing because the problem is a lot of us are so delusional and we lack this self-awareness that we don't understand why everybody's so upset, right? We can't see it. So a therapist can help us see what that issue is, right? And like, this is why I make these videos because so many of you are sitting back here judging Jacqueline Hill, looking at her, looking at, oh, she screwed up this launch and everything like that. But I want you to ask you, right? Like, what are you doing? Like, are you somebody who is just completely lacking self-awareness and you don't know why it seems like the whole world's out to get you? Is it because you're not noticing something about yourself? You need to work on that ish, all right? But anyways, I do hope Jacqueline Hill comes out on the other side of the thing. I hope this gets straightened out. I hope she does not give up on her dreams or her passions, but she definitely does need to improve, all right? But anyways, don't forget, check the description and the pinned comment down below. Rewire your anxiety is out now, baby. All right? But if you liked this video, please give it a thumbs up. If you're new, make sure you subscribe and ring that notification bell because I make a ton of videos. And a huge, huge thank you to everybody supporting the channel over on Patreon. You're all amazing. And if you would like to become a patron, get access to supercs and benefits like all of my books for free, click the top right there. All right? Thanks so much for watching. I'll see you next time.


Hello, it's Basel Chapman here and this is Tiger Foundation News Network 11 a.m. Eastern Time Market Update on the 7th day, Thursday, April, and we're looking at down 200 points at 34,293. It's almost a one-to-one, you know, TFNN. We kick off at 9 o'clock with this is the market kick off with Tommy O'Brien Jr.  I call them the Chapman Wave methodology just lower troughs and this particular instance we've gone from the 35,372 level down to the 1st of April, 34,533 then that sudden big spike up to 3512 and today's low is 3446. Now you've almost got the one-to-one to the downside. This is where there should be some kind of support coming in. Let's see if that's the case by the end of the day, a little bit of strength to come in to get off the lows. That's going to be important, but you've got a mixed market. Talking about a mixed market, look, there's the S&P down six, only six at 4474 under a 14-period moving average to QQQ, showing nice relative strength. Although it's down now, it was up earlier quite nicely, it's at 350 with a resistance at 358 in the 200-period moving average. I must mention this, the IYT, you can't ignore the fact that the IYT has just plunged as if some horrible news about energy has come out in the last week and it's gone, the IYT Transportation Index has gone from the 276th level on the 29th of March with 1, 2, 3, 4, 5, 6, 7. Huge red candles, except for one little dogey candle right at the 200-period moving average to check it out for strength, it didn't. So this is almost, I mean, what a move down to 240, that is a really big move in a week and a half, so you can't ignore it, but at the same time you've got some areas of strength certainly in the defense of stocks, but if you're looking at gold, it's stuck in a range, it's trading up 16, but it's still within a fairly narrow range. Crude oil had a huge move to the upside, now it's just digesting those huge gains, that's up 43 cents at 96. This is really important, together with the transports, we've seen a plunge in bonds, which means that yields are screened to the upside. All of that is really negative, and yet when you look at the market right now, we should be way, way lower, so either that's about to come, or in fact it's a rotational correction that we're looking at. I'm in the rotational correction camp for the moment. Stay tuned, you've got a great program coming up all day, and check out my opening call, my daily newsletter, and I will see you, don't forget my webinar coming up on Wednesday the 13th.