Welcome to the second lecture of the course on pattern recognition under the NPTEL phase two program. In the first lecture you have heard from professor C. Murti about the importance, significance and different applications and the purpose of pattern recognition. In the second lecture now we will consider an important aspect in the field of pattern recognition. In fact two aspects and we will try to understand the difference between the concepts of clustering and classification. The difference of clustering and classification can be understood best with an example. Let us start with the example of classification first which is easy to understand and then look at the problem of clustering. If you look into the slide what you now have actually are two images or two pictures of two different flowers. If we ignore the presence of the background, if we ignore the presence of background in these images, then if you are given the problem of classification to distinguish and say white flower is one class and that appears different from the second flower which is of class two. Let us say then you have to identify one property, one attribute or one difference between these two images in terms of something which belongs to the flower and that property could be color here let us say because on the left hand side what you see is an image of a flower and on the right hand side you see also an image of a flower but their colors are different on the left hand side the flower is of white and on the right hand side you have the color which is yellowish of the flower. So if I pick up color as an important property which we will actually specify by the name feature later on and define what is a feature for the time being in a very simple sense let us say I take the word characteristics or attribute or property of the object from which we are solving the problem of classification then color is a very important feature here. You go back to the slide and if we can extract color from these two images basically the flowers again I repeat ignoring the background then I can easily distinguish or anybody can easily distinguish between the flower on the left and the flower on the right of course there are a few richer features more than color like the shape of the pattern of each flower which we will consider later on. Now let me bring a third object which is completely different as you can see that this object is different from the other two it is not a flower of course it is an animal but if you had used or if you use color as an important feature here you will not be able to distinguish between the left flower on white as well as the animal which is also having a white color. This example shows that on certain occasions. So if you want to distinguish between the three different types of images shown on the screen to you just now color will not satisfy the criteria of classification between the flower and the animal. It might work in certain cases between the yellow flower and the white animal but between if the flower has also a white color and the animal also has a same color in this case white then you will not be able to discriminate between the two. So this example shows that you will need more than one property more than one feature more than one characteristic or attribute from objects from signals which we see which we perceive to distinguish one with respect to the other. We will move forward and see more examples of classification look at this problem you have several set of cars the problem of classification now is to distinguish between different models of cars let us say different models of car correct. If I use color again you can see that the car on the extreme left is the same as the one which is the color on the extreme right sample. So you have to use more than one feature may be the shape of the object the shape of the windows the position of the wheels with respect to the car various attributes of shape in addition to the color to distinguish between different categories of cars which are looking almost similar almost same I am not trying to distinguish or classify or categorize between flowers and cars cars and other animals but between between cars itself all cars look more or less the same they are four wheels they are front screen doors windows and so on and so forth so the shape and the size almost look similar of course there are other types of vehicles as well but between cars when the objects look very similar the problem of classification can be more difficult look at the next example in the same slide these are four different examples or fingerprints scanned from different individuals and there are experts who can actually distinguish between these four different categories of fingerprints how I will not go into details but there are features extracted from these type of line diagrams as they appear the fingerprints after they have been scanned and digitized and rectified the experts who will find certain patterns like reorientations, minutiae points re-openings and closings to distinguish between one fingerprint and other which is a very good biometric trait you can use fingerprint to uniquely identify an individual it has a huge application it is a huge application in various field of biometric access control and criminal identification in the case of 4 and 6 let us look at this as another example of biometric you are seeing here examples of 12 different face images 12 different face images what is the problem of classification in this case it is called a problem of face recognition the problem of face recognition involves trying to identify an individual trying to identify an individual recognize it from the patterns of the face now all faces look similar all of us have two eyes one nose, lips, ears etcetera and so forth the hairstyles may be differing there may be other patterns in the face so the variability of the patterns within the face from one individual to the other in terms of eyes, nose, wearable pixel variation may be structure and something else are been exploited by pattern recognition scientists to design a classifier which can identify an individual under certain conditions it may not work well under all conditions but in certain cases yes it is possible now if you look back to this problem of classification of trying to distinguish or classify between faces between fingerprints between faces and fingerprints and say between cars this problem was a little different from the first easy problem which I showed you between two flowers or between a flower and an animal why the samples were very different the images were different the objects were different one with respect to the other in the case of face recognition all the faces look very identical they could form properties if you extract properties their properties will be very similar similarly for fingerprint similarly for cars or similar objects so the problem of classification becomes difficult if you are trying to distinguish different type of objects within the same category different type of objects within category face recognition fingerprint recognition cars between type of say trying to identify patterns within a signature so they may be very similar and the problem becomes very close to a clustering problem let us get a little bit more specific with features and classification if you look into this very simple example which has been talked of in many books sorting incoming fish on a conveyor according to species using optical imaging or optical sensing so what you do with the sample fish there may be two classes of fish which we are trying to classify or categorize these are the two examples but you can of course take any other samples of fish which you want it could be the classic varieties of the hilsa and the rou which you find in India then what you do with these species you try to extract certain properties that could be used to distinguish between the two types of fishes the length of the fish the brightness or the lightness of the fish the lightness could be the brightness or the intensity the width of the fish number of shapes of the fin position of the mouth with respect to the body these are some examples of properties or characteristics which you can measure of course there are few other things which you can measure like the weight of a fish something which you probably cannot measure like the smell or the touch so these are important properties or characteristics which will help us to distinguish one sample of a category from the other in this case could be two different categories of fish so these are some examples as you can see in the slide are example of what are called on the right hand side examples of features which one has to extract so that this helps in distinguishing one class of object from the other one these are all the suggested features which can be used but of course you can use a few more if you can extract and then use it in your classifier this is a definition of the word feature which you may find possibly in some dictionary it is a property or characteristics of an object sometimes quantifiable sometimes not and then which is used that is the main purpose which is used to compare or distinguish between or classify two objects I repeat it is a property of an object which is used to compare or distinguish between two objects or classify two objects so henceforth in the rest part of the lecture we will deal with these features we have seen some examples of features to repeat them from the beginning of this lecture colour, shape, intensity size, minutiae features length, width shape features we will see some more examples position of certain important landmarks of the an account of certain landmarks in images or objects using these features the question comes as comes is how can you do classification and of course the other question is what is clustering but before we go there we will try to see some very important properties or desirable properties of features features must be invariant to translation, rotation scale, noise and other type of projective transforms this is much to do with images what does it mean in an image if you rotate the object keep the object anywhere in your screen scale the object make it bigger or smaller or if there are noisy artifacts in the image or if you have projective transforms as you can see that I am going away from you I am appearing smaller this is a classic example of scale variations due to projective transforms a classifier should be able to still recognize my face invariant to where am I compared to the sensor or the camera which is picking me up features are expected to be invariant to all sorts of affine transformations if you go back to the slide some examples are rotation translation scale, noise and projective transformation other desirable properties of features it must be distinct and unique for a given shape now if you have these two objects one is in USB mouse the other is basically a pen you can see that the features as for example even if you take intensity as an example and the short of a ratio of the height versus width if you take that as a feature they will be different for two different objects so coming back to the slide it must be distinct or unique for a given object the cost of computation must not be very high for a feature because otherwise the classifier will take lot of time to do the processing and must have graceful degradation due to discontinuities and missing parts that means if the picture is not very clean and there are certain discontinuities in the object which has been imaged still the features should not so much of variations the feature should not so much of variations it should still pick up similar identical value of the feature from the image these are some examples of features which are used in visual patterns you pick up you do pattern classification mostly with images you can perform classification with non-visual signals also we will come to that in a moment but let us look at visual patterns which is one of the most important example of pattern recognition we will explain later on but just let us name them these are something to do with shape features normalize central moments elongation, compactness, connectivity, oil and number they need to sometimes model the background or foreground texture of an object so you use measures like gray level co-occurrence matrix or GLCM you can use filters like gabber filter weblet filters and so on you can use color histogram and these abbreviations which you see are very rich features which have been just discovered or worked upon over the last decade or so HOG is histogram of gradients I repeat again histogram of gradients is hog DOG dog is called the difference of Gaussian HOG is histogram of gradient DOG dog is difference of Gaussian variant feature transform is sift and also surf curvature scale space is CSS chain codes, polar signatures corners DFT discrete Fourier transform DCT discrete cosine transform discrete weblet transform DWT short term Fourier transform STFT and Hilbert transform these are different type of transforms which are used MIT is middle axis transform LTP is linear binary pattern shape contest very rich features for representing shapes for motion people use optical flow vectors and also super quadrics for modeling blobs when they see it in images these are some examples of visual patterns which you can detect from images the pattern recognition area is applied for any type of signal visual as well as non visual something which you hear say an audio signal let's go back features used in other signals these are examples of features which are used in audio music, speech like the capsule coefficients LPC linear predictive coefficients spectrograms, speech, tone peak, chroma or harmony then of course you can have model parameters which can do for visual or non visual like the hidden markup model HMM the ARMA model autoregressive moving average condition of random fields CRF some more similar to GMRF people also extracted features like rhythm beat and tempo from music and of course you can measure weight, order, taste fine, you can use them as features also it's a huge list of features some of them are not exhaustive some of them I couldn't mention because there is a technology involved in some of them like PCA features we will talk of them later on all the LD or the ICA features we will talk of them as the course goes on with respect to feature we should know what is a feature vector because a single feature is not useful for the purpose of classification and a set of features are used together to perform a classification they form a feature vector so what is the difference between a feature and a feature vector well you can say a group of features are combined together to form a feature vector what is the dimension of the feature vector the number of components or the number of features which you have extracted to form the feature vector let us say you take three different features color the area of an object and say the eccentricity of an object I repeat again color area and any arbitrary moment like eccentricity or moment driven parameters like perimeter you can take perimeter area and color very three simple features color, area and parameter you want to use all these features because you know that if I use all these features together I can do a problem of classification much more accurately than picking up any one or two of these so when you take all of these three features together you form a feature vector where the first component of the feature vector is color the second could be area or it could be something else also does not matter so what do you expect this feature vector extracted from a sample belonging to one class or category of an object should be different from the feature vector extracted from another object and the problem of classification would be to distinguish between these two feature vectors belonging to two different class of objects or if possible learn and know from a priori samples as to what these features feature vectors would be what these feature vectors would be for sample belonging to class for samples belonging to class a and how they are different from samples belonging to class b and that is the problem of classification and this will lead us automatically to the idea of clustering as well as for example if you go back to the slide this is an example of a two dimensional feature we talked about this pair of features to be extracted from fish and if the two features which we extract are say brightness and length forming the two components x1 and x2 as they are indicated in your slide then capital X to your left is a feature vector the transpose indicating that we typically talk of this as a column vector rather than a row vector so the transpose is given as a row vector then the feature vector will be of dimension 2 one could be brightness the other could be width or they could be something different so if you select or identify a set of ten features from an object if select set of ten features from an object your feature vector will be of dimension 10 or even larger if you have more number of features feature vectors lie in a feature space this has to be learned it is something like the vector space samples of input represented by their features together are represented as points in feature space if a single feature is used then you are working on one dimensional feature space so now you have to learn and you have to know that a feature vector is now represented as a point you know a vector has a magnitude and orientation depending upon the value of its components but in a feature space which is like a vector space it is indeed the minimum but generally very high dimensions of course we can visualize up to three dimension only but if you have a large dimensional feature vector it is basically indicating a point in that feature space the feature space dimensionality is dictated by the feature space dimensionality is dictated by the dimension of the feature vector of course you can have variations like one dimensional case where these are examples of points in the example in one dimensional feature space what does this color indicate it is possible that the red colors could be indicating points of features extracted from one category of the object and the blue color could be representing another category to your right but we have just extracted one feature and hence the feature space is one dimension and the points are marked in one day features in two dimension also where it can have points in two dimensional space which you will see in the next slide but in general you can have an n dimensional feature space next what we are going to see is an animation which will take you into this feature space since our visualization is restricted to 3D so you will see this animation which will take you in 3D space and you will see a set of feature vectors sometimes they form a cluster sometimes they form two distinct clusters are a weird looking cluster we will see what is this cluster and what is this classification see the difference between the two in this animation and come back and continuing the discussion between of the difference between clustering and classification so let us look at the demonstration in the screen now this will give a visualization of a feature space in 3D you may actually feel that you are traversing in space in a shuttle what you have to realize is that each of these dots on the screen represents a point in a feature space a point in a feature space is also a feature vector now as we are passing by you will not notice any particular type of pattern or group or cluster or anything remarkable which would be happening here but as we move ahead in the screen you will start to notice a distinct set of points which are now appearing as a dense set of points a compact cluster a dense cluster the distance between a pair of points which are marked in yellow are much less in general compared to any two points which are in the background marked in white so you see a cluster being formed here which is marked in yellow this set of points form a dense cluster the distance average distance say if you are able to measure it between any pair of points is much less than if you take any two points which are marked in white and they are in the background let us say this distance is much larger than any pair of distances which you see here the question comes is how do you estimate that this is a dense cluster you must have some measure by which say the cluster density can be estimated from this feature space remember we are observing this space in three dimension but in general a vector can be of very large dimension and the same thing may occur in a large n dimensional space so how to measure the density if you move on in this space you will find after some time spheres two spherical volumes or spheres in different colors around the dense set of cluster points marked in yellow if you pick up any one of this any one of these sphere of spheres you will find what we have done is the points in yellow which are within this sphere are actually marked in red you can see here they are marked in red although they are overlapping remember this is a three dimensional visualization points yellow points within the red sphere are marked in green I have shown two spheres just to indicate that if we make a count or do an estimate of the number of points inside this sphere it will give you an indication of density the definition of density from the basic definition of density we know that it is mass divided by the volume so assuming that the volume spheres are identical if the number of points within a particular sphere is large then we estimate this the density at this point will be much larger than the density at some other place but in general the density estimated around any region within the cluster or any space within the cluster will be much larger than if I would have put the sphere here in the outer space where you do not have a cluster but a few sparse set of points it is possible that this cluster could be generated by a set of feature vectors obtained for a certain class or category or type of object what are these points they may not belong to a particular category or class of object so this is how you measure density of course you can replace this sphere or a unit cube and an estimate of the number of points which actually synonymously represents the mass at that region divided by the volume you can visualize this to be unit sphere or a unit cube will actually give you an idea of density so cluster density is one common measure used to detect the presence of a cluster I repeat again the density will be having reasonably large value when you measure it somewhere in this region of the cluster if you measure it somewhere else you will have a much lesser value of cluster density indicating the absence of a cluster so that is what we saw as a cluster do all clusters appear same well may not let us move a little bit forward in space on the left hand side of your screen now you see two different clusters one marked by red dots the other which we had earlier were in red dots they are two different clusters what is the significance of these two different colors which I have used to indicate the two clusters well it is possible that the feature vectors here indicated by this cluster could have been measured from images or signals or certain object categories resembling one type of object and this could have been obtained from another class of object you can have two classes class A and B, class 1 and 2 and this picture all resembles two different clusters in which I can put these two different spheres find the count of the number of green points within this sphere out of the red and the green points out of yellow within the red and estimate density of both these clusters indicating that I have two distinct clusters here there is no cluster elsewhere because when you measure density at the background somewhere here you will not detect the presence of any cluster because the cluster density value will be much less in the background this figure also illustrates the problem of classification vis-a-vis clustering we have two different clusters all right but the clusters are separated in space there is a distance between them and the distance between the clusters indicates how far are these two different classes or categories or object types we want to do pattern classification task remember the difference you have to detect clusters is one job of clustering after you have detected clusters let us say you have detected more than one cluster it is possible that the two different clusters could be belonging to two different object types and then you can actually find the distance between the two clusters by one of the simple methods is you can compute the average or the centroid of the set of clusters for the red points or the red markers for the yellow markers you can also find the mean or what is called as the class mean average or centroid of this cluster and look at the distance this distance is called the separability between clusters meaning this distance will give an indication of the separability between clusters the larger the classes or categories are separated in space the more will be the distance the less the separation we will see an illustration of that less the separation the distance will start to collapse or the distance will start to come down if you move ahead now what you see here of I have removed the color of the dots I have removed the color of the dots if you remove the color of the dots that means you don't know which point is belonging to which class you can still look at these two clusters in space very clear because in fact now I have removed the background in fact you can detect these two clusters using cluster density estimation techniques one of them which we have just discussed is a point inside a sphere you will get some idea of the cluster density you can also measure the distance between these two clusters if you are able to detect them but the question comes is if the class labels as they are called which were given by the color markers remember this cluster was marked in red this color was marked this cluster was marked by yellow markers what we only have coloring is the points which are inside the sphere the indicator which we have we have lost the color information then it becomes a different problem of classification sometimes it is called an unsupervised classification this is the case when you have to detect clusters and assume that each cluster belongs to a separate class or category or type of an object this could resemble a particular type of fruit a particular category of flowers let us say this still good point here which we now have at this point of time or till this point of time the clusters are still separated in space that means you have some distance based on which you can separately detect these clusters the problem can be more difficult as you can see now that I am bringing one cluster close to the other one and here very clearly you can see the region clearly that the points are starting to overlap this is the region where classification becomes a very difficult problem if you visualize all these set of points together can we have that visualization yes look at it now this could actually represent a cluster by itself that cluster will not have a regular appearance the cluster may not have a regular appearance as we had for these two distinct clusters when they were separated out but this could actually represent a cluster and it is this situation where the cluster starts to overlap that the problem of both clustering or the problem of classification if we introduce back the colors let us see now how it looks like look at this problem this problem is not that difficult but we are entering a situation where the classifier given the fact or the class labels that these points belong to one category class A the points in yellow belong to another category class B the classifier will work reasonably well perhaps if you have a powerful classifier design but it will make errors here the region where the cluster points start to overlap between classes so you look into this problem classification clusters clustering here which we are trying to discuss this can be considered as a problem of classification if you remove the colors it can become a problem of clustering even here you can detect two clusters but I am not sure whether a very naive classifier or naive clustering mechanism can detect two clusters prominently it might detect two, it might detect three it might detect four if the number of clusters are not known if the numbers of clusters are not known you might think that this is a cluster which has a very irregular shape that is possible we will give some examples later on of certain shapes which are geometrical in nature but they are not very smooth but of course the other problem which is not clustering is the problem of classification as you see here I have reintroduced the class levels with the help of color markers and the problem of classification is to discriminate features or feature vectors belonging to one class vis-a-vis or with respect to features in the other class the difficulty is what in this case for classification that there is an overlap the distance between the classes is much less is much less compared to what were we had earlier in this problem this is a much better situation for both classification and clustering where what is called as inter class distance between these two classes of feature points is much much larger much much larger compared to of course the scatter or the spread or the width of the individual clusters there is hardly there is in fact no overlap I can actually draw a line here and say to my left of this line I have all the red points think of an imaginary line here or a point along this line to the left I have all the red points with respect to this line as you can see here which I am drawing on the screen to right I have all the yellow points so classification becomes easy if I bring them close see if you bring them close as you can see that the cluster distance now is very minimal there is lot of overlap there is a problem of classification to produce accurate results so we have seen two scenarios now where classification and clustering could be easy these are the situations where classification with class levels or if I remove the color information clustering also could be difficult problem I will leave it to you for visualization if these two clusters overlap quite a lot more then you may actually have just one visible cluster out of the two and even if the class information of the color levels under overlap is given the classification algorithms might struggle to give certain good results so we will now look at we will now look at two different methods we will list a methods few methods of clustering and classification we have so far understood the difference between the two what it takes or what does the clustering problem of clustering solve vis-a-vis the problem of classification we have seen we have seen some animations some static pictures and also a table containing discriminating points between the two now we will just list a few methods of clustering and classification a few state of the art algorithms we will just name them mind you because now what this you are at a point where you can understand how you can write algorithms to solve this problem the rest of this course discuss many methods of clustering as well as classification but we will just name them now keep them in mind as we go through the rest of the course where these methods will be discussed in detail we go back to the slides you have a list of methods on your left hand side column which indicate which are names of methods used solving the problem of clustering representative points split and merge linkage there are many methods of linkage we will discuss that later on SOM indicates self-organizing map a special class of neural networks which can also solve the problem of clustering although actually they do solve a problem of mapping model-based methods vector quantization best of the lecture series of pattern recognition not today what are examples of classification well the classic best example is the best decision rule which will be discussed in detail then we have the linear discriminant analysis or the LDA which is called the fissures criteria then we have methods based on KN nearest neighbor called the KNN the feed forward neural network which uses architecture under the class of artificial neural networks or ANN it is called the feed forward neural network which uses a back propagation learning law support vector machines the most popular and the powerful one till date and random forests we will discuss most of these as we go on before we end up the talk we would also like to see the general categories of methods of clustering data the most books will be categorizing them into two different classes one is called the hierarchical or linkage based the other is called the partitional methods even within the hierarchical you will have a divisive policy and an agglomerative method of clustering I will repeat you have two methods called agglomerative and divisive methods of clustering under the hierarchical category of methods partitional methods some examples again two categories exclusive or probabilistic under exclusive you have the minimum spanning tree which uses the representation of graphs the K mean at the K may doids and under probabilistic you have the GMM called the Gaussian mixture model and the fuzzy C means FCM as it is called which is an extension of the K means or C means algorithm this categorization of methods of clustering is a very generic one it may be that when you go to certain literature and read from books the clustering this categorization of the different methods of clustering may appear little bit different than what I gave you but of course you still have methods which are based are partitional which are hierarchical and some of them are probabilistic and some of them are exclusive I have just confined myself to discussion of between these four different categories of lectures there are alternative view for algorithms of clustering also some would like to say an unsupervised method of learning is also clustering on unsupervised classification is also called clustering examples are K means and K may doids this is where most people will confused do you think unsupervised learning or classification is clustering well may not be clustering has no class levels unsupervised learning has no class levels but the purpose is different in the method of clustering you try to group data try to find structures within data in the case of unsupervised learning or classification you tend to form groups that is also true you may detect clusters that is also true but actually they are not same it is not wise to call a method of clustering as an unsupervised method of learning or classification in a very loose sense you can tell that but that may not be actually true other methods involve density estimation which we talked about when we are using spheres to indicate how to measure density in the feature space typical examples of density estimation couple of some of them could be parametric type that means you estimate certain parameters from the density Gaussian mixture of gaussians as it is called GMM is the classic example you can take non gaussian distributions also although they are not that popular the dirichlet or the beta distributions then there are other methods called the batch and bound piecewise quadratic boundary nearest mean classifier or maximum likelihood estimate these are just names of different methods of clustering based on which are all parametric density estimation there are non parametric density estimation also as given in this slide which are also often used methods based on histogram nearest neighborhood or neighborhood neighborhood kernel based methods graph theoretic methods iterative value seeking some of them we will discuss if not all of them in the remaining part of all lectures I would like to conclude the lecture today of clustering versus classification with the acknowledgement for my master students Mr. Pratik Sivastav for helping me in creating this animation and Mrs. Suranjana Samanta and professor C.A. Murthy for helping me to prepare the slides thank you and get back to the remaining set of lectures to understand all different methods of clustering and classification and many other applications thank you


The Akiti State Police Command has appealed to residents to provide them useful information on the whereabouts of fleeing members of a kidnapped syndicate. The command spokesman, Sunday Abutu, said this while parading a kidnapped syndicate arrested in the state capital. The suspects led newsmen to their hideout within Aduakiti, metropolis where they keep victims they abduct. Abutu said the gang was responsible for the kidnap of a popular businessman, a wife of a former surveyor general of the state, among others. During interrogation, the suspect confessed to be responsible for the kidnapping of one Olowo Balaji in Ikpotsi Akiti on the 29th of January 2020. They also confessed to have kidnapped one Al-Haji Suleyman Akinbami in Petro-Tila, who was kidnapped on the 10th of January 2021 in Aduakiti. The suspect, according to them, also kidnapped one Mrs Osalusi Dapo, the wife of a former Akiti state surveyor general. The kidnapped camping Olowo Bumi Obafon confessed to the crime and also led policemen to his den, a building located in the heart of Aduakiti, which he claimed belonged to his late father. It was on January this year that I met someone. So he now told me that he's into kidnapping business, that I should join him, that I can drive very well. Then my duty is to drive them, after driving them, I come back to Adu. So that of the surveyor general's wife too, I drove them, that is. So and I heard it is this guy that used to give them the work, that anywhere that he sees a rich person, you come and inform them. Meanwhile, the command has placed a bounty of five million Nara on fleeing members of the gang, as I remember of the public that those


Hey, hey there, dude's got a slugger here with me and I'm going to give you another reaction video. And today I am finally, finally, finally, finally, finally getting around to watching SF9's show called Secret Code 9, which has got some hints for their new album, mini album called Mamma Mia. And oh my god, I cannot wait for this album. And legit, I have been ignoring so many Twitter posts, like a lot, because like I've thrown out a lot of fantasies. And if you guys don't know who fantasies are, they are the fandom of SF9. The fantasy fandom is called fantasies.  I just stayed up till 5am because I had to edit the video and upload it and make a thumbnail. So I am freaking tired, but I am still going to do this video. I wasn't planning on doing it, but then I was like, screw it. Let's do this. Let's do this. But yeah, I was just ignoring a lot of Twitter posts because I'm like, I don't want any spoilers. I don't want any spoilers. So I think I like the couple of them where it was just Zuhou screaming, because it's Zuhou screaming. Come on, he's like my ultimate bias. But yeah, other than that, let's get straight into this. Ah, I'm freaking excited. I hope you guys are too. Let's do this. Okay, so the reason why I, it's been a while, well, since it, the reason why I have not reacted to it until now is because I've been waiting for anger subs to come out as well. Strongest way of writing a name, do you change your circle, bro? I don't know what bro said. Manifrozen, Mike. Oh, it's a good song in the background. Yes, I agree. My mouth is like that. Yeah, why are they here? Why are they here? It's right there. Sensational mission. Oh, no, that's what I saw. Zuhou screaming. Damn, sweater. Since I already know the title track, it's already spoiled for me, but I'll pretend to not know it. I'll pretend to not know it. Well, they do it, they, oh wait, that was in the clip before, but they do have to go on the water. That was gonna be funny. That was gonna be funny. Stay in ice water for nine minutes. Mission is excessive. The members complete nine minutes. Minus 15 degrees. I'm no key kind of Judas of you guys. I've never been in freezing cold weather. I mean, I've been in snow, but minus 15, that sounds like heaven for me.  So this is good for me. Really good for me. Super good for me. That thing's so, oh, zero one. Oh no, oh no, Zuhou is going mass. Oh, he's chasing the order. I'm fine with Zuhou being mass. I'm fine with that, because I get to see everyone else before Zuhou. No, I feel like they will do that since Ezen9 is so extra. I feel like they'll be like, oh, buy water. Like, he's just too handsome for this world, man.  Oh, you can do it. Oh, he was in for 29 seconds. Oh, oh, he was in for a minute 49. Oh, shooty-toot. Send me four minutes. Four minutes. You guys can do it. You guys can do it. What is, oh, I'm so bad. I feel so bad for each second. You get there. No, they aren't doing it. Oh my god, I thought they would do that. I thought they would do it. No. Oh, no, it was here. Yes, go, Janet. Oh, I know Darwin is just going to scream like, cha-cha-cha. It's the two minutes 32. You'll be fine. No, these guys are so mean. I knew this would work. He must have been in there for so long. Oh, I feel so bad. Oh my god, he was in there for way longer than two minutes 32. They are, dude.  He's supposed to be one- One piece. Good anime. I just want to receive prizes. Mom, I'm here. I want to win a prize. Damn, that was actually pretty good, but I still feel so bad for Suho. I just feel so bad for him, man. They're so mean to him. They're so mean to him. That's why I freaking love him. He just does everything, man. He's just like, I will go through this. Okay, I will do this, and that's what makes him awesome. Okay, so congratulations, Suho, for doing that. Oh my god. Man, everyone's so mean to you, dude. Just give them a little wee- But yeah guys, that's basically all for this video. I hope you guys did enjoy it. If you guys did, definitely show some support by leaving a like and also comment down below if you feel bad for any of the members. I definitely feel bad for Suho. I feel like a lot of people feel bad for Suho, and I will react to the second episode tomorrow. I'm not sure if the second episode has subs or not, but I'll check after this, and if it does have subs, I'll react to that tomorrow, and then I'll just react to them every time they come out and all that stuff, and with subs, I have to wait for the subs to come out, god damn it. But yeah, I will try my best to react to this series, because if it's not, why would I not react to them? And yeah, I'm in that. I hope you guys have a great day, and just keep on smiling. And adios, amigo.


Simple steps to set up a course in Cartra. So you heard about this incredible platform called Cartra where you can put your memberships in there or maybe you already have a membership and you've heard of the benefits of using Cartra and you want to switch over to Cartra. Well in this video I'm going to show you simple and easy steps to set up your course in Cartra and offer it to people who want to buy your course and we're starting right now. I'm JR Fisher and I've been selling online since 2009. Matter of fact I've sold millions of dollars worth of digital and physical products. Now if you're new to my channel I want you to do me a favor. I want you to look below. See down there where that big subscribe button is. If you've upsized the screen you need to downsize it to see it but you're going to see a subscribe button there and right next to it first click that subscribe button right next to it there's a bell. I need you to smash that bell as hard as you can and when you do it'll pop up and say turn on all notifications. Click that turn on all notifications. If you do that you'll be part of the Fisher family the VIP family that gets notifications as soon as I do a video. On top of that I want to gift you.  I'll tell you how to get that at the end of this video. So let's get going and let's learn how to put our courses on Cartra. Hey I'm kind of crazy. I'm kind of crazy about Cartra. Why? It's so good. It does so many things. I'm moved my entire business over to Cartra. Now we own a food company and we have a training company. We have coaching and guess where it's all at? Cartra. It's all there. Sure we have web pages and we use those but our main marketing platform is Cartra. Now why did I move it there? Well it's it's awesome. I mean it does so many things for you. And I was able to actually cancel eight different pieces of software because they're all rolled into Cartra. And one of them is memberships and that's what we're going to talk about in this video. One of the best advantages of Cartra is the ability to host these membership sites. Now these are secure sites. They're easy to build. They have templates in there and it's going to be perfect for whatever course that you're trying to teach. Now here's the cool thing. When you set up your membership in Cartra it will automatically generate the login pages, the user names, the passwords. It will automatically send an email to people with all this information so you don't have to worry about doing it. Now in this video I'm going to cover setting up your actual membership site structure. Okay how we're going to actually build it. I'm also going to cover how to upload content for all of your Cartra memberships. Okay that's very important. And then I'm also going to show you how to set up the access levels and the drip. In other words how we actually offer this content to the members of the membership over time. Now Cartra membership sites well they're pretty similar to an actual structure of a blog because you're going to have home pages, you're going to have your page posts, you're going to have your categories, all that type of thing. So if you've ever worked in something like WordPress you're going to love this because it's designed the same way. Now as far as content goes you can use anything you want. You can use audio files, you can use digital files, you can use PDFs. Whatever your content is you'll be able to upload that to Cartra for access to all your members. Now the cool thing I like is that the videos can actually be hosted at Cartra so they can actually play at the site. You don't have to use a Wistia or you don't have to use a YouTube or any of that type of thing. Those platforms to actually host your videos. You can host them in Cartra. Now the content itself can actually be delivered to all your members based on time, based on access levels. So you can actually drip content out to people automatically when they sign up for your course based on their access level. This means that you could you know drip it out to them daily, weekly, hourly, monthly, you know whatever you want to do so that you can actually give them the course in segments the way you want and you could also have different access levels so maybe you know your basic level the bronze level only got access to a certain number of the videos whereas your silver level gets an access to a whole lot more videos and then the gold level gets a whole lot more access and maybe you have a platinum level that they get a whole bunch more benefits and you can automatically set all this up in Cartra. Now Cartra is really an all-inclusive one-stop shop for membership sites. It's awesome setting up your course in Cartra is really awesome because you can add really really neat things that keep people interacting with your course you can add in their content of course but you can also add comments in there you could have a help desk in there there's all kinds of extra things you can have in there that most sites just don't offer. Now as I mentioned earlier the membership is similar to a blog on a regular website but what we do in Cartra is we can give them different access levels to that particular information. They'll also have a username and password so that they have to log in each time and we know who's using it and when they're using it. If you go to mine memberships and then click on the big green plus membership button the first step that will appear after you name your membership will be the content builder. The first page customers reach is the homepage so that's what you'll build first. At the top is your logo and a navigation bar. What's a really cool about the navigation bar is that it actually is automated and updated as you create your membership site. Simply drag and drop the content you want on your page from the menu on the left side of the screen and fill in the text as you want. Once you've created a main page with some welcome information by clicking and dragging the component you wish from the sidebar you can actually create a new category for your navigation bar. You just click and drag the new category icon from the top of the sidebar menu in the builder and you can drag and drop a new category into the top of your page. The category is a page to hold the post with your content. You can simply drag the content you want from each page from the left sidebar and enter the content as you go. Now you can actually create sub categories underneath each one of those two. Okay let's take a look at how we're going to upload the content for your membership site in Cartra. For your membership site to have the best content you'll want to host it somewhere. So luckily Cartra got that covered for you. Like I said you don't need YouTube. You don't need Wistia. You could upload images, audio playlists, videos and other files that will be available to your members without using an outside provider to actually host those files. Now you can get to the membership builder by going to my memberships and clicking the big green plus membership button. Or if you are adding content to an existing membership you've already created simply click the edit pencil icon next to that membership. In the builder simply find the type of content you want to upload in the left hand menu such as images, videos, playlists etc. And then drag and drop it onto the page. Uploading an image that's simple too. All you have to do is select your file. If you wish you could link the image to a URL of your choosing simply by entering the URL once the image uploads. Audio playlists they're a snap not a problem. It's that simple. Drag the element where you wish to place it then select the audio playlist file you want members to play on the site. Now with videos you're going to have other options. You don't have to do it the same way. You can use some of these outside services. The first thing that you're going to notice is that there's four different options when you're uploading a video in Cartra. You can use a Cartra video. You could use YouTube. You could use Wistia and you could use Vimeo. While the last three options do provide certain flexibilities using Cartra to host your video gives you additional possibilities like tracking and tagging viewers using forms within the video window and more. You don't have to leave the window to upload to Cartra. Simply click upload new video. It's that easy. Finally you can embed files for your members to download as well. Just drag over the download box icon. Click browse and select the file and you're good to go. Next thing I want to talk about is setting up your access levels and drip content for all your memberships. A powerful tool for your membership site is to create access levels and drip content. Drip content is so called because it is released over a set schedule dripping out by day or date. Creating drip content is easy in Cartra. Once you've created your content in your membership area then you want to go over to the third step which is access levels. Here you can create the different levels of membership alongside what content is released for different types of members. In the image on the screen now we've created an advanced membership level allowing us to lock certain content behind another sign-up wall. Each category you have created in the builder which are all pages inside your membership site can have a different unlocked date so that you can drip out content appropriately. Look I know there's a lot of membership sites out there. There's so many sites and so many softwares but the truth is with all those sites you don't get everything else that Cartra offers and you may be thinking well I'm not going to use all of this right now so why should I pay for it but you're not paying for it that's the key. What I found with all the softwares I had is that one or two of those softwares would pay for the cost of Cartra and then all the other things in Cartra have pretty much got free. So what I would tell you is if you're going to do a membership site you want to have something that's easy you can incorporate it with your system and the only way to do that is with Cartra. I don't know of another one that has a really good email platform and email sequences and sales pages and also has membership and the thing is when somebody goes through a course on your Cartra site they can get to the end and it can automatically trigger an email sequence that sells in your next course or your next offer or your next product and you can't do that on any other site. A lot of these other sites you don't even know if they went through the course or your course is sold alongside with other courses and different niches by different people so it takes them away from your brand. Look I know it's a lot of work and I will tell you that when we moved over we moved from infusion soft but we had done MailChimp we had done Aweber we had done Constant Contact we had done all these things and we found Cartra was better and I guess the stumbling point was oh my gosh we have so much stuff to move and I'll tell you it took us six months to move everything over to Cartra that was a lot of work but we're so happy now because everything is in one place we can get to everything it works seamlessly so you know I implore you if you have a course online if you have any type of membership site whatsoever I would definitely use Cartra. Now you can check it out for free by just clicking the link below I have a Cartra link down there you can go to the page no credit card or anything you can watch an entire video on all the stuff it does probably will go into a lot more detail than I did in this video then if you're interested I have a bonus for you you can get it for $1 as a trial just a dollar trial click the link on that page and you'll be able to try it out for one buck so that's pretty good so I hope you enjoyed this video if you did give me a thumbs up right hit that subscribe button down there next to the subscribe button you're going to find this it's a bell you got to hit the bell what does the bell do it makes you part of our family the Fisher family if you smash the bell and then you turn on all notifications I will actually let you know every time I do a new video so you don't miss a single one pretty cool right now on top of that I'm going to give you a course an e-commerce course worth $97 all you got to do is click in the link below you get it absolutely free no webinars to watch no credit card required enjoy yourself and get the training thanks so much for watching this video and I'll see you in the next one


All right. Good morning, everyone. Welcome back on this Monday on the 23rd couple days for Christmas and happy Hanukkah for all these Well, our friends out there. How's everybody doing? Good to see you all All right. Good. Good. Good. See you Ken. Good to see you all. All right chalk mark Don Cheryl everybody. All right Welcome back everybody. Hopefully everybody did all day Christmas shopping They said they broke a new work record This uh past weekend they said this is the most money ever spent even more on black friday They said so listen a lot of people making a lot of money. So you know what that means We're gonna have great great earnings that I think was that's gonna come out uh regarding about the trading now I want to just talk about a couple of things before we start going through the watch list You know, um, obviously everyone has a holiday parties and you know, probably went to a lot of holiday parties I was invited to so many of my couldn't make them all but everyone Knowing and and this is the same thing you encounter. Most people know what you do Everyone's always worried about hey, it's a stock market and crash and this and that you know what I tell those people Okay, I said listen if you think it's going down Just short it What what are you worried about just short it all the impeachment that I said just short the market Just short it. What are you worried about? You know, the market is so high right now If it goes back to where it was three years ago Shoot you're gonna make a killing just short the market. All right So they're like, well, okay. Well, what do you think it's gonna happen me personally? I think we're gonna go to 32 35 000. Okay, because you all know as traders when you break all-time highs Okay on a stock everyone. Where's the where's the resistance on the roof? Where's your next resistance level? Where's your next resistance? Now not going down Ed. We're going up It's the sky there isn't any right there is no resistance and then when ends up happening the short squeeze start coming in I think we were probably could have a short squeeze going into that would be unbelievable I'm not you know, I'm just saying not predicting anything, but you know what I've seen this happen before It could happen, you know because the thing that I keep hearing I hear a lot of people on the street like saying All the market's going down. I said just short it just short it Guess what if everybody shorts the stock right shorts the market and it doesn't go down What ends up happening it becomes a short squeeze What happens how many times how many of you guys here had seen short squeezes? How many tons of them right tons of them and what do we see in a short squeeze? Well Unfortunately the bad part about short squeeze it goes down So I really don't not a fan of a short squeeze, but I could see that that could probably happen Okay, so for all the people that are haters and Honestly personally, I just think a lot of people just jealous that they wish they could have got into the market when they should have And they're like and now they're like they're debating. Is it too late? You know, oh you're hearing, you know all these things coming out on on the financial stage I said just short the market Just short of what are you worried about if you if you're such a great trader, but they're like But I'm not really a trader we think I said I think it's going wrong. Okay How much I say how much money do you spend this weekend? How much money do you spend on on holiday gifts? You know, wow, this is a great Christmas party. I haven't a holiday party I haven't been to a party like this the one you did last year. So you guys spent more money you went from a You went from a local pub now. We're at a steakhouse. Holy crap That's a that's a pretty substantial substantial difference in cost right much triple All right So anyway, my point is this is that I know we're going to it's we're going to the slow time of the season But honestly, it doesn't look that slow this morning I think there's a couple of great little moving stocks that are moving But I just want to tell you, you know from from last week until now Going into it people get a little nervous going into the end of the year and so on the kids are from school And and now this is when you get to meet everyone um I tell everyone And you tell everybody the same thing if you really think the market's going down I assume you're going all in and you're shorting it just short the market just short it Okay, because what's going to happen is you're going to have a short squeeze Mike says that yep on cmc experts back in the late summer They were certain that another bear market like last year's drop Listen, the only reason why michael we had a bad drop last year and we had a christmas crash And that was one of the greatest opportunities where you guys could have made a lot of money We killed it that time is that is And this could happen even now the same exact thing like everything else Um, you have a uh, that was because the government shut down. That was really the only reason by the way our Itci is really taking off right now.  I've been doing this for 25 years. I've seen I've been through the internet bubble I've been through the financial crisis Okay, you know and talking about the financial crisis not not too boring. So I wanted to do some shopping But how many of you guys hated hated that we bailed out the banks?  I'm like, what are you worried about? That's just the greatest news ever. They're like, why should we bail them out? I said because me as a trader I'm going long. I'm like, you kidding me. This is the greatest opportunity ever in the history of trading How do you not how do you not buy they're like, uh, and it's so funny. It's like you had city bank at like two dollars Where's it going to zero? It was at a dollar people were selling. It was unbelievable So anyway, um, let's get back to trading And let's have some fun guys and let's look at some of the stocks So right now the biggest winner right now that I'm long right now. I told you this earlier I logged in around 745. I don't know where you guys are, but the tci Just took off. Look at that chart right there. It literally just popped just now I was getting worried about it was making a little bit of a low or low. There was a big big seller out there And uh, if you look right here those sellers just got executed If you look right here 24 was a resistance levels and those guys just got nail Now looks like we're going to 25. So looking pretty damn good on that trade right there Uh, wow That's nice.  Don't get me wrong I mean the thing ran from 730. I chased it. That was at 16 at 22 But it was holding really strong at 22 if you're in right now. You're doing pretty good. Honestly personally I'm done for the day.  That's how it goes. Anyway, is it ever too late to trade? No, there's a couple of other ones out of moving. Uh, let's start off with the we have also another one that's moving y a y o I don't know why guys don't log in early. You know, so funny.  Okay. Just you make it an effort Make it an effort to get here early. You know what you can go home and do and do all your holiday shopping right now You could have been done just on that tc the itci. You know, I mean, it's just it's just awesome It's what we do pre-market is where the money is All right This one also is moving pretty nicely. Uh, this stock, uh, we got a We've got a big seller right now. That's you know, he's been sitting out there for a while right around a buck 84 Got a 10 000 share iceberg order at a buck 82. So it's kind of really like holding right there I want to see that guy get executed. You can see him right here on the right hand side Stop I like it because the stock took a big hit. So that was doing pretty well Um, s a v a is another one came up by a watch list and obviously they're not as pretty as the ic Itci, but this one also it's got it's up 18 300 000 shares It's got some really nice orders out there on level three, but she's kind of flatline right now So a little skeptical about it, but some of you remember we did trade this stock Um, you know a couple of about a couple of weeks ago went from 160 ran to almost three bucks So we know this one does move s d r l another one that came up on the watch list Looks like she's starting to make a little bit of move You could see it right here. This one is doing really really nicely. It's up about 30 Um, it's kind of flat lined also right here, but you did gap up And there are some iceberg orders out here. You can see a 13 000 share seller at 222 Uh dp w another one that's moving up It's not starting to make a little bit of a foul still flag this stock got decimated You could see that stock was another short squeeze back in july took a big hit It also did it in uh, uh, September you could see we're from four to ten And we know the stock moves go back to your journals. Look how you trade it Look what we taught you and uh and see how that stock is trading now a couple of the big winners I do want to point out Once again, we only we don't just day trade here. We do swing trade. I want to talk about some of the swing trades Goose, okay So everyone knows this stock right here goose. We did it in our advanced class that stock, you know We didn't trade too many shares of it, but just kind of holding here, you know, I'm still feeling a little comfortable with it The sage trade That stock was just hands down a phenomenal winner right there. We all should have been in it at 61 dollars Okay, it's already at 76 another great swing trade that's working out pretty well for you guys And then the one that was from last week is this one right here r.a.d. You know, I still haven't I mean i'm killing it on that one right there I didn't get too much of the Sage but you know that this one right here is doing pretty well Uh, the one i'm really ticked off at myself though. I sold the uber Dammit it went up. I didn't realize it went up so much uber. I sold it at 29 50, you know, you know on the swing trade and uh, you know the problem with on this one I think I told you this earlier I just had too many shares of it, you know, I had too many shares of it I got burnt once before a few times. Thank god because that was at the 44 dollar price range You could see where I dropped down to but you know this one I sold the cheap I think a lot of us got out right around 29 Uh, 29 and a half and you could see where I almost went to 31 So we did a couple of good founders sold 90% of his share and you know what chef that was and that was right here And that's what i'm saying like you got me really getting you got me really nervous You know when they sold all those shares like why is the guy selling so many shares? That's that's that's ridiculous.  Something went from 34 all the way down to there, but she held up pretty strong here. So We'll keep an eye on anyway got a good list today. Listen guys Josh is going to be out today. Oh my god.  There's my little cash register There we go Oh No, no, it's not crashing wrong one There you go Beautiful sellers getting hit everywhere. Anyway, that's who we learned right? That's what that's what we're here for Uh, yeah, that was a huge seller at 26. He got filled. Wow Excuse right there. All right.  Uh, it's gonna start getting really slow tomorrow's christmas eve You know markets closed a lot of kids are off for a long time because it's been off since you know saturday So they're gonna be off for two weeks, you know, these Really screw things up as a trader when you have these holidays, you know, especially, you know Falling right in the middle of the week where it's not like christmas is on like on a monday or tuesday now You know, I mean on a monday now we're getting out on a wednesday and then then the next year's can be on a thursday So get a little bit better But you know this um, this is one of this happens every seven years and this is like one of the worst Situations that can happen in trading by falling on a day like this because you know what it's gonna prolong things a little bit longer But it's a great time to learn. So that's the key. All right. Good luck everyone Happy trading like I said, josh will be out. He's on vacation and then um, me and uh, ream rich will be here and greg and john will be banging out away and uh And we'll see you. We'll start commentating in the next few minutes. All right, by the way, these things still look strong Coming up to a big seller right now at uh, it looks like he's getting done Man got taken out right at 650. Look at that Guy got hit right there. Now it's going to 27 Cha-ching. Good luck everyone. Happy trading


Hey everybody, Dr. O here. In this video I want to talk about smear preparation. So a smear is a thin preparation of microorganisms on a slide. So the reason that we make smears is so that we can stain them to help identify which type of organisms we're looking at and to run differential or what I like to call diagnostic stains to know if an organism that we're looking at is dangerous or what family it belongs to. Okay? So what you're looking at here is the most powerful weapon that you have in your arsenal is a microbiology student. It is the wire loop. The wire loop is one of the few things that we can do in a microbiology lab where we know that we can sterilize it. So this is the wire loop. Just FYI if you took that little circle off the end and just left the piece of wire, that would be called a needle. We use that for some of the stabbing techniques that we do, but the wire loop is something you'll be using almost every week in a microbiology lab. So in order to prepare smears, the first thing we're going to do is we're going to flame. So we're going to flame this wire loop. As you can see here using a Bunsen burner, you can use a Bunsen burner or an incinerator. Now personally, you know, I'm using image, open education resources images, but I like to flame my wire loop with my hand underneath it, but that doesn't matter. I'll show you that in the lab if you're my student, but so you're going to flame this entire wire and the loop at the end of it until it's red hot. So that's how we have now sterilized this wire loop. And that means that when we, when we use our aseptic techniques, we're going to transfer pure cultures of organisms on a sterile wire loop. So we know that we're actually working with the organisms that we want to work with and we want to test. All right. So we take our wire loop, we flame it, then you're going to let it cool. See, I count to 10 Mississippi and then I start transferring micro organisms. Now, you'll know if you didn't wait long enough when you hear that, when you hear a sizzle, when you try to use this in a liquid broth. So make sure and if that happens, just start over, re-sterilize and count to maybe 13. So, so we're going to flame the wire loop. We're going to let it cool. And then we're going to add two loopfuls of organisms onto our slide. So you see a slide here that has a couple of loopholes of organisms on it. If you're starting with from a liquid culture, like a nutrient broth tube, then you just transfer two loops of organisms to the center of the slide and then spread it out. So it's pretty simple. Just using your regular aseptic technique. If you're starting with solid organisms from a, from a plate or a slant, then you're going to actually add two loopholes of water first and then add a clump of the solid bacteria and break it up here. So we've sterilized our looped. We've done our transfers. We now have two loopholes of organisms on our slide. Make sure you spread it nice and thin. You want to make sure it's a thin preparation of bacteria. Then the key here is to let it air dry. Super, super important that it air dries. Because after the air drying process, we will heat fix the slides. And if they're still wet when you do that, the chances that they actually will attach and stick to the slide and stay there when you cover them and stain and alcohol, et cetera, is slim to none. So if you, if you don't wait an extra couple of minutes here and you rush to do this, then you're going to, you're going to ruin your slide and not be able to actually see anything when you stain it anyhow. The other big problem is if it's still wet, when you heat fix the slide, that water will actually can destroy cells. So yes, we do want to kill the cells when we heat fix them, but you don't want to destroy them because if it changes their structure or it destroys their cell wall so they, they can't hold stain, we actually can't learn anything from our staining techniques. So super important to let them air dry. I like to kind of hold them up to the light, hold them at different angles, make sure they're dry before you heat fix them. And then we have heat fixing. So here's an example of using an incinerator to heat fix. There, you can, there are trays you can put on these and just leave them sitting or you can hold it like this. Incinerators work just fine, but they take a while. This will, this will generally take, can take several minutes to heat fix a slide this way. We primarily will use the Bunsen burner. And when we heat fix slides, now, now how I do it and how you do it will be different. But I like to just hold the slide and then just, just gently pass it through the flame of the Bunsen burner until I can feel the, the slide get warm. I would have a glove, a glove on that hand or else it would get super warm. But I don't generally have students do that. So I'll have you take a clothespin and just pass the smear through, through the, through the flame of the Bunsen burner six, six times, six, one second passes is generally about perfect. So we've transferred our organisms, we've let them air dry, we've now heat fixed them and now you're ready to stain them. So I just put a picture and an example here of what a, what a grand, grand stained organism will look like. So now you have your smear, now you can go and use your different staining techniques to, to see what you need to see. All right, have a wonderful day, be blessed.


So, what determines the activation energy of a reaction? In order to turn reactants into products, the bonds holding the reactant molecules together have to be broken so that the atoms can be rearranged to form the product molecules. The new molecules can't be formed until the old molecules break up. So, the thing that determines the activation energy is how strong are the bonds in the reactants. A reaction of which strong bonds have to be broken will have a large activation energy. Quite a lot of extra energy will be needed to break those reactant bonds and get things going. Without some kind of helping hand to provide this energy, like an added flame for instance, then the rate of this reaction is going to be slow. Conversely, reactants with weak bonds, which fall apart easily, may undergo a reaction with a small activation energy. And because the bonds that need to be broken are quite weak, the rate of the reaction will be higher because it's more likely that the bonds will get broken and the reaction can proceed more quickly. So, the activation energy is a way of determining how much energy is needed for a collision to be successful. Say two molecules collide, but the energy of the collision is less than the activation energy, then the molecules will simply bounce off each other and continue on unchanged. However, if the collision is more energetic than the activation energy, then the bonds of the reactant molecule will break and the atoms will be free to rearrange to form the product molecule.


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Hello and welcome to the session. The given question says, form the differential equation of the family of curves y is equal to a sin x plus b where a and b are arbitrary constants. Let's start with the solution and we are given y is equal to a sin x plus b where a and b are arbitrary constants. Now since as we can see here we have two arbitrary constants a and b therefore we will differentiate this equation two times and first let us differentiating it once. Let us denote this by equation number one. So differentiating equation number one with respect to x we get on the left hand side divided by dx is equal to a into derivative of sin x plus b which is cos into x plus b and derivative of x plus b is one plus sin plus b into derivative of a and a is a constant so it is equal to zero. Therefore we have divided by dx is equal to a cos x plus b. Let this be equation number two. Now as we have already discussed there are two constants a and b so we will differentiate it twice. So again differentiating it with respect to x and now we will differentiate equation number two with respect to x. On differentiating the left hand side we have d square y divided by dx square and on the right hand side we have a and derivative of cos x plus b is minus sin x plus b into derivative of x plus b is one and we have plus cos x plus b and derivative of a is zero. So this is further equal to d square y divided by dx square is equal to minus a sin x plus b plus b sy. So this implies d square y divided by dx square is equal to minus y and this is from equation number one or this further implies that d square y divided by dx square plus y is equal to zero which is the required equation. So this completes the equation by and take care.


Man, the Baltimore Ravens got great news yesterday. They got a huge relief yesterday. And the way that they initially shared the news, they trolled a lot of us. Watch this. Yeah, they doing it. They doing it. Hey, bro. Yeah, big homie. I'm just gonna go rock this one here with you Sunday. You know, I'm gonna do that. I'm gonna do that. We're gonna rock out. And y'all know exactly what y'all were doing with that. But hey, I respect it. And I actually admire it a bit because y'all were letting us all know before it was official that Roquon Smith was back because the previous day we had a little scare and the way that John Hubbaugh really didn't talk about it, it did not help at all. Because Roquon Smith had missed Wednesday's practice with a shoulder injury. So it was like, oh boy, where did this come from? But he was back out there on Thursday. And then today, even though today is more of a walkthrough, he was a participant as well. So having Roquon Smith back. And I know, like, while I do feel that, and I agree because I've seen some people put it too, that the Ravens could still get the job done against the Cardinals, even without Roquon Smith. I'm like, me? No, no, I want everybody who can play out there. I do not want it to be, no, no, no. You sit out this game. No, y'all know, like Ravens like doing this stuff, man.  Was gonna come into M&T Bank Stadium and cash out. But y'all took care of business in a major way, which was great. But you know how Baltimore Ravens can be in with an opponent that everybody like, oh, they gonna whoop, they gonna take care of business.  Ravens like doing this thing where they played down to competition. And this has been an issue for a long time. I wish they would fix it. I wish something would give to where they would just stop doing that. And not that NFL teams are not competitive and not competition because they're in an NFL too. But still, if you know a team is bad, take care of your business, man. So hopefully the Baltimore Ravens do that. But I did not want to be like, all right, all right, Roquan Smith, you sit out this game. We handle it. No, Roquan Smith, if you go to play, you go ahead and play. So having Roquan Smith back, oh, that just makes life as a Ravens fan. So much better, man. Because something that we've talked about, you know Roquan Smith is the $100 million dollar line back up for the Baltimore Ravens. And he has been earning every single dollar in that contract this year, every single one. Literally every single game. He makes a great play, every single game. And just the consistency of his play has been amazing. He has made such a big positive difference on this Baltimore Ravens football team. So with him returning to practice, with him being slated to play against the Cardinals and Hollywood, hey Hollywood, shout out to my guy, Hollywood. But anyway, with him being slated to play against the Cardinals, that's a beautiful thing. Cause again, I was a little worried at first. I was worried. And cause again, what really happened, what was John Hart, the way that he did it, I said, how about, why are you doing this, man? And I still don't agree with how he did it. Well, oh, well he's not out here. I just wait for the injury report. No, hey, that's the $100 million dollar player. Speak up on him, Hobbs. But again, that's just my opinion. But some other news that we got yesterday, which we touched briefly on, was Keith Mitchell. And that's a big relief as well, because me, I hadn't expected Keith Mitchell to practice at all this week. I know they said he was dealing with the hamstring injuries.  Sometimes the recovery can be like that. Other times the recovery can be like that, that, but with Keith Mitchell, Keith Mitchell has shown in the injuries that he's sustained thus far, he has an extremely quick recovery time. And this injury is no different because while Keith Mitchell, he didn't play, he only played a couple of snaps in the game against the Lions. And then he was out for the rest of it. He was limited in practice yesterday. And it said he practiced again today and we haven't got the official status of his practice yet, but he did practice. So that's a beautiful sign just for now and for next week and really just for the future because with Keith Mitchell practicing, even with it being on a limited basis, that bodes well for upcoming weeks when Keith Mitchell could be used. Now we'll see what even happens with this whole running back situation with all these rumors and whatnot. A lot of these rumors will be cleared up by next week because after October 31st, like the trade deadline, that's it, four o'clock PM on October 31st, you're either here, you're either Baltimore Raven or you're not, you're on your current team. So we'll see what goes down with that or what doesn't go down. I do think the Baltimore Ravens are gonna definitely do something because that's just what we've grown accustomed to them doing, making these moves. They made moves in the past year for guys like Marcus Peters for Yannick and Gakwa for Kaleas Campbell. Well, Kaleas Campbell wasn't at the trade deadline. That was in the off season, but still. And obviously last year was the big one with Roquon Smith. So Ravens, they are biased. They definitely bias when it comes to the trade market and we love that about them and that makes us excited for all of this that's going on right now. So we'll see soon, man. We'll see soon what ends up happening. Now, some other good news. Keevan Seymour, who had been out the past couple of games yesterday, on Wednesday he was limited, but yesterday he practiced in full. And again, he practiced today as well. So that's a beautiful thing because Ravens could use all the depth in the secondary that they can possibly get. And the more the merrier, the more guys you got back, it's always great. But the Baltimore Ravens got another return. Another return to somebody who had been out actually on injured reserve and all. And that was Tyler Wallace. So even more depth, like it's, oh man, thinking about this and like, it didn't hit me till like literally just now. Like Ravens are getting so many people back, man. Think about that. Ravens, they've lost a lot of guys with injury this year. Some guys for the year, like J.K. Dobbins, most likely Ardarius Washington, even though they didn't officially say it. Tyus Bowser, who knows what's gonna happen on him. David Ajabo, we still waiting on that with him. But they've lost a lot, but they've gotten a lot of guys back and they still getting more guys back. Like Ravens are getting stronger, man. They're getting stronger. Like, I could cry right now, man, because being a Ravens fan, it's tough, man. A lot of people ain't built for this. A lot of people ain't built for this. And I can see why. Like you dealing with injuries all the time. You deal with this secrecy, this non-transparency. And it's like, what is going on? But it ain't for the week.  They can go against a really bad team. They can either lose, they can play down the competition. Ravens like stressing you out. So being a Ravens fan, it ain't for everybody. But it's for us. But having Tyler Wallace back, getting him back. And he was a full participant past couple of days. So that was nice, man. The Ravens like, there's something about Tyler Wallace, man. The Ravens really, really love and admire Tyler Wallace and they value him. Because this is two years in a row, where the Baltimore Ravens, they placed him on Endure Reserve.  They placed him on Endure Reserve and brought him back. Now with Endure Reserve, there's only a limited amount of spots that you have with Endure Reserve, where you can bring somebody back, which I just learned that this year. So the fact that they used Endure Reserve for Tyler Wallace and brought him back, they value him a lot. They really do. For him to be getting ready to be back on the active roster, they love him. And I appreciate that. So I'm looking forward to what Tyler Wallace has to offer. And I'm not expecting him to get out on the field a million times or whatnot, because you got a lot in front of him like an O'Dell Beckham Jr. Bateman. I mean, we ain't gotta go through the whole depth chart. But him as a special teamer, we know that that's very, very important to the Baltimore Ravens. And it's been sometimes with a special team, even a little shaky, but overall it's been solid. But back to the injury report, O'Dell Beckham Jr., he was limited on Thursday with shoulder injury. So O'Dell Beckham Jr. just banged up, man. He just banged up. But again, O'Dell Beckham Jr., he's here for the long haul. Nah, I mean, I don't mean years to come, but he's here for the later part of the season. He ain't here for the beginning part of the season. He ain't here for all the stats, the pretty numbers and all. He's here for the later part of the season. Now, Adafi away. Adafi away was added to the injury report on Thursday, he was limited. But then it was said today that Adafi away, I don't believe he practiced. No, he did not practice today on Friday. Both him and Marcus Williams, they were the only two Baltimore Ravens not practicing. Now, with it being a walkthrough, because again, Friday's just walkthroughs, that may be indicative of O'Dell Adafi away status. This upcoming week against the Cardinals, maybe he might not play, we'll see. Cause usually if you don't practice, even though Friday's a walkthrough, but usually if you don't practice Friday, then you don't play. So, I mean, we'll see. We'll see. With Adafi away, they have had a lot of games without him this season. It was nice to have him back. I mean, he came back and got a sack for us to fumble and all that. It's like, all right, Adafi away. So hopefully he can go, but if not, it's next man up like that's Baltimore Ravens, man. That has been their theme like for a long time, definitely this year. So shout out to him, hopefully he can be healthy, but that's that, but Marcus Williams, him being out is no surprise there. We didn't expect Marcus Williams to play. They said he still got his arm injury, his pec injury dealing with that. And now he got the hamstring injury too that he's dealing with. And like I said before, it's not necessarily a bittersweet thing, because there's nothing sweet about being hurt, but with Marcus Williams dealing with his hamstring injury right now, that has to heal up, but that also gives him more time, completely off of the football field so his pec injury can heal up too, because me personally, I don't wanna see Marcus Williams out there with one arm. I don't wanna see that anymore. Let Geno Stone do his thing. Healthy Geno Stone is much better than a halfway Marcus Williams. Marcus Williams is a great player, but Geno Stone is holding it down. He got it covered for now. And he is also gonna get a nice paycheck this off season from somebody because of what's happening. So shout out to Geno Stone. Again, we gotta continue to commend Geno Stone because he has taken full advantage of his opportunity. Now, an opportunity that y'all can take advantage of, because I know a lot of y'all keep asking about where we get the jackets from, where the jacket come from, where it's set, we love the varsity jackets, and I love them too, they're clean, they're nice. They're really nice. You can go to standwithusclothing.com, the link directly to the jackets, the purple and black one, the white, red, green, and yellow one, and this black and gray one, the link to all of them is directly down below in the description. You click on that link, it'll take you right to the jackets. And you can use code engraving to get 10% off. So I'm trying to put y'all on, man. Y'all know I love y'all and I will always love y'all and I will always appreciate y'all and if I have an opportunity to put y'all on, I'm going to give y'all that opportunity. I love y'all team keeping clean. Make sure you subscribe to the channel, turn them notifications on so you do not miss this and any other updates when it comes to our Baltimore Ravens. This and any other videos when it comes to our Baltimore Ravens, this and really anything when it comes to our Baltimore Ravens, subscribe, turn notifications on, because I don't want y'all to miss out on anything. I love you so much. We out.