I'm very glad to be here to share this with you all. Yesterday, we were working on a curiosity and help company. And for that, I'm just giving you a free introduction on myself. And with me, the data science in MSD is important. And my projects are being focused on applied machine learning in drug discovery research. And also advanced analytics in the marketing and other finance departments. So today, the case study I'm going to show is actually adapted from the real one that I've been working on before. But the thing is, the data is all in the data lake. So it's a priority data set. So the data set I'm showing here is actually a mock-up data set. So which means, unfortunately, you're not able to use your laptop to follow up the tutorials. But the idea is to give you some demos so that in the future, hopefully, you can easily copy-paste the course for your own PySpark projects. And the workflow is going to be like this. I'm going to give a basic background of the questions that we'll try to solve and the data sets. Then I'll be covering four parts. They are pre-processing using PySpark. OK. So the second part is using ML library for especially random forest for binary classification problem. Then we try to find out how to use AUC score to actually test the model performance. And finally, we talk about one of the challenges in the data sets, which is very unbalanced and how we can handle that in PySpark. OK. So the question we're trying to answer is what is the probability that a given customer would like a certain product? So the data sets actually have two parts, the product's features and the customer's features. So what we want to predict is the probability that this customer will like this product based on all the features. And so this is just an overview of the data sets, of the mock-up data sets we are using here. So we see some of them are continuous variables. Some of them are categoricals. And the feedback column is the column we need to predict, which is a binary categorical column. OK. So let's start it. OK. The first step we do is load libraries. And as we can see, we have ML libraries. We have even pandas, but we may not really need pandas for this tutorial. And then we have NumPy, some other libraries. So, OK. As this step, we're assuming that, OK, in a company, this is how it works. So you have a big data team that's help you to set up the platform. Then you have an IT team, a traditional IT team, who will help you prepare the data sets and give you whatever views or tables you need. And then you, as a data scientist, just to sit down here and write Python scripts to do processing, modeling, and show them the results to actually demo your POC. And we're assuming that we have all data sets ready. And that's why that's how we can load our data sets into a PySmart data frame by using this line. And then we are selecting the features from the table that will do further processing and modeling. So I'm actually naming all the features here. And then you just simply drop the duplicates from the table. Then we start with Zoom-in. OK. How? OK. So is this OK? OK. Good. OK. So the first step is that we want to see what actually the target column distribution looks like. So because, as I mentioned, the target column supposed to have two categories. One is negative, and one is positive. And with a simple bar plot, we can see that it has actually several categories, neutral, negative, and positive. This is very real in the real world problem because we, first of all, the data sets are very skill. We have a lot of positive cases, which is a good indicator that people are liking our products. But on the other hand, we have too few negative cases which will be hard to train our model because we do not have enough information for the negative cases. And we have quite a lot of neutral cases. So one of the way we can do that because the challenge is to differentiate the positive from the negative or other cases. So why don't we just group the neutral and the negative together? So that's exactly what we do here. So this fuel line illustrates the purpose here. So firstly, we do a group by count to find out what is a count for each category in the feedback column. Then this response will be a list of rows, and then we further take out the category and the counts and do a bar plot. Then that's what you see here. Then the next step is that, because like I mentioned, I want to group all the neutral and negative together so that eventually I have two categories. Of course, I will drop the non-rows so that eventually we end up with two categories. And this is what we do. In PySpark, you will find out a lot of cases you have to use UDF, which is your user defined functions. And it's actually quite easy to do. So first of all, you have to define your own function, which is finalize. After that, actually there's a typo here. So after that, you pass to the function called UDF with a function you defined together with the output data type, which is string. So with this UDF, you pass to the function called which column. Which column is actually create a new column. And together with the input of the column to the function, finally you will come up with a new column for your data frame, which is DF. The DF remember is our original data frame having the data from the table. And for the user defined function, we actually convert all the neutral cases to negative cases and leave the positive case unchanged. So that's exactly what it does here. So I think it's quite straightforward. So the next step is to find out all the null values in the categorical columns and also casting the data types properly. Because we have all the columns here, we have selected and some of them going to be continuous variables. So that's why we convert to float and integers by using Cust. So that's column.Cust. Then specify the type you want to cast to. And then for the remaining columns actually, which are actually categorical columns. So I pass to the data frame and change. And the next step I did was to convert all the null values to a string called NA. So that the column will no longer have null values. So I think this is quite straightforward as well. Then let's move on. After we finish cleaning the data sets, the next step is to see which categorical columns actually have too many categories so that we may think of other strategies to deal with it. The first step we do is to do a count, goodbye count, to see for each of the categorical columns how many different categories are there in total for this column. So the way you do this is very simple. You just do a distinct count. So the distinct values and the count. Then we can see here one of the categories is called product feature three. And it has actually 500 something different categories, which is too many. So one of the way we can deal with the situation is that we can group those minorities into one categories and leaving the majority unchanged. For example, we have 500, but maybe 400 of them are actually minorities. So we can group them to a one category called minority. This is one of the ideas you can do. And the way we do it is like this. Again, we have to use UDF. But this part is a bit challenging because this UDF actually will accept two inputs columns. One is a category column. Another one is accounts. So like we did before, we have to do a goodbye count first. We goodbye this column and do a count. After that, we join this new data frame to a regional DF so that we have an additional column called count. With each row, we have a number associated with the category, represent the count of the category in the whole data sets. And after we join this column, we have to create our user defined function. As I mentioned, this function will accept two inputs. The first one is a regional column, which is a product feature. And the second one is a count. So based on the count, we have set the threshold like 150 to filter out those that below the threshold and then assign those values to a universal minority category string. And after we create this user defined function, we pass to UDF and specify the output type. Then we just again use the same function with column to create a new column called product feature three reduced. This will be the product feature three but reduced in terms of number of unique categories. Then we pass the UDF to it and specify the input columns, which is product feature and count. After that, we will drop the original product feature, which has 500 categories, as well as we drop the count columns, which is not useful. Okay, so after we finish, the last step will be any questions? Yeah, yeah, sure. How have you defined this threshold? Sorry? This threshold. Why is this one... Oh, okay. Let's go ahead. ...one thousand... Yeah, yeah. Because, okay, the story is that I did another plot to see for each of the categories how many counts associated with different categories. Then I found out that 150 is a good threshold so that I can filter out roughly around 400 categories. But it's just because the data sets in the cloud I couldn't run in here, so I couldn't actually show you. But what you have to do is do a bar plot for each of the categories to see the counts.  Yeah, but what is the purpose? The purpose is to collapse it to some amount of categories. For you, I want to have only eight categories. Or it is a percentage for the collection. This is actually the threshold, the count of the category below which the count of the category will be considered as a minority. No, the purpose of it to reduce the amount of categories. Yes, because we have 500 different categories. Some of them may only appear three times, five times, ten times. For all of these, I group them together to form a minority category. Okay, just answer my question. What is the amount of categories that you have? 500, in total. In total, one hundred. But after this transformation, how many? It's a hand or something. Hand or something. Yeah, hand or something. It's not ten, it's not twelve, and it is not ten. Okay, it's a one hundred. Yes, hand or something. Because I feel like I don't want to have too many. Two hundred is too many for me. I just use hand or something. But I didn't show here. Yeah, that's a good point. Yes. So, what this will achieve is that after you run this function, this original column called product feature three will only have a hand or something unique category left. Yep. I think if you look at the page, what's the point? There's so many categories. Oh, okay. Yeah, that's a good question. Because... It's not a problem why there's so many categories. I mean, we don't want to have five hundred categories. Yeah. But how many categories do we want to have? If you increase the stress of, for example, three hundred fifty, it will be something like seventy. Yeah. If you increase it for eight hundred, it will be probably twenty. What is the optimal amount of categories that we want to have? Okay. And how to choose the strategy properly? Actually, two of you have two different concerns. Both of which are reasonable. So, for your concern is that how many categories should we have which is a reasonable amount. This will, on one hand, depends on your data. Because if you find out that the top hundred category actually have quite a lot, and the hundred and one category onwards will, like, substantially below a threshold. This differentiation, if it is very obvious, so probably a hundred will be a good threshold. Right? So it's all dependent on data. So you can plot it and see, okay, maybe these onwards will all become a minority. It looks good on the data set and distribution. So then that is how I set the threshold. Or if you think a hundred is a, the maximum number of categories you want to have for random forest. Because random forest, sometimes, if you have too many, it doesn't make sense to train a model. Then in that way, which is his concern, then you can use a hundred as a threshold. So it's all up to what your concern is and how your data looks like. Okay. So the next part, which is the final part of the data processing is called one-hot encoding. So how many of you are actually familiar with one-hot encoding and know what it does? So, okay. So maybe I explain a little bit about one-hot encoding. Because for all category, categorical features, right, we cannot just push to the model to let the model train out. Because eventually the input data for the model is a matrix of numerical values. So in order to convert the categorical columns, like the cities or your set or gender, which are categorical values, you have to convert into a number. And there are two ways you can convert a categorical value to a number. One way is just to use an integer to represent, like Singapore will be one, like New York will be two, and et cetera. This is one of the ways. But this way, it's not the best way because there's no ordering relationship inside. New York is not two times of Singapore. So another way which is good is called one-hot encoding, if your column have like ten different categories or ten cities, you will end up as ten columns. And for each row, you will only have one number, which is one, which is associated with a column corresponding to the city. So you have ten columns. Nine of them will be zero. Only one of them will be one. If this row is, like, for example, Singapore, then this column represents Singapore will be one. So this is the idea of one-hot encoding, which is just to convert your categorical columns into a number, numerical form, so that your model can train out. But unfortunately, in PySpark, it's a bit hard to actually do one-hot encoding, compared with pandas or other packages. So the first step we have to do is use this string indexer to index the string into a label indices. So originally you have strings, like Singapore, New York, which is a string. Then you use this to convert to a label indices, one, two, three, four, five. After that, you pass to this one-hot encoder to actually convert the indices into the one-hot column, one-hot encoding columns, which is a sparse column. So this is the step you have to do it. The first thing you have to specify is the columns, which are categorical columns, and you want to do one-hot encoding. Then you just choose another name for each column after it has been converted. For example, feature three reduced. You want to name it feature three reduced cat back. Then you have the two lists. After that, you pass to one list. For each of the input from the original columns, you use one-hot encoder. You use the input column for the string indexer and output column. Then this output will further pass to the encoder, one-hot encoder function as input. Then the output from the one-hot encoder will eventually be your one-hot encoding columns or sparse columns. So this is very tedious, I know, but there's no other way we can do this in price bar. If you know, you can just let me know. So the sequence is like, you pass the original column to string indexer. Then use the output from the string indexer to go to the one-hot encoder function. Then use the output from the one-hot encoder as your final output column. So two steps. So because I want to make it as a pipeline, so that's why I flatten the string so that it will be like step-by-step for each of these columns. If you find out it's not easy to understand in the future just copy this code, it will do one-hot encoding for your columns. Okay, so the last step. Now you have all the features. After this tedious feature engineering part, you finally can do a modeling using random forest to train on your datasets and make predictions. One step before that is you have to select, you have all the features into one columns and to let the model know, okay, these are the amount of features I want you to train as predictors. And this specific column, which is feedback column, is a target column you have to predict on. So you have to let your model know this. And in order to do this, you have to use a function called vector assembler. And the input is the list of the names of the columns that you want to use as predictors. This is very straightforward. All of these columns are, we have seen it before, these are the features we have created from the original columns. Then you have to specify the output column. This is to specify a name, features, which represent all of the columns. A second step is to create a label indexer. And here you have to use a string indexer function. And the input to this is a binary response, which is a target column you want to predict. And you have to specify the output column, give a name, label. After you finish this, remember the temp is a pipeline that we have created previously. So you want to add these last two steps into the pipeline at the final two steps. And then eventually you can come up with your pipeline. So the pipeline is like you go through step by step of your future engineering all the way until these two last steps. So if you are familiar with Psyche Learn, you will know that a lot of the functions have the fit and the transform. A lot of the class have fit and transform functions. This is exactly the same here as Psyche Learn. So remember the df is your data frame. After you create your pipeline, you fit and transform on your data frame. It will go through all the steps you have to specify previously until the end. And after that I want to cache my data because eventually I have to do a lot of like assembling or ensemble so that I don't want to the pyspart to redo these steps again and again. So that's why I cache my data in the memory so that the output will be used directly. And then I do a split because I want to test my model's performance. So I use 20% of the data as test data and 80% as the training data. And remember to set a seed here so that every time you do the same split. Then I just want to look at the distribution of the negative and the positive labels in the training data. So it will be like this. It's the same ratio as we have seen before. It's around like 1 to 5 or 1 to 4. Okay. Finally you have created your data sets ready for modeling, which is a training data sets. Remember this is a pure numerical matrix. There's no strings, no categorical features. And this will be your input to train your random forest model. But before that you have to specify your random forest model to let the model know that feature columns represent a whole bunch of features in the data sets and the column called label is the target column you want to predict. And there's one or there's a few parameters one of them is a number of trees which you have to specify. After that you use the same way fit on your training data then you predict which is called transform in this package on the test data sets. So the transformed will be a list of predictions for your test data sets. So until here we have finished processing and modeling part. So any questions about that? So now you would like to see the performance of your model on the test data sets. Because this is a binary classification problem one of the way you can test your model is using AOC score which is another curve. And this score is normally between 0.5 and 1. Where 0.5 is like you flip a coin to randomly assign a prediction where 1 means like it's perfect correlated or the accuracy is perfect. So this is what we can easily do this by import this matrix from evaluation called binary classifier matrix. Then remember this transformed is actually a list of predictions we have got from the random forest on the test data sets. And we select the specific probabilities and the labels which is a ground truth. Then we assign all the labels and the probabilities we pass to the matrix and it will give the score which is 0.64 which is not very good but there could be other reasons like the data sets not very good or the model is not very good but it's still okay compared with 0.5 it improves by 0.14 and if you want to visualize your AOC score you can also easily do this which is the same way as you can do which is just to use map plotting features you can plot AOC score which is like this. So these areas represent the information or the advantage your model have gained over this area which is random guessing. So this area means like 0.14 something. So okay after you have finished this step we have come to the last step of this tutorial which is to which is to deal with unbalanced problem because remembering from the beginning we found the data sets very unbalanced. We have much more positive cases compared with negative cases. Unfortunately we cannot do much more in the random forward package on PySpark because we do not have the class balance implemented in the random forest as far as I know I don't think they have this so unlike other packages like Secular which is more robust and you can do a lot of features. So what we can do here is manually down sampling or up sampling. Down sampling means that because we have too many positive cases two more positive cases and negative cases so we can just take a sample of the positive cases and keep all the negative cases so we can bring down the positive and negative cases. This is called down sampling so this is just a distribution to see the outputs which is not too much to talk about. Let's see how we can do down sampling. First we specify the ratio of the positive cases to the negative cases because we want to bring down the ratio so we can specify the ratio is two which means the number of positive cases to the number of negative cases two to one. And we will keep all the negative cases because it's minority cases. Then we do a group buy same as we have done before. Group buy the positive and negative then we can get a count for each positive and negative. Then we calculate the ratio. Okay so this part is a bit tricky because the way I use is that for each of the positive cases I will random assign an integer within a bound. Then I use a threshold that I have calculated for all those rows whose random integer is below this threshold I will keep it. For all those cases whose random integer is above the threshold I will throw it away. So the one remaining will be roughly around two to one in terms of numbers. And this part will help you take care of this. Then eventually your DF sub-sample data will be a subset of your training data with reduced amount of positive cases. Then you use these data sets to retrain your random forest and make predictions and you realize okay it's improved a little bit which is 0.646. So by using this way you can increase your results a little bit. And another way is a bit trickier way that you can even do an example of down-samplings.  So what you can do is you do like multiple times of down-sampling. Every time you take a different subset from the positive cases then you do 10 times and then you will get 10 predictions. From those 10 predictions you take the average which is an example and hopefully this will be better than one time of down-sampling. Let's see how it works. This code is a bit more I mean complicated so I'm not going to go through this. But the basic idea is you have done like 10 times of down-sampling. But each time you get a prediction and you average them. Let's see what the results look like. So the first time without down-sampling is 0.645 and then increase, increase, increase, increase. So this example actually helps a bit compared with the previous down-sampling one time. So these are the two strategies that you can actually play with when you have an unbalanced data sets. But there could be other ways you can do this but there's just like some possible ways that can help you do this. So like I said you don't have to fully understand like all of this how does this work you just copy paste and it will do the job for you, hopefully for your future PySpark projects. Actually based on my experience working with PySpark it's not very easy compared with, it's much more hard like much harder to compare us using pandas and other packages. But why do you still need PySpark? Well one of the obvious reasons that you have too much data you cannot fit into your laptop or you need a cluster to do this. So at this moment I think the PySpark documentation is quite limited and also the package is not very, it's not a lot. There's a very few packages and Random Forest is only in the basic implement with its basic features. But so that's why we have to do a lot of extra work in order to enhance the performances. But hopefully this can give you an idea what you do with PySpark. So yeah, with that I finish the tutorial and if you have any questions you can ask. Which version? Oh, which version? Sorry, actually I can't remember. It should be around that because the last time the update was last year because that is when I finished the project. Sorry, I can't remember. It's not the latest one. At that time there's no class balance for Random Forest and you cannot even get future importance out of Random Forest models. I like Pandas. Just 40 minutes. If I have negative 2 and 10 positive why are you just Random Sample from here for? Why do you have to go through that long step? Oh yeah, because you have to random sample from your positive cases, right? So how do you random sample? Because there's no function that you can help. That's why I keep saying PySpark is not very easy for a lot of things. So there's not even a random sample feature function. A lot of functions are missing. So that's why when you are actually working with a PySpark project you have had it along the way. That's why hopefully this will give you life easier. I have one question. I'm just starting to explore PySpark and could you provide some of your experience? What are the limitations of PySpark compared to classical skip learning? Pandas or Secular? Yes. Which ones you should know to use PySpark? That's actually a very good question. The one sentence answer is that there are so many disadvantages and I mean I'm not saying PySpark is not good. It's an open source project and how to say it. You cannot compare with Pandas because Pandas and Secular are so well developed. You see all the PySparks not even 10% as good as that one. For some cases you have no other ways but you have to use PySpark. Like I mentioned you have too much data. You cannot fit on your laptop unless you have a very large HPC which has a lot of RAM. You have to come to a cluster and you only know Python for example. You don't know Scala so you have to use PySpark. I don't think the PySpark R is as good as PySpark. You can expect a lot more if you use PySpark R. That's the reason I finally end up with PySpark. After I finish all of these obstacles I feel like even though it's very hard to work with but you can understand the logic because it's very different from other packages like the lazy evaluation thing. So you have to understand some basic building blocks of Spark then you can step by step work your way to finish your projects. One of the ways is to use other people's code. This is very important. Even a simple step you have to think of you have to use a lot of ways to work around to accomplish that. You just use one line you can finish the job. But be careful if you eventually want to give up and convert to pandas data frame that's going to be another huge issue because you'll blow up your whole memory. Because sometimes I just want to I don't want to spend any more time I just convert to pandas data frame then I receive an email from my big data team you are actually cost of the whole cluster to stop because you're using all the RAM. That's the thing. It's very large. You cannot even use one data frame to store it. I mean the pandas data frame so there's no way you can do that. But for small data sets you can convert to pandas data frame then do a quick calculation which will make your life much easier but make sure the data frame is very small. So yeah. How much is left? It depends on your RAM because you have to negotiate with your big data team. It's all up to them. Because it's not just you there are many other projects who are sharing the skills or resources. It's hard to say. It doesn't matter on the rules. It matters on the size of the data like the gigabytes or terabytes so it depends on how much data set you have. If you only have like 10 gigabytes there's absolutely no reason to use this. Don't get in trouble. If it's one gigabytes like modern laptops have like 16 gigabytes why not just use a laptop?  So it's always the first question you have to ask yourself why do I need a big data platform? It's not because everybody using it, everybody talking about it you have to use it. That's not a good way. So think about your challenge think about your data sets. Do you really need it? Then you have to then you can't come to this platform. Okay. How big was the size of your data? How big was the size of your data? It's around 100 gigabytes for this particular task. And how long did it take to run your full notebook? It's actually quite long. Because the resources is very limited. It's only for POC so they don't give you too much too many executors so it's actually not very fast. So I think a few hours sometimes can take a few hours or even not finish. So it depends on your resources. This is very very is a lot. So it's hard to say. I'm tuning the two parameters. Tuning the two parameters. So the parameters is one of the thing. 200 is already quite a lot. Sometimes I have to use 50 for the number of trees because it cannot finish. So that's why one of the parameters you have to be careful is the number of trees you want to run in forest. The more trees, the longer time it will take. Actually I don't think it matters much. At least for my data sets. Because random forest, the good thing or the only good thing we use random forest is because it's easy and no parameter to tune. Comparison edgy boost or other models. So you just have to specify the number of trees which should be enough. For many cases. The maximum is 1,000 trees but I don't think it's necessary. You see. Consideration was actually the lack of 500. Yeah, it's one of the considerations of course. Like I said, 200 is already the maximum that I can afford. Otherwise, it's not going to finish within one day. Okay. Sorry. No. The down sampling is only okay. The thing is, when I was doing down sampling every time I get a subset of the data sets I run one random forest. Then I get another subset and run random forest. So one subset No. Okay. You are saying that the algorithm itself, right? No. They are doing bootstrap sampling out of your data sets. Then there is going to be some samples which is called all-out-back samples for every time you set the I don't think you can set any more parameters. The random forest package is very basic. If you go to the random forest documentation of the pass bar you will see that I don't think you can set that. Because it's very basic. You only can set the total number of trees of columns you use to sample from your total columns. So beyond that, you can check the random forest documentation but I don't think you can do that. Okay. What was some of the key steps that helped you to do all these samples? Yeah. That's a good question. Okay. If your business user or your stakeholders actually very curious about the accuracy, they wanted you to push it up a little bit. Then you really have to think about what can be the ways to actually increase it because this is not very high. Okay. So one of the ways you can get more features. Like I mentioned in the beginning, the feature we have is product feature and customer feature. Of course, this is a demo but the thing is this can be similar to many real-world cases. So from the product feature, you can get even more features. And the customers, for example, we only have the basic, the region, the job type and the gender of the customer. So we can even get more features. Like how frequently the customer come to buy our products and how many years this customer has been a loyal customer to our company. And even beyond that, we can even do a lot of feature engineering. Like we can do a group buy, for example, group buy this customer to count how many products this guy totally buy.  It will definitely increase your model's accuracy. But unfortunately, it's very hard to even create one feature in PySpar so I don't want to go into that area. So I always try to avoid those kind of sophisticated things to do in PySpar. But of course, that way will definitely increase your accuracy a lot. Yeah. So I think with that time, yeah, sure. In PySpar, I think there is more MLE which one are you using? Actually, okay, I'm not very familiar with what's the difference but the thing is I'm using MLE. But I'm also using some other tools from MLE. For example, this one, AUC score, yeah, this one. So the model is from MLE and the feature engineering. But honestly, I don't know the difference. No, no, I think this should be very similar. I can't really tell the difference between those two. So yeah, I think that's okay for my part. Any other questions? Okay, yeah, sure. Do you have a problem in saving your model? Sorry? Do you have a problem in saving your model? Saving? Oh, okay. No, I don't think so. Okay, this is some other projects, right? For example, you have a cluster of handle machines to run your random force. And eventually the random force model size will be very, very large. So it's going to be a trouble and concern for you to think about how you can carry the model along and every time you want to make predation, you have to load the model from the disk. So that is definitely the concern you have to worry about. If you have a large cluster, you have a lot of data. I don't think my cluster is very large, so that's why I take a few hours to finish the random force. So I don't think it's a big problem to save the model. I actually haven't tried that. I just saved the model in the server every time I retrain it because it takes a few hours to finish training. So I don't reuse the model from the... But I don't think it's a big problem. But of course, we have a large cluster which is a case that we had previously. The model size is going to be very large. So random force is very large. Okay. So, yeah. Any more questions for women? Okay, good. Thank you for...


So I have a little confession to make. As much as I've been doing for a very long time working in the IT field and working on infrastructure and things like that, I have always hired out the cabling. I know how to do it. I will do it as a one-off, but I never took the time to get good at it. So if you're a professional installer, this video is probably not for you. If you're someone like me who occasionally has to crimp a cable and you don't want it to end up looking like this, the obvious amateur move, the jacket pulled back, the cut done wrong, and it's out of its piece, and everyone recognizes this going, yeah, that's a terrible crimp, Tom. Yes, this may or may not have been done by me. I found this at the back of my shop. Okay, I'm better than this, but I don't like crimping cables. Now, for someone who's a professional installer, and they do this all the time, and you've seen Corey in some of my videos, I think the guy can do it in sleep. Watch professional installers. They're going to cut trim, cut trim, snap, put, and not even think twice about it. But when you're like me, and most of the time I'm not doing cable crimps, but occasionally I'm out in the field, and I'm like, crap, I need to either custom make a cable or fix an end on it, and there's just not another easy way to do it, there is a way to cheat at this. And when I say cheat at it, I mean, use this tool. This thing's pretty cool. This is the Ideal FT-45, and it can make your crimps a lot easier to do. So while the Ideal FT-45 may look like a standard crimper, at least from this side, this side's got a little blade.  Well, this is what's kind of cool about this, and this is what allows amateurs like me to make nice crimps. The cable pulls through. Now, you buy these from Ideal that pull through RJ-45s, they're not that expensive. They are more than your standard RJ-45 end. So yeah, there's a little bit of expense to this. But this is kind of a one-off, and just makes me look more professional at crimping cables, because I can get them done faster. So instead of trying to trim things right and being a professional, you can trim them wrong a little bit, pull them through. And as long as the ends are straight, I did have to trim the ends and get them through. But watch what happens when we push. They all go in, and away we go. Now they are nice in there. The jacket's where it belongs. It doesn't look all amateurs. Maybe we can wiggle this a little bit more. Get this in there, and now we have all these extras hanging out. And what do we do with all the extras? Well, we just take this, put it in, get this through here. And, whoops, one of them stuck under. There we go. And away we go. Now we have made a really nice-looking crimp. We've trimmed it. And no one knows that this was done by a non-professional. It just looks great now.   So it's really nice. I'm pretty happy with this. The crimps turn out well. They're easy enough to do. And it's not that expensive a tool if it's in my bag. And it will crimp a standard RG45 if you don't have the pull-through ones. But keeping a handful of these in my bag, just a handful of this. And this is my kit when I go out on that off chance that I have to crimp something. Because I've had the crimps go wrong. I've had the crimp not look as good when I'm done. But this tool's pretty handy. It's simple. It's easy to use. It's got a nice grip on it, nice feels. So it's nothing, it feels pretty durable like I could drop it. This is an all-metal construction, not plastic. So when it falls, I'm not worried about, oh, I won't have my handy-dandy cheater tool as I'm referring it to. Or I should say, as Corey refers it to, I asked him to see if he wanted to race me on who could do one faster with this or that. He still won. There's nothing beats someone who's really, really good at crimping things and watching Tom fuddle around with it. But I just want to throw this out there if you have to put some ends on. And it's not your day job to do so. It's one of those crap I have to put one on or I got to go put five or six of them on. I think they're worth it. If you're a professional staller and you want to do an entire site and you've got to put a lot of jacks on, maybe not so much. Because at some point, you just get really good at doing it the easy, well, the proper way. And it costs a little bit more for these, not substantially, but there is obviously a little bit of an expense incurred. But I think it's a great tool. It's definitely something I keep in my backpack for when I go on site and need to crimp something. And it's also nice because I can send some of my other technicians who are also in the same category as me. They just don't do as much of the infrastructure, but they run into a situation where they're like, oh, someone ripped the end off the cable. I don't have another cable that length. So I'm just going to crimp one real quick. And it does get that out of the way. They also put the cable orders on here.  It is written on the side of this, which I think is cool. Wouldn't it be cool if they put it in color? I would like that too. But I'll leave links where you can get these. They're available on Amazon. Links are not too expensive, pretty novel little tool. Thanks. Thanks for watching. If you like this video, give it a thumbs up. If you want to subscribe to this channel to see more content, hit that subscribe button and the bell icon. Maybe YouTube will send you a notice when we post. If you want to hire us for a project that you've seen or discussed in this video, head over to laurancesystems.com where we offer both business IT services and consulting services and are excited to help you with whatever project you want to throw at us. Also, if you want to carry on the discussion further, head over to forums.laurancesystems.com where we can keep the conversation going. And if you want to help the channel out in other ways, we offer affiliate links below, which offer discounts for you and a small cut for us that does help fund this channel. And once again, thanks again for watching this video and see you next time.


Major sponsors for Ableton on Air include Green Mountain Support Services, empowering people with disabilities to live home in the community, Washington County Mental Health, where hope and support come together. Media sponsors for Ableton on Air include Park Chester Times, Muslim Community Report, WWW, this is the Bronx dot info, Associated Press Media Additors, New York Power Online Newspaper, U.S. Press Corps Domestic and International, Anchor FM and Spotify. Partners for Ableton on Air include Yahad of New York and New England, where everyone belongs, the Orthodox Union, the Division for the Blind and Visually Impaired of Vermont, the Vermont Association for the Blind and Visually Impaired, Central Vermont Habitat for Humanity, and Montpelier Sustainable Coalition, Montefiore Medical Center of the Bronx, Rose of Kennedy Center of Bronx, New York, Albert Einstein College of Medicine of the Bronx. Ableton on Air has been seen in the following publications, Park Chester Times, WWW, this is the Bronx dot com, New York Power Online Newspaper, Muslim Community Report, WWW dot h dot com, and the Montpelier Bridge. Ableton on Air is part of the following organization, the National Academy of Television Arts and Sciences, Boston, New England Chapter, and the Society of Professional Journalists. Welcome to this edition of Ableton on Air, the one and only program that focuses on the needs, concerns and achievements of the differently able. I've always been your host, Lauren Seiler. Arlene is not here today, but we would like to say a special thanks to our sponsors, Washington County Mental Health, Green Mountain Support Services, and many others, including the partnerships with Einstein Hospital of the Bronx, Montefiore Hospital of the Bronx, and the Rose of Kennedy Center of the Bronx, and many other supporters and sponsors. With us to discuss the important topic of COVID and people with special needs, and a very important grant in New York City and the state of New York with COVID and people with special needs is Dr. Karen Bonak of Einstein Hospital of the Bronx, and Joanne Siegel of the Rose of Kennedy Center. Welcome to Ableton on Air. Hi. Hi, how are you? Can you tell us a little bit, well, can you tell us a lot about the grant and why and how it got started and the importance of it? Maybe I'll start with that. So I'd say a little over a year ago, I looked around and I saw that there was likely a need for vaccine education for people with developmental disabilities. I actually reached out to some of our clinicians and our leadership and particularly, you know, in different languages to get information out there. And I think people thought that maybe I was getting a little bit ahead of the curve. The vaccines were just starting to roll out and said hold off. At the same time, I contacted the New York State Developmental Disabilities Planning Council and put forth the idea of a vaccine education project aimed at developing different media initiatives and getting out science-based information and they agreed that it was a good idea. So this is just a broad overview of what we were funded for one year to produce different, you know, web and in-person segments geared towards various populations of persons with developmental disabilities, Spanish speakers, Chinese speakers, et cetera. And we also did some research and lo and behold, some of those folks from the clinical staff who initially were like we shouldn't get out in front have actually been some of our best spokespeople and been completely supportive of our project. So that's just a brief overview. How simplify or how do you have to simplify this information, you know, because people with special needs, depending on their challenge, whether it be autism, learning disability, they learn at a different speed and get information at a different pace. So how do you have to simplify, you know, people are also scared of, oh my God, vaccine, I'm scared to take it. How do you simplify this information for people with special needs? Well, two ways. Number one is we have people who have intellectual developmental disabilities speaking, making the videos. They can be found on our website. I think we can share them with you after the links. So they're speaking the same language. You're getting it from your peers. The other means by which we do this is the experts that we engage to do this education or experts in working with people with developmental disabilities. And to be honest, clear, plain, lay language is good for everybody. So, but not to be confused with being dumbed down. I have to say every time we do a webinar. Why did you say, I apologize, why did you say dumbed down? Some people think or may have perceived that people with intellectual developmental disabilities are not able to grasp concepts or scientific concepts. But you can transmit those concepts with a three-syllable word, maybe instead of an eight-syllable word. There's always a better way to say it. And those are principles of universal design. And we have found that the audiences that we have put on numerous webinars for, people with DD, their family members, easily grasp what COVID is, how the vaccines work. We explain it in a way that would be universally acceptable, which is the way we need to do it. So it is, and you know, they often comment that this was very respectful. You gave me science-based information in a way I can understand. So, Joanne, do you want to chime in with this as well? Yes. So what we do for each of the presentations, the speakers, we go over the slides to make sure that individuals that might be viewing understand them and can relate to them. We have included special presentations with the New York State Samus Self-Advocacy Association of New York State and have had panelists who are individuals with intellectual or developmental disabilities participate in our presentations. What we have found essentially is that many people are aware of what's happening. They watch the news. They hear the news. They are concerned. And they are very responsive to what is being presented and they ask many questions. I will say that at times for people who are living at home, remain at home, we do get questions about my family doesn't want me to get the vaccine but I would like to get the vaccine. And that comes up. And we try to address it as best we can. And we do indicate that that person needs to be discussing it with their family member because that is then it becomes an individual issue and a family issue. But these are valid, very valid questions that come up. And to date, I have to say that many of the people that you might think would not understand what is going on, they actually understand. They've been listening and they are very responsive to the education pieces that are going out there on the media. So I know we're pleased with that. And so that's an important point to make. Now, okay, so you deal with individual households. Is the training a little bit different or slightly different if you go into a group home setting, yes or no? And how? Okay, we have not gone into group home settings per se. Let me explain the background a little bit of some of the presentations. Essentially, we do our presentations as community outreach. And so we're presenting to family organizations, maybe advocacy groups. And we will go into this a little bit from various ethnic backgrounds. We have agencies that work with families that are Hispanic, Chinese, Korean individuals from Bengali backgrounds that we've made presentations with. We use interpreters. We use American Sign Language. So we're going to a very broad community outreach. Including, if you don't mind me asking, including Hebrew in case somebody needs that. Would you do other languages other than? We do have some limitations, okay? So we have limited to the five major languages other than English that are spoken in the United States because we just don't have capacity to include every single language. But we try our best to reach those major groups. And so to get back to who are the presentations for, therefore, family members and individuals, they can come on if they're living in the community. We work with agencies because, and I can go into some statistics on that. Organizations have asked for presentations because their staff are hesitant perhaps in getting a vaccine. So we've made many presentations to agency organizations for their staff, as well as their individuals. And we have also worked with care coordinating organizations that would be working directly with families living in the community, okay? And so that gives you an idea. Example of care organizations that, go ahead. Okay, in New York State, there are seven care coordinating organizations that work with families to develop plans of care for individuals that are eligible for state funding through the office, a New York State office for people with developmental disabilities. In New York City, there are three. Care design of New York. Yeah. Tri-County care. Yeah. And ACA. And those are the organizations. ACA stands for? Agency for Community Alliances, yeah. Okay. Now, how does the, I mean, this might have to do with it, but I noticed that you guys are also part of the disability university or something along those lines. How has this COVID education been part of that? Or like, is it more part of that? Because also you're helping the medical school teach this as well. And so... Well, let me just clarify that relationship. Okay. I'm sorry. No, no, no. So there's a lot of acronyms, like short names that are very confusing for everyone. Joanne and I co-direct the Rose F. Kennedy University Center of Excellence in Developmental Disabilities, UCEDD, okay? UCEDDs or Programs Without Walls. But as a requirement of the Federal Developmental Disabilities Act, they are required to be hosted within a university. So that's our relationship, is that the Einstein College of Medicine, where Joanne and I both hold faculty appointments. So Einstein is the host for our UCEDD. So our UCEDD, like you wouldn't drive up to Einstein and see a building that says UCEDD on the outside of the building. But we work closely with the Rose F. Kennedy, I would say Children's Evaluation and Rehabilitation Center. Another acronym, CERS. There are many adult self-advocates affiliated with that work, so it's not a great name. But that's... And there are 67 of these UCEDDs, and they all work on the type of activities we do. So a UCEDD has a four-fold mission, okay? One of them is Community Training and Technical Assistance, which is what... This is a large part of our vaccine education. Let's see if I can get all this right, okay? There's Information Dissemination, which is the emails, the Twitter that we put out, all the ways we disseminate content. There's Research, I have been taking the lead on, and then there's Advocacy, right? Is that it, Joanne? Yes. So these vaccine grants really encompass much of the... No, two UCEDDs are alike. They all carry out their missions in different ways, depending upon the catchment area. Our catchment area, the area of people we serve, is the five boroughs of New York City, as well as Long Island, which is... I'm not sure where most of your listeners are from, but it's... Well, we broadcast because, you know, special needs, topics and special needs is global, not just local. So we broadcast, you know, I've been a broadcaster for 30 years in this area, both in New York and now Vermont. So Vermont, you know, is New England, so you never know who's watching or listening. That's why we do these topics, because they're important. But yeah, I see your point there as far as that. Can I just go on? So many of the UCEDDs are doing some sort of vaccine education, and in fact, I think it was 11 of us. I think, right, Joanne, 11 UCEDDs received some special funding from the Centers for Disease Control to carry out this work. So that's a separate small grant that really complements the larger grant we got from our New York State Developmental Disabilities Planning Council. And as a group, we meet once a month to kind of share resources. Does anyone have a video in Korean? Oh, I have a video in Korean. Does anyone have a video of a mom in ASL? And does anyone have an infographic? So we actually, we work on a national scope as well. Now, we understand, I mean, as the nature of things, people are also scared of taking the vaccine. Now, my question around this is, are there, now you said videos. So how do the videos come into play when you're doing this COVID education? If someone is scared to take the vaccine? So there's two types of videos. One would be more like public service announcements on, not, it's explaining the importance of getting a vaccine. That's one component. But areas that really like anxiety and depression that have come up, particularly during the COVID pandemic, we have offered actual webinars where our psychologists have presented on mental health issues during COVID, anxiety, depression. And then for children, unfortunately, sometimes there's bullying around COVID. How is bullying play into this? It's very interesting because I didn't realize this until we spoke with some of the family members and that's why we kind of frame our presentations based on the feedback that families give us. So one of the families had mentioned that their child had gotten COVID and then, of course, there was a quarantine and the child then, but when the quarantine was over, came back to school and then experienced bullying by other students saying, we're not gonna associate with you, you had COVID, we might get sick, we don't wanna be near you. And so people have to learn how to respond in a fashion that is not hostile, but how do you respond to people when they are bullying? And so techniques were provided during that webinar which were really helpful. By the way, in the chat, I did put the links for our social media and so that's in the chat. Okay, so we'll go over that after the show. How does self-advocacy or how do the self-advocates of New York State help play into this education? Okay, so on December 10th of just this past year, at the New York State statewide conference for Sanis, Self-Advocacy Association of New York State, we had a 45 minute, no, it was a little longer, it was about an hour and 15 minutes, sorry, workshop on COVID-19 in which basic information was given by a medical person, but the rest of the presentation was really a panel presentation by self-advocates and so it was titled, COVID-19 Self-Advocates Speak Out. And they talked about their personal stories during the period of COVID and then they fielded questions remotely, this was all done remotely from the audience. So that was all latest event that we... Was this with sign language interpretation as well or no? No, Sanis did not provide sign language interpretation. It was their conference and we were invited to give a presentation, yeah. Okay, so in terms of what other activities or what other education, I mean, both of you can chime in. What other education is really important that we can talk about with the COVID situation? That's extremely important. Karen, do you want to talk about the clinics that were held? Sure. Yeah. So at the Rosef Kennedy Children's Evaluation and Rehabilitation Center, in June we had our initial clinic for children. I think it was in May that the vaccines were authorized. From five and up, right? No, May was they were authorized for 12 and up. Okay. In October they were authorized for five and up. Yeah, no, that's okay. It's hard to keep it straight. Because little children, infants are not able to get it yet. So it was authorized for children 12 and up in May by the Food and Drug Administration. In late June, our center, our clinical center, held a clinic and what was different about it is that we had, that one was staffed by I think primarily some of our, we have dental, we have actually dental clinic that provides services to people with developmental disabilities on the campus, on the site with the main health center there. And so some of the residents, those are people who are getting trained in dentistry, went there and they brought with them different toys and kind of distractions to help children who were really, really nervous, kind of with behavioral techniques in terms of distraction and feeling more comfortable. And then the follow-up clinic was in July and then it was just last Friday, I believe, right? We held another clinic for the five and up for children. And the idea was that to give parents the peace of mind, for many people with developmental disabilities presenting for medical appointment is anxiety producing on its own. What, what, okay, well, anxiety comes in many forms, correct? So how does, how does the anxiety play into, obviously you're going to try and calm the person down and then they get scared when they see a needle or vaccine.  Yeah, exactly. So these are, so the clinics that are held or again, it's not like going into your CVS where it's the sales clerk checks you in and then the pharmacist, these are people who like those who deliver the webinars and are able to speak in sort of plain language. These are people who are skilled at understanding these special anxieties and or sensory issues, right? That can often be more common and they're sensitive to that. They're able to take more time. It's not like, okay, we just have five minutes, we have to vaccinate the next person. So there's time, there's distractions, there's clinical skill. There was a room for people who needed to just kind of like really decompress and not have so much going on. So, yeah, they do that in a special Olympics. They have a special date. They have a special room that turns around and, huh? Okay. Yeah, we lost power from it. Are we okay? Okay, are you still recording? Yeah. Okay, sorry, let me repeat. Yeah, special Olympics does that very thing. Like at the end of each event, they still have another event. They can go and decompress and then come back and do more activities. So, yeah, certain other organizations do the same thing pretty much. But yeah, so go ahead. Can you explain a little bit more? Well, I think that's pretty much what I wanted to say. So it's an environment that is sensitive to the needs of this population who has the time and the tools and the clinical skills. And I believe that every child who presented to get vaccinated, they were able to successfully vaccinate and sometimes these parents had previously brought their child to another site and were not able to. So that's a real success. In your opinion, what causes people, especially some people with challenges, you know, developmental disabilities, what causes people not to want to get vaccinated? Is there a, are they that scared? I mean, what do you think? I mean, in many ways, I'm not talking about the children who don't make the decision when they're under 18. But in many ways, it's just like someone who doesn't have a developmental disability. We understand that there's a lot of the same misinformation and myths that are out there are also reaching. So do you think the media, do you think the media is just giving too much of that? Not the mainstream media. No, I'm saying there is, you know, it is known that there are 12 individuals. I think I read this somewhere who are responsible for spreading 75% of the misinformation on social media. Okay, the mainstream media, your ABC News, USA Today is not spreading this information. So no, that's not true. But people sometimes see something on Facebook and then they share that link. I know there are attempts to try and stop that if something is to label some of those posts of misinformation, but they're out there. People have, I don't even want to give them any credibility by repeating them. But what our webinars in particular do is it goes through from a scientific point of view, this is not possible. Yes, the vaccines did come onto the market quickly in terms of a certain type of vaccine. But just so you know, the process, right, this mRNA messenger RNA process has been around and worked on for quite a long time. I can't remember. At least 10 years. At least 10 years. You want to chime into that? Yeah, I also want to mention this and Karen will add to this. We've been involved in surveys of parents' attitudes toward the vaccine. And what it has shown us is that for young children and children with intellectual and developmental disabilities, the main concern of the parents is actually understandable. Their concern is they worry about will a vaccine cause more harm to their child in terms of their disability than not getting the vaccine. And some people, if you don't mind me chiming in here, some people claim religious exemption, which I pretty much, some religions don't allow vaccinations. So... There is, to my understanding, I don't believe there, I believe the Pope has endorsed vaccinations. Yes. My understanding is, I think it's like two, I think it's Seventh Day Adventist and Jehovah's Witnesses. Amish also. Yeah, I have some issues with, let's say, blood transfusions or something else. But that's not the main reason. In fact, that is among the people who might have some concerns about vaccines, like we split the sample, people who were eager to get it, and those who were not so eager among the not so eagles. That was perhaps the lowest reason. The other reason were concerns about the child's development, et cetera. But just to put a fine point on what Joanne said in our last survey of parents, we actually found that the willingness to get the vaccine was higher for parents of kids with developmental disabilities than it was in other research for parents of children in the general population. They're actually more eager, okay? And they're, when we ask what would be a big reason why you might not get it, very few of them mentioned, for that group, it was interesting misinformation. So I, you know, misinformation isn't the biggest reason. It's concern. And the other concern that's special to this population was a question. Were there, and this was with adults as well, were there people with developmental disabilities in the sample? Right? So they didn't ask when they were enrolling people in the vaccine trials, okay? But we know that, you know, by chance there was a likelihood that there were people, but they didn't ascertain that they didn't ask that. And this is actually, this is a side topic that... I'm sorry, I apologize. This is a side topic that maybe in the future we can do another show on about the inclusion of people with disabilities in research. But statistics about disabilities are not often included in these trials, so we don't know. Well, talk about research for and about people, especially since you said that. Yeah. You know, vaccines such as the polio vaccine and others in years past, do you know if they, or did they just start to include people with special needs in like research and development of vaccines? Because vaccines do save lives. So, right?  I'm not a medical doctor. Okay, I apologize. Nothing I say should be construed as medical advice. I'm actually a PhD in social work and research.  Yeah, yeah, yeah. I apologize. But, okay, have they just started including people with special needs? This has been going on for years, is it more recent? Absolutely, Larry, this should be another show. Okay. Okay, I'm sorry. Joanne and I are now working on a... No, no, no, I don't mean... I'm saying there's so much. We are working on a grant application to look at what are the roadblocks to including people with developmental disabilities and research. I just read a paper that came out a few years ago. Well, a paper that came out two days ago in the New England Journal of Medicine calls for more disability inclusion and research, of course. And then I have a paper that I read from three years ago that the number of studies that even ask if a person has developmental disabilities is very small. So we don't know. So this is an area that we need to expect. Okay, so let's get back to COVID, the COVID situation. So what is, since we only have some time, a couple of minutes left, what is... Well, obviously we don't know the future of this COVID vaccination. What might be the future of this COVID education and how much more will it go on, do you think? Well, we are just... We are hoping to renew our grant from the Development and Disposed Planning Council with a little bit of a different focus, to be honest. One of the main focus will be additional funding for different languages. It may sound like a small thing, but you can't imagine how many hours a week our team spends seeking out translation and interpretation services, making sure we have the correct American Sign Language interpretation. We have found that there is a great need for being culturally responsive. So that is a new kind of shift, not new. Joanne, do you want to add to that? Anything you want to add to that piece? No, I think that... Well, I think that Karen has said it succinctly. I do think, though, we originally were funded for the one year. We will be getting funding to go forward at least another year because no one anticipated the length of time that the pandemic would be with us and now with different variants emerging. We kind of have to then sit back and reassess where we're going. And so a little of this, the planning is on a day-to-day basis based on scientific information coming in. So I think we have our work cut out for us. I think there'll be more... Even after the pandemic, there will be an analysis of how people responded. How do we respond to the next pandemic that may arise? And how do we work with people with disabilities? Because we still are looking at... There's a lot of ethical issues in terms of... New York State has been very good in terms of distribution of the vaccines. As far as... Now, quick thing. So you put this out in webinars. You put this out in computer information. You put it out on Facebook. You put it out in pamphlets, correct? No, mostly in, I'm going to say, in internet and social platforms. Although, actually, I should mention we had an excellent webinar that was training done by our interim director, Dr. Lisa Schulman. She's a developmental behavioral pediatrician. It was specifically focused on answering the questions parents would have about vaccinating their children. That was extremely well-received, so well-received, in fact, that the Centers for Disease Control actually has that on their website. And that was written up, the transcript, a summarized transcript, or recording of what was said, was written up and published in Exceptional Parent. Okay. Just coming out. Yes, just coming out. Out of the press. Out of the press. So can you tell people where to turn, the website where people can get this information and how, you know, about the COVID webinars and so on and so forth? So I put in the chat, we have our social media platforms, and I put in the links on the chat. And so that's the best way to reach us. So we have a Twitter handle, and we have LinkedIn, so on our Facebook page. And our website actually lists many of our webinars. If one were to go to the Rose F. Kennedy, you could just put in RFK. RFK. Space. Space. U-C-E-D-D. U-E-C-E-D-D. No, no, no. You know what? I'll put it the link in because there's a lot of acronyms here. I'll put the link in. Okay. And this is all within the Albert Einstein College of Medicine and do a search on that for our use it. And it has our, actually the name of the, we try to have a popular name for the project. And it's called Vax Fax, DDNY. I'm putting the link in right now to the Vax Fax, DDNY. Dot.org, right? Well, no. I put just the whole link. You could just click on that. Okay. Okay. Let me, really quick. It won't get rid of you. Let me just, I just want to. Mm-hmm. So it would be, okay, www.vaxfax.ddny. Okay. Mm-hmm. And then, so then it also links people to the, the Albert Einstein website as well. Correct? It's through that. Yeah. Yeah. All right. Well, I would like to thank you for joining us on this edition of Ableton. For more information on the COVID education and what New York City is doing through the, along with the Centers for Disease Control and COVID education and people with special needs, you can go to the Einstein's website. First is www.einsteinmed.org, correct? Einstein. .edu. They just changed it. .edu. Okay. So www.einsteinmed.edu or www.vaxfax.ddny. Well, we would like to thank you for joining us on this edition of Ableton, and there will be plenty of more shows on medical information when it comes to people with special needs. Thank you so much. Thank you. Dr. Karen Bonak of Weinstein Medical Center and Joanne Siegel of the Roosevelt Kennedy Center. You sit. Thank you so much. This puts an end to this edition of Ableton on Air. As always, thank you to our sponsors, Washington County Mental Health, Green Mountain Support Services, and many other partners including Einstein Hospital of Bronx, New York, and the Roosevelt Kennedy Center of Bronx, New York as well. I'm Lauren Seiler. See you next time on the next edition of Ableton on Air. Major sponsors for Ableton on Air include Green Mountain Support Services, empowering people with disabilities to live home in the community. Washington County Mental Health, where hope and support come together. Media sponsors for Ableton on Air include Park Chester Times, Muslim Community Report, WWW, this is the Bronx.info, Associated Press Media Additors, New York Power Online Newspaper, U.S. Press Corps Domestic and International, Anchor FM, and Spotify. Partners for Ableton on Air include Yahad of New York and New England, where everyone belongs, the Orthodox Union, the Division for the Blind and Visually Impaired of Vermont, the Vermont Association for the Blind and Visually Impaired, Center Vermont Habitat for Humanity, and Montpelier Sustainable Coalition, Montefiore Medical Center of the Bronx, Roosevelt Kennedy Center of Bronx, New York, Albert Einstein College of Medicine of the Bronx. Ableton on Air has been seen in the following publications, Park Chester Times, WWW, this is the Bronx.com, New York Power Online Newspaper, Muslim Community Report, WWW.H.com, and the Montpelier Bridge. Ableton on Air is part of the following organizations, the National Academy of Television Arts and Sciences, Boston, New England Chapter, and the Society of Professional Journalists.


Up until now, we have been talking primarily about the internal working of systems. But in this section, we will start to present models for understanding systems within the context of the broader environment that they operate in. The first thing we need to discuss is what is called the system's boundary. The system's boundary demarcates a limit to the system's internal components and processes. Internal to its boundary, the system has some degree of integrity, meaning the parts are working together and this integrity gives the system a degree of autonomy. But that is all quite abstract, so let's take some examples. If we take a tree, for example, every part of the tree has been designed in some way to function as a part of the entire system. The bark, the leaves and trunk all serve some function with respect to the whole and thus they are integrated and through this integration, they are able to function independently from other systems in their environment. Thus the leaves in a tree are dependent upon the tree's trunk and all other elements to that tree, but they are independent from the leaves and trunks of other trees. That is to say, the tree as an entirety has a degree of autonomy. A system's boundary is then demarcated by where the nexus of relations that enable it to function, as an integrated and autonomous whole, reach their limit. Beyond this, the system loses its autonomy and has to interact with other systems and its environment. The boundary to a nation-state is another example of this. The nation's border is only a boundary if within this boundary public functions are integrated within the national system as an entirety and by the nation functioning as a whole, it can be autonomous from other nations. If one region of this nation has a different culture from that of its parent nation, instead sharing its heritage with a neighboring country, this will reduce the internal integrity of the nation, its autonomy to act as an entirety and reduce the degree of definition to its boundary. These examples should hopefully help to illustrate that boundaries may have a physical dimension but can't always be defined in physical terms. If we want to be able to achieve sufficient generality to talk about all types of systems, which we should remember is the aim of systems theory, then we need to understand boundaries within this slightly more abstract language of integrity and autonomy. Within the language of systems theory, systems are said to be open or closed. Open systems interface and interact with their environment by receiving inputs and delivering outputs external to their boundary. These boundaries are permeable, meaning that they may permit the exchange of materials, energy, information or ideas. Conversely, closed systems are more prone to resist incorporating new inputs and this resistance at their boundary makes them more strongly defined by the static properties of the boundary. By not adopting inputs, a closed system ceasing to properly serve a function within its environment may become deemed unnecessary to its parent environment and risks atrophy. An isolated system is more restrictive than a closed system as it does not interact with its surroundings in any way. The universe as an entirety might be an example of an isolated system but it is debatable as to whether such a construct could exist in reality. So let's take a few quick examples of open and closed systems to try and cement the idea. A hospital is an example of an open system continuously taking in new patients and discharging others, receiving medical equipment and removing old, hiring new personnel whilst retiring others. This rate of input and output to open systems make them dynamic. They are constantly changing and have to respond to the changes within their environment. An example of a closed system might be a boat on the sea. It is specifically designed not to take in water from the oceanic environment it is part of. Another example might be a group of teenage friends in a public park engrossed within the internal cultural dynamics of their peer group. They are capable of receiving only a very limited input of impressions from their broader environment. Finally, we get to the systems environment. All systems have a boundary and operate within an environment. This environment represents the sum total of other systems and input-output resources that the system interacts with during its operation. Thus, the environment consists of the sum total of resources and systems that lie outside of the boundary of the system of interest and interact with it providing its inputs and consumes its outputs. From this we should note that a systems environment is primarily relative to its functioning. So, a biological system that requires the input and output of natural resources operates within the natural environment. A business or enterprise system that requires the input of economic resources operates within a given market environment. And the political system of a nation operates within the international political environment. We will wrap up here and continue our discussion in the next section where we will be talking about the relations between elements and systems.