Hello, everyone. Now, we will discuss another application of address software that is on insurance industry. So, let us see one more application on insurance domain, how this software can help you using Monte Carlo simulation, you can take a better decision and it could be a better recommendation to that particular insurance industry, how they can manage their risk and they can take a statistical decision making. Let us see how it works. So, here you can see that software has come and now we will illustrate this application. Look at the data, we have collected this particular data of this example from their website. Let us see. So, here it is an insurance claim related problem. Suppose you have a data of last 20 years on 3 different type of claim, one is the auto claim, another is the general liability and the third one is the worker compensation. So, here for auto claim you have per year how many number of claim were been generated that has been mentioned here. So, 845 means number of auto claim for the year 1 or say last year, then 927 means number of auto claim for the next year, immediate preceding year. So, this way you have total 20 years number of auto claim and then also you have a data of general liability of last 20 years and worker compensation. And also you have say for each category of claim last one year like this year say let me you know make it colour, you will be able to understand for last one year you have say you know for auto claim you have 845 number of auto claim and here you have the amount of auto claim you can see here. Here you can see total 845 number of the amount of claim are been given for each particular claim. So, here you can see select the internal data here you can see total 845 number of claims are there and for each claim how much amount of money are been asked or been disbursed that also for instance claim in the auto segment are been also given here. So, only one year data are been given to you. So, 845 sample is sufficient we can get a guess that you know what could be the future amount of claim on an average for that particular segment of auto claim. Similarly, for general liability you have in first year 207 claim. So, that data are been given amount of claim are also been given right here you can see here. Similarly, so you can see total 207 claims are amount of claim are given various amount of claim are given. Similarly, for worker compensation amount per person or say per worker compensation amount are been also given as per as 108 claim are concerned. So, this one year data of amount of claim are given and total 20 year data of number of claim are also given. So, that means, in case on an average for next year if you do a calculation what could be the say number of amount of auto claim you have to multiply the average of that amount and say 845. So, this is a common sense, but what we are going to do now let us see first here you can see the average of number of auto claim we have calculated. In last 20 years the total claim are been mentioned. So, we have taken the average of them and also we have calculated sorry here you can see the average of the claim and the standard division of the data we have noted down and the mean of the data and the max of the data. The upper bound lower bound have also been calculated. Similarly, for general liability we can see the mean data and for worker compensation. So, here you can understand that what type of distribution it is following. So, we can also calculate that you can collect select the data and you can go to address and you can see you know distribution fitting. So, you can see it is triangular distribution it is following triangular distribution. So, we will see that later. So, this data number of auto claim are following one sided triangular distribution fine we will see that similarly for other type of claim also you can calculate what type of distribution it is following. Now, for number of amount of claim also we have calculated the mean data right the average of the mean data, the standard division of the data, the amount of claim and mean max all these 5th percentile and 95th percentiles we have also calculated 90 percent confidence interval you can also calculate based on the data this particular software. So, suppose this data you have just for the sake of information let me keep this color this particular cell with yellow color and say this one this is the average data right. So, what you can do in general people can do people can calculate the average prediction could be say next year the amount could be this multiplied by this one right. This could be the general prediction that this much of amount you should keep allocate for you know auto claim for the next year is the expected prediction because expected amount of say amount of claim per person are been averaged here it is 3409. So, in terms say money rupees thousands or say dollar whatever and also the on an average the number of auto claim are been also calculated. So, you can multiply that you may calculate the expected prediction of your total amount of auto claim similarly you can calculate for general liability as well as the worker compensation this is the common sense people can calculate common a people can calculate, but here we are not going to do that what we will do we will do some risk analytics here. So, here you can see this is what number of claim which are now following triangular distribution suppose here we have defined uniform distribution for the sake of illustration I have mentioned, but it is following triangular distribution and also type of claim we have used triangular distribution you can check that data also whether it is following triangular distribution or not you can check that also, but suppose we have assumed it you know come here you can see the defined distribution fit it here you can see it is following some beta general distribution not a matter. So, we can consider them also beta general distribution here you can see now it is earlier you have considered as a triangular distribution now you have replaced it as a beta general distribution this one I have defined uniform distribution, but if you wish you can change this as triangular distribution as per the data pattern you can define that also. Select the data and define distribution fit it go to new and then just select the particular model sheet. And select this particular cell and write to cell sorry write to cell and just select this particular cell done. So, now it is been changed now this data earlier we have considered uniform and triangular now this is triangular and this one is some other distribution beta general as per the data pattern we have selected it. Similarly, general ability also you can calculate I have kept uniform risk uniform and the risk triangular we can change based on the pattern of the data you can check and you can do it I will illustrate only the auto part auto case. So, now suppose these two data we have this data is nothing, but this particular representative of the data which is following triangular distribution and this data which on this particular type of claim are now following some beta distribution which are been generated from this particular 845 sample. So, now you have these two now here we have multiplied you can see the multiplication simple multiplication you can do which I have shown you here. Suppose if you do the simple multiplication here for your information let me do it here simple multiplication. So, if you do the simple multiplication this even in the previous sheet I have shown you that if you take the expected value and if you do a multiplication what you will get here I have mentioned that but look at here this you will get simple multiplication of these two set. So, here it is nothing but expected you know auto claim for the next year amount of auto claim. So, here you can see that this is nothing, but the average of multiplication of average of the data. But now if you come to the model here you can see say multiplication I have done but here now that number of claim and the amount of claim are been multiplied through random sample. So, in every simulation now you will be generating one sample of auto claim and the amount of auto claim. So, you will multiply them and you will if you generate 1000 of simulations you will generate you will be able to get 1000s of output of amount of auto claim. So, in the previous case you had only one average value here this one, but here now you are generating random sample. So, what we are getting let us generate it so we have selected you know only auto case I am explaining similarly you can take the total sum of allocation of for the insurance industry whether it can be an example of say ICIC Lombard or you know HDFC, AMC wherever you can think on that SBA insurance. So, you know you can LIC also you can think on these examples. So, here suppose I am focusing only on the auto case. So, now here it is a risk output if you run it say instead of 100 sample let us make it 1000 sample will run this one. So, you can observe that the output case. So, here or output results here you can see the mean is around 33 lakhs on an average and the standard deviation is almost 14 lakhs 13.9792 lakhs note down. So, mean is almost 33 lakhs and you know standard deviation is almost 14 lakhs note down it you can see mean and say standard deviation. So, what is the mean for this particular data selected we have already run the result. So, here almost how much 33.10 lakh and standard deviation is 13.92. So, suppose mean is 33.1 lakh and standard deviation is almost 13.92 lakh variation imagine even only for auto claim forget about the total grand total you can do that you can drag it and you can do it just auto claim I am sharing with you. So, you can see that data variation because every time you are generating and the entire sample of 1000 data confidence interval you can also you know like the previous illustration you can also you know get to know now what I am going to do. Here what we have done we have generated one claim of auto for a particular year and say you know amount of claim the mistake that we have done here is that let me keep it in the right hand side the mistake that we have done here is that for every simulation suppose you have run we have done 1000 simulation. For every simulation what mistakes we have got here is that or we have done it committed here is that suppose if you generate say 700 number of claim suppose in a iteration you have generated 700 number of claim. And then you have generated suppose 3410 say 3500 these are the thumb length kind of things of some of the maybe one of the sample iteration you found, but it is not the average. So, suppose now if you come here suppose in a particular iteration of your 1000 simulation suppose you have generated 700 number of claim it should not be dollar it is a 700 number of claim right and amount of claim suppose it is coming out to be in a particular simulation 3500 then what happened you have multiplied now you have multiplied that you have multiplied this value 700 multiplied by 3500.  So, what mistake you are doing here is that you are actually considering that or you are assuming that every people of 700 claim or every auto claim you will have the same amount of claim every person will come for the auto claim and they will claim 3500 and that simulation you have generated here which is incorrect right for every person the amount of claim should also change for that consideration you are not including in your simulation. What we will do? We will use a you know comment here like you know some you know formula here that is called risk compound and in that case they will do another one more simulation inside the simulation. That means when you generate say you know 700 number 700 number of auto claim or say you know 800 or whatever in every simulation you will change that might be 900 700 every simulation every iteration you are doing it. So, suppose you have generated 700 number of auto claim. So, for that when you run the second claim the second cell like this one from your beta distribution. So, in that case your amount of claim will also vary for each 700 people or 700 claim. So, in that case you might say the difference variation will be high no variation will come down because you are trying to capture the reality practical cases. So, if you do that through this risk compounds formula look at here it is compound just I have multiplied and through the risk compound formula and now if I run this here it is taking 1000 simulation and if you run this you will see the variation. Look at the outcome for auto claim only the variation what is the mean? Mean is almost 33.50 almost same 33.50 you can see earlier it was 33.10 lakhs here it is 33.50 almost same. But look at the standard deviation standard deviation has come down 3.49 only let me write down here 3.49 lakh of standard deviation and imagine this the reduction in your confidence interval that you are getting from this particular risk analytics version of insurance claim. And here you can see the variation in the previous case even if you consider the simulation and the random scenario of the practical aspects but still you are committing a mistake what mistake therefore every claim you are multiplying the amount of claim should be same for that entire year. But that should not be the same in practice your amount of claim should vary from person to person. So, therefore, if you include that through this compound formula here you can see your on an average the mean will same mean mean of amount total amount of claim for the auto industry will be the same for auto insurance. But standard deviation is reducing look at here only 3.5 lakhs but earlier almost 14 lakhs. So, this much of variation if you reduce imagine that your confidence interval also increase also 90% confidence interval if you see 28.87 and 39.99. So, this much you know reduction you are able to do in reducing your confidence interval as well as you can see a lower bound here how much a lower bound 28 lakhs and upper bound is how much it is almost you know 44 if you come here the basic multiplication with different random input and here you can see 6 lakhs lower as low as it can go to 6 lakhs and as much as it can go up to 85 lakhs. So, variation is high and the lower bound upper bound is also high and your confidence interval also 90% you can see 90% confidence interval here you can see and here you can see 1.2726 lakhs only right maybe you know it is not 6 it could be you know probably 60 it could be 60 you can check that. So, here you can see the differences in your outcome analysis but I can run this again close it here you are generating 1000 simulation perhaps earlier one it was 100 simulation but now we are generating 1000 simulations first we will get a better insights here here 13.41. So, it was 13.41 you can see right and on an average mean is same and here you can see the lower bound like you know to some extent it is like you know 7 lakhs and upper bound is 78 lakhs and 90% confidence interval also you can see around 56 lakhs 13 lakhs to 56 lakhs 13 lakhs to 56 lakhs 90% confidence interval but if you use the risk compound where you know in every simulation every iteration of your simulation you are changing the amount of claim from from each person of claim or each amount of you know each candidate of auto claim. So, in that case you can see your variation also it is at gone up to 27.43 and 90% confidence interval see 20 almost 29 to 40 just. So, the confidence interval also been reduced as well as the reduced standard deviation. So, this kind of analysis you will be able to do and you can accordingly you can plan of your like an allocation of fund for the insurance industry whether auto, general liability or worker compensation or you know overall fund allocation for the next year that prediction you are able to do well in advance with a better you know understanding of that particular analytics aspects of insurance claim. So, now this is the example that I have discussed about insurance claim part now we will discuss the example of product mix with uncertainty and this type of problem it is a optimization problem under uncertainty this type of problem are very popular in manufacturing sector or say you know inventory planning segment or say production planning domain. So, let us see one application of that in production planning or manufacturing sector how does how that simulation based risk analytics can help you in taking a better decision. Here you know so many data are there, but I will summarize the information that what aspects we are going to cover here. So, that I will explain so that you can understand by seeing the lecture or you can practice by taking the sample data at your home by downloading the trial version of this particular software. Here you can see some you know unit cost of the production are been given say you know labor cost, assembly line cost, testing line like manufacturing line one manufacturing like here we call it is a testing line one and testing line two and there are you know this if you read this particular problem statement I think they need to produce some you know 5 type of laptop or electronic devices say think about example of say Dixon technology say. Suppose here you have 5 products say 5 cell phone Dixon technology is going to develop produce for the next month say and the demand for that particular or say order flow for that particular type of cell phone are been given here right say 4 type of cell phone and it has been given and the production line so the input data sets are been given here say you know for all 5 type of cell phone say say labor cost labor hour right the raw material requirement or the resource requirement labor hour for assembly labor hour for line one line two production plan one production line one production line two and the cost of component parts selling price if Dixon sell the product to the clients how much profit they will get the revenue they will get that is also been given and the overage cost if there is excess inventory suppose whatever example you can see you know whether it is electronics product or any other example also you can see if there is excess inventory you might have to pay some and holding cost etc. you can or you know discount price you can mention that also and demand and only know production quantity here we have mentioned the production quantity initially we will assume it say you know initial case right 100 then we will run it this you can assume 0 also suppose we are assuming the initial assumption of your production quantity for each type of product suppose 100 100 100 and this is the total production say but effectively you do not know what will be the final production based on the demand cost etc. and here you can see the number of sold based on this sum you can calculate your number of sold product for two type of product and left over inventory excess inventory and the you know the condition the assembly line condition here you can see assembly line the total used I mean calculated like this some product of this and the assembly time total production and the assembly time similarly you can calculate the testing line 1 here you can see that line 1 and the you know the amount of hour you multiply the quantity and the time required for the production and testing line 2 you can see look at the amount products row number 24 and the time required so this you have a availability right you have a limit so here you can see you have a assembly line budget or say you know total available time for the month and similarly manufacturing line 1 manufacturing line 2 testing line 1 testing line 1 and testing line 2 whatever you want right this is a sample example you can relate to any other practical example but here sample illustration I am trying to show you so these are limit so what you have calculated now let us see now you have your total revenue you can multiply the number of sold and the selling price right you can check the selling price you can multiply that similarly you can subtract the entire all the cost look at the cost total unfilled cost unmade demand cost like penalty cost testing line 1 cost quantity product produced into multiply the time and the corresponding cost you can see right same way you can calculate all the other cost and if you subtract that from your revenue you will get your total profit for model 1 say cell phone 1 and you can calculate your total profit here right some and the risk output you have to make it so that you can get the output under risk environment now here what we have done sample illustration you can increase more information more uncertainty to the data so here we have assumed that demand for each product are following triangular distribution they are not fixed you do not know what could be the sale what could be a variation in the demand that you do not know and based on the past data you have assumed that they follow triangular distribution so you have considered only one type of uncertainty that is the sales uncertainty or say you can say the demand for the product are being changed because next past from the past data you have calculated your demand but you do not know what could be the future demand remember this is a practical problem I am talking about I have given example of prediction probably you can relate it to those you know any needs vendor problem you can relate it to any production planning problem or any other you know real life example where you can set this product mix problem of manufacturing or production planning and you can solve your practical example. And now here what is the objective now the objective is maximize your profit objective is to maximize the profit subject to the restrictions subject to this restriction here I have put only the assembly line restrictions and the testing line 1 and testing line 2 restrictions but you can add more like you know budget restriction your say credit card restrictions your investment restriction you can say space restriction machine utilization restrictions many other restrictions you can put and you can actually solve this problem this and based on what based on this production quantity this production quantity could be 0 also initially it could be say 200 also you do not know initially you are assuming it but software will the excel solver will optimize this particular you know production quantity subject to that you have a conditions restrictions here subject to the restrictions here I have kept only couple of restrictions but in practical problem you might have a many more restrictions right and you have to maximize your revenue sometimes you may need to maximize minimize your manufacturing cost or production planning cost or inventory cost or warehousing cost or you may need to maximize your supply chain profit. So, depending on the problem statement your scenario will change but overall the conditional restriction of optimization here we are focusing on the linear optimization you can extend that concept to the non-linear optimization also but this linear optimization will come into the picture and you have to optimize the production total profit subject to the maximum production with limited expenditure or your available resources. So, this type of problem we call it is a maximization problem we call it is optimization problem and there what do you do max or mean depending on the problem like in our case it is a maximization of the profit but in other case it could be other problem statement also. Suppose you know Cx whether C is nothing but the cost structure the representative of cost structure or the profit structure vector and Ax are nothing but the variables say you know Xn say Cn. So, this is called you know variables and these are your cost component subject to Ax less equals to b, b are nothing but your restrictions or limits, constraints. So, based on these constraints we will calculate the best production whether X as I mentioned it could be X1, X2, . This X will maximize or will optimize subject to maximization or minimization of the problem and these conditions this type of problem or b could be a restriction say b could be b1, b2, . Depending on the number of conditions and Ax are nothing but your total utilization of resources put together in order to produce the number of quantity of Ax. So, this is called the general linear programming problem you can convert that into nonlinear programming also in case your variables are coming in a in a nonlinear manner. So, this is a general structure of optimization that aspects you can solve using basic solver, but here we will be using it using evolve of this particular software under the context of risk analytics. Now, we will run this particular problem under risk environment. So, demand are considered as a risk now uncertain now you can include the conditions also as a risk parameter like availability could be have a could have a variation say you know low R bound and the standard deviation etcetera and many other aspects also you can bring and you can make it more practical problem. Just only one aspect I am showing there where you can integrate the uncertainty along with the optimization problem. Just go to that risk part and do not run the simulation here now reduce the number of simulation say 100 say for the sake of you know running the software other it may take time and go to risk optimizer model definition. Here it is like similar like excel solver here you are optimizing the problem. So, which cell you want to maximize or minimize here you want to maximize your it is a minimization not it is a maximizing it could be supply chain minimization warehouse cost minimization here you are maximizing your production profit after production total profit because selling price we have mentioned. So, it is a maximization problem which cell you are maximizing your total profit look at total profit we have selected. So, that we are going to maximize and these are your in a total variable limit who are the variable you can see see how to add it just go and select this particular variables say particular variables you select it and you can suppose I have added here here suppose it is upper limit say 1000 suppose or say make it more say 1500 and lower limit say 0 the variable. So, one variable one particular cell I have added we can add few more variables so say step could be any it could be with 5 interval step whatever you want right whatever you want. So, as per the data let me put so that we can run it you can make it integer variable could be integer which will take only integer variable it can be any continuous version from 0 to say 1000s or 2000 it could be discrete with 5 steps increment of your production quantity whatever you want you can put it suppose another variable I would like to add suppose this variable I will have to add this one right click this and put the lower bound so 0 and the upper bound could be say 2000 if you want to put. So, this way you can add all the conditions right here I have dragged since we have kept all them in a same row so you have dragged all the data and I have kept the lower bound and upper bound if you wish you can reduce the lower bound upper bound also say and now come to the conditions we have only these three conditions if in your practical problem you have more conditions you can add that also how to add that condition you can delete the data also. So, suppose this condition so here is condition this condition right this condition should be less than greater than equal depending on the situation here it is less equals to what is the upper bound of that assembly line this one done. So, here I have added one more conditions but earlier if you drag all three like you know I can show you if you drag all three conditions you can also in a single instance you can include all them all are less equals to in a practical problem it might be some may be less than conditions some may be greater equals to conditions some may be equality conditions so you have to you know check all this. So, we have added all of them but no need to repeat all them if you do not include them like this suppose in a complex problem you have so many conditions and later you realize that this condition is becoming redundant or not required to include and I want to see the variation in the what if analysis. So, you can delete them also you can just no need to delete just you know click and click them your only if you click the particular row or you know condition and the variables only they will come in active rather rest of them will not be active in your optimization process. What the mistake I have done here is that you know same data sets I have kept once again but if you have more different conditions you can include that actually different condition you can include and you do not need to delete all them right you know like the way you condition maybe it is because of range. So, the variable range so therefore, it is creating probable one look at here in the condition like you know I feel that if I do not click this still it will work look at it is working now. So, here you can come back again. So, here you know in case you have a many conditions maybe it because of range you cannot change different range of the variable. So, that for you need to keep for each of them one by one you can put the range or all put together you can keep a range. But for conditions constants you can add constant you can delete or you do not need to delete all just and click it. So, this way you can define your data entry to the evolve or check how many iteration you want to do trials maybe reduce it to 100 now. So, that we can save the time and then go to here again now do not run your simulation here start the optimization here because in this case you will be doing optimization as well as the simulation. In every iteration of your simulation you will generate different demand uncertain demand and you will get the optimization output it is going on it will come you will get to know. So, both will be going on in this particular case of optimization under risk. So, here optimization is also going on as well as like the LP optimization I have shown you using solver evolver here as well as the uncertainty has been also integrated to your optimization solver system. So, this is more practical in reality actually this type of problem if you can develop with a better understanding with a better application mode probably you will be able to handle different case study of optimization of real practical problem or cases using this particular software risk part can also be added to the system. Here you can see your total profit risk optimizer you have used which is nothing, but you know the optimization of your total profit of your system as subject to the uncertain conditions and the uncertain demand. And here you also you can see the mean value maximum standard deviation and all these things you can see lower bound upper bound and all these analysis also you can do. You can see a tornado graph scatter plot which demand are of the product are making only demands we have considered uncertain, but in other practical cases you can consider different other parameters also which will which can be also of uncertain or risk parameter. In that case you can see the impact of that particular parameter uncertain parameter to your total profit. Here you can see you know model 4 has the more variation. So, it will have a maximum impact your total profit. So, this type of analysis we have shown earlier. So, you can see the overall you know analysis of this particular application of risk analytics where you are using the optimization of your entire supply chain of the manufacturing system at the same time your production planning system at the same time you are integrating the uncertainty to the data. So, this type of aspects can also be addressed through the risk analytics of simulation version of software. So, these are the couple of applications that we intended to illustrate through this module of simulation and risk analytics. There are many more applications which you can you know illustrate or you may get insights of them by you know downloading the different case applications through the software. If you download the 14 day style version you may get many case applications whether it is an insurance industry, banking industry, supply chain industry or you know logistic case applications, marketing application also. So, many applications you will be able to get through this case of the examples from the software and you can illustrate they have the solution also. So, solution analytics as well as the simulation part all put together you can do predictive analytics, you can do prescriptive analytics also like for this example, you can think this particular last example product mix problem. Here we have done the prescription that optimization as well as the prediction also like demand or uncertain what could be expected total profit, demand planning and the production planning. The expected production planning we are doing well in advance as well as the expected prediction also with confidence interval and you are incorporating the risk part into your model. So, you know this particular module would help you to address practical problem under risk analytics as well as as well as Monte Carlo simulations. So, I believe you understood the entire aspects of Monte Carlo simulation. Look at in each and every application couple of applications I have discussed right. In each and every applications you could see that the background study is simulation. When you are integrating the uncertainty or risk part of any parameter of any particular problem, you are generating different you are not taking the average value, you are actually generating in every instances of a iteration of simulation, you are generating different input of that particular parameter or variable.  And then from there you are taking mean variance standard deviation, cut-off skewness, lower bound, upper bound, confidence interval all are you with you, turn on the graph which making maximum impact to your output that analysis also you are able to do through this particular analytic software of risk modeling. So, this could be an integrated aspects of you know predictive modeling as well as you know business forecasting. So, we thought of covering this particular software also as a part of your simulation modeling under risk analytics. So, with that as I mentioned there is no limit for this illustration of this particular software with different case applications. I leave it to you to practice different problem at home and you know get more confidence in understanding the risk analytics part through this particular decision tool suite software. And you can make different you know many statistical analysis also can be done decision tree like in predictive modeling can also be done through this particular software and you may get more insights of it with practical case applications. With that let us conclude this particular module of risk analytics of Monte Carlo simulation and a part of business forecasting. Thank you.


to another edition of Senior Connections, the show that is about the Senior Activity Center of Sheboygan. My name is Wendy Schmitz, I am the supervisor of the center, and today we're going to have a follow-up session on our last program, which was about our recent trips to Cuba. Today, my guests are Anne Grittinger and Maria Westra, Anne went on the September trip, and Maria came with me and my husband on the October trip. So Anne, could you introduce yourself a little bit please? Certainly. I'm Anne Grittinger, and I actually live outside of Usperg. It was our first experience going to Cuba, although we traveled other places and loved to travel now that we're retired. And you went with? I went with my husband and with 11 other people from, or 10 other people from the Sheboygan area. And Maria? I'm Maria Westra, and I always, I like to travel, and when the Cuba trip came up, it was like, what better place to go with a group of people? So I asked my friend, Anne, to go along, and didn't take her too much convincing. And Anne, what is it about Cuba? We asked Margaret this in our last show. What was it about that particular trip that intrigued you? Well, I thought it was interesting to find out more about the culture. I'm very interested in the arts. Do a lot of volunteering with the Art Center, and so the opportunity to see the art and the architecture, the music, were all very interesting to us. And your husband had a specific reason for wanting to go. Do you mind briefly touching on that? Well, he wanted to go. He wanted to see, he was hoping to see some of the more of the biology of the area. He wanted to see the crocodiles. He wanted to see some of those things. Unfortunately, they was pointed out that it's three miles down the road that way. But we didn't go that way, but there were still lots of things for him to see. And as a painter? And as a painter, yes, he's doing a whole series of lighthouses right now, so he was interested in taking pictures of lighthouses and some of the water scenes there. Very nice. In our last show, Rita was the interviewer, and she asked Margaret and I basically about our general impressions of Cuba. And of course, the reason we explained that the reason why we were able to go is because now the embargo has been lifted somewhat, and we're able to do a cultural exchange from America to meet the people of Cuba. So what I thought today would be fun would be to continue the conversation and talk about some of the people who we were able to meet. We started our trip in Havana, and I believe that our schedules were probably different, but Maria and I, our very first trip outside Havana was to the children's performance at the Art Center in Santa Clara, right, Maria? Well, it doesn't really matter what the timing was. How would you describe the experience of meeting the children, and what were they doing for us? Oh my gosh, they were just so exuberant, so exciting to see us when they greeted us and they handed us their flower, their national flower, which is the jasmine, I believe. And they were just really to go pick and to see them perform, there was a group of 60 children at this school, they were into the music, the dance, and the arts, I believe they're painting. Wow, they're just outstanding.  And one of the things about the trip that I enjoyed was that we got to participate. It was a little bit like getting Lutherans to participate. I don't think that we're quite as exuberant as you described them to be. I know it took a little while for us to warm up in some cases, but we did get to dance with the children and it was hard to resist. And did you get that same opportunity? Yes, we did. The children came out to the audience. They took us by the hand and led us on. The production that we saw was Romeo and Juliet, Go to Cuba. Oh, really? And they said, it'll take too long if we translate along the way, but we think you'll get the idea and you can ask questions at the end. So it was interesting, they started out with a farmer and his dispute with a crow and the children just picked at that wonder flay and then they went on to do the little Romeo and Juliet story, which we all knew. And then they pulled us out and had us dance with them. Now I know that for some of the people on our trip it was the highlight of the trip, was that particular performance, some people said. What struck you the most about it, Anne? The children's enthusiasm about the arts and the dancing and their singing and their openness with strangers coming into their school. And I know you had mentioned when you came back that what struck you was how they managed to be very resourceful about their costumes. Do you remember about their shoes? Yes, in our, if we want to recital here in the States all the children would have either the black or the pink or their little white slippers and they had slipper socks and things like that and it didn't matter, it was the performance that was the important thing for them. Lovely, they reminded us a lot of our granddaughters and in the performance and the children and they were eager to talk to us and tell us, oh, this one's my brother and this one's my cousin and so it made it feel more like a family sort of gathering. Yeah, one of the highlights that I've talked about for me personally was when we went to the Literacy Museum, which was actually outside Havana. Maria, can you talk a little bit about what the Literacy Museum was all about and who we met there? The Literacy Museum was, well, when Fridao Castro came into power in 1960 he wanted the Cubans all to be literate, have the ability to read. So he did a campaign to recruit anyone who wanted to go out and teach the people how to read and this was to the remotest areas up in the mountains and some of these children that went out, they had to leave their homes for months at a time. They went up into the mountains to help the farmers learn to read but part of this involved the children or whoever went to help the farmers with their chores and then in the evening after all their work was done they would sit with lanterns and do all, teach them how to read and whatever had to be done. We didn't get to meet the people per se who were part of that campaign but there's a wonderful picture on the wall of an eight-year-old boy who left his family and went to live with a farming community and I think what struck me is he's exactly the same age as me and now he's a veterinarian and just the idea of letting an eight-year-old boy leave your home and go off and this must have sounded like some wild mission. Well it was interesting because they showed us the video that you probably also saw about the whole campaign and then at the end the lady who was at that museum and said and Freddie here who was our bus driver was one of the teachers. Oh really? Yes, so it was really sort of fun to talk to Freddie about his experiences being a teacher too. Yeah, one of the things that struck me was there was a lady on that video and she said that, she said it doesn't matter what I've done in the rest of my life. She said that one year gave my life purpose and I, as an ex-teacher at some point in my life I was so touched by that and I know that the teachers in our group were, it was, to me, my trip could have stopped right there. I felt like that was my reason for going to Cuba. So that was another place we went to and then Anne, I know that you told me about a time when you were particularly touched and that was when we went to see the choir. Could you tell us a little bit about the choir? Well we went to see a choir perform. It was a very, very hot day but it didn't matter. We learned by that time, have your fan. And this is a wonderful choir. They sing acapella. They had just returned from a tour of France where they'd sung all over the place. No pitch pipe, it was all acapella, no pitch pipe for tone. She had a tuning fork, which she would touch at the point that she needed to tap it and then touch her mandible so that you get the vibration and the tone in her head. She'd do a little humming to give everybody their parts, hum the little first part to get them into the right key and they'd start singing. And it was absolutely beautiful. They sang a rendition of Shenandoah and they came around and sort of surrounded us into the aisles and sang it. It brought tears to my eyes. It was so beautiful. It was so beautiful. It's interesting that you talk about how hot it was because Maria, do you remember just like everywhere else we went? We're listening to this incredible, internationally famous choir and of course we're drinking the mojito that you get when you go anywhere. Did you get me? Oh my gosh, yeah, we did. It seemed a little strange to be in this auditorium and we weren't able to take pictures there. Obviously that would have been distracting I think but we're able to hang on to our obligatory mojito which I got quite a taste for while I was there. This house was right after lunch so maybe they thought we had more hours. We went before lunch, didn't we? Yes, it was lovely. One of the questions that I had when we were watching the choir was how on earth can all these people have taken off work to come and do an hour long or whatever the performance was that we enjoyed but that was actually their job, wasn't it? That's their job. That's their job. And they travel around the world and doing that which reminds me we know that not only have travel restrictions eased for Americans to go to Cuba but they've also eased for some Cubans to travel and I think that was surprising for me and Maria do you remember when we went to Remedios and we went to a church that's being renovated and we met the town historian and we met the artist that is renovating the church and he had actually traveled quite extensively outside Cuba and has been invited to do residencies in other places. I can't remember his name unfortunately Do you remember the work that he did that gold altar screen, that whole wall that was covered? Was that, it was gold leaf, wasn't it? Yes, yes. And then they had restored some of the side pieces and they were still, it was still under construction but you could walk around and see the different things from the different periods of history in that church. And we also learned at the literacy museum that the system of reading that has been developed in Cuba is worldwide. In fact, the only place that it is not being taught is in America and that the experts who've developed the programs are well traveled people that they are used as resources all over the world. So I think that kind of blows a stereotype many people that we've talked to since coming back think that Cubans aren't allowed to travel and they are if they're sharing their expertise with other people. Who else did we meet? We met so many people and did so many things it's sometimes hard to remember what we did. And I know that you had a slightly different experience when we went to the Museum of Fine Art and that you made a very personal connection. Can you tell us about that? We didn't have that same opportunity to meet somebody. We met a very lively and wonderful guide that took us through the Museum proudly showing off modern history which isn't quite as, it went from a time farther back than just really contemporary but very happy to show us. And because we had had a Cuban artist at the John McCullough Arts Center a few years ago who had a big exhibition there. I asked him if he knew about this artist and perhaps I didn't pronounce his name in a Cuban accent but he wasn't quite sure what I was talking about. So I told him I'd send him the newsletter and the write up, the curatorial notes sent it to him along with who took it for me and he was excited about that. He's also his favorite singer was Dianne Warwick. So I sent him a CD of Dianne Warwick. That was a little tricky to find one of those around in the stores anymore. But sent it to him and he was thrilled and he did know about the artist. Then it came back and he had had an exhibit there also in 2009 and so we were chatting about that and so that was that personal connection where you could talk about artists that we both knew and the representational things, things that don't necessarily reflect real positively on a government where they're finding exception to the government and in his work this artist was showing some of the problems.  He's in Florida. They're talking on the phone and yes, it's raining here too because it's only 90 miles apart. So it was really interesting and hopefully with some of the easing of the restrictions and if there is enough money to do it, he'd like to come and visit the United States. Not to live here, but he would like to see some of our art museums also. Right and you are in contact with him. We're emailing. Now. Back and forth now. And Maria, you are in contact with our guide. Leanne, yes. And she was quite thrilled to hear from us that we're still all a buzz about Cuba and putting the word out there for other people to travel. And do you imagine that we will see her one day? I'm hoping so in one way or yes, I think so. We can see to that. And did you get the impression when we were there that her goal is to travel? I know she wants to go to Spain, which is where her family originated from and she wants to go to France. But did you get the impression that she wants to reside anywhere else other than Cuba? No, I don't. She is very Cuban at heart and I think she always will be. She wants to explore the world just like most of us too. But no, definitely not. I think Cuba is her home. And she also very much wanted to take advantage of the Cuban educational system. Oh, for sure, for sure. And because she knows so many languages, I think that's why I'm not sure she had gone to France to study or she had that availability. But I'm thinking she will go there to enhance her understanding. I think that's one of the wonderful things with the guides. They were very personable with us. They answered questions as honestly as they could and just made it very much known that they were very proud of their country and let us know what the history was and dealt with anything from economics, the money system, the ration system, all of those things that we had heard about. But to hear it from someone that's experiencing it, that's living it, our guide was the basically bit breadwinner for her family that consisted of her grandmother, her parents, herself, and her daughter. Although her grandmother and her parents are now pensioners, but that wouldn't be sufficient. But it was really nice to hear about their personal experience living in Cuba. Maria, you and I have talked a little bit about that. Maria and I don't have American passport, so in fact, we would be able to go to Cuba from either the countries where we have our passports from or through Canada or Mexico. But would you refer to what you and I have sometimes talked about, the fact that we were happy to be on this trip because of the fact that, like Anne said, we got the personal stories and we got the lessons, as it were, from our guide. Rather than going as an individual traveler. Oh, definitely. I think this was one of the better ways to go through Premier, through the Chamber of Exploration group, because there's just so much to see. And I don't know if it would be possible to get to see all these things, especially in a short period of time, because to interact with the people, to experience the different foods in the restaurants and just see all the UNESCO sites. And the fact that Havana is a heritage world site, that so many of the city squares or areas of importance have been restored, like the churches. And now there is storing along the melacon, one of the main highways that runs along the Caribbean. Those old colonial buildings are for 500 years old and they're restoring them to their splendor that they once were. And same with the palace. I just can't imagine. I can imagine finding my way around Havana and certainly reading it up ahead of time, knowing what to see. But it was such an intense experience Of course, for me, I would never have got up as early as we did. So I would have missed out half of the trip, probably. But I don't know about your guide, Anne, but one of the things we noticed is that Leanne was well known. She knew people wherever we went. And so we had these immediate connections with people. Let's talk about, I know it was one of your favorite places. It was certainly one of mine. And that was when we went to, is it Del Mar? Where the street ceramic art is? Did we go through Del Mar? Yes, during the marketplace. Is that what you said? No, Fuster. Fuster? Yeah. Ah, yes. Fuster is a Cuban artist who's an environmental artist. He's a ceramicist, a painter, and uses recycled as well as tiles that he fires there. Also paintings. He is well known. He has exhibited in the Twin Cities. He has paintings and ceramics on exhibit in New York City. We were invited to his home, his gallery, at Wander around the courts. And he hasn't limited his art to himself, which is a wonderful thing. It's like this small, narrow street, residential street, but all the neighbors have pitched in and they take all the tiles. And so all the, it's a ceramic tiled fence line almost with down the whole street, everybody's gates are all decorated. So it all matches and then you walk into this and it's just this whole environment filled with color and tile. And Maria, it's hard to describe what that neighborhood looked like other than the art. Some of our group would say it was poverty stricken. Some would say it was very simplistic and some would say that it was crumbling. But it's not what you expect when you first, it's just pure joy to see this art coming out of nowhere kind of thing. And I think whether you say poverty, it's what is your standard? Is it the US standard? It's like saying when we went, they showed us both the regular grocery store that you would use your Cuban pesos and your other grocery store that you would use your coupes in that was a bit bigger and fancier. It was air conditioned, but it was small. It was very small. It is nowhere near our big supermarkets, but that's normal for them. And so to criticize and say, this is just pop, it's what you're, what are you accepting? Are you content with this or do you have to have all these other things? And they're much more content with a whole lot less than we are here in the United States. I think Maria and I came back with the same impression. They're very resourceful. Yes. Now I know, I don't think your group got the opportunity to do this, but we were very fortunate. My husband would say it was the highlight of his trip. On the last evening that we were there, we got to go to the Buena Vista Social Club and we got to actually meet the elderly Cuban musicians. Maria, can you talk a little bit about what that was like? We kind of let loose that night. I was just, I don't know, I've never been to one of those before, so I'm not sure. What were you expecting? I think I was expecting a theater because we were going to see a music group and what did it look like? It was more like just a, I don't want to call it a bar, it's just a cafe, I guess they would call it there. I mean, my gosh, they're just, we were appropriately all tourist in there and everybody just, but who was that you say the singing was just, it was really phenomenal. They just went on and on and everybody, something belted and who is that one lady there, that big blonde lady? She reminded me of Cooch Cooch. And she was 85 years old. And she just went on. And I wish that I'd known a little bit more about the history of the musicians because people were standing up, having their photographs taken with them and they just wanted to touch them and we got to meet them and shake their hands and dance. And then again, at the end too, as you say, everybody has to get up and do the final dance, so to speak, you could go and use zigzag through around the tables and you just have a good time. They're just so musically inclined. They're just so exuberant. It just flows out of them versus we try to do those dances and it just doesn't come as easy. I'm not sure if it's an everyday occurrence, but every place that we went for lunch or for dinner, there were musicians. Of course, they all had their CDs to sell us, but it was wonderful just to experience the kind of music and I kept waiting for something that was gonna be a little bit more Cuban and every once in a while we'd hear it but they were playing for the audience or they played tunes that they thought Americans would like. And there's a huge African influence also. And I think that points to the fact that when we went to see the children, we were actually going to like a cultural center that children go to when they're not in regular school and that's one of the things we learned is that because of that culture and because of that political system, everybody has access to the arts. And I joke about the fact that I'm not a very musical person but that we came back and every morning when you get up in the morning, you're kind of singing and dancing, you weigh into the elevator because it becomes such a part of you, doesn't it? When you're there, the whole music thing. Well, again, time has gone extremely quickly and I thank you very much for coming on the trip and for being my guest today and we look forward to seeing the viewers next time. Thanks a lot.


Nice and neat, two thinning, I just leave me two with magnetic boat levels on the profile so keep an eye on it. That's our steel tight.  And it is the next day because the weather got too bad yesterday. It kept being on and off really fine rain so everything was just getting soaked and then as we pack up get dry up again when we try again, get wet again. One of the frustrating days so we just, we packed up a bit early. Went to Hodgens to hire up one of these in case we use the stillsaw. Keep everybody happy but yeah so hopefully today should be a bit better. The weather is going to be raining up until next week so let's just hope that they're a bit wrong because we need to make some progress on this thing but that is what we got up to yesterday. We've got a few courses up so we're just going to carry on and get that finished. And just a little side note quick as well. Made a start trying to get this cat flap sorted but we accidentally dug out a bit too low because we forgot to consider these cats have to try and get through it and we went to damp course by mistake so we're just going to pick that up one more course. Made a start fixing it up but we were in out of water so we're going to get that pup back in and cut this accordingly. We won't be able to do that just yet though because that can wait. Probably until we've got a few more course of brick up so it's a bit stronger. For now we're going to get back on this side and just blast it up as tall as we can and rinse and repeat over there trying to get some block working today hopefully. Let's see. I don't want to jinx anything. There's only wood. I find some wood. What's the wood? There it is. So let's get a mix on and let's do something shall we? Looks like we know what we're doing. Just spinning. We're a bit organized this time. We've got loaded out and got a mix in before break so we're ready to get started. A bit of an update. We're about five courses up from where we started. I think six on the other side because we have to do a bit extra but anyway we're going to keep going at this until we can't reach any higher. I think we've got enough bricks we'll see. I think yeah that's ready for jointing that so I'll get the old joint out give that a tidy up and then wait for dad to finish his damn coffee. I'm waiting for you here dad and the invisible watch but yeah so let's keep making hay while the sun shines or whatever that scene is. Just another update here. We've got the tear wires in all the way across. I don't know if you can see them but I'll make sure that you can. All the way down and here's the pack of them as evidence so if you're in the middle of taping right now where's the tear wires you can delete that right now and I'll take you over to here as well and I'll also show you the screw ties are in too oh god but yeah it's all in it's all tied together so put your keyboards down sit back and enjoy just know that the tear wires are in here peace of mind let's get some bricklaying done shortly before dinner so it's all ready for um block work or chalice is going to jump on but you're going to get those fill those top joints and that's where we're reaching way above our heads I had a bit of a hobble going on this side and that's quite a reach but um nice neat two thing we've got a few uh pretty a few more to go there yet well we have got a few more to go there yet but not till we've got something to stand on a scaffold and where have you will be um I'll probably have a ladder this side to get these trestles that side to get that bit and the cavity so yeah he's Alex is getting him getting sorted on there getting the DPC down where have you and it's starting to get a bit tired it doesn't look it but the more wall you build the more stuff you've got out about the garden so that can all be brushed off now because that stack of bricks gone that's been there since day one on the brickwork I'm right I'm all set up here spot boards ready profiles ready we'll come back to the cat flap when this profile brackets out the way and then just a quick one on the profile I just leave me to a magnetic boat levels on the profile so keep an eye on it and when it starts pulling you just accordingly that's when you drop this thing I think it's called line stabilizer so when you when you get to about six or seven and then the profile starts to pull over which everybody does you just click that onto the wall and then screw it up to your brickwork and that's solid more solid than all the uh all the clamps and everything up here in the pieces of 4b1 and 3b2 and clamps and that's it everything self-contained on the blakes lot less stuff to carry around a lot less stuff to worry about a lot less stuff to lose and there you go Alex just in the shade now so it while he gets himself set up there I'm going to get cracking on this side get a corner block on that end and off we go get off for some dinner now I just threw those four on there yep through those on there got a mixing ready for after lunch Alex is cracking on with the block working there he's coming on nicely and then plan is to try and get the vessels up on that today I have to know the jointing just ready for spoon you down now and yes it's windy not nice is it being the puppet but uh unfortunately the GoPro died just as I was finishing off but that side's up to trestle height now I keep going to the lintel height not yet a few more for that but that side's ready for scaffold and dad has been very busy setting up his own little makeshift scaffold to get all this bit up but the reason because it's seven to damp yeah seven to damp well is here and I'll just there you go right up to uh about six foot that so yeah yeah they're a bloody nice match I can tell you that and they're really nice to set as well because you can probably tell they're just so square which like well rectangular which I know sounds weird but there's so many bricks are just yeah these they're closer to engineering brick as well with the way they are and the way they don't take water there you go there's like a ibstock tradesmen light ibstock tradesmen light bloody beautiful brick and as you can tell it definitely looks different to it this morning it looks like we've done something now and we've put a big dint in all of the bricks and blocks which is always nice to see them go so that's more or less all the blocks there isn't it yeah so practically all of them are here then yeah so make a good made a good dint in it today we're coming back tomorrow let's get the bucket back and get on yeah let's get ourselves home ready for tomorrow where we can just try and get this side up to a scaffold height and uh get the scaffold set up there and just knock a knock a bit out happy days it's a pretty big big gap isn't it so yeah so we end up babbling let's uh catch you tomorrow right and it's tomorrow hello friends been here friends here to greet me again but obviously today our plan is going to be to get the scaffold set up there we've got to make we brought the extra scaffold as well to get ready for these steels on monday and we are gonna get that side up to lindl height that's the priority there get bike get all that blockwork and all that done and then i think i'll probably jump on that side while dad jumps on this side so he'll do the blockwork here and the brickwork get that up and i'll get the the blockwork and the rest of the brickwork up and my friend's gonna eat my gloves looks a bit um but yeah so not really much else to say but let's get on to it still a quick update here this was buddy fiddly but is the opening we're gonna have for this cat flap so we've just we left out a gap here for the blocks and then we've cut out a gap in this block and once we get the everything figured out we'll go patch this bit up here just so we can try and get this higher than the skirting boards when they're going so that's that now i know it looks a bit all over the place but now that we've gone past that the bond can go back to normal now so we had to mess around with stuff around and do unideal things but that's the best way that that could have worked because obviously we had to have a proper thing for that to sit on the way that's worked out stowed is so we're gonna build this on it and we'll cut out a reciprocating yeah but like i said now that we're past it i can get back to the bonded and that corner so we'll get you set back up well you're getting on for about half 11 now oh my god 12 o'clock um well that was a shock of finding that out yeah alex is on that corner still yeah alex is on this corner now he's got over the awkward bit you can get cracking on that now we've got to try and get these up at least the fronts to little height i've just been on the um the stilly with the water suppression bought that yesterday because we were threatened with the fireman by the neighbors so this is what happens when you use water suppression the freaking mess and when you're trying to work on the water you work underneath just go with it as best you could and hose it down but yeah it's all done now all the cuts are out for the toothers so um i'm gonna get up there now knock all that out we'll get this side loaded out get stuck into this after dinner so um yeah well um i'll stick it on time that's a bit well i'm not right missed it again sorry about that but um it actually took longer to do the cuts than to tap them out so once you go in with the stilly from the side in the front you just tap them they fall out luckily no breakages took the cavity out as well a bit of a mess down there we'll sort that out so now we're all ready profile on that corner string line across to that corner because i went up a bit high with that side and get this leveled up to lintel height and the priority is brickwork up to lintel height and at least the block work on that front corner and then that lintel light again i mean alex might be able to reach out on that today which will be handy maybe i've got much to do this afternoon then we've got about 140 to put on this side now it's probably dinner time now are you ready for dinner because i'm not no right we'll work on a bit then yeah we had we had a fry up Saturday didn't we i had a sausage buddy alex had a bacon buddy and i got a nice latte so a bit full off to that so right it's tough to get this loaded because the trestles are up quite high but they're up quite high meaning we can reach the top of the pike so we don't have to move it so um yeah that that pauses me thinking i might just take those bricks off and drop the scaffold back down again because it's got to be moved on monday to set up for the steels okay so get back to you in a bit that's our steels height both ends big ass steal that but yeah it's got the it's got all day tomorrow to go off now which we need because the big old with with that and last three beds are big because i don't know what's going on with this house but they're all over the place so the last three have had to catch up up to there up to there we check with the laser level and the last we just went bonkers so it's all leveled up now and then all we've got left then is a bit of brickwork here tiny bit here and that pike there you have it show you the other side that cleverly just rate that corner back because if you're doing that we would still be here till seven o'clock so so yeah we wouldn't have enough water all the time so that's good is it so that's guys ready for finished height and now we can just simply get a line up and run them in obviously next time you see us it'll be the next video where we're getting the steels in so for now though thank you very much for watching this video if you liked it be sure to leave the thumbs up for us because that's greatly appreciated and if you're new to the channel be sure to hit subscribe and hit that bell so you're notified when we upload a video again we greatly appreciate you doing that uh this has been bricklaying with Steve and Alex and see when these steels are up


So I've been studying at tertiary level for the last eight years or so now and something that I've learnt the hard way is how to trust myself. I know what I'm capable of and I know my strengths are and what I'm not so great at and had I made decisions based on that knowledge I think I'd have saved myself an enormous amount of stress. I started taking a research paper in the second semester and it's a self-directed research paper on a topic that I'm really interested in. Had I done that same topic in a taught lesson I probably would have got much better results. It's easy to get swept away with the exciting things of doing something new and that's fantastic. Stick with it. Just bear in mind, listen to yourself.


Democracy Watch Initiative DWI has cautioned President-elect Borla Tinibu and the National Working Committee of all progressives, Congress, APC, against imposing leaders to represent various constituencies at the incoming FEND National Assembly, which is blatant encroachment on the legislative subversion of popular will and deviation from democratic principles of separation of power between three arms of government. There is today this in the communique after a one-day colloquium with theme inclusive participation for sustainable democracy in Kaduna. The convener Nasru Abdukadir Dambataz says there is no better system of government than democratic process to produce leaders through free and fair elections, noting that all contestants to the Senate presidency and speakership should be given fair constitutional rights irrespective of their region. All candidates for the House of Representatives speakership from the North should also be encouraged to subject their aspirations to voting. We however question that the capacity, character and competence should take priority above every other consideration during the voting process and call the candidates that lose to concede honorably and cooperate with those that win in order to make progress. The colloquium finally resolved to call the attention of the President-elect Aswajibola Ahmed Tunugu to be vigilant as to the political opportunities that surround him with the intent of forming another cabal who are behind this scheme aiming to pitch him against his traditional political friends and sections of the country. Blow the President-elect to reclaim the people's confidence in his untented democratic credentials and commitment to entrench sustainable democracy, restoring the glory of governance, reclaiming the sanctity of constitutionalism and respect for the rule of law by coming out openly to renounce any purported arrangement of preferred candidates. They appeal for capacity, character and competence to take priority above every other consideration during the voting process while calling all the candidates that lost to concede honorably and join in building the nation. There is no better system of government than democracy. There is also no better way to produce leaders than democratic process of free and fair elections. The current attempt to impose leaders on people, people of undoubtful maturity and proven experience elected to represent their various constituencies in the incoming 10th National Assembly amounts to a brutal encroachment on the sanctity of the legislature, blatant subversion of popular will and distant deviation from the democratic principle of separation of powers between the executive, legislature and judiciary. The leadership of the All Progressive Congress itself, a product of democratic election and which subjected its presidential ticket contest to elections, should now be the same party attempting to manipulate the system to one of selection and imposition. The colloquium therefore condemned the stubborn insistence on the resort to all tactics of imposing leaders on institutions as important as the National Assembly by the National Working Committee of the party under its chairman Ablad. Hello, hope you enjoyed the news. Please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


Okay, so we're getting closer to our intermediate goal of being able to maximize the entropy and find out what macrostate of a system is most likely. But before we can do that, we need to talk about optimizing not just single variable functions, but functions with more than one variable, multivariate functions. So to make sure we understand what we're talking about, let me sketch a picture of a two-dimensional function, some function that depends on two variables, x and y. So I can draw x, I can draw y, and then I'll attempt to draw some function that depends on both x and y and has a particular position where it's at a maximum. So the goal is to understand what to do to find the maximum or indeed the minimum of a function with more than one variable. And of course, for multivariate function, we can calculate more than one type of derivative. We can calculate partial derivatives. So we've made the jump now from calculus one to calculus three in talking about multivariate functions. And I can calculate for this function, I can calculate the derivative in the x direction, the derivative in the y direction. And of course what that means is derivative in the f direction is telling me how much the function is changing as, so how much does f change as I change x? When I move purely in the x direction, and a little bit trickier to draw, but if I attempt to draw what this function looks like, if I make a change only in the y direction, then this is telling me about the slope dfdy when I move only in the y direction. So I can calculate the slope in any direction, but of course the important thing about the maximum is when I'm sitting at the top of the hill right here, it doesn't matter whether I look in this direction or this direction or this direction or this direction, the top of the hill is flat in every direction. So the maximum or the minimum of the function will obey the requirement that dfdx, the derivative of the function in the x direction is zero when I move in this direction, the slope is zero at the maximum. And likewise, the derivative of the function when I move in the y direction, it's also flat. In every direction, regardless of whether I'm moving in x or whether I'm moving in y, the derivatives in all these directions have to be zero. For a more complicated function, if I have more than just two variables, if I have maybe three or maybe a hundred variables, a function that depends on lots and lots of variables, the requirement for being at an optimum or an extremum is the derivative with respect to any one of those directions has to be zero. The partial derivative has to be zero if I'm at an extremum of the function. So that's the prescription for how to find the maximum or minimum of a multivariate function is just take all the partial derivatives with respect to any one of the variables and set them all equal to zero. For example, let's suppose our function is x squared and 2y squared minus 6x plus five. So there's a function. It's not the same as this function, but it's a parabolic function that's going to be fairly easy to take derivatives of. And if I want to know where is the, and this function will have a minimum, it's a concave up parabolic function. So the minimum of this function will be where df dx and df dy are both equal to zero. So I have to be able to take the derivative of the function, partial derivative with respect to x, pretending the y's are constant, taking the derivatives with respect to x. I just get 2x from the x squared term, and the minus 6x term gives me minus 6. Partial derivative with respect to y, 2y squared when I take the derivative becomes 4y. And all the other terms go away when I take the y derivative. So those are the partial derivatives with respect to x and y. If I'm at the minimum, then each of those terms has to be equal to zero, and those are not terribly difficult to solve. So 2x minus 6 has to be equal to zero. That's only true when 2x is equal to six or when x is equal to three. So that's the value of x at the minimum. 4y is equal to zero only when y is equal to zero. So that is the minimum of the function. The function goes through a minimum when x is equal to three and when y is equal to zero. So now we have done an example calculating the minimum of a multivariate function. So it almost seems as if we're ready to tackle a real problem. So let's see how that works. Let's say, remember two things. I know how to calculate entropy. If I know the probabilities, entropy is minus some of the p log p's multiplied by a constant, Boltzmann's constant, if we need a numerical value. Let's suppose my example is flipping a coin. So we know the answer to this question. When I flip a coin, there's a 50% probability it's going to come up heads, and a 50% probability it's going to come up tails. Let's suppose, though, I want to calculate the entropy of that coin flip. I could plug in 0.5 for heads, 0.5 for tails, calculate the entropy of that coin flip. But let's ask the question a slightly different way. Let's say, what would the probability of heads and tails be that would make that coin flip as random as possible? Would it be 100% heads and 0% tails or 50% heads and 50% tails? Which of those two or which of any of the possible probabilities give us the most entropy or the most disorder, the most randomness? So we've got some probability of heads, some probability of tails. And I would like to know, what are the probability of heads and tails that maximize the entropy? Actually, let me maximize the entropy per coin flip. So I need a bar on top of my entropy because I'm calculating the entropy per occasion. So the entropy, molar entropy looks like minus k p log p. So p heads log p heads and p tails log p tails. That is a two dimensional function. That is a function of p heads and p tails. So I can take the derivatives of that function with respect to the two variables, p heads and p tails, and set them both equal to 0. So I would like to know if I take the derivative of this function with respect to p sub h and set it equal to 0, what does that tell me about p sub h and likewise for p sub t? So that derivative of p times log of p, that's very similar to the derivative we did in a previous lecture. So product rule tells me that's 1 times the log of p plus p times derivative of p 1 over p. So that's log p heads, that's what I get when I take derivative of p times log p and add to that p times the derivative of log, that one gives me the 1. So all together that's minus k times log p h plus 1. And a very similar expression is going to be true for probability of tails. So ds bar dp tails. I'll write that a little. There we go. That's going to be minus k log p tails plus 1. So those are the two equations I have to solve for p heads and p tails in order to find out what maximizes the entropy. Rearranging the first one, first of all I can divide by k and get rid of the k on both sides. Equation is minus log p h minus 1 has to be equal to 0. So log p h has to be minus 1 or p h has to be equal to, after I undo the natural log by taking the exponential I'll get e to the minus 1 or 0.37. Mathematically the same is when we were considering optimization of a multiple example we considered when we did optimization of a one variable function. So find out that p heads is 0.37. Math is exactly the same for the second equation. So setting 0 equal to minus log p tails minus 1 also leads to p tails equal to the same result 1 over e or 0.37. So what this example has told us is what will make the entropy as large as possible, what will make my coin flip as disordered or unpredictable as possible with the highest entropy is if I have a 37% chance of getting heads and a 37% chance of getting tails. Not 50-50 like we expected, in fact something seems to have gone badly wrong here and what went wrong is that we've been assuming all along that heads and tails have to add up to 100%. My only two outcomes are heads and tails.  So mathematically the heads and tails must add to 100% but that's not what happened in this problem because we didn't require that heads and tails add up to 100%. So the problem we've solved is not actually the one that we're interested in. These do in fact maximize the entropy but they are not the maximum of the entropy if we require that heads and tails add to 100%. So we're not actually quite ready to maximize the entropy just yet, first we have to consider how to do this maximization with a constraint, a constraint that the heads and the tails have to add to 100% and that's what we'll do next.