Islamic Economy in Saudi Arabian Companies Essay -- Economics

1. Saudi Companies Fund A. The institution of SAMA could not be a profit-making institution, it had to conform to Islamic law. SAMA has from time to time financed one-half of government debt. From 1962 to 1983, the governments earning grew in surplus, and all the debt was repaid. Whenever the government needed SAMA bolstered government reserves. B. In 1966 a major banking control law clarified and strengthened SAMA's role in regulating the banking system. Foreign banks submitted application for license. SAMA sent its recommendation to the ministry of finance. The ministry set conditions for granting licenses to foreign banks. C. Saudi companies introduced a great deal of regulation abiding with Sharia law. This law also defined requirements against deposits. There were several restriction to abide by SAMA's implementation of monetary policy. 2. The Saudi financial system A. The Saudi financial system had three autonomous government institutions. They were the pension fund, the general organization of Saudi insurance, and Saudi fund for development. These government instit...

The Greek Polis

The polis is the city-state of ancient Greece where Greek politics, commerce and creative culture was centered. It developed from the Archaic period and is considered the ancestor of the terms city, state and citizenship. It was the central urban area that may have some degree of rule over the surrounding countryside. Among the Greek polis were Athens, Thebes and Sparta which were considered independent with each other. The polis is typically bounded around a citadel or an acropolis and would essentially have a market called the agora and would usually have one or more places of worship and a gymnasium. The Greeks considered the polis or city-states as a unique institution because in ancient times, it was the first gathering of people wherein the rulers or officials were elected by the people and laws were passed. Although the polis was composed of several tribes or demes, it was ruled not by a particular king or oligarchy but rather considered as a political entity ruled by the inhabitants or citizens themselves. To some extent, there is a high degree of citizen involvement and participation in the management of the public organization in a polis. The polis was considered as the most significant contribution in politics of the Greek people. The fundamental difference between the ancient Greek polis and a modern day country city like that of Western Europe or America is that the Greeks did not consider the polis as under any sovereignty while a modern age city is necessarily under a sovereign country or state. In other words, the polis is an autonomous entity while a modern city is positively positioned in a sovereign state. The polis is not identified by territory but rather by the citizenship of its people whereas a modern city is generally set apart by land area and population. Their similarities lie greatly in them being both centers of commerce, artistry, politics and the melting pot of state development.

Regional distribution centre report

Introduction: My company that I work for is currently expanding and has recently opened several new superstores on the outskirts of large urban areas. The existing distribution centers, which supply stores, are finding it difficult to meet the increased demand. So, the company has decided to set up a new distribution centre in an area, which has a mixture of old supermarkets and new superstores. Mileage: Mileage is one of the most important factors on this survey and research that I am doing and Prime thing that I have to look for is how far it is from the motorways and the supermarkets. In my opinion location C has got the best mileage and the one with the least mileage is D. Accessibility: Accessibility is not as important as mileage but it is still a convenient factor of the research and has to be considered very deeply. If the place has plenty of doors and garages, then it would be more convenient than a store with less doors and garages for the trucks or lorries to take our stock to the superstore. The best one again is location C and the worst one again is location D. But we have to remember that some locations are better at other things than at some things. Suitable Land: Land is vital for the company because we need lots of space for our staff to store the products from our regional distribution centre.6 acres would be good enough for us. The location that strike me first location A, as it has 20 acres of land which is over three times more of what we actually wanted. And the worst one was location C. In the factors above us, it was the best and now it is the worst for the land. And not everything can be perfect, you cant have a location which scores five out of five for everything. Cost Of Land: Now we are coming to the financial side of the operation and it is the thing that we have to consider before buying the location that we decide. Price is an enormous factor for everything not just regional distribution centers. The best priced location was location D, this was the location which had scored poor ratings in all the other factors and now has appeared top for the price. And the worst priced location was location C. This location did really well with the mileage and the accessibility but is very pricey. It would not be a good idea to buy this location if the company does not have a large budget. Setting Up Cost: Well, some locations do not need much work but some do and this could cost the company quite a bit. The location that really well was location E which has not exactly had that good factors in it. And the location that has done really badly or the worst was location B, this location is out of the picture as it was not good use to us. Labour Force: Where ever a business moves and there are job opportunities people would try really hard to get one so that's why we are considering to move to a place where there is a high unemployment and, the people should be skilled or have had experiences and we should be able to offer them low salaries. If we can make this happen, this could have us thousands of pounds every year. The location that has a good labour force is location D. This is truly the best location so I am considering of buying this. And the location that has done not so well in this important category was location A. Evaluation: In my opinion and my research I think that Location C is the best location because the cost of the land is really good, there is plenty of cheap labour, it scored top on the environmental factors and it almost came top in setting up. But two main things that it lacks is the mileage as it is very far from motorways and the accessibility is not good at all.

Tortuous, Torturous, and Tortious

Tortuous, Torturous, and Tortious Tortuous, Torturous, and Tortious Tortuous, Torturous, and Tortious By Maeve Maddox All three words, tortuous, torturous, and tortious derive from the Latin verb torquere, â€Å"to twist.† Pronunciation: tortuous [TOR-choo-us] torturous [TOR-chur-us] tortious [TOR-shus] The Latin adjective tortuosus means â€Å"full of twists or turns.† The Latin noun tortura means twisting, but it can also mean torment or torture; torturing a person often involves twisting body parts. The English adjective tortuous retains the sense of twisting or winding, whereas the adjective torturous conveys the sense of painful. A winding road is said to be tortuous. In medicine, a â€Å"tortuous bowel† is a bowel that has several bends and twists in it. A â€Å"tortuous aorta† has anatomical abnormalities that cause it to be distorted in the path it takes. The adjective torturous means â€Å"involving or causing pain.† Both words are used literally and figuratively: tortuous We decided to take the surface route instead, even if it meant a much longer drive along narrow and tortuous roads. The singer has followed a fairly  tortuous path to fame, with many ups and downs in his personal life, before achieving his current success. torturous Urge Massachusetts To End Torturous Shock Treatment of Disabled Individuals Why women love  high heels, no matter how  torturous  they are The plot is based on a silly premise, is childishly developed and is not worth reading, even if you could muddle through the torturous use of the English language. Not surprisingly, inattentive writers tend to mix up these two adjectives: I have a torturous bowel, what are my options for colon screening? The road twisted and turned its torturous way up and down mountains, through gorges, over streams to the little Italian villages. a campaign to highlight the tortuous treatment inflicted upon animals in cosmetic lab testing Hunger strikers in the SHU are calling for an end to hostilities between racial groups within prisons all the way down to county jails in order to call attention to inhuman and tortuous treatment of prisoners. The word, tortious, is the adjective that goes with the legal term tort. In the case of tort, the sense of twisted applies to behavior. In U.S. law, a tort may be defined as â€Å"a negligent or intentional injury against a person or property, with the exception of breach of contract.† Here’s an example of the use of tortious: [The Court ruled that] the managing member and president of an LLC could not be liable for tortious interference with contract for firing the Plaintiff. Want to improve your English in five minutes a day? Get a subscription and start receiving our writing tips and exercises daily! Keep learning! Browse the Vocabulary category, check our popular posts, or choose a related post below:When to Capitalize Animal and Plant NamesHow to Play HQ Words: Cheats, Tips and TricksHow Long Should a Synopsis Be?

Data mining titanic dataset Essays

Data mining titanic dataset Essays Data mining titanic dataset Paper Data mining titanic dataset Paper Titanic dataset Submitted by: Submission date 8/1/2013 Declaration Author: Contents Dated: 29/12/2012 The database corresponds to the sinking of the titanic on April the 15th 1912. It is part of a database containing the passengers and crew who were aboard the ship, and various attributes correlating to them. The purpose of this task is to apply the methodology of CRISP-DMS and follow the phases and tasks of this model. Using the classification method in rapid miner and both the decision tree and INN algorithms, I will create a training model and try apply the class survived or didnt survive. If I apply a decision tree to the dataset as it is, I get a prediction rate of 78%. I will try various techniques throughout this report to increase the overall prediction rate. Data mining objectives: I would like to explore the pre conceived ideas I have about the sinking of the titanic, and prove if they are correct. Was there a majority of 3rd class passengers who died? What was the ratio of passengers who died, male or female? Did the location of cabins make a difference as to who survived? Did chivalry ring through and did Women and children first actually happen? Data Understanding: Describe the data: Figure Class label: Survive (1 or O) 1 = survived, died. Type = Binomial. Total: 891. Survived: 342, Died: 549 Attributes: 10 attributes 891 rows The dataset have primarily a categorical type of attribute so there is low information content. This might indicate a decision tree would be an appropriate model to use. I can see that the number of rows in the dataset is indeed 10 to 20 times the number of columns, so the number of instances is adequate. There doesnt seem to be any inconsistencys in the data. Pappas: 1st, 2nd, or 3rd class. Type: polynomial. Categorical, 3rd class: 491, 2nd class: 216, 1st class: 184 0 missing Name: Name of Sex: Male, female. Type: binomial. Male: 577, Female: 314 0 missing Age: from 0. 420 to 80. Average age: 29, standard deviation of 14+-, Max was 80. 177 missing Sibs (Siblings on board): Type: integer. Average less than 1, highest 8. This suggested an outlier, but on inspection the names where there were 8 siblings corresponded. (The name was sage, 3rd class passengers, all died. ) O missing Parch: number of parents, children onboard. Type: integer. Average: 0. 3, deviation 0. 8. Max was 6. O missing Ticket: ticket number. Type: polynomial. To me these ticket numbers seem quite random and my first inclination is to discard them. O missing Fare: Cost of ticket. Type: real. Average: 32, deviation +- 49. Maximum 512. There seems to be quite a disparity in the range of values here. Three tickets cost 512, outliers? O missing Cabin: cabin numbers. Type: polynomial. 687 missing From looking at this data I think I can discount one of my initial questions about cabin numbers. If there was more data it might be an interesting factor as regards cabin locations and survival. As it stands the quality of the data is not good, there are Just o many missing entries. I. E. Greater than 40%. So I will delete (filter out) the cabin attribute from the dataset. The age attribute could cause a problem with the amount of fields missing. There are too many to delete. I might use the average of all ages to fill in the blanks. Explore the data: From an initial exploration of the data, I was able to look at various plots and found some interesting results. I have tried to keep my findings to my initial questions that I wanted answered. Was there a majority of 3rd class passengers who died? You can tell from Figure 2 that this was true. This graph Just shows survival by class, 3rd class fairing the worst. Again this is shown with a scatter plot but with the added attribute sex. You can see on the female side of the first class passengers, only a few died. Interestingly it shows that it was mostly male 3rd class passengers who perished, and it is demonstrated that more males then females died. There is a clear division in classes demonstrated. This graph answers my other question. What was the ratio of passengers who died, male or female? From this we can see that mainly males did not survive. Although there were more males on board (577), about 460 perished. From the females (314), about 235 survived. Another attribute that needs attention is the age category. I wanted to find out if the women and children first policy was adhered to, but there are 177 missing age values. This is going to complicate my results on this. From leaving the 177 as they are, I get this graph: but this is not conclusive in Figure 5. I thought that the fare price might indicate a childrens price and therefore allow me to fill in an age, but the fare price doesnt seem to have much pattern. Another idea I thought might help would be to look at the names of passengers, I. . Miss might signify a lower age. (In 1912 the average age of marriage was 22, so anyone with title miss could have an age less than 22. ) Names which include master might indicate a young age as well. Figure 5 also indicates possible outliers on the right hand side. From this graph I could easily see the breakdown of the different class of passenger and where they embarked from. It is obvious that Southampton had the largest number of passengers get on board. Question had the highest proportion of 3rd class passengers compared to 2nd and 1st class at that port, and its also interesting o note that this was an Irish port. This graph further explores the port of embankment and shows the survival rate from each, as well as the different classes. To me it seems that the majority of 3rd class passengers were lost who came from Southampton port, although they did have the highest amount of 3rd class passengers. A closer look at Southampton port. The majority who didnt survive were 3rd class (blue), also noted is the handful of 1st class passengers (green) who died, yet Southampton had the highest number of 1st class passengers to board. See figure 6. Verify data quality There were a number of missing values in the dataset. The highest amount of missing data came from the cabin attribute. As it is higher than 45% (687 missing) I decided to filter out this column. There are also 177 missing values from the age attribute. This amount of missing data is again too large a percentage to ignore and needs to be filled in. I can see that the dataset contains less than 1000 rows, so I think that sampling will not have to be performed. There doesnt seem to be any inconsistencys in the data. There are still 2 missing pieces of information from the embankment attribute. I see that they are 1st class passengers so from my graph on embankment I think I can put her embankment from Churchgoer. The other passenger is a George Nelson, which I will add to Southampton. I decided to filter out names also. I dont see how it can help in the dataset. It may have helped with age, by looking at the title as I said, but for this I Just used the average age to replace the missing values. Another approach to filling in the missing age fields might be linear regression. Remove possible outliers? I can see that there may be some outliers. For instance in the fares attribute, there re three tickets which cost 512 when the average is 32. They were first class tickets, but the difference is huge. Data Preparation: Here is the result of using x validation on the dataset before any data preparation has taken place. I will now sort out the problem of 667 cabin numbers missing. With it being higher than 40%, Vive decided to delete the attribute entirely. Vive also deleted the name attribute, as I dont see how it will help. By deleting cabin, name and ticket, here is the result I get: I replaced the missing age fields with the average of ages, this increased the accuracy lightly and gave these results with x validation: I used detect outliers and picked the top ten and then filtered them out. This gave this result: The class recall for survived has not improved much. Increasing the number of neighbors in the detect outliers operator improved things, also limiting the filter to deleting 5 made a better accuracy. I decided to use specified binning for the ages and broke the ages into three bins. For children aged up to 13, middle aged from 13 to 45, and older from 45 to 80. I tried different age ranges and found that these ranges yielded the best results. It did increase the accuracy. I also used binning for the fares, splitting them into low, mid, and high which also improved results on the confusion matrix. I used detect outlier to find the ten most obvious outliers, and then used a filter to get rid of them. I have decided to remove cabin from the dataset, and also there are 177 missing age values which I have tried various approaches in changing. I changed the ages to the average age, but this gives a spike in the number of ages 29. 7. Example of average age problem: Modeling: I tried to implement both the decision tree and inn algorithms, seeing as the dataset as primarily categorical. I found that inn yielded the best results regarding accuracy. This was set at k=l . The accuracy was not great at 73%. The parameter of K is too small and may be influenced by noise. INN: 5 worked the best at 82. 38%. This seems to be the optimal value for k, and the distance is set right. Class precision is about even on each class. Decision tree: The decision tree algorithm didnt give me as much accuracy, and I found that turning off pre pruning gave me a better accuracy. From the decision tree, the age binning seemed to predict middle aged males (13 to 45) with a low fare well. The class recall for survived was not great at 67. 85%. Generate Test Design I used x-validation to perform cross validation on the data. I initially used 20 for the number of validations, but then found 25 achieved a better result. I used the apply model and performance operators as these are best used for classification tasks and work well with the polynomial attribute. This then presented me with a confusion matrix where I could measure the accuracy of my model by comparing the accuracy, recall and precision. I found that throughout my various testing of operators and valuating the confusion matrix, raising the class recall on true 1 (survived) most difficult. After all my efforts I managed to raise it to 73. 6%. I. E. 91 were incorrectly predicted as surviving. Figure Final result Workspace: From my initial objectives I was able to determine the answers using rapidness. I wanted to find out if those who perished were in the majority 3rd class passengers. I found this to be true, and also that the majority who died were male 3rd class passengers. Female passengers and children fared better than most which leads me t o believe that the rule of women and children first applied. This may have been sighted more to the first and second class passengers as demonstrated in Figure 3. Because the dataset had such a large amount of data missing concerning age, this was more difficult to determine. I found the embarked attribute to be interesting in the graphs I could generate from it. There seemed to be a large number of 3rd class passengers who died that had embarked from Southampton. If all the cabin numbers were present I wonder if Southampton 3rd class passengers had cabins close to where the iceberg hit? Did this have a bearing on their survival? From the different algorithms I used I found that Inn yielded the better results.

Career Advice From a Professional Stage Makeup Artist

Career Advice From a Professional Stage Makeup Artist What does it take to become a makeup artist for the theater? Well, when seeking career advice, it’s best to contact an expert. Judi Lewin has worked as a professional hair and makeup artist for over thirty-five years. We caught up with her during a film shoot to ask her some important questions about her career. Although she has been working in the movie and television industry, we asked her if she would talk about her extensive career as a makeup designer for the legitimate stage. Getting Started While living in her hometown of Toronto, Judi had a natural talent for makeup. Whether she was applying her makeup skills on herself or her friends, she had a knack for making people look their best. It wasn’t long before many of her â€Å"theater friends† requested her help. Soon, she found herself transforming the faces- and hairstyles- of actors. Her first professional experiences took place at local theaters in Toronto, with her earliest productions being community theater musicals, such as A Chorus Line, My Fair Lady, and Annie. Her love for her craft motivated her to take on show after show and, after a couple of years of amateur productions, she began to work on a professional basis. Judi has since worked in such theaters as the Canadian Opera Company, the Santa Barbara Civic Light Opera, and the Pasadena Playhouse. Recently, she worked as the Key Makeup Designer for the new show, Happy Days- A New Musical, which was based upon the classic hit television show. Methods After accepting a job from a director, Judi reads through the script. After reading it through, she reads it a second time and makes notes, listing the characters and paying close attention to the setting. Then, she works with the director to find out â€Å"what specifically the director wants to see.† She does everything she can to learn about the director’s vision in advance before she begins the design process. Judi explained that in smaller, lower-budgeted productions, the director’s input might be all that she requires. However, the bigger the show, the more likely producers, executives, and others will want to express their opinion- and that’s when the business of the makeup artist can really get complicated. Do Your Research Judi recommends that up-and-coming makeup designers constantly gather historical images- this means finding photographs, illustrations, and other images from any era in the past to serve as inspiration or source material. Also, try to find information and visual material from and about as many different countries and cultures as possible. By gathering these images from the internet, old books, and second-hand stores, a makeup artist will increase their knowledge of different looks, eras, and lifestyles, from â€Å"aristocracy to someone who lives in the slums.† Working with Actors It is common for actors to feel insecure- sometimes about their looks, but sometimes about their performance. Occasionally, Judi has worked with a few prima donnas, but she has a weapon against rudeness. â€Å"Kill them with kindness,† she says. â€Å"Be lovely and polite.† She also mentioned that because so much time is spent together, actors often reveal personal details to their makeup artists. Her rule about that is, â€Å"Nothing leaves the makeup room.† Career Advice According to Judi, here are two reliable ways to become a professional makeup artist: Attend a Makeup School (She recommends The Makeup Designory in Burbank, California and New York City).Intern with a professional who works in both theater and television (That way the intern will learn two very different techniques). Benefits of the Theater One of the reasons Judi loves working in live theater so much is simply because it’s alive! â€Å"In theater, things need to happen now!† Her stage experience is a sharp contrast to her film work, in which the general rule is to work on the makeup until it is perfect. During a musical, the makeup artist has a limited amount of time to get things right- this makes the experience both challenging and fun. The excitement of live theater invigorates Judi Lewin and her fellow artists.

Informative presentation, persuasive PowerPoint Presentation

Informative , persuasive - PowerPoint Presentation Example Training is a sort of meeting that draws individuals with the same agenda or line of work together giving them an opportunity to mingle and share their experiences as well as their thought of how to tackle the challenges. Training is an integral part for a company seeking to climb the ladder and be successful in their area of business. It holds the key to unlocking unrealized talents and untapped skills while at the same time improving the environmental conditions for the business as effects of training come into effect. While we do conquer that training is essential, it is important to outline some of the reasons why companies and businesses insist on training its people. First off, it helps employees to understand the job market as it is in its present state, to move along with the trends in the market and this helps the business appeal to new share holders and financiers, to be a force to recon with for potential employees and for employees to learn how to conduct themselves while on official duty. Communication cuts deep within the organization and not just the ordinary exchange of pleasantries and this is also taught while in training. Training programs also focus on getting the best all round employees and imparting a different skill set so that they can handle duties assigned to them in future which they may have not necessarily been prepared for while at the university/college. Training programs follow a strict schedule and timeline. It has to be planned for in advance and communicated to the people set to undergo it. Other things that need to be done include pairing up experts with trainees where they learn through apprenticeship. This means that the expert will work on a project while the trainee takes notes and this goes on for about a week before they are given the go ahead to attempt and do what they have been witnessing. This, however, lies with the complexity of the field of operation in