The Future of Drinking Water Essay Example | Topics and Well Written Essays - 1500 words - 1

The Future of Drinking Water - Essay Example The primary sector comprises the primary production of raw materials or food, while the secondary sector concerns on the processing or refining of the produced goods or raw materials. As to the distribution of the finished products, the whole concern of it lies on the tertiary sector. However, all of these sectors require the availability or abundance of quality drinking water for them to function smoothly as possible. For instance, a quality and abundant drinking water source is necessary to ensure the sustainability of the basic needs in life, and consistency in the achievement of human health, and other related human needs. Furthermore, the availability of quality drinking water is necessary in order to successfully produce food or raw materials for the production of other products in the secondary sector. For example, a quality drinking water is necessary in the livestock industry so that there will be quality produced products that can be readily used in the processing industry. The processing industry on the other hand would require availability of clean and excellent water supply as integral component of the production of high quality standard processed foods. The above illustrations just simply depict the importance of drinking water in the production of raw materials and down to the processing and production of secondary product offerings.

Workplace romance Thesis Example | Topics and Well Written Essays - 3750 words

Workplace romance - Thesis Example Some coworkers may take workplace to be of no interest to them even as others may see their love affair responded to with a high level of defense. Companies and organizations are confused in respect work place romance since they ought to give a free and conducive environment for workers as opposed supposed to creating fear and tension among workers. Another subject of confusion in relation o workplace romance relates to the right organ or department to handle such cases. Some managers feel that such relationships could affect the productivity of the company while others feel that workers are citizens who have rights to socialize in whichever way they like even in the organizational setup. This has seen companies in situations where they have been unable to come with approaches and policies that address office dating. It has become challenging when people from different departments fall in love, for example, the administrator having a love affair with a junior officer in a certain dep artment. Secondly, it has been of less effect when the administrator develops a romantic relationship with his or her secretary, when the same administrator is the one who is in charge of implementing the laid down policies. This paper will discuss different perspectives and opinions of different authors toward work place romance. According to Bytautas, Klenin, Marinescu and Appelbaum (2007), employers have experienced work place relationships for a long time. They indicate that employers are in a position to realize that workers have love relationships with another employee. Bytautas et al. indicate that companies have realized the effects that come with work place relationships. It is has negative effects on production since the respective couples tend to spend their time together thinking of each other instead of concentrating on the delivery of service. Bytautas et al. suggest that work place romance has been a result of an

Industrialization after the Civil War Assignment

Industrialization after the Civil War - Assignment Example As the report stresses the exhaustion of the war, ironically, created an atmosphere of peace. This gave vent to the entrepreneurial aspirations of the citizens. But the opportunities were not uniform or equitable. The privileged White Anglo-Saxon Protestant (WASP) community, which owned most of the properties, continued its economic and social dominance. It was this preordained economic privilege for the community that would give rise to such great industrialists as Henry Ford, John Rockefeller, etc. These luminaries were the first great capitalists of the country. Indeed they would develop great companies like Standard Oil and Ford Motor Company, which would supersede in scale and revenues even some older establishments in Europe.From this paper it is clear that the rapid pace of industrial growth had a major socio-cultural impact. It affected the life of the average working American in many different ways. The most important change they witnesses is the transformation of the mode o f livelihood from independent small-farmers to wage-earning factory workers. In fact, the late 19th century labor press was saturated with discussions on such fundamental changes. This transformation was not a smooth and swift process at all. Most small farmers put their independence ahead of job security. They moreover perceived receiving wages from a capitalist master as a slur on their dignity.  

The US foreign policy from 1945-1991 Case Study

The US foreign policy from 1945-1991 - Case Study Example This research will begin with the statement that the term Cold War was coined by Pulitzer prize-winning publicist Herbert Bayard Swope and refers to the intense feelings of hostility and the profound international tension and struggle for power between the the USA and then the USSR, both of which headed formidable alliances i.e. Allies and Russian satellites, respectively.   It started in 1945 and ended in 1989.   It was ‘cold’ because the relations between the two superpowers were icy but never came to a flashpoint verging on a shooting war.   The conflict involved political rivalry and advantage as well as an upper hand in the balance of power. The conflict was replete with clashes of competing ideologies i.e. between the democratic capitalist system of America and its allies and the socialist/communist system of the USSR and the satellite nations comprising the nations of the Warsaw Pact. The rivalry consisted of propaganda, military alliances, atomic arms devel opment, reconstruction programs and the rivalry to win the hearts and minds of the neutral countries, most especially the third world countries which might provide military bases, natural resources, and markets. As early as 1929, the USA and the USSR had kept each other at bay and at arms' length despite differences in political ideologies. This estrangement was intensified by the USA's policy of isolationism in the 1930's which muted whatever feelings of mistrust they had for each other. But relations were improved when the USA and the Soviet Union unexpectedly found themselves fighting side by side against fascist Germany in World War II. The warm relations, however, rapidly dissolved when midway through the war, the USA realized that the USSR was determined to retrieve all the territories in Eastern Europe that it lost prior to World War I and these are eastern Poland, Latvia, Lithuania, Estonia and parts of Romania and Finland. It became clear to USA that Stalin in his paranoid fear of Germany and its military might want all of Eastern Europe to serve as its buffer states and shields from German aggression. President Roosevelt stressed that USA as adherent of the principle of self-determination wanted these states to choose for themselves the kind of government they want. The other western allies looked at Poland as strategically the bulwark of Europe which when falling into the hands of the Soviets would open the floodgates of Russians invading Western Europe. The US fears were vindicated during the 1943 Teheran Conference when Stalin finally laid his cards on the table: that he expected territorial concessions in the form of the states of Eastern Europe. Justice, to Stalin demanded that Russia be recompensed from the deaths of 16 million Russians and the massive destruction and damage to Soviet properties and machinery during the first three years of war. It dawned on President Roosevelt that reality, tradition and history dictated that self-determinatio n among the eastern European states would be anathema to Russia as any freely elected government in Eastern Europe would be hostile to Soviet ideology. Both the Yalta Conference and the Potsdam Conference in 1945 further strained the relations almost to the breaking point as Russia treacherously set up a communist government in Lublin, Poland followed by its undermining of elected non-communist governments in Czechoslovakia and Hungary. It was inevitable that the Cold War ensued in early 1946 after Berlin and Germany were divided into 4 factions i.e. Russian, American, British and French.

Symbolic Learning Methods Essay Example for Free

Symbolic Learning Methods Essay Abstract In this paper, performance of symbolic learning algorithms and neural learning algorithms on different kinds of datasets has been evaluated. Experimental results on the datasets indicate that in the absence of noise, the performances of symbolic and neural learning methods were comparable in most of the cases. For datasets containing only symbolic attributes, in the presence of noise, the performance of neural learning methods was superior to symbolic learning methods. But for datasets containing mixed attributes (few numeric and few nominal), the recent versions of the symbolic learning algorithms performed better when noise was introduced into the datasets. 1. Introduction The problem most often addressed by both neural network and symbolic learning systems is the inductive acquisition of concepts from examples [1]. This problem can be briefly defined as follows: given descriptions of a set of examples each labeled as belonging to a particular class, determine a procedure for correctly assigning new examples to these classes. In the neural network literature, this problem is frequently referred to as supervised or associative learning. For supervised learning, both the symbolic and neural learning methods require the same input data, which is a set of classified examples represented as feature vectors. The performance of both types of learning systems is evaluated by testing how well these systems can accurately classify new examples. Symbolic learning algorithms have been tested on problems ranging from soybean disease diagnosis [2] to classifying chess end games [3]. Neural learning algorithms have been tested on problems ranging from converting text to speech [4] to evaluating moves in backgammon [5]. In this paper, the current problem is to do a comparative evaluation of the performances of the symbolic learning methods which use decision trees such as ID3 [6] and its revised versions like C4.5 [7] against neural learning methods like Multilayer perceptrons [8] which implements a feed-forward neural network with error back propagation. Since the late 1980s, several studies have been done that compared the performance of symbolic learning approaches to the neural network techniques. Fisher and McKusick [9] compared ID3 and Backpropagation on the basis of both prediction accuracy and the length of training. According to their conclusions, Backpropagation attained a slightly higher accuracy. Mooney et al., [10] found that ID3 was faster than a Backpropagation network, but the Backpropagation network was more adaptive to noisy data sets. Shavlik et al., [1] compared ID3 algorithm with perceptron and backpropagation neural learning algorithms. They found that in all cases, backpropagation took much longer to train but the accuracies varied slightly depending on the type of dataset. Besides accuracy and learning time, this paper investigated three additional aspects of empirical learning, namely, the dependence on the amount of training data, the ability to handle imperfect data of various types and the ability to utilize distributed output encodings. Depending upon the type of datasets they worked on, some authors claimed that symbolic learning methods were quite superior to neural nets while some others claimed that accuracies predicted by neural nets were far better than symbolic learning methods. The hypothesis being made is that in case of noise free data, ID3 gives faster results whose accuracy will be comparable to that of back propagation techniques. But in case of noisy data, neural networks will perform better than ID3 though the time taken will be more in case of neural networks. Also, in the case of noisy data, performance of C4.5 and neural nets will be comparable since C4.5 too is resistant to noise to an extent due to pruning. 2. Symbolic Learning Methods In ID3, the system constructs a decision tree from a set of training objects. At each node of the tree the training objects are partitioned by their value along a single attribute. An information theoretic measure is used to select the attribute whose values improve prediction of class membership above the accuracy expected from a random guess. The training set is recursively decomposed in this manner until no remaining attribute improves prediction in a statistically significant manner when the confidence factor is supplied by the user. So, ID3 method uses Information Gain heuristic which is based on Shannon’s entropy to build efficient decision trees. But one dis advantage with ID3 is that it overfits the training data. So, it gives rise to decision trees which are too specific and hence this approach is not noise resistant when tested on novel examples. Another disadvantage is that it cannot deal with missing attributes and requires all attributes to have nominal values. C4.5 is an improved version of ID3 which prevents over-fitting of training data by pruning the decision tree when required, thus making it more noise resistant. 3. Neural Network Learning Methods Multilayer perceptron is a layered network comprising of input nodes, hidden nodes and output nodes [11]. The error values are back propagated from the output nodes to the input nodes via the hidden nodes. Considerable time is required to build a neural network but once it is done, classification is quite fast. Neural networks are robust to noisy data as long as too many epochs are not considered since they do not overfit the training data. 4. Evaluation Design For the evaluation purposes, a free and popular software tool called Weka (Waikato Environment for Knowledge Acquisition) is used. This software has the implementations of several machine learning algorithms made easily accessible to the user with the help of graphical user interfaces. The training and the test datasets have been taken from the UCI machine learning repository. Two different types of datasets will be used for the evaluation purposes. One type of datasets contain only symbolic attributes (Symbolic Datasets) and the other type contain mixed attributes (Numeric Datasets). Performance of the different learning methods will be evaluated using the original datasets which do not contain any noise and after introducing noise into them. Noise is introduced in the class attributes of the datasets by using the ‘AddNoise’ filter option in Weka which adds the specified percentage of noise randomly into the datasets. Symbolic Datasets are those which contain only symbolic attributes. Symbolic learning methods like ID3 and its recent developments can be run only on datasets where all the attributes are nominal. In Weka, these nominal attributes are automatically converted to numeric ones for neural network learning methods. So, preprocessing is not required in this type of datasets. Numeric Datasets are those which contain few nominal and few numeric attributes. Since symbolic learning methods like ID3 and its recent developments can be run only on datasets where all the attributes are nominal, these datasets first need to be preprocessed. A ‘Discretize’ filter option available in Weka is used to discretize all the non-symbolic attribute values into individual intervals so that each attribute can now be treated as a symbolic one. Initially, the entire data being considered is randomized. Two types of evaluation techniques are being used to analyze the data. (a) Percentage Split: In general, the data will be split up randomly into training data and test data. In the experiments conducted, the data will be split such that training data comprises 66% of the entire data and the rest is used for testing. (b) K-fold Cross-validation: In general, the data is split into k disjoint subsets and one of it is used as testing data and the rest of them are used as training data. This is continued till every subset has been used once as a testing dataset. In the experiments conducted, 5-fold cross validation was done. 5. Experimental Results Experiments were conducted on two symbolic datasets and two numeric datasets. The two symbolic datasets are tic-tac-toe and chess. The two numeric datasets are segment and teacher’s assistant evaluation (tae). DataSet 1 : TIC-TAC-TOE (a) 5-fold cross validation (i)Without any noise: Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 (ii) Percentage of noisy data = 10% Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 Time to build 0.03 6.16 0.02 0.06 0.01 % correct 67.4322 81.8372 75.8873 73.5908 71.2944 % incorrect 28.0793 18.1628 24.1127 26.4092 28.7056 % not classified 4.4885 0 0 0 0 Time to build 0.06 6.35 0.06 0.01 0.02 % correct 86.1169 97.4948 85.8038 87.5783 83.1942 % incorrect 11.691 2.5052 14.1962 12.4217 16.8058 % not classified 2.1921 0 0 0 0 (b) Percentage split with training data being 66% and the rest is testing data (i)Without Noise: Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 (ii)Percentage of Noisy data = 10% Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 Time to build 0.05 6.5 0.01 0.01 0.02 % correct 85.5828 97.546 83.1288 88.0368 82.2086 % incorrect 11.0429 2.454 16.8712 11.9632 17.7914 % not classified 3.3742 0 0 0 0 Time to build 0.04 6.15 0.02 0.02 0.01 % correct 68.4049 80.6748 73.9264 72.3926 71.4724 % incorrect 28.2209 19.3252 26.0736 27.6074 28.5276 % not classified 3.3742 0 0 0 0 For the tic-tac-toe dataset, in the presence of noise, neural nets had better prediction accuracies than all the other algorithms as expected. Though C4.5 gives better accuracy than ID3, its accuracy is still lower in comparison to Neural Nets. If the pruning factor (confidence factor was lowered) was increased, the prediction accuracies of C4.5 dropped a little. But in the absence of noise, the performances of ID3 and Multilayer Perceptron should have been comparable. But the performance of Multilayer Perceptron is quite superior to ID3. DataSet 2 : CHESS (a) 5-fold cross validation (i)Without any noise: Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 (ii) Percentage of noisy data = 10% Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 Time to build 0.36 47.75 0.21 0.18 0.19 % correct 81.1952 86.796 89.0488 84.6683 88.4856 % incorrect 18.8048 13.204 10.9512 15.3317 11.5144 % not classified 0 0 0 0 0 Time to build 0.21 47.67 0.15 0.05 0.1 % correct 99.562 97.4656 99.3742 99.3116 99.2178 % incorrect 0.438 2.5344 0.6258 0.6884 0.7822 % not classified 0 0 0 0 0 (b) Percentage split with training data being 66% and the rest is testing data (i)Without Noise: Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 (ii)Percentage of Noisy data = 10% Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 Time to build 0.33 41.73 0.24 0.19 0.19 % correct 80.1288 85.7406 87.5805 82.6127 87.6725 % incorrect 19.8712 14.2594 12.4195 17.3873 12.3275 % not classified 0 0 0 0 0 Time to build 0.13 43.55 0.06 0.06 0.08 % correct 99.448 97.1481 99.08 98.988 99.08 % incorrect 0.552 2.8519 0.92 1.012 0.92 % not classified 0 0 0 0 0 For the chess dataset, in the absence of noise, the performance of ID3 is better than that of Multilayer perceptron and takes lesser time. For the noisy data, back propagation predicts better accuracies than that of ID3 as expected, but the performance of C4.5 is slightly higher than back propagation. The reason for this could be that the feature space in this dataset is more relevant. So, C4.5 builds a tree and prunes it to get a more efficient tree. DataSet 3 : SEGMENT (a) 5-fold cross validation (i) Without any noise: Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 (ii) Percentage of noisy data = 10% Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 Time to build 0.07 9.64 0.04 0.04 0.03 % correct 68.9333 80.8667 81.2667 79.6 80.5333 % incorrect 21.3333 19.1333 18.7333 20.4 19.4667 % not classified 9.7333 0 0 0 0 Time to build 0.05 10.3 0.02 0.23 0.12 % correct 88.0667 90.6 91.6 94 94.3333 % incorrect 5.2 9.4 8.4 6 5.6667 % not classified 6.7333 0 0 0 0 (b) Percentage split with training data being 66% and the rest is testing data (i) Without Noise: Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 (ii) Percentage of Noisy data = 10% Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 Time to build 0.07 11.73 0.03 0.04 0.03 % correct 72.9412 82.549 82.1569 82.549 81.3725 % incorrect 19.6078 17.451 17.8431 17.451 18.6275 % not classified 7.451 0 0 0 0 Time to build 0.06 9.87 0.03 0.02 0.03 % correct 89.8039 87.6471 92.1569 93.7255 90.1961 % incorrect 4.1176 12.3529 7.8431 6.2745 9.8039 % not classified 6.0784 0 0 0 0 Segment, being a numeric dataset, all the attribute values had to be discretized before running the algorithms. In the absence of noise, ID3 performs slightly better than back propagation and the performance of J48 (implementation of C4.5 in Weka) is much better than ID3 and backpropagation. But a very interesting observation was found. In the absence of noise, the performance of an unpruned tree generated by C4.5 was quite superior to the rest. In the presence of noise, the performances of back propagation and C4.5 were comparable. DataSet 4 : TAE (a) 5-fold cross validation (i) Without any noise: Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 (ii) Percentage of noisy data = 10% Time to % % build correct incorrect ID3 0.02 53.6424 37.0861 Multilayer Perceptron 0.16 38.4106 61.5894 J48 0.02 52.9801 47.0199 C4.5 unpruned 0.01 56.2914 43.7086 C4.5 confidence factor = 0.1 0.01 54.3046 45.6954 (b) Percentage split with training data being 66% and the rest is testing data (i) Without Noise: Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 (ii) Percentage of Noisy data = 10% Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 Time to build 0.01 0.17 0.01 0.01 0.01 % correct 38.4615 44.2308 44.2308 50 44.2308 % incorrect 40.3846 55.7692 55.7692 50 55.7692 % not classified 21.1538 0 0 0 0 Time to build 0.02 2.23 0.03 0.02 0.01 % correct 44.2308 57.6923 51.9231 55.7692 42.3077 % incorrect 34.6154 42.3077 48.0769 44.2308 57.6923 % not classified 21.1538 0 0 0 0 Classifiers % not classified 0 0 0 0 0 Time to build 0.02 0.18 0.02 0.01 0.01 % correct 54.3046 54.9669 48.3444 50.9934 47.0199 % incorrect 35.0993 45.0331 51.6556 49.0066 52.9801 % not classified 10.596 0 0 0 0 TAE, being a numeric dataset, its attribute values had to be discretized too before running the algorithms. But after observing the results, it is very clear that the random discretization provided by Weka did not generate good intervals due to which the overall accuracy predicted by all the methods is quite poor. Again, interestingly an unpruned tree built by C4.5 seems to give high prediction accuracies relative to the rest in most of the cases. In this case, for cross-validation approach and noisy data, surprisingly the performance of back-propagation was very poor. One reason for this could be that only few epochs of the training data were run to build the neural network. In the absence of noise, accuracy prediction of Multilayer perceptron was either comparable or greater than that of ID3. 6. Conclusion No single machine learning algorithm can be considered superior to the rest. The performance of each algorithm depends on what type of dataset is being considered, whether the f eature space is relevant and whether the data contains noise. In the absence of noise, in some cases, the performance of ID3 was comparable or sometimes better than back-propagation and was faster but in some cases Multilayer perceptron performed better. When noisy datasets were considered, back propagation definitely did better than ID3 though it took more time to build the neural network. But in the presence of noise, in some cases, C4.5 gave faster and better results when the attributes being considered were relevant. But some surprising observations were made when the attribute values of the numeric datasets were discretized, the prediction accuracy of an unpruned tree generated by C4.5 algorithm was much higher than the rest. This shows that the unpruned tree generated by C4.5 is not the same as that generated by ID3. References: 1.Mooney, R., Shalvik, J., and Towell, G. (1991): Symbolic and Neural Learning Algorithms An experimental comparison, in Machine Learning 6, pp. 111-143. 2. Michalski, R.S., Chilausky, R.L. (1980): Learning by being told and learning from examples An experimental comparison of two methods of knowledge acquisition in the context of developing an expert system for soybean disease diagnosis, in Policy Analysis and Information Systems, 4, pp. 125-160. 3. Quinlan, J.R. (1983): Learning efficient classification procedures and their application to chess end games in R.S. Michalski, J.G. Carbonell, T.M. Mitchell (Eds.), in Machine learning: An artificial intelligence approach (Vol. 1). Palo Alto, CA: Tioga. 4. Sejnowski, T.J., Rosenberg, C. (1987): Parallel networks that learn to pronounce English text, in Complex Systems, 1, pp. 145-168. 5. Tesauro, G., Sejnowski, T.J. (1989): A p arallel network that learns to play backgammon, in Artificial Intelligence, 39, pp. 357-390. 6. Quinlan, J.R. (1986): Induction on Decision Trees, in Machine Learning 1, 1 7. Quinlan, J.R. (1993): C4.5 – Programs for Machine Learning. San Mateo: Morgan Kaufmann. 8. Rumelhart, D., Hinton, G., Williams, J. (1986): Learning Internal Representations by Error Propagation, in Parallel Distributed Processing, Vol. 1 (D. Rumelhart k J. McClelland, eds.). MIT Press. 9. Fisher, D.H. and McKusick, K.B. (1989): An empirical comparison of ID3 and backpropagation, in Proc. of the Eleventh International Joint Conference on Artificia1 Intelligence (IJCAI-89), Detroit, MI, August 20-25, pp. 788-793. 10. Mooney, R., Shavlik, J., Towell, G., and Gove, A.(1989): An experimental comparison of symbolic and connectionist learning algorithms, in Proc. of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), Detroit, MI, August 20-25, pp. 775-780. 11. McClelland, J. k Rumelhart, D. (1988). Explorations in Parallel Distributed Processing, MIT Press, Cambridge, MA.

To improve a manual system

To improve a manual system Introduction Our project is to improve a manual system to an automated system, so we selected a local bookshop which had limited number of functions so its going to be easier for our project. The scope of the project is to analyze the need for a bookshop system, and to find advantages to formulate an effective computerized bookshop system that will eliminate the existing problems. In this process of analyzing, information were obtained from the staff as well as the customers Proposed system is expected to have simplified Bookshop system that will eliminate the difficulties and disadvantages of not having a system. The system is also expected to satisfy both the customers as well as the staff members in having efficient and effective Bookshop System. Systems Request Summery The Manager of the Book Shop requested us to design and implement an automated IT system to replace their current manual system. He wanted a very effective and efficient system which they expect more profits from this investment to the IT system. He wanted a system which is really user friendly because he didnt want the staff members to be unhappy with working in the new system. Findings As we visited the Book Shop and spoke with the staff members and the customers we found out the followings as main points for the design and what they expect us to do; Most of the staff members had a normal knowledge of computing. Staff has a fear that they might not be combatable with the new IT system. Customers have to wait long to search for books and billing. Recommendations We recommend the shop to have a complex system which will cover all the areas but the simple user interface so that any user can use the system easily. Use barcode system for the book. There for the customers wating time can be reduse. Keep a backup data of the users daily. In case of some thing happen to the system. Use camers in side the library. If some users doing damages to propety esily can identified. Create a Computer area for the users. To acess to the internet and to search the books. Time and Cost Estimation As we estimated the total program will take around 75 days to be developed. We are planning to finish the project within 72 days, but we kept 5 days extra incase of emergency. As the book shop isnt a company which is not gaining huge profits they wanted us to stick to a smaller budget. After our main analysis we decided to complete the project for 385,000 LKR. Expected outcomes By using automated system we hope to overcome these disadvantages of the manual system. In manual system all the things in the system have to do manually. So it causes to make lot of mistakes. Then using automated system we cam over come these things. Using automated system make this advantages to the system. Can keep full information about whole sellers who providing books and stationery items. In the manual system we have to keep information about whole sellers in books or some times that are not wrote some where and keep that in mind of the owner/manager. Then they forget or loosing papers, however they lost information about whole sale vendors. And other thing is all the vendors are not giving items at same prize. Some are cheaper than others. So buying cheep items can make more profit. With large number of whole sale vendors, it hard to compare and choose vendor to buy items for sale. In automated system it is easy because all the information of whole sale vendors is in one place and analyst it briefly. Not only the price but also we have to care about quality of items. Other wise if our customers not satisfy with goods our sales will do down. So we have to care about quality also. In this system can keep history of whole sale vendors can we can build confidence about goods they give to us. And if we have recode of who is the vendor provide each item if we feel something wrong while selling that we can change or complain about that. From that we can keep confidence between us and costumers. Can keep information about stock. When selling items it must to have enough stocks. If it doesnt has cant continuing selling process. And if doesnt have the item what customer want confidence between customer and shop will break. Then keeping stock details are the best way to avoid these disadvantages. And currently they are replacing lot of items once at a month. Then some are finished sooner some are not finish in that month, so that method is not very good. And it need more space for stock. By using a automated system it is easy to keep stock details. Manager can see easily what item is getting finish and he can replace it. Then space needed will be less. Keeping stock space less will allow the manager to add more stock when he needs according to the type of goods. Can keep reservations. If customer needs to reserve book it can reserve on the system then it can keep on stork till customer buy it. It helps to enhance confident of the customer. On the system it is vary easy to do it. Can keep information about customers. Customers are the main thin. Then keeping customers information is vary important. In manual system doesnt have method to keep customers information. When give special gift or discount to customers it is vary use full. Help to observe sales. Observing sales is useful to control businesses. Like controlling stocks, getting people to work, investing more for each item etc.

Interpretations of William Faulkners A Rose for Emily :: A Rose for Emily, William Faulkner

Interpretations of William Faulkner's A Rose for Emily William Faulkner's "A Rose for Emily" has been interpreted in many different ways. Most of these rely solely on hints found within the story. I believe that his life can also help one analyze this story. By knowing that Faulkner's strongest influence was his independent mother, one can guess that Miss Emily Grierson's character was based partly on Maud Falkner. William Cuthbert Faulkner was born in New Albany, Mississippi on September 25, 1897. His family moved to Oxford, Mississippi when Faulkner was five years old (Larinde). His parents were Murry and Maud Falkner (Zane 2). Faulkner added the "u" to his last name on his Royal Air Force application for unknown reasons (5). Faulkner's great-grandfather, Colonel William C. Falkner had moved from Tennessee to the Mississippi Delta in 1841. The Colonel was a Civil War hero, plantation owner, railroad builder, and even a writer (Larinde). Faulkner's grandfather and father were both respected, though not wealthy. They were also both alcoholics. Faulkner and his father never had a very good relationship. He and his mother, though, were very close. Maud gave him his love of art and literature. She influenced Faulkner more than anyone else with her strong independence (Zane 3-4). She may have been the inspiration for the strong, independent character, Emily Grierson. "William Faulkner was a quiet but mischievous child, polite and rude, loving and withdrawn" (4). He did well in grade school, but began showing signs of truancy during adolescence. Faulkner dropped out of high school in eleventh grade. In 1918, Faulkner attempted to enlist in the U.S. Army but was turned down. He then applied to the Royal Air Force where he adds the "u" to his last name. He was soon discharged and returned to Oxford, Mississippi. Here he attended the university for two year. "In the decade that followed, Faulkner donned a host of other identities, alternately and aristocrat, a bohemian, or a derelict" (Zane 5). Faulkner established himself as a major novelist in 1929 with the book The Sound and the Fury (Larinde). He wrote twenty novels and many short stories (Zane 1). His greatest achievements were the Nobel Prize for literature in 1950, the National Book Award, and Pulitzer Prizes. All of these awards came after he was fifty (7). Although Faulkner lived in Canada, New Orleans, New York, Hollywood, and Virginia, most of his life was spent in his native Mississippi (Faulkner 177).