Effects of Using Computers

Effects of Using Computers As the world is developing rapidly, people rely more and more on science and technology. When they define the concept of science and technology, â€Å"most people today think of silicon chips, iPods, high-definition TVs, and visual cell phones† (Wilson 320). Among those high-tech inventions, cars and cell phones are two scientific inventions that mostly affect people’s lives. For example, they can get to the destination faster or simply make a phone call to talk instead of taking time to write a letter. However, all of these effects do not stop there. As people can observe, they no longer work manually or by hand because everything have become computerized. Therefore, it is not unreasonable to believe that â€Å"in the future, computing is not computers any more; it is about living,† according to Nicholas Negroponte (27). In fact, the invention of computers has had both positive and negative effects on people’s development, such as, in humans’ social lives, in their jobs, and in human entertainment. First of all, humans’ social lives rely on computer invention because of its helpfulness. With the aid of technology, using computers with internet access is not an unusual way to communicate anymore. Since computers were invented, communication has become easier and quicker because â€Å"computers will join together to communicate with us and for us† (qtd. in Leone 13). For example, international students, who do not live with their family, can keep in contact with their parents via webcam. They can not only talk but also see each other’s face. Talking via webcam like that helps those international students feel like they are living far away from home. Maintaining better communications means maintaining a better relationship, so people might say that â€Å"computers can foster human contact† (Swerdlow 17). When people are bored or disappointed, they usually log in to chat room or facebook where they can meet many different and interesting people. For example, my friend’s brother immigrated to the U. S. ten years ago and left his girlfriend in Vietnam. The only thing that helped them keep in contact was the computers. They just kept chatting via webcam about how they lived without the other. Even though they did not live together, talking via webcam helped them maintain their relationship. Three years ago, they decided to get married. There are not many people reaching this destination, but computers provide people one way to keep their distant relationship. However, using online chatting to communicate has its own side effect which badly affects people’s privacy. Although computers have made communication easier, quicker, and more convenient, it has also brought the privacy concerning. For example, every time people visit a dating or chatting site, all of the activities that they make will be saved and â€Å"can be converted into permanent records that, when combined with innumerable other such records, can form a detailed profile of who we are† (qtd. in Wagner 59). It is scary to see our profiles online, meaning that people’s privacy is known as public information which they never expect to see. In addition, most people use a credit card to shop online, and every time they use it, that credit card can be matched to specific titles and be stolen easily. Using credit card to buy something online is so convenient; however, it is not safe because some online hackers can steal people’s account number and start using it illegally. In fact, using computer to access the Internet has impacted people’s privacy. Secondly, computers play a big role in people’s jobs because computers help people perform work faster and more correctly. Long time ago when computers were not invented, hundreds of people collaborated to trade stocks by using blackboards, chalk, and phones, and with much calculation, stock trading now is so much easier and quicker. It means people just simply turn on their computers, and all the information about stocks will appear on their screens. Sometimes, â€Å"human power is becoming increasingly ineffective in controlling the way information technology shapes our economic and political lives† (Swerdlow 21). For example, in the supermarket, there are too many products with many different price ranges, and computers help employees to complete the sum up transactions faster. In fact, no matter what jobs people are working, they need computers because the world has been computerized. Some people think that there is a lot of work that computers cannot perform. Yes, there is some work that needs physical movement or physical force, but computers can help people perform other tasks that require a complex thought process. According to Levy, a writer of Quarterly Journal of Economics in 2003, â€Å"computers cannot easily substitute for humans in these jobs but they can complement humans by providing large volumes of information†, he implies (30). For example, part of the truck drivers’ jobs is usually to carry goods in and out of the truck, and computers can not help them to do it. However, it can provide the truck drivers the navigation system to get to the destination quicker. Computers are an intelligent machine that can help people do various work through its various functions, so people cannot deny the crucial role of computers in people’s jobs. However, applying computers in the workplace also has a certain disadvantage. The major effect of computers is that computers create an unemployment problem. Unemployment is a serious conflict because employers no longer need much labor to run their companies when computers can help them in various aspects. In reality, â€Å"[computers] technology is enabling companies to extend their operations and enlarge their profits while reducing their workforce and the pay and security of those who remain by contracting out work to cheaper labor around the globe and by replacing people with the machines† (Noble 40). For example, employers are used to hire accountants to keep their business documents, but when they have computers, those accountants are no longer needed because the employers can handle their documents by themselves with the accounting software. Moreover, the increased use of computers in the workplace causes another serious consequence which is the decrease of employees’ work skills. According to Joan Greenbaum, a programmer at IBM company, he writes â€Å"in the language of work rationalization, the worker who does the same task over and over again†¦is being deskilled because she does not have a chance to use her own intelligence and knowledge† (63). Employees will not practice anything at all if they keep working on one work over and over again. They have computers which really help them do a major of work, so they do not really need to use their knowledge to perform their work. In fact, computers are really useful for employees to work on a variety of tasks, but there are some bad consequences following the computers’ advantages. Last of all, computers have influenced humans’ entertainment because they have some functions that televisions or cell phones do not. Mostly, children listen to music or watch movies with a CD/DVD player to entertain themselves, but when they know how to use computers, their use of those inventions is less than after they discovered the effects of computers. Evidently, â€Å"U. S. residents 13 and older consider computers more important for home entertainment than the CD player, stereo or DVD player† (Evangelista E1). A major of teenagers use computers to entertain because of the computers’ functions. For instance, televisions can not be used to go online, but computers can. Televisions do not have â€Å"hard drive† to store music and movies, but computers do. Moreover, computers also help teenagers exploit other different activities on the Internet, and one of which is game online. Playing games online has become very popular because of the advantages it offers. People do not need to come by a game store to find one because there are many interesting games on the Internet. People can also gamble online. Therefore, there is no doubting the truth that computers help people entertain themselves more effectively. However, entertaining via computers connected to the Internet may cause bad effects on people’s lives, especially teenagers. Among game online and movies, the violent images are the most common issue that most parents do worry about. They seem to act exactly like what they have seen in the movies or the games on the Internet. For instance, one of my friend’s younger brother, who is now sixteen years old, usually watches gangster movies, and his favorite one is â€Å"Young and Dangerous†, a Hong Kong gangster movie. He tends to act like a gangster and tries to be as cool as the main character, which he really loves to be. He even gets the same that character’s dragon tattoo on his back. About three years ago, he fought with another group of students of another school and was put in jail for six months. Therefore, online games and movies may mentally influence teenagers. Additionally, games and movies online also has a bad effect on teenagers’ physical development because they can spend most of their time sitting in the front of a computer, having their eyes glued to the monitor, and playing from early morning until midnight without eating anything. Playing games online is seemly like having drug abuse because when teenagers get into it, they will have difficulty getting out. Focusing too much on game online, teenagers have lack of exercise which directly affects on their physical growth, and they will become hypoactive which means they are less active and communicative. Therefore, most people, especially teenagers, should realize that watching movies and playing games online too much can cause serious consequences. As time has passed, scientists have invented many great high-tech inventions, and computer invention is one of those. In fact, every high technology has not only advantages but also disadvantages, so computers are not an exception. Computers have both positively and negatively impacted humans’ social lives, their jobs, and also their entertainment. Now, when people define the concept of science and technology, the first invention which they mention is the computers because computers have become a part of humans’ lives. Computers have many sophisticated functions to perform a variety of work, but people should remember that computers may cause bad effects.

Based Data Mining Approach for Quality Control

Classification-Based Data Mining Approach For Quality Control In Wine Production GUIDED BY: | | SUBMITTED BY:| Jayshri Patel| | Hardik Barfiwala| INDEX Sr No| Title| Page No. | 1| Introduction Wine Production| | 2| Objectives| | 3| Introduction To Dataset| | 4| Pre-Processing| | 5| Statistics Used In Algorithms| | 6| Algorithms Applied On Dataset| | 7| Comparison Of Applied Algorithm | | 8| Applying Testing Dataset| | 9| Achievements| | 1.INTRODUCTION TO WINE PRODUCTION * Wine industry is currently growing well in the market since the last decade. However, the quality factor in wine has become the main issue in wine making and selling. * To meet the increasing demand, assessing the quality of wine is necessary for the wine industry to prevent tampering of wine quality as well as maintaining it. * To remain competitive, wine industry is investing in new technologies like data mining for analyzing taste and other properties in wine. Data mining techniques provide more than summary, but valuable information such as patterns and relationships between wine properties and human taste, all of which can be used to improve decision making and optimize chances of success in both marketing and selling. * Two key elements in wine industry are wine certification and quality assessment, which are usually conducted via physicochemical and sensory tests. * Physicochemical tests are lab-based and are used to characterize physicochemical properties in wine such as its density, alcohol or pH values. * Meanwhile, sensory tests such as taste preference are performed by human experts.Taste is a particular property that indicates quality in wine, the success of wine industry will be greatly determined by consumer satisfaction in taste requirements. * Physicochemical data are also found useful in predicting human wine taste preference and classifying wine based on aroma chromatograms. 2. OBJECTIVE * Modeling the complex human taste is an important focus in wine industries. * The main purpose of this study was to predict wine quality based on physicochemical data. * This study was also conducted to identify outlier or anomaly in sample wine set in order to detect ruining of wine. 3. INTRODUCTION TO DATASETTo evaluate the performance of data mining dataset is taken into consideration. The present content describes the source of data. * Source Of Data Prior to the experimental part of the research, the data is gathered. It is gathered from the UCI Data Repository. The UCI Repository of Machine Learning Databases and Domain Theories is a free Internet repository of analytical datasets from several areas. All datasets are in text files format provided with a short description. These datasets received recognition from many scientists and are claimed to be a valuable source of data. * Overview Of Dataset INFORMATION OF DATASET|Title:| Wine Quality| Data Set Characteristics:| Multivariate| Number Of Instances:| WHITE-WINE : 4898 RED-WINE : 1599 | Area:| Business| Attrib ute Characteristic:| Real| Number Of Attribute:| 11 + Output Attribute| Missing Value:| N/A| * Attribute Information * Input variables (based on physicochemical tests) * Fixed Acidity: Amount of Tartaric Acid present in wine. (In mg per liter) Used for taste, feel and color of wine. * Volatile Acidity: Amount of Acetic Acid present in wine. (In mg per liter) Its presence in wine is mainly due to yeast and bacterial metabolism. * Citric Acid: Amount of Citric Acid present in wine. In mg per liter) Used to acidify wine that are too basic and as a flavor additive. * Residual Sugar: The concentration of sugar remaining after fermentation. (In grams per liter) * Chlorides: Level of Chlorides added in wine. (In mg per liter) Used to correct mineral deficiencies in the brewing water. * Free Sulfur Dioxide: Amount of Free Sulfur Dioxide present in wine. (In mg per liter) * Total Sulfur Dioxide: Amount of free and combined sulfur dioxide present in wine. (In mg per liter) Used mainly as pres ervative in wine process. * Density: The density of wine is close to that of water, dry wine is less and sweet wine is higher. In kg per liter) * PH: Measures the quantity of acids present, the strength of the acids, and the effects of minerals and other ingredients in the wine. (In values) * Sulphates: Amount of sodium metabisulphite or potassium metabisulphite present in wine. (In mg per liter) * Alcohol: Amount of Alcohol present in wine. (In percentage) * Output variable (based on sensory data) * Quality (score between 0 and 10) : White Wine : 3 to 9 Red Wine : 3 to 8 4. PRE-PROCESSING * Pre-processing Of Data Preprocessing of the dataset is carried out before mining the data to remove the different lacks of the information in the data source.Following different process are carried out in the preprocessing reasons to make the dataset ready to perform classification process. * Data in the real world is dirty because of the following reason. * Incomplete: Lacking attribute values, lacking certain attributes of interest, or containing only aggregate data. * E. g. Occupation=â€Å"† * Noisy : Containing errors or outliers. * E. g. Salary=â€Å"-10† * Inconsistent : Containing discrepancies in codes or names. * E. g. Age=â€Å"42† Birthday=â€Å"03/07/1997† * E. g. Was rating â€Å"1,2,3†, Now rating â€Å"A, B, C† * E. g. Discrepancy between duplicate records * No quality data, no quality mining results! Quality decisions must be based on quality data. * Data warehouse needs consistent integration of quality data. * Major Tasks in done in the Data Preprocessing are, * Data Cleaning * Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies. * Data integration * Integration of multiple databases, data cubes, or files. * The dataset provided from given data source is only in one single file. So there is no need for integrating the dataset. * Data transformation * Normalization a nd aggregation * The dataset is in Normalized form because it is in single data file. * Data reduction Obtains reduced representation in volume but produces the same or similar analytical results. * The data volume in the given dataset is not very huge, the procedure of performing different algorithm is easily done on dataset so the reduction of dataset is not needed on the data set * Data discretization * Part of data reduction but with particular importance, especially for numerical data. * Need for Data Preprocessing in wine quality, * For this dataset Data Cleaning is only required in data pre-processing. * Here, NumericToNominal, InterquartileRange and RemoveWithValues filters are used for data pre-processing. * NumericToNominal Filter weka. filters. unsupervised. attribute. NumericToNominal) * A filter for turning numeric attribute into nominal once. * In our dataset, Class attribute â€Å"Quality† in both dataset (Red-wine Quality, White-wine Quality) have a type †Å"Numeric†. So after applying this filter, class attribute â€Å"Quality† convert into type â€Å"Nominal†. * And Red-wine Quality dataset have class names 3, 4, 5 †¦ 8 and White-wine Quality dataset have class names 3, 4, 5 †¦ 9. * Because of classification does not apply on numeric type class field, there is a need for this filter. * InterquartileRange Filter (weka. filters. unsupervised. attribute. InterquartileRange) A filter for detecting outliers and extreme values based on interquartile ranges. The filter skips the class attribute. * Apply this filter for all attribute indices with all default options. * After applying, filter adds two more fields which names are â€Å"Outliers† and â€Å"ExtremeValue†. And this fields has two types of label â€Å"No† and â€Å"Yes†. Here â€Å"Yes† label indicates, there are outliers and extreme values in dataset. * In our dataset, there are 83 extreme values and 125 outliers i n White-wine Quality dataset and 69 extreme values and 94 outliers in Red-wine Quality. * RemoveWithValues Filter (weka. filters. unsupervised. instance.RemoveWithValues) * Filters instances according to the value of an attribute. * This filter has two options which are â€Å"AttributeIndex† and â€Å"NominalIndices†. * AttributeIndex choose attribute to be use for selection and NominalIndices choose range of label indices to be use for selection on nominal attribute. * In our dataset, AttributeIndex is â€Å"last† and NominalIndex is also â€Å"last†, so It will remove first 83 extreme values and then 125 outliers in White-wine Quality dataset and 69 extreme values and 94 outliers in Red-wine Quality. * After applying this filter on dataset remove both fields from dataset. * Attribute SelectionRanking Attributes Using Attribute Selection Algorithm| RED-WINE| RANKED| WHITE-WINE| Volatile_Acidity(2)| 0. 1248| 0. 0406| Volatile_Acidity(2)| Total_sulfer_Diox ide(7)| 0. 0695| 0. 0600| Citric_Acidity(3)| Sulphates(10)| 0. 1464| 0. 0740| Chlorides(5)| Alcohal(11)| 0. 2395| 0. 0462| Free_Sulfer_Dioxide(6)| | | 0. 1146| Density(8)| | | 0. 2081| Alcohal(11)| * The selection of attributes is performed automatically by WEKA using Info Gain Attribute Eval method. * The method evaluates the worth of an attribute by measuring the information gain with respect to the class. 5. STATISTICS USED IN ALGORITHMS * Statistics MeasuresThere are Different algorithms that can be used while performing data mining on the different dataset using weka, some of them are describe below with the different statistics measures. * Statistics Used In Algorithms * Kappa statistic * The kappa statistic, also called the kappa coefficient, is a performance criterion or index which compares the agreement from the model with that which could occur merely by chance. * Kappa is a measure of agreement normalized for chance agreement. * Kappa statistic describe that our predicti on for class attribute for given dataset is how much near to actual values. * Values Range For Kappa Range| Result| lt;0| POOR| 0-0. 20| SLIGHT| 0. 21-0. 40| FAIR| 0. 41-0. 60| MODERATE| 0. 61-0. 80| SUBSTANTIAL| 0. 81-1. 0| ALMOST PERFECT| * As above range in weka algorithm evaluation if value of kappa is near to 1 then our predicted values are accurate to actual values so, applied algorithm is accurate. Kappa Statistic Values For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 0. 5365| 0. 5294| J48| 0. 3813| 0. 3881| Multilayer Perceptron| 0. 2946| 0. 3784| * Mean absolute error (MAE) * Mean absolute error (MAE)  is a quantity used to measure how close forecasts or predictions are to the eventual outcomes. The mean absolute error is given by, Mean absolute Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 0. 1297| 0. 1381| J48| 0. 1245| 0. 1401| Multilayer Perceptron| 0. 1581| 0. 1576| * Root Mean Squared Erro r * If you have some data and try to make a curve (a formula) fit them, you can graph and see how close the curve is to the points. Another measure of how well the curve fits the data is Root Mean Squared Error. * For each data point, CalGraph calculates the value of  Ã‚  y from the formula. It subtracts this from the data's y-value and squares the difference. All these squares are added up and the sum is divided by the number of data. * Finally CalGraph takes the square root. Written mathematically, Root Mean Square Error is Root Mean Squared Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 0. 2428| 0. 2592| J48| 0. 3194| 0. 3354| Multilayer Perceptron| 0. 2887| 0. 3023| * Root Relative Squared Error * The  root relative squared error  is relative to what it would have been if a simple predictor had been used. More specifically, this simple predictor is just the average of the actual values. Thus, the relative squared error takes the to tal squared error and normalizes it by dividing by the total squared error of the simple predictor. * By taking the square root of therelative squared error  one reduces the error to the same dimensions as the quantity being predicted. * Mathematically, the  root relative squared error  Ei  of an individual program  i  is evaluated by the equation: * where  P(ij)  is the value predicted by the individual program  i  for sample case  j  (out of  n  sample cases);  Tj  is the target value for sample case  j; andis given by the formula: * For a perfect fit, the numerator is equal to 0 and  Ei  = 0.So, the  Ei  index ranges from 0 to infinity, with 0 corresponding to the ideal. Root Relative Squared Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 78. 1984 %| 79. 309 %| J48| 102. 9013 %| 102. 602 %| Multilayer Perceptron| 93. 0018 %| 92. 4895 %| * Relative Absolute Error * The  relative absolute error  is very similar to the  relative squared error  in the sense that it is also relative to a simple predictor, which is just the average of the actual values. In this case, though, the error is just the total absolute error instead of the total squared error. Thus, the relative absolute error takes the total absolute error and normalizes it by dividing by the total absolute error of the simple predictor. Mathematically, the  relative absolute error  Ei  of an individual program  i  is evaluated by the equation: * where  P(ij)  is the value predicted by the individual program  i  for sample case  j  (out of  n  sample cases);  Tj  is the target value for sample case  j; andis given by the formula: * For a perfect fit, the numerator is equal to 0 and  Ei  = 0. So, the  Ei  index ranges from 0 to infinity, with 0 corresponding to the ideal.Relative Absolute Squared Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality | K-Star| 67. 2423 %| 64. 5286 %| J48| 64. 577 %| 65. 4857 %| Multilayer Perceptron| 81. 9951 %| 73. 6593 %| * Various Rates * There are four possible outcomes from a classifier. * If the outcome from a prediction is  p  and the actual value is also  p, then it is called a  true positive  (TP). * However if the actual value is  n  then it is said to be a  false positive  (FP). * Conversely, a  true negative  (TN) has occurred when both the prediction outcome and the actual value are  n. And  false negative  (FN) is when the prediction outcome is  n while the actual value is  p. * Absolute Value | P| N| TOTAL| p’| True positive| false positive| P’| n’| false negative| True negative| N’| Total| P| N| | * ROC Curves * While estimating the effectiveness and accuracy of data mining technique it is essential to measure the error rate of each method. * In the case of binary classification tasks the error rate takes and components under consideration. * The ROC analysis which stands for Receiver Operating Characteristics is applied. * The sample ROC curve is presented in the Figure below.The closer the ROC curve is to the top left corner of the ROC chart the better the performance of the classifier. * Sample ROC curve (squares with the usage of the model, triangles without). The line connecting the square with triage is the benefit from the usage of the model. * It plots the curve which consists of x-axis presenting false positive rate and y-axis which plots the true positive rate. This curve model selects the optimal model on the basis of assumed class distribution. * The ROC curves are applicable e. g. in decision tree models or rule sets. * Recall, Precision and F-Measure There are four possible results of classification. * Different combination of these four error and correct situations are presented in the scientific literature on topic. * Here three popular notions are presented. The introduction of the se classifiers is explained by the possibility of high accuracy by negative type of data. * To avoid such situation recall and precision of the classification are introduced. * The F measure is the harmonic mean of precision and recall. * The formal definitions of these measures are as follow : PRECSION = TPTP+FP RECALL = TPTP+FNF-Measure = 21PRECSION+1RECALL * These measures are introduced especially in information retrieval application. * Confusion Matrix * A matrix used to summarize the results of a supervised classification. * Entries along the main diagonal are correct classifications. * Entries other than those on the main diagonal are classification errors. 6. ALGORITHMS * K-Nearest Neighbor Classifiers * Nearest neighbor classifiers are based on learning by analogy. * The training samples are described by n-dimensional numeric attributes. Each sample represents a point in an n-dimensional space. In this way, all of the training samples are stored in an n-dimensional pattern space. When given an unknown sample, a k-nearest neighbor classifier searches the pattern space for the k training samples that are closest to the unknown sample. * These k training samples are the k-nearest neighbors of the unknown sample. â€Å"Closeness† is defined in terms of Euclidean distance, where the Euclidean distance between two points, , * The unknown sample is assigned the most common class among its k nearest neighbors. When k = 1, the unknown sample is assigned the class of the training sample that is closest to it in pattern space. Nearest neighbor classifiers are instance-based or lazy learners in that they store all of the training samples and do not build a classifier until a new (unlabeled) sample needs to be classified. * Lazy learners can incur expensive computational costs when the number of potential neighbors (i. e. , stored training samples) with which to compare a given unlabeled sample is great. * Therefore, they require efficient indexing techniqu es. As expected, lazy learning methods are faster at training than eager methods, but slower at classification since all computation is delayed to that time.Unlike decision tree induction and back propagation, nearest neighbor classifiers assign equal weight to each attribute. This may cause confusion when there are many irrelevant attributes in the data. * Nearest neighbor classifiers can also be used for prediction, i. e. to return a real-valued prediction for a given unknown sample. In this case, the classifier returns the average value of the real-valued labels associated with the k nearest neighbors of the unknown sample. * In weka the previously described algorithm nearest neighbor is given as Kstar algorithm in classifier -> lazy tab. The Result Generated After Applying K-Star On White-wine Quality Dataset Kstar Options : -B 70 -M a | Time Taken To Build Model: 0. 02 Seconds| Stratified Cross-Validation (10-Fold)| * Summary | Correctly Classified Instances | 3307 | 70. 6624 % | Incorrectly Classified Instances| 1373 | 29. 3376 %| Kappa Statistic | 0. 5365| | Mean Absolute Error | 0. 1297| | Root Mean Squared Error| 0. 2428| | Relative Absolute Error | 67. 2423 %| | Root Relative Squared Error | 78. 1984 %| | Total Number Of Instances | 4680 | | * Detailed Accuracy By Class | TP Rate| FP Rate | Precision | Recall | F-Measure | ROC Area | PRC Area| Class| | 0 | 0 | 0 | 0 | 0 | 0. 583 | 0. 004 | 3| | 0. 211 | 0. 002 | 0. 769 | 0. 211 | 0. 331 | 0. 884 | 0. 405 | 4| | 0. 672 | 0. 079 | 0. 777 | 0. 672 | 0. 721 | 0. 904 | 0. 826 | 5| | 0. 864 | 0. 378 | 0. 652 | 0. 864 | 0. 743 | 0. 84 | 0. 818 | 6| | 0. 536 | 0. 031 | 0. 797 | 0. 536 | 0. 641 | 0. 911 | 0. 772 | 7| | 0. 398 | 0. 002 | 0. 883 | 0. 398 | 0. 548 | 0. 913 | 0. 572 | 8| | 0 | 0 | 0 | 0 | 0 | 0. 84 | 0. 014 | 9| Weighted Avg. | 0. 707 | 0. 2 | 0. 725 | 0. 707 | 0. 695 | 0. 876 | 0. 787| | * Confusion Matrix| A | B | C | D | E | F| G | | Class| 0 | 0 | 4 | 9 | 0| 0 | 0 | | | A=3| 0| 30| 49| 62| 1 | 0 | 0| | | B=4| 0 | 7 | 919| 437| 5 | 0 | 0 | | | C=5| 0 | 2 | 201| 1822| 81 | 2 | 0 | || D=6| 0 | 0 | 9 | 389 | 468 | 7 | 0| || E=7| 0 | 0 | 0 | 73 | 30 | 68 | 0 | || F=8| 0 | 0 | 0 | 3 | 2 | 0 | 0 | || G=9| * Performance Of The Kstar With Respect To A Testing Configuration For The White-wine Quality DatasetTesting Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 99. 6581 %| 100 %| 70. 6624 %| 63. 9221 %| Kappa statistic| 0. 9949| 1| 0. 5365| 0. 4252| Mean Absolute Error| 0. 0575| 0. 0788| 0. 1297| 0. 1379| Root Mean Squared Error| 0. 1089| 0. 145| 0. 2428| 0. 2568| Relative Absolute Error| 29. 8022 %| | 67. 2423 %| 71. 2445 %| * The Result Generated After Applying K-Star On Red-wine Quality Dataset Kstar Options : -B 70 -M a | Time Taken To Build Model: 0 Seconds| Stratified Cross-Validation (10-Fold)| * Summary | Correctly Classified Instances | 1013 | 71. 379 %| Incorrectly Classified Instances| 413 | 28. 9621 %| Kappa Stat istic | 0. 5294| | Mean Absolute Error | 0. 1381| | Root Mean Squared Error | 0. 2592| | Relative Absolute Error | 64. 5286 %| | Root Relative Squared Error | 79. 309 %| | Total Number Of Instances | 1426 | | * Detailed Accuracy By Class | | TP Rate | FP Rate | Precision | Recall | F-Measure | ROC Area | PRC Area| Class| | 0 | 0. 001 | 0 | 0 | 0 | 0. 574 | 0. 019 | 3| | 0 | 0. 003 | 0 | 0 | 0 | 0. 811 | 0. 114 | 4| | 0. 791| 0. 176 | 0. 67| 0. 791| 0. 779 | 0. 894 | 0. 867 | 5| | 0. 769 | 0. 26 | 0. 668 | 0. 769 | 0. 715 | 0. 834 | 0. 788 | 6| | 0. 511 | 0. 032 | 0. 692 | 0. 511 | 0. 588 | 0. 936 | 0. 722 | 7| | 0. 125 | 0. 001 | 0. 5 | 0. 125 | 0. 2 | 0. 896 | 0. 142 | 8| Weighted Avg. | 0. 71| 0. 184| 0. 685| 0. 71| 0. 693| 0. 871| 0. 78| | * Confusion Matrix | A | B | C | D | E | F| | Class| 0 | 1 | 4| 1 | 0 | 0 | | | A=3| 1 | 0 | 30| 17 | 0 | 0| | | B=4| 0 | 2| 477| 120 | 4 | 0| | | C=5| 0 | 1 | 103 | 444| 29 | 0| || D=6| 0 | 0 | 8 | 76 | 90 | 2 | || E=7| 0 | 0 | 0 | 7 | 7 | 2| || F=8| Performance Of The Kstar With Respect To A Testing Configuration For The Red-wine Quality Dataset Testing Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 99. 7895 %| 100 % | 71. 0379 %| 70. 7216 %| Kappa statistic| 0. 9967| 1| 0. 5294| 0. 5154| Mean Absolute Error| 0. 0338| 0. 0436| 0. 1381| 0. 1439| Root Mean Squared Error| 0. 0675| 0. 0828 | 0. 2592| 0. 2646| Relative Absolute Error| 15. 8067 %| | 64. 5286 %| 67. 4903 %| * J48 Decision Tree * Class for generating a pruned or unpruned C4. 5 decision tree. A decision tree is a predictive machine-learning model that decides the target value (dependent variable) of a new sample based on various attribute values of the available data. * The internal nodes of a decision tree denote the different attribute; the branches between the nodes tell us the possible values that these attributes can have in the observed samples, while the terminal nodes tell us the final value (class ification) of the dependent variable. * The attribute that is to be predicted is known as the dependent variable, since its value depends upon, or is decided by, the values of all the other attributes.The other attributes, which help in predicting the value of the dependent variable, are known as the independent variables in the dataset. * The J48 Decision tree classifier follows the following simple algorithm: * In order to classify a new item, it first needs to create a decision tree based on the attribute values of the available training data. So, whenever it encounters a set of items (training set) it identifies the attribute that discriminates the various instances most clearly. * This feature that is able to tell us most about the data instances so that we can classify them the best is said to have the highest information gain. Now, among the possible values of this feature, if there is any value for which there is no ambiguity, that is, for which the data instances falling wi thin its category have the same value for the target variable, then we terminate that branch and assign to it the target value that we have obtained. * For the other cases, we then look for another attribute that gives us the highest information gain. Hence we continue in this manner until we either get a clear decision of what combination of attributes gives us a particular target value, or we run out of attributes.In the event that we run out of attributes, or if we cannot get an unambiguous result from the available information, we assign this branch a target value that the majority of the items under this branch possess. * Now that we have the decision tree, we follow the order of attribute selection as we have obtained for the tree. By checking all the respective attributes and their values with those seen in the decision tree model, we can assign or predict the target value of this new instance. * The Result Generated After Applying J48 On White-wine Quality Dataset Time Taken To Build Model: 1. 4 Seconds| Stratified Cross-Validation (10-Fold) | * Summary| | | Correctly Classified Instances| 2740 | 58. 547 %| Incorrectly Classified Instances | 1940 | 41. 453 %| Kappa Statistic | 0. 3813| | Mean Absolute Error | 0. 1245| | Root Mean Squared Error | 0. 3194| | Relative Absolute Error | 64. 5770 %| | Root Relative Squared Error| 102. 9013 %| | Total Number Of Instances | 4680| | * Detailed Accuracy By Class| | TP Rate| FP Rate| Precision| Recall| F-Measure| ROC Area| Class| | 0| 0. 002| 0| 0| 0| 0. 30| 3| | 0. 239| 0. 020| 0. 270| 0. 239| 0. 254| 0. 699| 4| | 0. 605| 0. 169| 0. 597| 0. 605| 0. 601| 0. 763| 5| | 0. 644| 0. 312| 0. 628| 0. 644| 0. 636| 0. 689| 6| | 0. 526| 0. 099| 0. 549| 0. 526| 0. 537| 0. 766| 7| | 0. 363| 0. 022| 0. 388| 0. 363| 0. 375| 0. 75| 8| | 0| 0| 0| 0| 0| 0. 496| 9| Weighted Avg. | 0. 585 | 0. 21 | 0. 582 | 0. 585 | 0. 584 | 0. 727| | * Confusion Matrix | A| B| C| D| E| F| G| || Class| 0| 2| 6| 5| 0| 0| 0| || A=3| 1| 34| 55| 44| 6| 2| 0| || B=4| 5| 50| 828| 418| 60| 7| 0| || C=5| 2| 32| 413| 1357| 261| 43| 0| || D=6| | 7| 76| 286| 459| 44| 0| || E=7| 1| 1| 10| 49| 48| 62| 0| || F=8| 0| 0| 0| 1| 2| 2| 0| || G=9| * Performance Of The J48 With Respect To A Testing Configuration For The White-wine Quality Dataset Testing Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 90. 1923 %| 70 %| 58. 547 %| 54. 8083 %| Kappa statistic| 0. 854| 0. 6296| 0. 3813| 0. 33| Mean Absolute Error| 0. 0426| 0. 0961| 0. 1245| 0. 1347| Root Mean Squared Error| 0. 1429| 0. 2756| 0. 3194| 0. 3397| Relative Absolute Error| 22. 0695 %| | 64. 577 %| 69. 84 %| * The Result Generated After Applying J48 On Red-wine Quality Dataset Time Taken To Build Model: 0. 17 Seconds| Stratified Cross-Validation| * Summary| Correctly Classified Instances | 867 | 60. 7994 %| Incorrectly Classified Instances | 559 | 39. 2006 %| Kappa Statistic | 0. 3881| | Mean Absolute Error | 0. 1401| | Root Mean Squa red Error | 0. 3354| | Relative Absolute Error | 65. 4857 %| | Root Relative Squared Error | 102. 602 %| |Total Number Of Instances | 1426 | | * Detailed Accuracy By Class| | Tp Rate | Fp Rate | Precision | Recall | F-measure | Roc Area | Class| | 0 | 0. 004 | 0 | 0 | 0 | 0. 573 | 3| | 0. 063 | 0. 037 | 0. 056 | 0. 063 | 0. 059 | 0. 578 | 4| | 0. 721 | 0. 258 | 0. 672 | 0. 721 | 0. 696 | 0. 749 | 5| | 0. 57 | 0. 238 | 0. 62 | 0. 57 | 0. 594 | 0. 674 | 6| | 0. 563 | 0. 64 | 0. 553 | 0. 563 | 0. 558 | 0. 8 | 7| | 0. 063 | 0. 006 | 0. 1 | 0. 063 | 0. 077 | 0. 691 | 8| Weighted Avg. | 0. 608 | 0. 214 | 0. 606 | 0. 608 | 0. 606 | 0. 718 | | * Confusion Matrix | A | B | C | D | E | F | | Class| 0 | 2 | 1 | 2 | 1 | 0 | | | A=3| 2 | 3 | 25 | 15 | 3 | 0 | | | B=4| 1 | 26 | 435 | 122 | 17 | 2 | | | C=5| 2 | 21 | 167 | 329 | 53 | 5 | | | D=6| 0 | 2 | 16 | 57 | 99 | 2 | | | E=7| 0 | 0 | 3 | 6 | 6 | 1 | | | F=8| Performance Of The J48 With Respect To A Testing Configuration For The Red-wine Qual ity Dataset Testing Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 91. 1641 %| 80 %| 60. 7994 %| 62. 4742 %| Kappa statistic| 0. 8616| 0. 6875| 0. 3881| 0. 3994| Mean Absolute Error| 0. 0461| 0. 0942| 0. 1401| 0. 1323| Root Mean Squared Error| 0. 1518| 0. 2618| 0. 3354| 0. 3262| Relative Absolute Error| 21. 5362 %| 39. 3598 %| 65. 4857 %| 62. 052 %| * Multilayer Perceptron * The back propagation algorithm performs learning on a multilayer feed-forward neural network. It iteratively learns a set of weights for prediction of the class label of tuples. * A multilayer feed-forward neural network consists of an input layer, one or more hidden layers, and an output layer. * Each layer is made up of units. The inputs to the network correspond to the attributes measured for each training tuple. The inputs are fed simultaneously into the units making up the input layer. These inputs pass through the input layer and are then weighted an d fed simultaneously to a second layer of â€Å"neuronlike† units, known as a hidden layer. The outputs of the hidden layer units can be input to another hidden layer, and so on. The number of hidden layers is arbitrary, although in practice, usually only one is used. The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the network’s prediction for given tuples. * The units in the input layer are called input units. The units in the hidden layers and output layer are sometimes referred to as neurodes, due to their symbolic biological basis, or as output units. * The network is feed-forward in that none of the weights cycles back to an input unit or to an output unit of a previous layer.It is fully connected in that each unit provides input to each unit in the next forward layer. * The Result Generated After Applying Multilayer Perceptron On White-wine Quality Dataset Time taken to build model: 36. 22 seconds| Stratifi ed cross-validation| * Summary| Correctly Classified Instances | 2598 | 55. 5128 %| Incorrectly Classified Instances | 2082 | 44. 4872 %| Kappa statistic | 0. 2946| | Mean absolute error | 0. 1581| | Root mean squared error | 0. 2887| |Relative absolute error | 81. 9951 %| | Root relative squared error | 93. 0018 %| | Total Number of Instances | 4680 | | * Detailed Accuracy By Class | | TP Rate | FP Rate | Precision | Recall | F-Measure | ROC Area | PRC Area | Class| | 0 | 0 | 0 | 0 | 0 | 0. 344 | 0. 002 | 3| | 0. 056 | 0. 004 | 0. 308 | 0. 056 | 0. 095 | 0. 732 | 0. 156 | 4| | 0. 594 | 0. 165 | 0. 597 | 0. 594 | 0. 595 | 0. 98 | 0. 584 | 5| | 0. 704 | 0. 482 | 0. 545 | 0. 704 | 0. 614 | 0. 647 | 0. 568 | 6| | 0. 326 | 0. 07 | 0. 517 | 0. 326 | 0. 4 | 0. 808 | 0. 474 | 7| | 0. 058 | 0. 002 | 0. 5 | 0. 058 | 0. 105 | 0. 8 | 0. 169 | 8| | 0 | 0 | 0| 0 | 0 | 0. 356 | 0. 001 | 9| Weighted Avg. | 0. 555 | 0. 279 | 0. 544 | 0. 555 | 0. 532 | 0. 728 | 0. 526| | * Confusion Matrix |A | B | C | D | E | F | G | | Class| 0 | 0 | 5 | 7 | 1 | 0 | 0 | | | A=3| 0 | 8 | 82 | 50 | 2 | 0 | 0 | | | B=4| 0 | 11 | 812 | 532 | 12 | 1 | 0 | | | C=5| 0 | 6 | 425 | 1483 | 188 | 6 | 0 | | | D=6| 0 | 1 | 33 | 551 | 285 | 3 | 0 | | | E=7| 0 | 0 | 3 | 98 | 60 | 10 | 0 | | | F=8| 0 | 0 | 0 | 2 | 3 | 0 | 0 | | | G=9| * Performance Of The Multilayer perceptron With Respect To A Testing Configuration For The White-wine Quality DatasetTesting Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 58. 1838 %| 50 %| 55. 5128 %| 51. 3514 %| Kappa statistic| 0. 3701| 0. 3671| 0. 2946| 0. 2454| Mean Absolute Error| 0. 1529| 0. 1746| 0. 1581| 0. 1628| Root Mean Squared Error| 0. 2808| 0. 3256| 0. 2887| 02972| Relative Absolute Error| 79. 2713 %| | 81. 9951 %| 84. 1402 %| * The Result Generated After Applying Multilayer Perceptron On Red-wine Quality Dataset Time taken to build model: 9. 14 seconds| Stratified cross-validation (10-Fold)| * Summary | Co rrectly Classified Instances | 880 | 61. 111 %| Incorrectly Classified Instances | 546 | 38. 2889 %| Kappa statistic | 0. 3784| | Mean absolute error | 0. 1576| | Root mean squared error | 0. 3023| | Relative absolute error | 73. 6593 %| | Root relative squared error | 92. 4895 %| | Total Number of Instances | 1426| | * Detailed Accuracy By Class | | TP Rate | FP Rate | Precision | Recall | F-Measure | ROC Area | Class| | 0 | 0 | 0 | 0 | 0 | 0. 47 | 3| | 0. 42 | 0. 005 | 0. 222 | 0. 042 | 0. 070 | 0. 735 | 4| | 0. 723 | 0. 249 | 0. 680 | 0. 723 | 0. 701 | 0. 801 | 5| | 0. 640 | 0. 322 | 0. 575 | 0. 640 | 0. 605 | 0. 692 | 6| | 0. 415 | 0. 049 | 0. 545 | 0. 415 | 0. 471 | 0. 831 | 7| | 0 | 0 | 0 | 0 | 0 | 0. 853 | 8| Weighted Avg. | 0. 617 | 0. 242 | 0. 595 | 0. 617 | 0. 602 | 0. 758| | * Confusion Matrix | A | B | C | D | E | F | | Class| | 0 | 5 | 1 | 0 | 0| || A=3| 0 | 2 | 34 | 11 | 1 | 0 | | | B=4| 0 | 2 | 436 | 160 | 5 | 0 | | | C=5| 0 | 5 | 156 | 369 | 47 | 0 | | | D=6| 0 | 0 | 10 | 93 | 73 | 0 | | | E=7| 0 | 0 | 0 | 8 | 8 | 0 | | | F=8| * Performance Of The Multilayer perceptron With Respect To A Testing Configuration For The Red-wine Quality Dataset Testing Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 68. 7237 %| 70 %| 61. 7111 %| 58. 7629 %| Kappa statistic| 0. 4895| 0. 5588| 0. 3784| 0. 327| Mean Absolute Error| 0. 426| 0. 1232| 0. 1576| 0. 1647| Root Mean Squared Error| 0. 2715| 0. 2424| 0. 3023| 0. 3029| Relative Absolute Error| 66. 6774 %| 51. 4904 %| 73. 6593 %| 77. 2484 %| * Result * The classification experiment is measured by accuracy percentage of classifying the instances correctly into its class according to quality attributes ranges between 0 (very bad) and 10 (excellent). * From the experiments, we found that classification for red wine quality using  Kstar algorithm achieved 71. 0379 % accuracy while J48 classifier achieved about 60. 7994% and Multilayer Perceptron classifier ac hieved 61. 7111% accuracy. For the white wine, Kstar algorithm yielded 70. 6624 % accuracy while J48 classifier yielded 58. 547% accuracy and Multilayer Perceptron classifier achieved 55. 5128 % accuracy. * Results from the experiments lead us to conclude that Kstar performs better in classification task as compared against the J48 and Multilayer Perceptron classifier. The processing time for Kstar algorithm is also observed to be more efficient and less time consuming despite the large size of wine properties dataset. 7. COMPARISON OF DIFFERENT ALGORITHM * The Comparison Of All Three Algorithm On White-wine Quality Dataset (Using 10-Fold Cross Validation) Kstar| J48| Multilayer Perceptron| Time (Sec)| 0| 1. 08| 35. 14| Kappa Statistics| 0. 5365| 0. 3813| 0. 29| Correctly Classified Instances (%)| 70. 6624| 58. 547| 55. 128| True Positive Rate (Avg)| 0. 707| 0. 585| 0. 555| False Positive Rate (Avg)| 0. 2| 0. 21| 0. 279| * Chart Shows The Best Suited Algorithm For Our Dataset (Measu res Vs Algorithms) * In above chart, comparison of True Positive rate and kappa statistics is given against three algorithm Kstar, J48, Multilayer Perceptron * Chart describes algorithm which is best suits for our dataset. In above chart column of TP rate & Kappa statistics of Kstar algorithm is higher than other two algorithms. * In above chart you can see that the False Positive Rate and the Mean Absolute Error of the Multilayer Perceptron algorithm is high compare to other two algorithms. So it is not good for our dataset. * But for the Kstar algorithm these two values are less, so the algorithm having lowest values for FP Rate & Mean Absolute Error rate is best suited algorithm. * So the final we can make conclusion that the Kstar algorithm is best suited algorithm for White-wine Quality dataset. The Comparison Of All Three Algorithm On Red-wine Quality Dataset (Using 10-Fold Cross Validation) | Kstar| J48| Multilayer Perceptron| Time (Sec)| 0| 0. 24| 9. 3| Kappa Statistics| 0. 5294| 0. 3881| 0. 3784| Correctly Classified Instances (%)| 71. 0379| 60. 6994| 61. 7111| True Positive Rate (Avg)| 0. 71| 0. 608| 0. 617| False Positive Rate (Avg)| 0. 184| 0. 214| 0. 242| * For Red-wine Quality dataset have also Kstar is best suited algorithm , because of TP rate & Kappa statistics of Kstar algorithm is higher than other two algorithms and FP rate & Mean Absolute Error of Kstar algorithm is lower than other algorithms. . APPLYING TESTING DATASET Step1: Load pre-processed dataset. Step2: Go to classify tab. Click on choose button and select lazy folder from the hierarchy tab and then select kstar algorithm. After selecting the kstar algorithm keep the value of cross validation = 10, then build the model by clicking on start button. Step3: Now take any 10 or 15 records from your dataset, make their class value unknown(by putting ’? ’ in the cell of the corresponding raw ) as shown below. Step 4: Save this data set as . rff file. Step 5: From â€Å"tes t option† panel select â€Å"supplied test set†, click on to the set button and open the test dataset file which was lastly created by you from the disk. Step 6: From â€Å"Result list panel† panel select Kstar-algorithm (because it is better than any other for this dataset), right click it and click â€Å"Re-evaluate model on current test set† Step 7: Again right click on Kstar algorithm and select â€Å"visualize classifier error† Step 8:Click on save button and then save your test model.Step 9: After you had saved your test model, a separate file is created in which you will be having your predicted values for your testing dataset. Step 10: Now, this test model will have all the class value generated by model by re-evaluating model on the test data for all the instances that were set to unknown, as shown in the figure below. 9. ACHIEVEMENT * Classification models may be used as part of decision support system in different stages of wine productio n, hence giving the opportunity for manufacturer to make corrective and additive measure that will result in higher quality wine being produced. From the resulting classification accuracy, we found that accuracy rate for the white wine is influenced by a higher number of physicochemistry attribute, which are alcohol, density, free sulfur dioxide, chlorides, citric acid, and volatile acidity. * Red wine quality is highly correlated to only four attributes, which are alcohol, sulphates, total sulfur dioxide, and volatile acidity. * This shows white wine quality is affected by physicochemistry attributes that does not affect the red wine in general. Therefore, I suggest that white wine manufacturer should conduct wider range of test particularly towards density and chloride content since white wine quality is affected by such substances. * Attribute selection algorithm we conducted also ranked alcohol as the highest in both datasets, hence the alcohol level is the main attribute that d etermines the quality in both red and white wine. * My suggestion is that wine manufacturer to focus in maintaining a suitable alcohol content, may be by longer fermentation period or higher yield fermenting yeast.

The Effects of Globalization and Economic Expansion on Russia Research Paper

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Business Emails Essay Example | Topics and Well Written Essays - 750 words

Business Emails - Essay Example I know that if I have had this experience, you must also have other stores that are in the same situation. I would even be willing to help share the cost of producing the catalog. Well Jack, think about this proposal. As I said, your product is second to none but we need to give the customer greater access to it. A full line catalog will increase my sales significantly and I'm not opposed to standing part of the expense of having a catalog printed. Let me know what decision you come to as soon as you can. We are entering our busy season and it would be good to move on this as soon as possible. When we entered into a contract for you to supply our store with custom made desks, we were guaranteed 8 weeks delivery. However, the last 2 orders took 12 weeks to get to the customer. There were several phone calls from you requesting more detailed information from the customer and each time the delivery was further delayed. This has caused problems for our customer and has reflected negatively upon our store and our sales staff. When the desks arrived, they were of excellent quality and there were no complaints on the product, but the late delivery and installation caused my customers to reschedule other design work that was pending. Please understand that there was a pe... We do not wish to enforce that clause at this time. We would prefer to have the product delivered as promised. I suggest we design a specification sheet that is more detailed than the one we currently use. Our staff can get all the information you need on their first customer contact. I understand that your custom made department is difficult to schedule and we have taken that into account. Again, we like your product and wish to continue to use your company to fill that customer need. We simply need to get the desks to our customers as promised to maintain our relationship with them. Please contact me as soon as possible to design a more complete order form. I look forward to hearing from you and working with you in the future. To: MEllington@Notmail.com From: TMartin@Thefurniturebarn.com Subject: Country of Origin May 17, 2007 Ms. Ellington, We have been purchasing fabric for our commercial office furniture for several years and have had few complaints. We recently noticed that some of the fabrics in your catalog were not exactly as labeled. For example, we purchased an Italian fabric for a customer and it was marked as 'Made in Indonesia'. I understand the use of the word 'Italian' may have a generic meaning, but our clients do not perceive it that way. To better inform our customers, I would like to get a country of origin list for your fabrics. I know this will take you some time to put together, but it would greatly aid our commercial clients in making a fabric decision. In addition, it could be used as a sales tool by offering the customer additional information about their custom made furniture. Please get back to me as soon as you can and let me know if there is anything that I can do to help you compile the list. If you could get me the

Marketing Grocery Essay Example | Topics and Well Written Essays - 1750 words

Marketing Grocery - Essay Example Macro-environmental Factors Macro-environmental factors are the environmental factors that affect the marketing strategies of the organization although it has limited chances of manipulating them. They include political-legal, socio-cultural, international and technological factors. The organization can define these factors in terms of scanning for better understanding of all opportunities and threats it may face together with the required strategic devices to adjust so as organization can attain and maintain competitive advantage (Kotler & Armstrong, 2006). Macro-environmental factors originate from outside of the organization and they cannot be changed by the organization’s actions. Specifically an organization can get great challenges when there changes in this factor of environment but the organization itself cannot affect the environment. Legislation The legal environment forces organizations to become complex while affecting business operations directly. It is difficult for businesses to operate their activities without meeting obligations relating to regulations of the law. Some of the regulations that may affect bussines organizations include consumerism regulations, competitive and relations of employees. Most of regulations are associated with regulatory agencies. The US has the powerful regulatory agencies that include Occupational Safety and Health Administration (OSHA), Equal Employment Opportunity Commission (EEOC), Environmental Protection Agency (EPA) and many others. Compliance cost of the regulations is very expensive although most of them are passed on consumers at the end. This means that most of the product prizes may be high to meet the requirements of all the regulations. Therefore, the organizations commodity-marketing price depends heavily on the legal requirements (Schmidt, 2005). Socio-cultural Factors Socio-cultural factors of environment comprises of traditions, values and lifestyles that provide the characteristics upon which the organization operates. Socio-cultural factors of environment affects the ability of an organization to get resources, come up with its own services and operate within a society. Social-cultural factors comprises of all aspects within the society that has the ability to influence the performance of an organization. They can include expanding educational levels, population demographics, values and norms together with the attitude towards social responsibility (Schmidt, 2005). Technological Factors Technology i s a factor that affects the development of strategic plans of an organization. Variation in technology may lead greatly influence the demand of the organizations goods and services. It may also affect its processing techniques and the required raw materials for manufacturing goods. The changing of technology can influence an organization in two ways. First, it may provide new opportunities for the organization to explore and get better returns. On the other hand, it may cause threats to the survival status of the organization, the product or industry. Technological improvements continue to increase at a very high rate, which requires that all firms be a constant revolution to survive. Balance of Payment Balance of payment is the net difference in goods that bought and sold by business people of a country. It

Weather, Hurricanes, & Solar Radiation Essay Example | Topics and Well Written Essays - 500 words

Weather, Hurricanes, & Solar Radiation - Essay Example There are hurricanes that first appear on June or July and that means they will stay a little longer than those hurricanes that appear in August (Elsner, 2009, p. 61). Generation of hurricanes begins to cool with the cooling of the water in late autumn. At this time, the weather pattern fails to favor the development of tropical development. North Atlantic seasonal cycle pronounces its peak activity during August and September where only 17 percent of activity happens beyond the three months duration of august to October (Elsner, 2009, p. 65). The examination of North Atlantic took into account the mean number of for all hurricanes in a year’s time and for total. North Atlantic experienced hurricanes every season due to the presence of the budget heat and seasonality effects. Budget heat effects influence the hurricanes to occur mostly from June through to December. During this time, there is perfect balancing of heat that earth absorbs inform of radiation. If this absorption did not occur, then the earth would have extremely high or low temperatures. The seasonal variability has many, but important parameters associated with the North Atlantic Hurricanes. One such parameter is the El Nino that is helpful in determining, through analysis, whether the season would be active. El Nino takes into account a number of atmospheric variables. El Nino has a characteristic of warm sea temperatures on the surface especially over the equatorial pacific. The temperature relates to westerly winds of up to 200-millibar on the sea. The El Nino also has a link with the Caribbean surface pressure and the western Atlantic. When El-Nià ±o occurs, stronger Westerlies bring fourth fleeing over the hurricane producing areas of the western Atlantic as well as generating higher surface pressure. These occurrences suppress the hurricane development especially if it occurs earlier than October to November. El Nino southern oscillation, therefore, plays a vital role in

Ecological Dimension of Globalization Essay Example | Topics and Well Written Essays - 1750 words

Ecological Dimension of Globalization - Essay Example Venturing into a new nation drives a company to integrate the cultural characteristics, and the government rules and regulations. Thus it creates a major integration of cultures around the globe. Globalization influences the economic, the political, the social as well as the ecological balance. Even the companies around the world have realized this that the long term growth depends on saving the natural resources and utilize them properly such that the ecological balance is maintained throughout. In recent times the ecological perspective of globalization has a major contribution to the growth and development of a nation and has therefore raised the most significant concern centering on globalization. Ecological Globalization: Buckley observes that the ecosystems take into account the admixing of substances through transmission of air particles, movement of water body and movement and migration of the animals and the people around the globe. All these form major routes of connectivit y in the ecosystems. There is a single atmosphere connecting the globe. The transportation of gases, minerals, even the biodegradable materials cause a great deal of harm to the natural environment. This causes a particular phenomenon called ‘greenlash’ which is caused when transformations in the environment bounded in a smaller area have an astonishing effect in broader areas. The heavy draught in 1930 had a severe effect on the farmers across Midwest of U.S. The dearth of crops led to soil erosion and degradation and it caused powerful dust storms. This huge blow of dusty winds resulted in the so-called ‘infamous Dust Bowl’, which degraded the quality of air and affected the health patterns of the public at large throughout the country. Due to increasing Globalization taking place, there has been an inadvertent introduction of harmful species and pathogens like fire ants from South America and the SARS virus being transported from China, which could have an overwhelming effect on the society at large. Sources revealed that the USA currently allocates about $120 billion per year to eradicate the harmful species causing a lot of harm. So proper diagnosis of the ecosystem will help in unearthing the unknown movement of the harmful species and thus could save the ecology. Buckley suggests provision of information regarding processes that encircle a larger area of time and space and also proper analysis of the processes that cover the genomic and expand to continental from every bit to decades. Moreover understanding the social and behavioral patterns of the human movements in scientific models and unleashing the connectivity patterns among the ecosystems will provide a lot of help to gauge accurate predictions of any future ecological transformation. (Buckley) Study of a Physicist group on Carbon dioxide capturing from air: Rudolf observes that in the recent times some of the greatest minds have been thinking over the issue of reducing the threat emanating from the climatic change that has preoccupied the world, thanks to the effect of Globalization, which although has helped in making the bigger world a smaller place, but has contributed to ecological imbalance. In recent times with greater globalization and technological advancement the pollution in air has increased manifold. So the eminent experts are now considering a newer and effective vision of capturing carbon dioxide from air. This concept has undergone major difference with pulling out carbon dioxide from the reactors and coal-based plants before the gas makes its entry into the air. But doubts have been raised in serious terms whether the project of capturing carbon dioxide from air is economically viable as pulling out 1 ton of Carbon

Environmental sae Assignment Example | Topics and Well Written Essays - 250 words

Environmental sae - Assignment Example The various footprints left by the modern progressive man are bringing us close to an unstoppable catastrophic end. Understanding our role in this damage and providing a healing touch is a shared responsibility of every global citizen. My connect with nature dates back to memories of childhood where thankfully technology was much primitive and we were not exposed to the myriad of gizmos that an average kid today is offered. Our playtime meant outdoor activities where each moment we were touching and connecting with Nature. Vacations meant a tour to our native countryside, where we were awakened by the rustling breeze, tweeting birds and the chirping of the insects. We often snacked on the farm grown fruits and vegetables. Our hands did not hold any videogame but were soiled with mud. Touching and feeling the soil is such a great euphoria, you find an instant connect with nature. Camping nostalgia were filled with the unfolding mysteries of the night, of trying to decipher what the whispers of the nocturnal life conveyed to us, of what the retired life forms anxieties were. As much as we enjoyed absorbing ourselves in storybooks, we cherished lying endlessly under a clear sky gazing at the stars. I remember often t aking to nature trails, where we learnt about numerous animals, insects, birds, plants, habitats and all that you now hunt in libraries. The learning was natural and permanent. The love for the environment was instilled effortlessly and for life. The intimacy I had with Nature has created an eternal bond. I am immensely thankful to the upbringing I had which has led to my acquaintance with the earth in such a positive manner. My rearing days in which nature was so much a part, has molded my entire persona and brought calmness to my ethos. Childhood learning is etched permanently in our character. It has built herculean sensitivity and a responsibility towards the environment. The growing up years in Nature’s arms has not only

A review on the play Buried Child By Sam Shepard Term Paper

A review on the play Buried Child By Sam Shepard - Term Paper Example The play explicitly reveals how different family values and the concept of an American family were a few decades back from how they are perceived today by the American nation in general. The concept of the American Dream is also kept highly obvious throughout the play as different portions from the play can be easily connected by the readers or viewers to construct the message conveyed by the author, which identifies how the youth remains incapable of carrying out the American Dream either due to physical or emotional reasons. Disappointment and disillusionment displayed by all the main characters in the play due to the basic requirements of the American Dream not getting fulfilled also forms a major part of this celebrated play. It is easy to judge from the play that Shepard has tried to convey the apparently boring information about myriad actual frustrations and problems faced by the American nation a few decades back while taking care to present that grave and sensitive informati on in a highly engaging and entertaining way. The author of the play Buried Child, Sam Shepard happens to be one of the mainstream American playwrights, actors, and film directors. Being closely associated with the entertainment media and having maintained his first-class status almost through out his active years makes Shepard a highly distinguished and wildly talented artist which is the reason why he also got awarded for writing the play under discussion in this critical review. In addition to advancing his myriad talented ideas in the field of drama and production, he also served as a learned teacher for quite many years busily teaching the naà ¯ve students crucially important tactics of acting and writing which explains why many of his literary works bagged worldwide critical acclaim. He is recognized as a truly shrewd and keen playwright who is

Meaning of Life and Fast Lane Essay Example for Free

Meaning of Life and Fast Lane Essay leave well enough alone | let well enough alone Meaning: If you leave well enough alone, or let well enough alone, you dont try to improve or change something thats already good enough. For example: The kids seem happy enough now so lets just leave well enough alone and forget about finding a new school for them.? skate on thin ice- Meaning: If youre skating on thin ice, youre doing something risky, or youre in a situation that could quickly become dangerous. jockey for position- Meaning: If you jockey for position, you try to get yourself in a good position in relation to others whore competing for the same opportunity or the same goal. let the cat out of the bag Meaning: If you let the cat out of the bag, you let someone know a secret. You could have knocked me over with a feather. Meaning: You can say you could have knocked me over with a feather to show how surprised you were when something happened, or when you heard about something. add fuel to the fire If you add fuel to the fire, you do something to make a bad situation even worse. Ahead of the game You are ahead of the game if you have an advantage over your competitors in any activity in which you try to do better than others, such as in business, academia, sports, etc. all the rage Informal If something is all the rage, its very popular or its in fashion at the moment. asking for trouble If someone is asking for trouble, theyre doing something risky that could lead to a problem. itchy feet Informal If you have itchy feet, you feel the need to go somewhere different or do something different. in the long run If you talk about something in the long run, you mean over a long period of time. At cross-purposes If youre at cross-purposes with someone, you think youre both talking about the same thing but youre actually talking about different things. at loose ends If youre at loose ends, you feel restless and unsettled because you dont have anything to do. a blessing in disguise You can say something is a blessing in disguise if it appears to be bad at first, but it results in something very good in the end. back to square one If you have to go back to square one, you have to stop and start again, usually because something isnt working as well as expected. bite your tongue | hold your tongue If you bite your tongue, or hold your tongue, you force yourself not to say something you really want to to say. the icing on the cake | the frosting on the cake If something is the icing on the cake, or the frosting on the cake, it makes a good situation or a good result even better. joie de vivre If you have joie de vivre, you feel the joy of living. make hay while the sun shines If you make hay while the sun shines, you make good use of the chance to do something while it lasts. Fast friends good, loyal friends. The two of them had been fast friends since college. See also: friend fast one a clever and devious trick. (Compare this with pull a fast one. ) That was a fast one. I didnt know you were so devious. This was the last fast one like that youll ever catch me with. life in the fast lane a very active or possible risky way to live. (See also in the fast lane. ) Life in the fast lane is too much for me. See also: lane, life make short work of something to deal with or finish something quickly We made short work of the food that was put in front of us. fast and furious if an activity is fast and furious, it is done quickly and with a lot of energy The first half of the game was fast and furious with both teams scoring three goals each. Ngn av dessa till din story a queer fish Meaning: If someones a queer fish, they are a bit strange and can sometimes behave in an unusual way. For example:Your great grandfather was a queer fish, Johnny. He used to write funny poems and then hed read them aloud to everyone on the train on his way to work let the cat out of the bag Meaning: If you let the cat out of the bag, you let someone know a secret. For example: Wed planned a surprise party for Donna, but some guy she works with let the cat out of the bag, so now she knows.? Dont forget that this is a secret, so whatever you do, dont let the cat out of the bag.? †There? s an elephant in the room† – Meaning: If you make a killing, you make a lot of money from a sale or a deal of some sort. For example: My aunt made a killing when she bought some shares in a company as soon as they were issued, and sold them a few weeks later for three times what she paid.? Lots of people made a killing when property values went so high back in the nineties. †Bark is worse than his bite†, †put your money where your mouth is† – prove it.. alot of not air? all hell broke loose Meaning: You can say all hell broke loose if a situation suddenly became violent or chaotic. Bad news travels fast ’ Bad news means news about bad things like accidents, death, illness etc. People tend to tell this type of news quickly. But good news (passing an exam, winning some money, getting a job etc) travels more slowly. Least said soonest mended Possible interpretation: When we do or say something bad to someone, a long apology and discussion does not help. In such a case, the less we say the better. Its written all over your face. If you say its written all over your face, youre saying that the expression on someones face is showing their true feelings or thoughts. Group 20 ENOUGH IS ENOUGH Words Relating to More Than Enough (did you get it? ) ample copious lavish myriad plethora profuse prolific superfluous surfeit Memory tips: use these mnemonics (memory devices) to boost your vocabulary. Make up your own memory clues for words in this lesson that are personally challenging. Add these tips-and your own-to your Vocabulary Notebook. Copious let yourself see the word copies within copious, and think â€Å"lots of copies. † Certainly â€Å"lots of copies† leads to the defining ideas of abundant and plentiful. Plethora Let the ple lead you to plenty. When you write plethora in your Vocabulary Notebook, underscore the ple with a colored pen or marker. Superfluous The prefix super means over and above. This knowledge is helpful because superfluous means â€Å"above what is needed; extra. â€Å" Surfeit Like super-, sur- is also a prefix meaning over and above. So a surfeit is an amount over and above what is needed. Using apperception, (http://www. merriam-webster. com/dictionary/apperception) link a word you most likely already know, surplus, to the new word, surfeit. These two-syllable synonyms even have the same number of letters! Solidify the meaning of surfeit in your memory. Ample think of the word sample but get rid of the S. and since Ample means more than enough you can think of many samples! Lavish think of marangsvisch with lakris sas! And put the letters LA from the word Lakris instead of S. Profuse(overflodande) think about refuse and proactive, because proactive is â€Å"overflowing† with vitamins. Change the re to pro Prolific, the word productive has the same meaning, so take the PRO from productive and add it with lyric which at least makes me think of lific. So think productive lyric. Myraid (skiftande mangd) think My ride†¦ Group 19 (2nd cluster for Fall Term) Wonderful You Are! Words Relating to Praise and Respect (did you get it? ) Acclaim accolade adulate esteem eulogize exalt extol laud panegyrize revere venerate Laud If you know applaud, then simply connect the new word laud to the word you already know, applaud. The meanings of these two verbs are closely connected. Plaudits means praise. Revere You remember Paul Revere from American History, right? Now, I am sure you’ll agree that it’s only right to respect, or revere one of our nation’s Founding Fathers! Another tip: you can repeat this chant to yourself over and over â€Å"Revere the Reverend. Revere the Reverend. Revere the Reverend† Acclaim think of ass, but with cc, and slajm (slaim)! Accolade think of assa, but with cc and chocolade Adulate ad-ul-ate Esteem think of S team, but with double e Eulogize think ekoloogisk / EU logisk Exalt think Exaltera, without era, since exalterad means vara upprymd like exalt. Extol, ex stol Panegyrize think, pannkakor risgrynsgrot Venerate Think â€Å"ata vanner†, but switch place, let vanner lead you 2 Vener, and ata-ate.

Impact of the Civil War on the South of America

Impact of the Civil War on the South of America What was new about the New South? The following will discuss what, if anything was new about the New South that emerged in the United States after 1877. Prior to the American Civil War the old South had predominantly been an agrarian economy in which blacks were slaves who had worked on the cotton plantations, factories, or had been domestic servants. Cotton had been the major commodity of the economy, which had mainly been exported to Britain. The American Civil War had been fought over the issue of slavery and whether the Southern States had the right to cede from the United States to preserve the institution of slavery (Hobsbawm, 1975 p.184). The Civil War brought social and economic changes to the South. Its cotton exports had been drastically reduced, its agricultural and industrial output declined sharply, whilst much of its infrastructure was destroyed. During the civil war President Lincoln had proclaimed the emancipation of all slaves, whilst blacks had fought with distinction on the Union side. The devastat ion brought to the South by the civil war meant that a period of reconstruction was needed afterwards. Leading white Southerners such as Henry Grady called for a New South. The blacks that were freed, as a result of the Confederate States losing the civil war, also anticipated a New South. The blacks in the Southern States expected their lives to be better following the Union’s victory and the era of reconstruction. In many respects strong arguments can be made that their lives got worse rather than better. Du Bois for one contended that blacks â€Å"had fought slavery to save democracy and then lost democracy in a new and vaster slavery† (Du Bois, 1935 Chapter 1). The result of the American Civil War in theory was that the four and a half million blacks in the United States were all free and equal with the white population. However, the end of the Reconstruction made those equal rights a mockery in the New South (Brogan, 1999, p.348). That the New South was not a new place for the better for its black population was due to the way in which the American Civil War ended. Lincoln’s assassination was the South’s revenge for losing the War. Lincoln’s successor, Andrew Johnson was less capable of ensuring that the South changed in ways that benefited its black population. From his presidency onwards, the North did very little to ensure Southern blacks had any meaningful rights (Brogan, 1999, p.348). Southern blacks were only able to exercise their political rights whilst the Union forces remained in the South, those rights ceased to exist in reality once the South was left to run itself. The suppression of Southern blacks was arguably worse once they had been formally freed than when they had been slaves. Racial discrimination, the fear of violence and poverty meant that the New South was no better than the Old South had been (Hobsbawm, 1975, p.143). Neither the South in general or its repressed bla ck population in particular, gained as much from the United States rapid industrialisation from the 1870s onwards as the North did (Hobsbawm, 1987, p.35). In the New South there was a strong desire amongst the defeated Confederate States to make its black population subject to its strict political and economic controls for as long as possible. The abolition of slavery had not seen the end of the cotton plantations. However, jobs and better pay were given to the whites rather than blacks. Blacks were given the lowest paid jobs and they could be punished for not taking them. For many blacks the newness of the New South was the increased harshness of the discrimination they were subjected to. Whilst the whites in the New South had been unable to defeat the Union during the American Civil War, they were in a position to make life very unpleasant for the black population of the New South. Much discrimination was given legality through the ‘Black Codes’ of the Southern legislatures that severely restricted the rights of former slaves. Slavery had, in many respects, been restored in a less obvious form (Brogan, 1999, p.352). Thos e blacks that tried to exercise their legal rights found legal and political obstacles placed in front of them, which effectively deprived them of all those rights. They also faced violence and intimidation on a regular basis (Bradbury Temperley, 1998, p.153). The Southern states were able to prevent the Constitutional Amendments that abolished slavery and gave freed slaves their rights having a positive impact as they were responsible for their enforcement, rather than the national government (Murphy et al, 2001, p.315). States such as Louisiana had no intention of giving blacks any rights on the grounds it was unconstitutional to do so (Du Bois, 1935, p.454). A series of measures which were known as Jim Crow laws were used by the Southern States to segregate and repress their black populations. Although they claimed the segregated services were of equal quality, this was a sham to excuse neglecting their black communities (Cobb, 1992). Overall Jim Crow Laws delayed the economic d evelopment of the New South, whilst they institutionalised racial discrimination and segregation. The cost of providing segregated services lowered the quality of education, housing, and transport in the New South. Segregation had even been endorsed by the Supreme Court as long as services were of equal quality, which few bothered to check. Such discrimination was contrary to the way Henry Grady believed the New South should have developed. Grady argued that the best way to industrialise the New South was to treat blacks as equal partners rather than inferiors. Therefore social justice and equality were just as important as capital and machinery in building the New South (Mauk Oakland, 1995 p. 108). Grady believed that the New South would be the perfect democracy as long blacks were treated equally. The civil war had been an opportunity for the South to stop its outdated reliance on slavery and cotton (Harris, 1890 p. 15). Segregation, as well as being morally questionable, kept th e South relatively poor and backward in relation to the rest of the country (Hobsbawm, 1975 p.184). Poverty was a new feature of the New South. Poverty paradoxically enough had not been an issue for blacks in the South when they had been slaves. Although, they had no freedom, slaves were provided with basic levels of accommodation and food, on the logical basis that unhealthy slaves did not work as well as healthy ones. Southern slave owners had generally treated their slaves well enough for their numbers to increase at the same rate as the white population (Bradbury Temperley, 1998 p. 153). Defenders of slavery had maintained that it kept the Southern states economically competitive, kept the black population at subsistence, whilst ensuring that all white men could find paid work (Brogan, 1999, p.371). Poverty, as freed slaves found to their cost, was as restrictive of their freedom as actual shackles had been. Freed slaves had to compete with whites to gain jobs. Poverty was closely linked with racial discrimination, in that whites were given better jobs and better working condi tions, even when there were better-qualified blacks to do the jobs. Discrimination in the provision of education, housing and medical care also contributed to keep the blacks repressed and in poverty (Cobb, 1992). Blacks were disenfranchised by their poverty, whereas loopholes were used to ensure that poor whites kept the vote (Hobsbawm, 1987, p.24). Another new feature of the New South was the increased levels of urbanisation. Cities such as New Orleans and Birmingham increased in size during the reconstruction era. The urbanisation of the New South was result of the industrial expansion encouraged by the Southern states and the migration of people trying to escape rural poverty. Migrating to the cities did not reduce racial discrimination and it barely increased opportunities for black people. Birmingham was the only city to achieve industrialisation on a major scale in the New South. The South was economically held back by its deliberately uneducated blacks and its under educated whites (Brogan, 1999, p.372). Southern blacks had also migrated to northern cities such as New York to increase their opportunities and to escape racial discrimination. The North was still prone to such discrimination even if it did give greater opportunity and blacks faced lower threats of violence. The Southern states had been motivated to enact the ‘Black Codes’ to restrict migration to both Southern and Northern cities (Brogan, 1999, p.363). Unemployment was a more obvious problem in the New South than it had been in the old South. Unemployment and low paid employment in a country with no public welfare provision was a serious problem, especially for blacks that were discriminated against and could not afford the basic necessities of life (Hobsbawm, 1987, p.103). Employers and plantation owners in the New South as a whole tended to keep the relationship between poor blacks and poor whites as unfriendly as possible. Factory and plantation owners feared that that if black and white workers had a good relationship they would form effective trade union movements and threaten the profits of the owners (Lewis, 1994). Discrimination in favour of white workers alienated blacks from them, whilst owners and employers kept control of their workers by threatening to use black workers as strike breakers. Such tactics were effective at preventing the emergence of trade unions but did nothing to improve race relations in the New South (Brogan, 1999, p. 371). The creation of Birmingham, Alabama was a symbol of all that was new in the New South. The place had not existed before 1871, and calling it Birmingham after one of the most industrialised cities in Britain was a statement of intent. Birmingham, Alabama was to be the industrial heart of the New South (Vann Woodward, 1951). Henry Grady himself cited Birmingham as the best example of his plans for a New South, yet historians have argued as to whether the development of Birmingham was similar to the industrial development envisaged by the plantation owners prior to the civil war (Lewis, 1994). Post civil war reconstruction gave the Southern States the opportunity as well as the need to reconstruct their economy. Falling prices for raw cotton meant that plantation owners switched their attention to manufacturing finished cotton products in new cotton mills. Attempts were also made to diversify the Southern economy away from cotton by developing coal, steal, and iron production. During th e reconstruction period the Federal government had tried to enhance the economic prospects of the South by having the railroads rebuilt and extended to improve the transport links with the rest of the United States. Southern plantation owners, investors from the rest of the United States, as well as foreign investors funded industrial development. One feature of the New South did not change from the old South; it was still economically weaker than the North (Spiller et al, 2005 p. 80). The economic changes of the New South only benefited a few plantation and factory owners, some of who became much wealthier than they had been before the civil war (Hobsbawm, 1987 p. 24). A new feature of the New South was the high level of violence directed against the black population by white racists. In the immediate post-civil war period the formation of the Ku Klux Klan demonstrated the popularity for white supremacist ideas in the Southern states. The Ku Klux Klan added murderous intentions to their racist outlooks. The emergence of the Ku Klux Klan led to many thousands of lynchings and murders throughout the New South. Blacks found it very difficult to protect them-selves from racially motivated violence on such a large scale. They received no meaningful levels of protection from the police, the courts or the state authorities, which often sympathised with white supremacist views and were therefore unwilling to take action against the Ku Klux Klan or individual racists. Racism and prejudices were built into the ‘Black Codes’ that made a mockery of the post civil war Constitutional Amendments. The Federal courts and governments were unwilling to i ntervene in the affairs of the New South, as far as the Federal governments was concerned the Constitutional Amendments were fully operative in the South. Nobody in Washington DC seemed to be bothered to act upon the plentiful evidence of racial murders and discrimination in the New South. Between 1887 and 1917 official United States government figures showed that 2,734 blacks were murdered in racially motivated crimes, the vast majority in the New South. Before that period the death toll had been even higher, and only the presence of the Union army before 1877 had prevented further bloodshed (Murphy et al, 2001 p. 320). In some respects there were few new aspects in the New South. The combined effects of the Black Codes and Jim Crow laws meant that the New South restricted the freedoms of freed slaves to such an extent that slavery might as well have been retained. Economic, social, and political restrictions meant that insignificant numbers of blacks could vote in elections, own their land or gain education in the Southern States (Cobb, 1992). Low wages, unemployment, high rents, and direct discrimination were as effective as the Black Codes at keeping black people poor and powerless (Du Bois, 1935 p. 454). It is no wonder that many blacks believed that after reconstruction the New South made their lives worse than before. For them the only difference the old and new South was that they were underpaid for working on other peoples’ land and in other peoples’ factories rather than being paid at all. Only a small number of freed blacks had been able to make successes of their lives befor e the Jim Crow laws began to restrict opportunities. Only 4,000 freed slaves managed to purchase land in the New South, and most of them could not buy enough land to run successful farms (Murphy et al, 2001 p. 316). The New South was not a content place; the whites still fumed at their defeat in the civil war and re-imposed a quasi slavery upon the nominally free blacks (Hobsbawm, 1975 p. 143). Therefore, there were new aspects to the New South, although those aspects were not all positive or progressive in their nature. The Southern States were changed socially and economically as a result of the American Civil War. The economic consequences of the civil war were apparently severe. Agricultural and industrial outputs had been reduced, whilst the infrastructure of the Southern States had been badly damaged in the war. The war had disrupted the export of raw cotton which, had been the basis of the old South’s economy. Plantation owners had claimed that their plantations would be unprofitable with the abolition of slavery, a claim that proved unfounded due to the low wages they paid to white and black workers alike. The freed slaves found that life in the New South was in fact harsher in some respects than slavery. This was due to the increased racism and discrimination that was a new feature or perhaps at least a more obvious feature of the New South. The idea of the New South was promoted by the likes of Grady, as well as the new industrial centres such as Birmingham, Alabama and Atlanta. Overall in the period after 1877 industrial output in the Southern States did increase with the development of cotton mills, coal, steel, and iron production, although it still lagged behind the rest of the United States. Industrial development did not improve the lives of most people in the New South, just factory and plantation owners and the profits of outside investors. The legacy of the civil war was a long and bitter one, with the Southern whites repressing the blacks to compensate for defeat and demonstrate their alleged supremacy. Bibliography Bradbury M Temperley H, (1998) Introduction to American Studies 3rd edition, Longman, London Brogan H, (1999) The Penguin History of the USA, Penguin, London Cobb J C, (1992) The Most Southern Place on Earth: The Mississippi Delta The Origins of Regional Identity, Oxford University Press, Oxford, and New York Du Bois W E B, (1935) Black Reconstruction in America, London Harris J C, (1890) Henry W. Grady: His Life, Writings, and Speeches, Cassell Publishing Co, New York Hobsbawm E, (1975) The Age of Capital 1848-1875, Weidenfeld Nicholson, London Hobsbawm E, (1987) The Age of Empire 1875-1914, Weidenfeld Nicholson, London Lewis (1994) the emergence of Birmingham as a case study of continuity between the antebellum planter class and industrialization in the new south agricultural history (spring 1994) p. 62-79 Lewis (2003) Mauk, D Oakland, J (1995) American Civilization Routledge, London Murphy D, Cooper K Waldron M, (2001). United States1776-1992 Collins, London New south plantation kingdom -the new south writings and speeches of Henry Grady, (1971) The beehive press savannah, Georgia Spiller J, Clancy T, Young S, and Mosley S (2005) The United States 1763 – 2001, Routledge, London

To Dam, or Not To Dam Essay -- Marc Reisner Ecology Environmental Essa

To Dam, or Not To Dam â€Å"The River, slightly milky from glacial sediment, tumbles down rocky chutes, boils through tight canyons, and glides across beds of agatelike stones. In the distance, poking through storm clouds, are plunging slopes dense with virgin hemlock and fir†¦Intruding into this primeval scene are two decrepit dams† (Reisner, 382). In this essay by Marc Reisner, his standpoint on the issue of dams is very well seen. Reisner talks of the ecological damages that dams create. The debate over dams has been heated in recent years, their harmful effects and overall abundance is the topic for such debates. But are dams as bad as everyone says they are, do dams do more harm than good, or more good than harm? Emphasis should be placed on comparing the ecological effects verses the economical benefits. There are many pros and cons for the ecological side of this debate. One pro is that dams help areas that would otherwise be waterless and barren support life. Taken from a pamphlet prepared by the Committee on Public Awareness and Education, â€Å"Water is the vital resource to support all forms of life on earth. Unfortunately, it is not evenly distributed over the world by season or location. Some parts of the world are prone to drought making water a scarce and precious commodity, while in other parts of the world it appears in raging torrents causing floods and loss of life and property. Throughout the history of the world, dams and reservoirs have been successfully in collecting, storing and managing water needed to sustain civilization† (CPAE, 1). This is a major benefit to the ecology of the world, as the quote stated water is the basis of life, the human body depends on water for survival. Although ther... ...to benefit the United States. Even those who oppose dams benefit from them in one way or another, destroying them would give our nation’s economic system a detrimental blow. Finally for the solution for this dilemma, I don’t believe there is an general solution. Each dam should be looked at individually, if the costs outweigh the benefits, then it should be destroyed, and vice versa. The issue of dams will not be easily solved. Ever since the first dam was built there has been controversy, and as long as one stands so will the debate. References McKibben, Bill. Daybreak. In A Forest of Voices: Conversations in Ecology – 2nd Edition (pp. 156-169). Mayfield Publishing Company. Committee on Public Awareness and Education. (1997, May). Benefits and Concerns About Dams – an Argumentaire. Retrieved March 3, 2003, from http://www.icold-cigb.org/BandC.PDF

The Multicultural Education Essay -- essays papers

The Multicultural Education John Searle addresses the â€Å"major debate†¦ going on at present concerning†¦ a crisis in the teaching of the humanities.† [Searle, 106] He goes on to defend the canon of works by dead white males that has traditionally made up the curriculum of liberal arts education. I disagree with many of his arguments, and believe that multiculturalism should be taught in the university, but this is just the tip of the iceberg. Openmindedness will take much more than just minimal changes in curriculum. In order for works by different races and women to be judged and studied alongside works by white men, they have to be seen as equal to works by white men. They have to be studied for their literary content, not for the statement they make about feminism or race. We don’t just need to evaluate them by the same standards, we need to change the standards. The standards set by the traditional liberal arts education have been set by white males and are inherently biased. Ne w standards need to be set that are as openminded as we want students to be. This is a trend that needs to be started way before college. A diverse curriculum should be taught throughout a person’s education, because that is what will produce well rounded, openminded individuals that will change the tradition of oppression in society. Searle says, â€Å"We should not be embarrassed by the fact that a disproportionately large percentage of the major cultural achievements in our society have been made by white males.† [Searle, 118] To this, I say yes we should! We should be embarrassed that there are people who don’t see that this â€Å"disproportionately large percentage† is not due to the overwhelming intelligence of the white male, but to centuries of oppression. Our culture hasn’t nurtured the intellectual efforts of women or minorities, their ideas and pursuits have been repressed, probably out of fear. We have a society dominated by white males, it shouldn’t be surprising that literature is too. We need to change the way our society view women and minorities. Trying to do this by changing the curriculum of college students is pointless. We need to start from the beginning, with the children. Children need to be taught that they exist as a part of the world, rather than just as a part of America. If worldliness is encouraged at a young age, it will replace the â€Å"us† and â€Å"them† mentali... ...be expanded to new ideas and cultures. University education should be an extension of, not a replacement for, grade and high school education. I think that, in general, we are on the road to a more openminded society. Children and young adults are more accepting than their parents and their grandparents, and I think if we encourage and appreciate this trend, it will continue on to their children. But, it is not the case in all families, and even if it were, family is only one of the influences in a child’s life. As they get older, they are increasingly influenced by outside factors, such as school, the media and culture. We need to encourage children and open their minds with all the tools we have available to us as a society. Children are the ones that will make up the bulk of the influential population in twenty years. Broadening their horizons is broadening the future’s horizons as well. As we change the way our society views women and minorities, as they are embraced as equals, we will start to see literary works of the same caliber, if not better than the works encompassed by the traditional canon. Then , we will be on the road to having a more diverse university education.