A Summary of Salvation by Langston Hughes free essay sample

Summary of Salvation by Longs Hughes Salvation was written by Longs Hughes. This story is about when Hughes going on thirteen, he was saved from sin. However, his not really saved. Church had had a special meeting for children. The meeting was about to bring the young sinner who had not yet been brought to Jesus. He was waiting for a light. Because hes aunt and many great old people told him that when his saved he will saw a light, and meeting happened to him Inside, and Jesus will came to his life.God would be with him from then on. So he was waltzing for God to come to him. However, he was waltzing and waiting, but nothing happened. Finally all the children had gone but only him and a boy called Wesley were still Walt for Jesus to come. Its getting late, they were still waiting. We will write a custom essay sample on A Summary of Salvation by Langston Hughes or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page Finally, Wesley said to him that he doesnt want to Walt anymore, then he Just get up and saved. Wesley told a Ill and left, so now Hughes was all lone on the mourners bench.His aunt and other prayers came and sat around him, they pray for him. Hughes still waiting. He wanted to see him, unfortunately, nothing happened to him, he didnt see the light. His aunt cried because Jesus didnt come to see Hughes. Its getting really late. Hughes started to blame himself for holding everybody up so long. He now wanted to know what Jesus thought about Wesley. The one who didnt see God either. Wesley told a lie, but nothing happened to him.God didnt punish him. So Hughes lie, too. He got up and be saved. The last lamp of God finally be saved. Everybody were so happy. However, in that night, Hughes cried. Alone in the bed. His aunt heard that, and told his uncle he cried because he saw Jesus today. He couldnt tell her the truthhe didnt see Jesus. He couldnt tell anyone that he had lied. From that time, Hughes didnt believed there was a Jesus any more because he didnt come when he needed help.

Early Action Schools Complete List of EA Colleges

Early Action Schools Complete List of EA Colleges SAT / ACT Prep Online Guides and Tips Would you like to know where you'll be going to college as soon as possible? If you apply early action, then you might have your plans all set by winter break of your senior year. That's a big leap forward compared with waiting for regular notifications in March or April! A large number of schools offer an early action deadline in addition to a regular decision deadline. This guide will go over what you need to know about applying early action and give you a comprehensive list of all the schools that offer it. To start, how does early action work? What You Need to Know About Early Action Early action can be a great option if you've done your college research and have prepared all the different parts of your applicationby the November deadline. Data shows that a greater percentage of early action candidates get accepted than regular decision candidates. However, this higher admission rate might reflect the competitiveness of early action applicants rather than suggest that applying early gives you a special advantage. A good rule of thumb to go by is this: apply early if you're organized and have a strong application ready to go. Wait until regular decision if your application would be stronger with a couple more months of preparation. Remember, your highest priority should always beto send the best application you can. The most common deadlines for early action are November 1 and November 15.You'll typically hear back from these colleges in mid-December. Hopefully, the news makes for a happy New Year! Even though you hear back early, you're not obligated to respond to an offer of acceptance any earlier than the national response date, which is May 1.So feel free to hang onto that acceptance letter as you wait to hear back from the rest of the colleges you applied to. You can then compare offers and financial aid packages once you've received all your admissions decisions. Just like with regular decision, your application may be accepted or denied. But there's also a third option: getting deferred. This means that your application will be pushed into the regular decision pool to be reviewed again in February or March. If you're deferred and remain very interested in the school, you could send along mid-year grades or test scores if you think they'll help your application. You could also call the admissions office to find out whether there's anything you can send to strengthen your candidacy. For the most part, you can apply to as many early action collegesas you want; however, a few schools have restrictive or single-choice early action,which means that you can't apply early action anywhere else. (Note that you can still apply regular decision elsewhere.) Schools with restrictive early action policies includeHarvard, Princeton, Stanford, and Yale. Besides these four universities, though,what colleges offer early action deadlines? Want to build the best possible college application? We can help. PrepScholar Admissions is the world's best admissions consulting service. We combine world-class admissions counselors with our data-driven, proprietary admissions strategies. We've overseen thousands of students get into their top choice schools, from state colleges to the Ivy League. We know what kinds of students colleges want to admit. We want to get you admitted to your dream schools. Learn more about PrepScholar Admissions to maximize your chance of getting in. Can't wait to find out which campus you'll be admiringfall foliage on? Applying early action might be for you! Complete List of Early Action Schools by State Below is the complete list of schools withearly action, organized alphabetically by state. Some popular schools include Caltech, MIT, Georgetown, UNC, the University of Chicago, the University of Michigan, Notre Dame, UVA, and Villanova. Search for your school of interest by state, or use the ctrl + F function on your keyboard to type in the first few letters of the school and be brought right to it. Note: There are currently no schools in Alaska, Arizona, Hawaii, Kansas, Missouri, Montana, Nebraska, Nevada, North Dakota, South Dakota, or Wyoming with Early Action admissions. We'll be sure to update this article if this changes. Alabama Auburn University Birmingham-Southern College Arkansas Hendrix College University of Arkansas California Azusa Pacific University Biola University California Baptist University California Institute of Technology (Caltech) California Lutheran University Chapman University Concordia University Irvine Loyola Marymount University The Master's University Menlo College Mills College Mount Saint Mary’s University Point Loma Nazarene University Saint Mary's College of California Santa Clara University Simpson University Soka University of America Stanford University University of the Pacific University of Redlands University of San Francisco Vanguard University of Southern California Westmont College Whittier College Colorado Colorado College Colorado State University University of Colorado Boulder University of Denver Connecticut Fairfield University Sacred Heart University United States Coast Guard Academy University of New Haven Yale University Delaware Delaware College of Art and Design District of Columbia Catholic University of America Georgetown University Howard University Florida Eckerd College Lynn University University of Miami University of Tampa Georgia Agnes Scott College Emmanuel College Georgia College and State University Georgia Institute of Technology (Georgia Tech) Georgia State University Mercer University Morehouse College Oglethorpe University Spelman College University of Georgia Georgia Tech (Davidhermanns/Wikimedia Commons) Idaho College of Idaho Northwest Nazarene University Illinois Augustana College DePaul University Illinois College Illinois Wesleyan University Knox College Lake Forest College School of the Art Institute of Chicago University of Chicago University of Illinois at Chicago Wheaton College Indiana Butler University DePauw University Earlham College Grace College Hanover College Purdue University Rose-Hulman Institute of Technology University of Evansville University of Notre Dame Wabash College Iowa Coe College Cornell College Wartburg College Kentucky Bellarmine University Centre College Transylvania University University of Kentucky Louisiana Centenary College of Louisiana Tulane University Maine Maine Maritime Academy Saint Joseph’s College of Maine Thomas College Unity College University of Maine University of Maine at Farmington University of Maine at Machias University of New England Maryland Goucher College Loyola University Maryland McDaniel College Mount St. Mary's University Salisbury University St. John's College University of Maryland University of Maryland, Baltimore County Washington College Massachusetts Assumption College Babson College Bay Path University Becker College Berklee College of Music Bridgewater State University Clark University Curry College Dean College Emerson College Emmanuel College Framingham State University Gordon College Hampshire College Harvard University Hellenic College Lasell College Lesley University Massachusetts College of Art and Design Massachusetts College of Liberal Arts Massachusetts College of Pharmacy and Health Sciences Massachusetts Institute of Technology (MIT) Massachusetts Maritime Academy Merrimack College Montserrat College of Art Northeastern University Regis College Salem State University Simmons College Stonehill College Suffolk University University of Massachusetts Amherst University of Massachusetts Boston University of Massachusetts Dartmouth University of Massachusetts Lowell Wheaton College Worcester Polytechnic Institute Worcester State University Michigan Central Michigan University College for Creative Studies Kalamazoo College Michigan State University University of Michigan University of Michigan in Ann Arbor Minnesota College of Saint Benedict Gustavus Adolphus College Hamline University Minneapolis College of Art and Design Saint John's University Mississippi Millsaps College New Hampshire Saint Anselm College Southern New Hampshire University University of New Hampshire New Jersey Bloomfield College Caldwell University Felician College Georgian Court University Kean University Monmouth University Princeton University Rider University Saint Peter's University Seton Hall University William Paterson University New Mexico St. John's College New York Adelphi University Bard College College of Mount Saint Vincent College of Saint Rose Columbia University, School of General Studies Concordia College New York Fordham University Hofstra University Iona College Ithaca College Le Moyne College LIM College LIU Brooklyn LIU Post Manhattanville College Marist College Molloy College New York Institute of Technology Niagara University Pace University Parsons School of Design (The New School) Pratt Institute The Sage Colleges Siena College SUNY Albany (University at Albany) SUNY Binghamton (Binghamton University) SUNY Buffalo (University at Buffalo) SUNY Cortland SUNY New Paltz SUNY Oneonta SUNY Polytechnic Institute SUNY Purchase (Purchase College) Utica College Wells College North Carolina Elon University High Point University Lees-McRae College Lenoir-Rhyne University North Carolina State University Queens University of Charlotte University of North Carolina at Chapel Hill University of North Carolina at Charlotte University of North Carolina at Wilmington Warren Wilson College Western Carolina University Ohio Case Western Reserve University Cleveland Institute of Art College of Wooster John Carroll University Miami University Ohio State University Ohio Wesleyan University University of Akron University of Cincinnati University of Dayton Wittenberg University Oklahoma University of Tulsa Oregon Eastern Oregon University George Fox University Lewis Clark College Linfield College Oregon State University University of Oregon Willamette University Pennsylvania Duquesne University La Salle University Lycoming College Saint Joseph's University Susquehanna University Temple University University of Scranton Ursinus College Villanova University Washington Jefferson College Westminster College Rhode Island Bryant University Providence College Roger Williams University Salve Regina University University of Rhode Island South Carolina College of Charleston Furman University Presbyterian College University of South Carolina Wofford College Tennessee Rhodes College Sewanee: University of the South Texas Abilene Christian University Austin College Baylor University Southern Methodist University Southwestern University Tarleton State University Texas Christian University Texas Lutheran University Trinity University University of Dallas University of St. Thomas Baylor University in Waco, Texas Utah University of Utah Vermont Bennington College Marlboro College Saint Michael's College Sterling College University of Vermont Virginia Christendom College Christopher Newport University George Mason University Hampden-Sydney College Hampton University Hollins University James Madison University Longwood University Old Dominion University Patrick Henry College Radford University Randolph College Randolph-Macon College Sweet Briar College University of Mary Washington University of Virginia University of Virginia's College at Wise Washington Cornish College of the Arts Gonzaga University Northwest University Seattle Pacific University Seattle University Whitworth University West Virginia Shepherd University Wisconsin Beloit College Carthage College Lawrence University As you can see, there are a lot of early action schools. But does it give you an advantage to apply early to a school? The short answer is yes. Applying early can communicate your enthusiasm for the school and highlight your commitment to going there. Moreover, schools tend to accept more early action applicants than they do regular decision applicants so they can geta higher yield.Yield refers to the percentage of students who accept their offers of admission. Since early action applicants are serious about attending that school, they're more likely to accept an offer of admission. In terms of yield, the more students who accept their offers of admission, the higher a school's yield will be. And the higher a school's yield is, the easier it will be to predict enrollment numbers and avoid having to make a waitlist. At the same time, applying early doesn't necessarily make poor grades or low SAT/ACT scores look better, especially since early action students tend to be some of the strongest applicants. Timeline for Applying Early Action to College If you choose to apply early action, then you have to immerse yourself in college planning a few months earlier than you would for regular decision deadlines (though either way, you'll benefit from preparing early!). The parts that require especially early planning are theSAT/ACT, your recommendation letters, and your personal essay.In the fall, you'll also want to submit your transcript request form to your guidance office and take your time filling out the Common Application or school's individual application. Here's a brief timeline showing you how to apply early action. Step 1: Take the SAT/ACT For the SAT/ACT, it's a good idea to leave yourself plenty of SAT/ACT test dates. Students almost always improve when they retake the SAT/ACT, especially if they do focused, targeted test prep in-between test dates. If you apply early action, your last opportunity to take the ACT is September, while your last chance to take the SAT is October (both during your senior year). Since this is right up to your early action deadline, you probably won't be able to view your scores before deciding whether or not to send them. With all the other busy things going on in the fall of your senior year, there's no doubt it's better totake the SAT/ACT earlier than this. We suggest taking the SAT/ACT first in the fall of yourjunior year, again in the spring of your junior year, and a third time (if needed) in the summer or fall after your junior year. Think about how much time you can devote to test prep and how many times you'd like to take the tests to achieve your SAT/ACT target scores. As you can see, planning out your SAT/ACT could start more than a year before your actual early action deadline! Step 2: Ask for Letters of Recommendation In terms of recommendation letters, give your teachers and/or counselor at least one month to write your letter. This means you should ask for your recs before October 1 your senior year. It can be a good idea, too, to ask at the end of your junior year, since these teachers will remember you most clearly at this point. They'll likely appreciate how on top of college applications you are! Step 3: Plan and Begin Writing Your College Essay Just like with letters of rec, you want to spend some time planning and writing your personal essay and any other supplemental essays. I recommend working on it over the summer.Even reading essay prompts can help you begin brainstorming potential topics. You can then spend a few months drafting, getting feedback, and revising your essay until it's ready for submission. Step 4: Finish Your Application Finally, in September and October, you can work on the rest of your application, proofreading all the information in it and writing about your extracurricular activities in a compelling manner. By startingthe application process in the spring of your junior year (earlier including the SAT/ACT), you should be ready with a thoughtful and well-executed application by November for sure! Want to build the best possible college application? We can help. PrepScholar Admissions is the world's best admissions consulting service. We combine world-class admissions counselors with our data-driven, proprietary admissions strategies. We've overseen thousands of students get into their top choice schools, from state colleges to the Ivy League. We know what kinds of students colleges want to admit. We want to get you admitted to your dream schools. Learn more about PrepScholar Admissions to maximize your chance of getting in. What's Next? Do you have your sights set on the Ivy League? Learn what it takes to get into Harvard and other highly selective schools from this Harvard alum. Now that you know which schools offer early action, check out this guide on all the early action deadlines!It goes over the most popular early action schools and offers acomprehensive list of all the colleges with early action deadlines. Are you also interested in early decision? This guide covers all early decision schools and their deadlines. Want to improve your SAT score by 160points or your ACT score by 4 points?We've written a guide for each test about the top 5 strategies you must be using to have a shot at improving your score. Download it for free now:

On the book LADY CHATTERLEYS LOVER Research Paper

On the book LADY CHATTERLEYS LOVER - Research Paper Example At first, the term fidelity does not appear to be consistent with the main plot in Lawrence’s Lady Chatterley’s Lover. The title itself implies infidelity. The main plot centers around an adulterous wife, Connie Chatterley whose husband is rendered impotent as a result of an injury sustained in the war. Lady Chatterley, an aristocrat then takes up an affair with Mellors, the gamekeeper (Lawrence 2009). The question of fidelity arises in a way that challenges normative values existing at the time. While Lady Chatterley is unfaithful to her husband and breaks ranks with her own class, she is faithful to her lover (Niven 1979, 184). Although Mellors is complicit in Lady Chatterley’s adultery and is married himself, he himself is entirely faithful to Lady Chatterley. According to Gabriel and Smithson (1990), â€Å"Mellors seeks the approval of one woman only† (69). The lovers’ fidelity to each other however, calls for infidelity to their respective spou ses. However, from Lawrence’s perspective, he was not concerned with what might be characterized as â€Å"photographic fidelity†(Wuchina 2009, 172). In other words, Lawrence was more concerned with feelings that commanded fidelity rather than a sense of detached duty. This message is communicated through Mellors who, reflecting on his intimate encounters with Lady Chatterley, observed that: The connection between them was growing closer. He could see the day when it would clinch up, and they would have to make a life together (Lawrence 2009, 142). Wuchina (2009) points out that Mellors has â€Å"no second thoughts, or guilt† (174). This is because, â€Å"in its essentials, the relationship, the mutual attraction, is essentially legitimate† (Wuchina 2009, 174). The legitimacy is founded on the fact that Lady Chatterley was in a loveless marriage and was making a particularly difficult sacrifice. In fact, Mellors observes of Lady Chatterley: She was nicer t han she knew, and oh, so much too nice for the tough lot she was in contact with!..But he would protect her with his heart for a little while. For a little while, before the insentient iron world and the Mammon of mechanized greed did them both in, her as well as him (Lawrence 2009, 136). Mellors was obviously referring to the fact that Lady Chatterley was quite young. She was only 23 years old and was trapped in an unusual situation, one that she was too young and perhaps too naive to cope with. Lady Chatterley was for the most part confined to the companionship of her wounded husband and his circle of friends with whom she was essentially bored. As Daum (2008) observes, this is a situation that the young Lady Chatterley had to endure each day and it could not have been easy to cope with (3). Yet in this youthful innocence, the moral code of the times commanded fidelity from Lady Chatterley. Lawrence (2009) immediately draws attention to the fallacy of the moral code of the times. The novel opens with the caution â€Å"ours is essentially a tragic age† (5). Lady Chatterley was trapped in a time where, the First World War and its consequences were still fresh. She was therefore tethered to a marriage in which she could not find happiness and had yet to learn the meaning of life. As the plot moves along, a poignant issue necessarily arises. Is it fair to expect the young Lady Chatterley in the circumstances in which she finds herself to be faithful to her marriage

LEADERSHIP AND ORGANIIZATIONAL BEHAVIOR (520) Case Study

LEADERSHIP AND ORGANIIZATIONAL BEHAVIOR (520) - Case Study Example 171). In order to foster good relationship with the customers, it is imperative that the employees know how the customers form perceptions, what factors play a role in affecting their perceptions about the salespersons or the company’s products for the better and worse, and what behaviors in the salespersons are appreciated by the customers. Since statistics are available that reflect that the employees who have had good terms with the customers have outperformed their coworkers in terms of sales, understanding the process of formation of perceptions and development of attributed by the customers becomes very important for Joe’s employees. Suitability of Leaning Theories Several learning theories are suitable for Joe to apply in this situation that include but are not limited to operant conditioning learning theory and social learning theory. While there are certain theories that apply more in the case under consideration as compared to others, none can be completely re futed as most learning theories apply at least to some extent. ... Employees come in the company and try their fortune by executing their individualistic behaviors; for some, it works whereas for others, it does not. Employees are only able to find out the impact of their behaviors on their ability to make sales after they have executed their behaviors as part of the company’s workforce. Likewise, since the employees are provided with the opportunity to earn certain commission on the list price, they are motivated to make more sales to make more commission which makes it obvious that the reward is tied to the performance, thus justifying the suitability of the operant conditioning theory for Joe in this case. On the other hand, the social learning theory is also suitable for application in this case because many behaviors are learnt by the employees in the workplace. For example, employees can witness that their coworkers who have fostered good relations with the customers are able to make more sales as compared to other employees who do not manage to develop as good relations with the customers. Having observed this, employees feel intrinsically motivated to take steps to develop good relations and association with the customers. However, certain end up achieving this successfully whereas others fail to develop good relations with the customers, and the social learning theory provides rationale for this difference. According to social learning theory, while people may observe what behaviors lead to success in a particular setting, it is not imperative that this learning brings a change in their behavior. Although the employees know the importance of fostering good relationships with the employees, yet they fail to enhance their sales because this learning did not cause them to

Comparative Analysis of German, French and American Human Rights Law Essay

Comparative Analysis of German, French and American Human Rights Law - Essay Example This essay discusses that crucial importance of political rights and liberties in today’s evolving and fast-changing world cannot be overemphasized. It has been opined that political rights and liberties are of paramount importance because of their impact on other rights, such as social and economic rights. The universal condemnation of state-sponsored repression is due in large part to the globalized ideal of human rights where we see a whittling down of the concept of sovereignty in favor of the acceptance of international norms of human rights. Indeed, the protection of human rights is one of the fundamental aspirations of international law. In international law, the primacy of the State is the core principle of the international legal regime as it is traditionally known. It is the duty of international law, therefore, to interlock authority with power, and to ensure that authorized decision-makers regulate the actions of States. When the United Nations was created in 1948 by a world still reeling from the ravages of the Second World War and intent on healing the wounds wrought by it, it was tasked to become the primary agency in defining and advancing human rights. From then on, various other agencies were created, addressing specific human rights concerns. Notable examples of this are the International Labor Organization and the UNICEF. Within the jurisdiction of the individual states, however, human rights legislation evolves mainly as a result of case law, i.e., the jurisprudence based on decisions made by the Supreme Court on human rights disputes brought before it. Indeed, Indeed, society has come a long way towards preserving human rights, and righting the wrongs of the past with justice and accountability. Says Abrams and Ratner3: Societies long reluctant to investigate or prosecute human rights abusers have begun to do so with greater frequency. These include both those inquiring into the abuses of their own officials or former officials, as well as those investigating or prosecuting individuals who have committed abuses in other countries. This paper attempts to trace the role that case law has played in the legal systems of Germany, France and the United States with respect to the development and evolution of human rights. This paper shall also look into some of the more important and landmark decisions made in the respective jurisdictions and evaluate the degree to which these decisions have impacted on human rights. As the space for this paper is rather limited and the field of human rights is vast, this paper will focus on human rights law as it applies to freedom of religion and circumstances when it competes with the interests of the state to preserve certain values, e.g., neutrality and national security. Germany When people think of Germany and human rights law and religion, thoughts inevitably first turn to the end of the second world war, where Nazi soldiers had been prosecuted for gross war crimes committed against the Jews. The end of World War II ushered in a milestone for international criminal responsibility. The axis powers were completely annihilated and the allied powers were now determined not to repeat the mistakes of the past. It was only through punishing the guilty that the horrors and wounds of the victims could be assuaged. The allied states created the International Military Tribunal (IMT) for the prosecution of the men

K Means Clustering With Decision Tree Computer Science Essay

K Means Clustering With Decision Tree Computer Science Essay The K-means clustering data mining algorithm is commonly used to find the clusters due to its simplicity of implementation and fast execution. After applying the K-means clustering algorithm on a dataset, it is difficult for one to interpret and to extract required results from these clusters, until another data mining algorithm is not used. The Decision tree (ID3) is used for the interpretation of the clusters of the K-means algorithm because the ID3 is faster to use, easier to generate understandable rules and simpler to explain. In this research paper we integrate the K-means clustering algorithm with the Decision tree (ID3) algorithm into a one algorithm using intelligent agent, called Learning Intelligent Agent (LIAgent). This LIAgent capable of to do the classification and interpretation of the given dataset. For the visualization of the clusters 2D scattered graphs are drawn. Keywords: Classification, LIAgent, Interpretation, Visualization 1. Introduction The data mining algorithms are applied to discover hidden, new patterns and relations from the complex datasets. The uses of intelligent mobile agents in the data mining algorithms further boost their study. The term intelligent mobile agent is a combination of two different disciplines, the agent is created from Artificial Intelligence and code mobility is defined from the distributed systems. An agent is an object which has independent thread of control and can be initiated. The first step is the agent initialization. The agent will then start to operate and may stop and start again depending upon the environment and the tasks that it tried to accomplish. After the agent finished all the tasks that are required, it will end at its complete state. Table 1 elaborates the different states of an agent [1][2][3][4]. Table 1. States of an agent Name of Step Description Initialize Performs one-time setup activity. Start Start its job or task. Stop Stops its jobs or tasks after saving intermediate results. Complete Performs completion or termination activity. There is link between Artificial Intelligence (AI) and the Intelligent Agents (IA). The data mining is known as Machine Learning in Artificial Intelligence. Machine Learning deals with the development of techniques which allows the computer to learn. It is a method of creating computer programs by the analysis of the datasets. The agents must be able to learn to do classification, clustering and prediction using learning algorithms [5][6][7][8]. The remainder of this paper is organized as followos: Section 2 reviews the relevant data mining algoritms, namely the K-means clustering and the Decision tree (ID3). Section 3 is about the methodology; a hybrid integration of the data mining algorithms. In section 4 we discuss the results and dicussion. Finally section 5 presents the conclusion. 2. Overview of Data Mining Algorithms The K-means clustering data mining algorithm is used for the classification of a dataset by producing the clusters of that dataset. The K-means clustering algorithm is a kind of unsupervised learning of machine learning. The decision tree (ID3) data mining algorithm is used to interpret these clusters by producing the decision rules in if-then-else form. The decision tree (ID3) algorithm is a type of supervised learning of machine learning. Both of these algorithms are combined in one algorithm through intelligent agents, called Learning Intelligent Agent (LIAgent). In this section we will discuss both of these algorithms. 2.1. K-means clustering Algorithm The following steps explain the K-means clustering algorithm: Step 1: Enter the number of clusters and number of iterations, which are the required and basic inputs of the K-means clustering algorithm. Step 2: Compute the initial centroids by using the Range Method shown in equations 1 and 2. (1) (2) The initial centroid is C(ci, cj).Where: max X, max Y, min X and min Y represent maximum and minimum values of X and Y attributes respectively. k represents the number of clusters and i, j and n vary from 1 to k where k is an integer. In this way, we can calculate the initial centroids; this will be the starting point of the algorithm. The value (maxX minX) will provide the range of X attribute, similarly the value (maxY minY) will give the range of Y attribute. The value of n varies from 1 to k. The number of iterations should be small otherwise the time and space complexity will be very high and the value of initial centroids will also become very high and may be out of the range in the given dataset. This is a major drawback of the K-means clustering algorithm. Step 3: Calculate the distance using Euclideans distance formula in equation 3. On the basis of the distances, generate the partition by assigning each sample to the closest cluster. Euclidean Distance Formula: (3) Where d(xi, xj) is the distance between xi and xj. xi and xj are the attributes of a given object, where i and j vary from 1 to N where N is total number of attributes of a given object. i,j and N are integers. Step 4: Compute new cluster centers as centroids of the clusters, again compute the distances and generate the partition. Repeat this until the cluster memberships stabilizes [9][10]. The strengths and weaknesses of the K-means clustering algorithm are discussed in table 2. Table 2. Strengths and Weakness of the K-means clustering Algorithm Strengths Weaknesses Time complexity is O(nkl). Linear time complexity in the size of the dataset. It is easy to implement, it has the drawback of depending on the initial centre provided. Space complexity is O(k + n). If a distance measure does not exist, especially in multidimensional spaces, first define the distance, which is not always easy. It is an order-independent algorithm. It generates same partition of data irrespective of order of samples. The Results obtained from this clustering algorithm can be interpreted in different ways. Not applicable All clustering techniques do not address all the requirements adequately and concurrently. The following are areas but not limited to where the K-means clustering algorithm can be applied: Marketing: Finding groups of customers with similar behavior given large database of customer containing their profiles and past records. Biology: Classification of plants and animals given their features. Libraries: Book ordering. Insurance: Identifying groups of motor insurance policy holders with a high average claim cost; identifying frauds. City-planning: Identifying groups of houses according to their house type, value and geographically location. Earthquake studies: Clustering observed earthquake epicenters to identify dangerous zones. WWW: Document classification; clustering web log data to discover groups of similar access patterns. Medical Sciences: Classification of medicines; patient records according to their doses etc. [11][12]. 2.2. Decision Tree (ID3) Algorithm The decision tree (ID3) produces the decision rules as an output. The decision rules obtained from ID3 are in the form of if-then-else, which can be use for the decision support systems, classification and prediction. The decision rules are helpful to form an accurate, balanced picture of the risks and rewards that can result from a particular choice. The function of the decision tree (ID3) is shown in the figure 1. Figure 1. The Function of Decision Tree (ID3) algorithm The cluster is the input data for the decision tree (ID3) algorithm, which produces the decision rules for the cluster. The following steps explain the Decision Tree (ID3) algorithm: Step 1: Let S is a training set. If all instances in S are positive, then create YES node and halt. If all instances in S are negative, create a NO node and halt. Otherwise select a feature F with values v1,,vn and create a decision node. Step 2: Partition the training instances in S into subsets S1, S2, , Sn according to the values of V. Step 3: Apply the algorithm recursively to each of the sets Si [13][14]. Table 3 shows the strengths and weaknesses of ID3 algorithm. Table 3. Strengths and Weaknesses of Decision Tree (ID3) Algorithm Strengths Weaknesses It generates understandable rules. It is less appropriate for a continuous attribute. It performs classification without requiring much computation. It does not perform better in problems with many class and small number of training examples. It is suitable to handle both continuous and categorical variables. The growing of a decision tree is expensive in terms of computation because it sorts each node before finding the best split. It provides an indication for prediction or classification. It is suitable for a single field and does not treat well on non-rectangular regions. 3. Methodology We combine two different data mining algorithms namely the K-means clustering and Decision tree (ID3) into a one algorithm using intelligent agent called Learning Intelligent Agent (LIAgent). The Learning Intelligent Agent (LIAgent) is capable of clustering and interpretation of the given dataset. The clusters can also be visualized by using 2D scattered graphs. The architecture of this agent system is shown in figure 2. Figure 2. The Architecture of LIAgent System The LIAgent is a combination of two data mining algorithms, the one is the K-means clustering algorithm and the second is the Decision tree (ID3) algorithm. The K-means clustering algorithm produces the clusters of the given dataset which is the classification of that dataset and the Decision tree (ID3) will produce the decision rules for each cluster which are useful for the interpretation of these clusters. The user can access both the clusters and the decision rules from the LIAgent. This LIAgent is used for the classification and the interpretation of the given dataset. The clusters of the LIAgent are further used for visualization using 2D scattered graphs. Decision tree (ID3) is faster to use, easier to generate understandable rules and simpler to explain since any decision that is made can be understood by viewing path of decision. They also help to form an accurate, balanced picture of the risks and rewards that can result from a particular choice. The decision rules are obta ined in the form of if-then-else, which can be used for the decision support systems, classification and prediction. A medical dataset Diabetes is used in this research paper. This is a dataset/testbed of 790 records. The data of Diabetes dataset is pre-processed, called the data standardization. The interval scaled data is properly cleansed. The attributes of the dataset/testbed Diabetes are: Number of times pregnant (NTP)(min. age = 21, max. age = 81) Plasma glucose concentration a 2 hours in an oral glucose tolerance test (PGC) Diastolic blood pressure (mm Hg) (DBP) Triceps skin fold thickness (mm) (TSFT) 2-Hour serum insulin (m U/ml) (2HSHI) Body mass index (weight in kg/(height in m)^2) (BMI) Diabetes pedigree function (DPF) Age Class (whether diabetes is cat 1 or cat 2) [15]. We create the four vertical partitions of the dataset Diabetes, by selecting the proper number of attributes. This is illustrated in tables 4 to 7. Table 4. 1st Vertically partition of Diabetes Dataset NTP DPF Class 4 0.627 -ive 2 0.351 +ive 2 2.288 -ive Table 5. 2nd Vertically partition of Diabetes Dataset DBP AGE Class 72 50 -ive 66 31 +ive 64 33 -ive Table 6. 3rd Vertically partition of Diabetes Dataset TSFT BMI Class 35 33.6 -ive 29 28.1 +ive 0 43.1 -ive Table 7. 4th Vertically partition of Diabetes Dataset PGC 2HIS Class 148 0 -ive 85 94 +ive 185 168 -ive Each partitioned table is a dataset of 790 records; only 3 records are exemplary shown in each table. For the LIAgent, the number of clusters k is 4 and the number of iterations n in each case is 50 i.e. value of k =4 and value of n=50. The decision rules of each clusters is obtained. For the visualization of the results of these clusters, 2D scattered graphs are also drawn. 4. Results and Discussion The results of the LIAgent are discussed in this section. The LIAgent produces the two outputs, namely, the clusters and the decision rules for the given dataset. The total sixteen clusters are obtained for all four partitions, four clusters per partition. Not all the clusters are good for the classification, only the required and useful clusters are discussed for further information. The sixteen decision rules are also generated by LIAgent. We are presenting three decision rules of three different clusters. The number of decision rules varies from cluster to cluster; it depends upon the number of records in the cluster. The Decision Rules of the 4th partition of the dataset Diabetes: Rule: 1 if PGC = 165 then Class = Cat2 else Rule: 2 if PGC = 153 then Class = Cat2 else Rule: 3 if PGC = 157 then Class = Cat2 else Rule: 4 if PGC = 139 then Class = Cat2 else Rule: 5 if HIS = 545 then Class = Cat2 else Rule: 6 if HIS = 744 then Class = Cat2 else Class = Cat1 Only six decision rules are for the 4th partition of the dataset. It is easy for any one to take the decision and interpret the results of this cluster. The Decision Rules of the 1st partition of the dataset Diabetes: Rule: 1 if DPF = 1.32 then Class = Cat1 else Rule: 2 if DPF = 2.29 then Class = Cat1 else Rule: 3 if NTP = 2 then Class = Cat2 else Rule: 4 if DPF = 2.42 then Class = Cat1 else Rule: 5 if DPF = 2.14 then Class = Cat1 else Rule: 6 if DPF = 1.39 then Class = Cat1 else Rule: 7 if DPF = 1.29 then Class = Cat1 else Rule: 8 if DPF = 1.26 then Class = Cat1 else Class = Cat2 The eight decision rules are for the 1st partition of the dataset. The interpretation of the cluster is easy through the decision rules and it also helps to take the decision. The Decision Rules of the 3rd partition of the dataset Diabetes: Rule: 1 if BMI = 29.9 then Class = Cat1 else Rule: 2 if BMI = 32.9 then Class = Cat1 else Rule: 3 if TSFK = 23 then Rule: 4 if BMI = 25.5 then Class = Cat1 else Rule: 5 if BMI = 30.1 then Class = Cat1 else Rule: 6 if BMI = 28.4 then Class = Cat1 else Class = Cat2 else Rule: 7 if BMI = 22.9 then Class = Cat1 else Rule: 8 if BMI = 27.6 then Class = Cat1 else Rule: 9 if BMI = 29.7 then Class = Cat1 else Rule: 10 if BMI = 27.1 then Class = Cat1 else Rule: 11 if BMI = 25.8 then Class = Cat1 else Rule: 12 if BMI = 28.9 then Class = Cat1 else Rule: 13 if BMI = 23.4 then Class = Cat1 else Rule: 14 if BMI = 30.5 then Rule: 15 if TSFK = 18 then Class = Cat2 else Class = Cat1 else Rule: 16 if BMI = 26.6 then Rule: 17 if TSFK = 18 then Class = Cat2 else Class = Cat1 else Rule: 18 if BMI = 32 then Rule: 19 if TSFK = 15 then Class = Cat2 else Class = Cat1 else Rule: 20 if BMI = 31.6 then Class = Cat2 , Cat1 else Class = Cat2 The twenty decision rules are for the 3rd partition of the dataset. The number of rules for this cluster is higher than the other two clusters discussed. The visualization is important tool which provides the better understanding of the data and illustrates the relationship among the attributes of the data. For the visualization of the clusters 2D scattered graphs are drawn for all the clusters. We are presenting the four 2D scattered graphs of four different clusters of different partitions. Figure 3. 2D Scattered Graph between NTP and DPF attributes of Diabetes dataset The distance between NTP and DPF attributes of Diabetes dataset varies at the beginning of the graph but after some interval the distance becomes constant. Figure 4. 2D Scattered Graph between DBP and AGE attributes of Diabetes dataset There is a variable distance between DBP and AGE attributes of the dataset. It remains variable throughout this graph. Figure 5. 2D Scattered Graph between TSFT and BMI attributes of Diabetes dataset The graph shows almost constant distance between TSFT and BMI attributes of the dataset. It remains constant throughout the graph. Figure 6. 2D Scattered Graph between PGC and 2HIS attributes of Diabetes dataset There is a variable distance between PGC and 2HIS attributes of the dataset. But in the middle of this graph there is some constant distance between these attributes. The structure of this graph is similar to the graph of figure 5. 5. Conclusion It is not simple for all the users that they can interpret and extract the required results from these clusters, until some other data mining algorithms or other tools are not used. In this research paper we have tried to address the issue by integrating the K-means clustering algorithm with the Decision tree (ID3) algorithm. The choice of the ID3 is due to the decision rules in the form of if-then-else as an output, which are easy to understand and help to take the decision. It is a hybrid combination of supervised and unsupervised machine learning, using intelligent agent, called a LIAgent. The LIAgent is helpful in the classification and prediction of the given dataset. Furthermore, 2D scattered graphs of the clusters are drawn for the visualization.

Destined to Fail :: Free Essays Onlinevv

Destined to Fail Imagine having to wake up every morning and going to a broken down old building for seven hours a day. In the building you complete the same tasks which are easier in other buildings five minutes away, but since yours is poor it is difficult to, if at all, complete these tasks. The outlook is so bleak that it almost seems as if you are destined to fail. For children in Camden, New Jersey this is school. Students in Camden are faced with an obvious, appalling educational disadvantage when viewed against suburban schools, such as in Cherry Hill which are only five minutes away. The crux of the problem with the Camden public schools is the impoverished state in which it is forced to educate its children. The main cause for the destitution in the Camden public schools is the serious lack of funds for educational materials including those for school facilities. The schools are in such dire straits that most do not have the necessary materials with which to teach. Students at times do not even have their own textbooks and science labs lack the necessary equipment to teach lessons properly. If a student is lucky enough to receive a textbook it is either outdated, falling apart, or at the incorrect level of learning. In one Camden school, eleventh grade history class is taught from an eighth grade history text, (Kozol 152). This unfortunate condition applies not only to school supplies but also to the school itself. School facilities are in a state of trouble, many are falling apart or have serious problems which inhibit learning. In one of the Camden high schools, the malfunctioning heating system not only makes the building extremely hot all year round, but also melted approximately forty of the fifty computers in a lab, (Kozol 149). Is this the proper environment for education? Would you want to go to a school like this? Disadvantages such as these do not provide a proper atmosphere or environment conducive to learning. They also add a number of components to the problem of the lack of funds and increase the students' feeling that they are destined to fail. The lack of proper educational materials prevents students from learning. Since it prevents students from passing state mandated tests which control funding, they have to spend approximately eight months of the school year, usually in high school, preparing for these exams.

Perspectives

Pavlov (1927), founder of classical conditioning used dogs in his experiments. The key terms within his experiment were stimulus and response. The unconditioned stimulus of the child's fear would be the presence of animals and the unconditioned response would be the behavior of crying. The unconditioned response would become conditioned as It's associated with the stimulus (Doherty, Hughes, 2009). Skinner (1966) developed operant conditioning, focusing on reinforcement or punishment to elicit changes in behavior.He found reinforced behavior becomes strengthened and repeated whereas behavior not reinforced becomes extinct and weakened. For child X, his previous experience with animals may have been negative; therefore he may prefer the experience not to occur again (Miller, 2011). Watson (1924) believed Individual differences and experiences mould our behavior as emphasized below. â€Å"Give me a dozen healthy infants, well-formed, and my own special world to bring them up in and I'l l guarantee to take any one at random and train him to become any type of specialist I might select†¦ (quoted in Schaffer, 2004, peg. 336). Influenced by Pavlov, Watson believed behavior can be controlled through understanding relationships between stimulus and response. Child Ax's home or educational setting could change to adapt a pet policy within the environment, to become confident to eradicate his fear. Bandeau (1986) emphasized on behavior as imitation with four elements; attention, mental representation, mitotic response and motivation. Child X could have seen someone showing negative affection towards an animal (attention allowing him to remember his observations (mental representation).This may be the reason as to how he behaves In the same way (mitotic response) when he felt the urge to cry (motivation) (Levine, Munich, 2011 This theory highlights people learn from imitation as a direct reinforcement of their own behavior within their environment. â€Å"The psychodr ama approach focuses on the role of internal processesÃ'›. In shaping personality, and thereby behavior. † (Clansman, Had, 2009, peg. 224) our preconscious mind or they are totally inaccessible within our unconscious mind.Our unconscious thoughts can become conscious through dream interpretation, free association and transference. Many unconscious thoughts are experiences best forgotten (Gross, 2010). Child X could have experienced a negative incident with animals causing him to erase this event from his mind. Freud recognized three structures of personality resulting in clashes. Old is the basic personality wanting everything and will do anything to feed it's desires through operating a ‘pleasure principle'. For child X, the id would make him cry while looking at animals making it uncontrollable.Superego is the sensible structure conditioned by the environment and has a conscience of both right and wrong, so would tell child X not to seek attention by crying. Ego is a mediator between id and superego; therefore controls both structures (Hermann, 1994). However, as child X grows older, his superego ill control his id through moral principles resisting temptations of crying. Humanists are optimistic and recognize behavior through own free will (Gross, 2010). Mason (1968) and Rogers (1951) regarded personal growth and fulfillment in life as basic human nature.Both theorists emphasis on growth and fulfillment for a person to be able to self-actualities (Nee, 1996). Mason believes individuals have capability to progress towards the level of self- actualization highlighted through hierarchical stages (see appendix 1). However, if there is a failure to meet lower level needs, progression to the next stage is delayed. Although there are many needs to be met at the bottom there is a potential to achieve for all (Nee, 1996). Child X may have experienced a dangerous situation with an animal; therefore his safety needs would need to be met for him to progre ss onto the next stage.Rogers believed humans have one basic aim; to self-actualities by fulfilling their own potential. His theory highlights self-esteem as the ‘real self and the ‘ideal self. Being able to achieve what one is capable of allows self-actualization and positive regard from others to promote self-esteem (Doherty, Hughes, 2009). If child X was shown positive regard when in the presence of animals, he may remove his fear and begin to self-actualities. â€Å"Cognitive psychology is concerned with†¦ Perception, learning, memory, language, emotion, concept formation and thinking. (Essence, 1995, peg. L) Cosmogonists view people and their environment as important. Piglet's (1969) constructivist theory is based on age ability of stage learning. His theory describes children's perspective on their world (Levine, Munich, 2011). Pigged identified four stages of learning (see appendix 2) believing past experiences shape children's organization of the world. Ref lecting on Piglet's stages, child X would be in the very early stages of the pre-operational stage as he cannot see his fear of animals from another perspective.Using symbolic features within this stage may allow him to make links between reality and fantasy (Dates, Grayson, 2004) forming close links to the psychodrama approach regarding accessing the unconscious mind. Child X may not access his unconscious mind due to unpleasant past experience. Weights (1978) emphasized social interactions through scaffolding and understanding of the world (Curtis, Change, 2005). Like Pigged, he constructed a stage theory (see appendix 3). Child X may understand emotions and experiences if knowledge is stored within him.Making him understand there is nothing to fear about with animals, may be beyond his intellectual capability because of his global developmental delay. He may not have reached the stage of maturity within ZAP to remove his fears. However, through reconstruction and social interacti ons, he may become used to the presence of animals within his environment. The cultural context within stages may influence his fear as family contexts may imply a ‘no pets' policy, Hereford imitating the family attitude.Behaviorist's emphasis on connections between the environment and the behavior and ignore physiological and cognitive events occurring. Pavlov and Skinner experimented on animals whereas Bandeau and Watson experimented with children. The behaviorism perspective is concerned with nurture as the environment is the stimulus of it's theories. It does not take into perspective holism, therefore against the humanistic approach (Clansman, Had, 2009). Humanists found the psychodrama approach to be too pessimistic in comparison to their optimistic approach.This approach is individualistic and studies internal world of the person rather than external. Measles hierarchy suggests moving upwards in regards to achievement similar to the stage theories for other perspectives . Although his theory is not age related, it is similar to Hoosegows as individuals' progress accordingly. However, Pigged identified children cannot progress onto the next stage without having developed fully in the previous. All these theorists have one thing in common; failure to meet lower level needs results in a delay or fixation to develop (Gross, 2010).

Destruction of the Brazilian Tropical Rain Forest

Destruction of the Brazilian Tropical Rain Forest Deforestation is one of the biggest global problems, it being a practice that is hard to control, because world forest reserves spread over international borders. Although global governments and environmental protecting organizations have always put measures to curb the practice, still deforestation remains one of the biggest threats to the survival of not only the world’s flora and fauna, but also to the survival of the human species. Advertising We will write a custom essay sample on Destruction of the Brazilian Tropical Rain Forest specifically for you for only $16.05 $11/page Learn More One of the most affected forest reserves are the world’s tropical rainforests for example, the Amazon and the African tropical rainforest, which covers a better portion of the Congo basin, Corte Devoir, Zaire and some sections of West Africa. In America, the Brazilian Amazon rainforest is one of most affected rainforest, a fact that research findings attrib ute to the nature of development initiatives and extensive agricultural ventures undertaken by the people of Brazil. Over the recent past Brazil has been on the limelight, because of its contribution, as far as the Destruction of the Amazon tropical-rainforest reserve is concerned. Such destructions are primary causes of the current unpredictable climatic changes that are root causes of the prevailing environmental, social, and economic problems facing countries, for example, drought and global warming. The Amazon tropical rainforest is the home of thousands of flora and fauna species, which are rare in other forest reserves, because of the favorable environmental conditions of the forest. In addition to thriving of flora and fauna, since time memorial the Amazon tropical forest has been the home of many indigenous tribes, a number that is decreasing rapidly hence, unless the government puts in place mitigating measures to curb deforestation, likelihoods of these tribes disappearin g are high. In Brazil alone, human activities have led to the disappearance of more than ninety Brazilian Indigenous tribes, which played a central role in the production medicinal herbs. On the other, it is important to note that, the Amazon forest is of significance as far as climatic control is concerned, because of its significance in the recycling of carbon dioxide. Compounding these factors and the fact that most Brazilian environmental protection bodies’ initiatives to conserve the forest have yielded little, the issue is of great concern, because the Amazon is a global and not a national heritage.Advertising Looking for essay on ecology? Let's see if we can help you! Get your first paper with 15% OFF Learn More One primary cause of the increased deforestation in Brazil is the increased rate of subsistence farming, as most individuals are have encroached and cleared the Amazon forestland for agricultural projects. Two main farming practices com monly practiced by Brazilians are breading and rearing of cattle and planting of soybeans. These practises require large pieces of land hence, the continued destruction of the Amazon forest to sustain the practices. Another factor that has contributed to the increased destruction of the Brazilian Amazon is the increased infrastructural development ventures by the government, for example, the construction of the two thousand miles road project of 1970s, which passed through the forest. On the other hand, because the forest is reach in varied tree species of highest timber, another primary cause of the increased rate of deforestation is logging, a practice that consumes more than twenty three thousand three in a month. Although agriculture contributes greatly to Brazil’s economy, it being one of the biggest producers of animal and soybean products globally, in most cases most agricultural practices adopted by farmers do not take into consideration the effects of their practic es on the well-being of the environment. Brazil’s deforestation has great effects on the global climatic conditions, because of the role played by the Amazon rain forest on climate control. Excessive production of greenhouse gases and clearing of vast amounts of vegetation has led to the increased build up of green house gases. Such accumulations are major causes of global warming, a fact that has led to increased global calamities, for example, flooding and drought. In addition, because of the richness of the Amazon forest with many species of flora and fauna, destruction of the forest reserve has led to the disappearance of the world’s indigenous and rare species of flora and fauna; hence, the need for workable solutions to the problem. In conclusion, although the Brazilian government has put up measures to control the rate of deforestation, there is need for it to review its land control and tax policies, as a measure of ensuring its farmers do not reap big profit s from their farming activities, at the expense of the environment.Advertising We will write a custom essay sample on Destruction of the Brazilian Tropical Rain Forest specifically for you for only $16.05 $11/page Learn More

A Comparison of Arthur Dimmesdale and Pearl essays

A Comparison of Arthur Dimmesdale and Pearl essays In The Scarlet Letter by Nathaniel Hawthorne, Reverend Arthur Dimmesdale and Pearl are two essential characters. Because they are father and daughter, they have some similar qualities, but also some different ones. The apple does not fall far from the tree: the apple is the child of the tree (the parent); therefore, it inherits similarities in personality, but it also retains its own individual qualities. Dimmesdale and Pearl share few similar traits, but Hawthorne makes these similarities significant. Passion greatly affects the lives of both Dimmesdale and Pearl. Dimmesdale commits adultery a sin of passion. Pearl inherits all this enmity and passion [ . . . ] by inalienable right (Hawthorne 87). From the moment Hester Prynne gives birth to her daughter, the sin of adultery marks Pearl permanently just as the scarlet A marks her mother. Throughout the entire novel, Pearl serves as a symbol of Dimmesdale and Hesters passion. Although the same force of passion affects Dimmesdale and his daughter, he makes the choice to commit adultery while Pearl does not have the power to decide to be borne out of a sin. This sin inflicts grief upon both the father and daughter. Dimmesdale, overcome with a great horror of mind, feels a gnawing and poisonous tooth of bodily pain because his guilt haunts him (Hawthorne 136). It continues to haunt him for as long as he refuses to confess to the sin. Grief also bears a heavy weight on Pearl, not just her father. Hawthorne writes, Nothing [is] more remarkable than the instinct, as it [seems], with which the child [comprehends] her loneliness (86). Not only does Pearls father refuse to acknowledge her as his daughter, but also the children of the town refuse to allow her to play with them. How can she not be grief stricken? The mutual love between her mother and herself helps Pearl to cope with the grief; neithe...

Analyze Marriott International Inc Research Paper

Analyze Marriott International Inc - Research Paper Example A real hands-on director, he systematically relished visiting Marriott’s progressively glamorous places, and spending time with the ever-growing positions of friends who, in his awareness, were the key secret of his firm’s accomplishment. (Marriott International Inc., 2000). Currently, Marriott International, Inc. is a foremost international hospitality corporation with approximately 2,800 functioning units in USA and 67 other nations and regions. In keeping with Marriott’s supreme valued custom of service, the JW Marriott Hotels & Resorts brand is, itself, a new take on treat, offering detail-oriented individual and reliable service. On the other hand, from the amenity structures you expect to the least facts that amuses, JW Marriott superiorities itself in offering those unforeseen touches that lift each visitor’s vacation. As a fresh affiliate of the JW Marriott clan, the JW Grand Rapids is devoted to conserving the philosophy that the Marriott’s initiated some decades back. A culture that distinguishes its connections as the most valuable possessions and appreciates that how the firm serves its visitors is a straight echo of how much they are esteemed. Its Headquarters are located in Bethesda; on the other hand, the company has 102868 Employees in USA and 195551 Employees worldwide. Marriott is an international lodging firm based in Bethesda, Md. The business functions and franchises guesthouses and licenses holiday possession resorts under 18 sorts. The majorities of the employees say Marriott gives unique assistances. (Marriott International Inc., 2000). Whereas they vary by site, the business operates 715 possessions in USA alone .Those seeking an outdated 9 - 5 work day might not find this office the greatest fit, depending on the work. However most workers say the corporation inspires work-life stability, need to stay long-term and say its a welcoming environment. At Marriott, 81% of

The future status of English as the global language is assured Essay

The future status of English as the global language is assured - Essay Example As mentioned above, English is one of the fastest growing languages of world that adopts thousands of new words to embellish its vocabulary. It is this changing nature that prevents many from getting mastery over this language, which may be a real threat to its global status. Despite such a global reach, English is also susceptible to forces of language fragmentation or even disappearance altogether. Some of the linguists have observed that an increase in the democratization of governments will reduce the use of English globally. This is because it relegates the status of the political elite who are chiefly the speakers of non-native English. This downgrading of the English language has already occurred with the advent of independence in the post world war two eras in countries such as Tanzania, Philippines, Malaysia and Sri Lanka. Another notable factor concerned with English in India is that the employment of English in the business and educational fields is reducing, especially wi th the recent introduction of a key business newspaper entirely written in Hindi. Furthermore, the rapid growth in Indian higher education will lead to an influx of citizens who speak Hindi or another vernacular language thus lessening the number of citizens who can speak in English. The problem of translation the English become a vital issue in third world nations and people in these nations have often faced difficulties in translating English works in to their native language. This issue often questions the universal acceptability of English language as a Global language and it also reveal the fact that in future the status of English language as a Global language is not assured. The problem is visible in both literature and communication. Many other world languages have been receiving in third world nations without any practical problems and one can find the fact that their translation is