Review of the Wrestler Essay Example for Free

Review of the Wrestler Essay THIS EMOTIONAL FAIRY TAIL FOLLOWS THE STORY OF THE HAS-BEEN BROKEN DOWN, BEATEN, AND EMOTIONALLY STUNTED RAM A ONCE POPULAR WRESTLING STAR OF THE 80IES NOW STRUGGLING TO MAKE RENT FOR HIS TRAILER FIGHTING IN FRONT OF BARLEY 10S OF FANS SUDDENLY HITS HIS LOWEST POINT AND HAS A CRIPPLING HEART ATTACK. DOCTORS TELL RAM HE CAN NEVER FIGHT AGAIN, THIS SHACKING HIM AS WRESTLING WAS HIS ONLY LOVE AND THE ONLY THING HE HAS STUCK WHICH THROUGH OUT HIS LIFE UNLIKE HIS EX WIFE OR DAUGHTER BUT WITH NOTHING LEFT HE DECIDES TO REUNITE WITH HIS DAUGHTER AND START A LIFE WITH A GIRL HE MEETS AT A STRIPE CLUB, HAVING TO WORK A REGULAR JOB WITH HIS PRICK BOSS AT A LOCAL FOOD STORE. OF COURSE THIS ALL GOES WRONG WHEN RAM SNAPS AT A CUSTOMER WHO RECOGNIZES HIM AS A WRESTLER, RAM UPSET WITH THIS GOES ON A BINGE OF SEX AND DRUGS THEN BECAUSE OF THIS MISSES HIS REUNITE DINNER WITH HIS DAUGHTER SO THEN RAM WITH NOTHING LEFT DECIDES HE IS NOT CUT OUT TO LIVE A NORMAL LIFE AND WRESTLER A BIG MATCH AGAINST LONG TIME OPPONENT AYATOLLAH AFTER A. EMOTIONAL SPEECH WITH HIS GIRLFRIEND HE GOES AHEAD WITH THE WRESTLING MATCH SHOWING THAT WRESTLING IS HIS MAIN PASSION AND TRUE LOVE DURING THE MATCH RAM HAS HEART ISSUE HE CLIMBS TO THE TOP ROPE IN PAIN FROM HIS HEART DELIVERS HIS SIGNATURE MOVE THE CAMERA BLACK OUT LEAVING YOU WITH THE IMPRESSION HE HAS DIED. THE WRESTLER IS AN AMAZING PIECE OF ACTING WRAPPED WITH LONG EMOTIONAL SHOTS AND SPEECHES AND TRULY OUTLINES ONE MANS LOVE FOR SOMETHING ABOVE EVERYTHING ELSE SO MUCH HED RATHER DIE THAN LIVE WITH OUT IT AND ALTHOUGH ITS SLOW BUILD CAN SOMETIMES EVEN FIND YOU BORED THE PAY OFF OF THE PURE RAW EMOTION AND TALENTED ACTING YOU WONT BE DISAPPOINTED.

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.

Theories on How the Moon was Formed

Theories on How the Moon was Formed Earth’s sole natural satellite was first scientifically observed through Galileo Galilei’s telescope since 1610. The celestial body Galileo was observing makes a complete orbit around Earth in 27 earth days at a distance of 384 thousand km1. The Moon rotates and spins at the same rate which causes it to keep the same side or face towards Earth during the course of its orbit1. The satellite moderates the Earth’s wobble on its axis through a gravitational pull which is responsible for stabilizing the weather, and also for creating a tidal rhythm that has been helping humans for thousands of years. The Moon is also responsible for helping nocturnal animals see at night through its light reflecting from the Sun onto the Earth. Earth’s moon is a rocky solid body containing a cratered surface from impacts, with an exosphere (a very thin and weak atmosphere) and lack of liquid on its surface that cannot support life1. Although this celestial body cannot support l ife, it has helped life on Earth since the beginning. How was the moon created? There are several lunar origin theories which will be explained further in this paper. There were three pre-Apollo major theories that have been speculated for centuries2. These are: capture theory, fission theory, and the double planet theory3. The fission hypothesis was proposed by Charles Darwin’s son, George Darwin in 1878. He thought that the Moon and the Earth were a part of each other2 and that the Earth had been spinning so fast that material broke off from the Earth which formed into the Moon. The reason why he thought this was because of Kepler’s third law, and also because of his observation that the Moon’s orbital period was growing around the Earth suggesting that it must have been closer to Earth at one point. Kepler’s harmonic law relates the orbital period of a planet to its average distance from the sun showing that closer planets travel at greater speeds and also have shorter orbital periods4. This was a popular theory for the longest time even though it had its problems. Another scientist, Osmond Fisher, encouraged the ide a and thought that the Pacific Ocean was actually a scar left from the separation of the Earth and the Moon 2. This theory was eventually disproved and later on, researchers showed that in order for the Moon to separate from the Earth, the Earth must have been spinning so fast that it was rotating around the sun at least once every two and a half hours3 which scientists believe couldn’t have happened. Also, a scientist named Forest Ray Moulton showed through mathematics of the stability of fluid mechanics, the Moon could not have been formed through fission2. The second major theory that was hypothesized was the co accretion theory, double planet hypothesis, or the condensation theory. This theory suggests that the Moon and Earth formed together at the same time by co-accretion through the original Nebula that formed the solar system (suggested by Pierre-Simon Laplace) 2. This theory is observed through binary star systems and has the greatest astronomical observational support. It also has the help of the Roche limit proposed by Edouard Roche that shows the physical limit to how close the Moon can be as a celestial body disproving the fission theory as well. This limit showed that the Moon could only have existed as a ring of debris similar to Saturn and Jupiter2. Unfortunately, problems were observed with this theory since scientists could not explain why Venus did not have a moon, and why the Earth did not share the same properties as the Moon such as the type of core each had (Earth is dense, the Moon is not), a differing gravity forc e, and the amount of Iron each body had3. The third pre-Apollo major theory that was proposed was formulated by Thomas JJ See. He suggested that the Moon was a captured satellite and that it was actually formed further out in the solar system as far as Neptune2, and somehow, the Moon became close enough to the Earth that the gravitational pull of the Earth captured it. This theory could explain why the Moon and Earth do not share the same properties and is also evident in the universe itself with Mars and other planets. However, this too had its problems because it is very unlikely that a celestial body with the Moon’s shape and elliptical orbit could have found the Earth the way it did. If it was slightly different (which it should have been), it would have crashed into Earth or would have been thrown away from it3. After the Apollo 11 lunar landing with the first men on the Moon, there was a new hypothesis generated through the help of a little piece of moon rock. The moon rock showed that volatile substances with low boiling points such as water were rare as well as metals such as potassium and sodium3. This in itself discredited the fission and double planet theories because if these were true, the Moon would have the same composition as the Earth. The latest theory is also known as the canonical moon theory: the Giant Impact Hypothesis3. It in a way combines all three theories to form one that makes the most sense overall. This hypothesis proposes that the Earth was struck by another celestial body the size of Mars called Theia5 (capture hypothesis). The impact of this collusion expelled large amounts of material (the fission hypothesis) 2, and since Theia had a less dense mantle, Earth’s core was untouched by the impact5. The material which was a ring of very hot debris6 eventually c oalesced or condensed into Earth’s sole satellite (co-accretion hypothesis) 2. This also implies that the Moon would have formed very hot or possibly molten which also disapproves that the Moon was formed solely through the capture hypothesis since if the moon was captured it would not heat up as much as it did. Moreover, the substances on the Moon are more common to silicon and aluminum which are substances with high boiling points3. Although the Giant Impact Hypothesis is what most scientists believe to be the origin of the Moon, there has been new research by geochemist Junjun Zhang from the University of Chicago that looked at titanium isotopes, t50 to t47 in 24 separate samples of moon soil and rock5. The geochemist tested titanium since Theia should have left its signature on the Moon after the giant collision and it is very unlikely that Earth could have exchanged titanium since it has a very high boiling point5. However, research showed that similar to oxygen isotopes from previous research, titanium shares a good proportion of the Earth’s mantle7.This is troubling since Theia was thought to be a ways away from the Earth. Moreover, Robin Canup from the Southwest Research Institute in Boulder, Colorado shares input and states that oxygen isotope composition of Mars differs from Earth by a factor of 50 so it is improbable for the Moon to have the same proportions of oxygen and titanium7. Another study was conducted in 2012 by Matija Cuk from SETI (Search for Extraterrestial Intelligence) and Sarah Stewart from Harvard University7 and suggests that if the Earth was spinning faster than it is now (to have two or three hours for a day), the planet could have thrown enough material to form the Moon. After forming the Moon, the gravitational pull could have eventually slowed down the Earth’s spin rate eventually producing the 24 hour day we have today7. In order to understand how the universe works, more research needs to be conducted including a mission to Venus7 so that we can better understand how and why the Earth and Moon have the composition they do. We already know the composition of Mars so it is important to know how the other planet beside us, Venus, operates as well. Although we have theories of how the Moon was formed, even the canonical Giant Impact Hypothesis seems to be wrong due to recent research about the Earth and Moon’s properties. I think it is very likely that Matija Cuk and Sarah Stewart’s hypothesis is correct, that is, the young Earth may have spun fast enough to form a moon. The Earth could have been closer to the Sun than it is today which is highly probable due to the dark energy slowly expanding our universe. Moreover, the debris may have shaped into the Moon’s form, a spherical satellite, which can be observed through an example of binary star systems. Eventually, the Moon could hav e been big enough to stabilize the Earth’s orbit, to conduct how long our days are today, and further support life on Earth by providing ocean tides to influence the Earth’s climate.

A GROSS FORM OF DELIGHTFUL SATIRE Essays -- essays papers

A GROSS FORM OF DELIGHTFUL SATIRE "The stoical scheme of supplying our wants by lopping off our desires, is like cutting off our feet when we want shoes." -Jonathan Swift "We have just enough religion to make us hate, but not enough to make us love on another." -Jonathan Swift Like all true satirists, Swift was predominantly a moralist, one who chastises the vices and follies of humankind in the name of virtue and common sense. Throughout his writing, Swift constantly raised the question of whether the achievements of civilization-its advancing technology, its institutions, its refinement of manners-cannot be seen as complex forms of barbarism. With this theme in mind, Swift wrote some of his best works: A Modest Proposal, Gulliver’s Travels, and A Tale of a Tub. Although he is mastery at prose, he is also known for his poetry. It can be said that the subjects within his writings could be taken from his religious belief in the non-perfection of man. Swift believed that human reason was necessary to divine guidance. According to Herbert Read, Swift was the first poet who dared to describe nature as it is with all its deformities, and to give exact expression to a turn of thought no matter the subject. And because his life was one long mutiny- mutiny against darkness of fate, the injustice of men, the indignity of our bodily functions-his work is one long scrutiny into dark depths. Therefore, he attacks the idealistic idea of feminine beauty by ironically drawing attention to the female body’s excretory functions. Unfortunately, Swift emphasizes women, despite his deep love and friendship for individual women, as a symbol of man’s bestiality. He victimizes women by his own secret over-idealization of her. This is seen in his poems, The Lady’s Dressing-Room, Strephon and Chloe, and A Beautiful Young Nymph Going to Bed. Swift becomes obsessed by the morbidly physical. The gap between spirit and flesh cannot bridge, for flesh has become uncleansable to him. With Swift being seen by Robert Ellis--quoted by Herbert Read-as having neurasthenia, anything that comes regularly and in routine is liable to become intolerable, it is easier to understand some of his writings. This idea gained him much ridicule from critics because thinkers of his day stressed the essential goodness and rationality of humans. Swift, certainly, shares this i... ...od which he was writing and the subjects that were generally written about. Because his descriptions are so detailed, and the imagery is so deep, Jonathan Swift proves himself as a writer to be studied and admired. Bibliography: WORKS CITED Brown, Laura. â€Å"Reading Race and Gender: Jonathan Swift.† Critical Essays on Jonathan Swift. Ed. Frank Palmeri. New York: G.K. Hall & Co, 1993. 122. Davis, Herbert. â€Å"Swift’s View of Poetry.† Poetry Criticism. Ed. Drew Kalasky. Vol. 9. Detroit: Gale Research Company, 1994. 259 Donoghue, Denis, Ed. Jonathan Swift. Australia: Penguin Books, 1971. 307. Huxley, Aldous. â€Å"Do What You Will.† London: Chatto & Windus, 1956. Johnson, Maurice. â€Å"The Sin of Wit: Jonathan Swift as a Poet.† Literature Criticism from 1400 to 1800. Ed. Dennis Poupard. Vol. 1. New Jersey: Gale Research Company, 1984. 502. Read, Herbert. â€Å"The Poems of Swift.† Literature Criticism from 1400 to 1800. Ed. Dennis Poupard. Vol. 1. New Jersey: Gale Research Company, 1984. 453. Watkins, W.B.C. â€Å"Absent Thee from Felicity.† Literature Criticism from 1400 to 1800. Ed. Dennis Poupard. Vol. 1. New Jersey: Gale Research Company, 1984. 461.

Vivisection: Progress as Paradigm :: Animals Science Papers

Vivisection: Progress as Paradigm "Progress is an optional goal, not an unconditional commitment, and its tempo has nothing sacred about it. A slower progress in the conquest of disease would not threaten society, but would be threatened by the erosion of those moral values whose loss, possibly caused by the too ruthless pursuit of scientific progress, would make its most dazzling triumphs not worth having." –Hans Jonas, bioethicist, 1969 I. Introduction The debate over animal experimentation for scientific advancement is serious and highly controversial. It brings our assumptions about the value of human life and scientific advancement into question. Analysis of this controversy does not purport any easy solutions: there are many points of view. However, it is apparent that the tones are shifting to entertain alternative methods. In allowing the interests of our own species to override the greater interests of members of other species, can we be equated with racists? Sexists?[1] To oppose the use of live animals in scientific experimentation do we not oppose all cruelty to animals, and should we not all be vegans? Should we not charge congress on all fronts for every connection between us and non-human animals? All of these questions will be touched on in this paper, but I will focus more directly on the vivisection controversy, for which I will borrow the Animal Liberation Front's definition: "Any use of animals in science or re search that exploits or harms them." I will give a brief history institutionalized experimentation and challenge the antagonistic viewpoints presented about the efficacy of the use of live animals in research, and offer some budding alternatives. II. History of Institutionalized Experimentation Experiments involving animals for scientific interests began centuries ago, but became institutionalized with Francois Magendie (1787-1855). Magendie was known as a hardworking and brutal physiologist. Barbara Orlans describes some of his experiments in In the Name of Science: Issues in Responsible Animal Experimentation: "Magendie isolated a section of the dog intestine so that it was attached to the rest of the body only by a single artery and vein. This of course was done without anesthesia. Magendie injected various powerful poisons including prussic acid into the intestinal segment and found that the animal was poisoned just as if the normal connections had been intact. He obtained a similar result by injecting a leg detached except for its crural artery and vein.

Salem Witch Trials and Account

Salem Witch Trial: Hangings Michelle Woodring Mr. Yates American Literature 1 May 2010 Salem Witch Trial: Hangings Theses— There were many casualties of the Salem Witch Trials and there are still many mysteries today. * Salem History and Background * Witch Craft Starting * Symptoms of Witch Craft * People Accused of Witch Craft * Trials * Hangings * End of the Witch Hunt Michelle Woodring Mr. Yates American Literature 1 May 2010 Salem Witch Trials: Hanging’s In1688, John Putnam invited Samuel Parris to preach in the Village church (â€Å"An Account of. . †). A year later inflation adjustments, and free firewood, Parris accepted the job as Village minister r(â€Å"An Account of†¦Ã¢â‚¬ ). He moved to Salem Village with his wife Elizabeth, his six year old daughter Betty, niece Abagail Williams, and his Indian slave Tituba (â€Å"An Account of†¦Ã¢â‚¬ ). Sometime during February of 1692 young Betty Parris became strangely ill (â€Å"Salem Witch Trialsà ¢â‚¬ ). The cause of her symptoms seemed to match those of Ergot (â€Å"Salem Witch Trials†). Betty’s behavior in some ways mirrored that of witch craft (â€Å"Salem Witch Trials†).It was easy to believe with an Indian war raging less than several miles away that the devil was close in hand (An Account of†¦Ã¢â‚¬ ). Talk of witch craft increased when other playmates of Betty’s began to exhibit similar unusual behavior (â€Å"An Account of†¦Ã¢â‚¬ ). William Griggs, a doctor, was called in to examine the girls he suggested that the girl’s problems might have a supernatural origin (â€Å"An Account of†¦Ã¢â‚¬ ). The number of girls afflicted continued to grow rising to seven (â€Å"An Account of†¦Ã¢â‚¬ ). The girls contorted into poses, fell down into frozen postures, and complained of biting and pinching sensations (â€Å"An Account of†¦Ã¢â‚¬ ).Sometime after February 25 and 29 the first three accused of witch craft w ere Tituba, Sarah Good, and Sarah Osborn (â€Å"Salem Witch Trials†). Tituba was an obvious choice (â€Å"Salem Witch Trials†). Sarah Good was a beggar (â€Å"Salem Witch Trials†). Sarah Osborn was old and had not attended a church for over a year (â€Å"Salem Witch Trials†). Tituba was the first witch to admit to doing witch craft (â€Å"Salem Witch Trials†). Soon the spectral forms of other women attacking the afflicted girls came into the court rooms (â€Å"An Account of†¦Ã¢â‚¬ ).Martha Corey, Rebecca Nurse, Sarah Cloyce, and Mary Easty were accused of witch craft during a March 20 church service (â€Å"An Account of†¦Ã¢â‚¬ ). Dorcas Good, four-year-old daughter of Sarah Good, became the first child to be accused of witch craft when three of the girls complained that they were bitten by the specter of Dorcas (â€Å"An Account of†¦Ã¢â‚¬ ). Deliverance Hobbs became the second witch to confess for doing witch craft (â€Å"An A ccount of†¦Ã¢â‚¬ ). The first accused witch to be brought to trial at the local church was Bridget Bishop (â€Å"An Account of†¦Ã¢â‚¬ ).At Bishop’s trial on June 2 a field hand testified that he saw Bishop’s image stealing eggs and then saw her transform herself into a cat (â€Å"An Account of†¦Ã¢â‚¬ ). Bishop’s jury returned a verdict of guilty on June 10 (â€Å"An Account of†¦Ã¢â‚¬ ). Bishop was carted to Gallows Hill and hanged (â€Å"An Account of†¦Ã¢â‚¬ ). As the summer of 1692 warmed, the pace of trials picked up (â€Å"Salem Witch Trials†). John Proctor was openly critical of the trial and paid for his skepticism with his life (â€Å"Salem Witch Trials†). No execution caused more unease in Salem than that of the village’s ex minister, George Burroughs (â€Å"An Account of†¦Ã¢â‚¬ ).Burroughs was identified by several of his accusers as the ringleader of the witches (â€Å"An Account of†¦Ã¢ € ). Among the thirty accusers of Burroughs was nineteen-year-old Mercy Lewis that offered unusually vivid testimony against Burroughs saying that he had came to her in the middle of the night and made her sign the Devils book (â€Å"An Account of†¦Ã¢â‚¬ ). Burroughs became guilty and took to Gallows hill to be hanged he recited the Lord’s Prayer perfectly which greatly moved people (â€Å"An Account of†¦Ã¢â‚¬ ). The Lord’s Prayer: Our father which art in heaven, Hallowed be thy name.Thy kingdom come. Thy will be done, As in heaven, so on earth. Give us this day our daily bread. And forgive us our debts, as we Also have forgiven our debtors And lead us not into temptation, but Deliver us from evil: for thine is the Kingdom, and the power, and the glory Forever. Amen. One victim of Salem witch-hunt was not hung his name was Giles Corey (â€Å"Salem Witch Trials†). He died from being pressed to death by stones (â€Å"Salem Witch Trials†). T hree days after Giles Corey’s death on September 22 eight more convicted witches were hung (â€Å"Salem Witch Trials†).They were the last victims of the witch-hunt (â€Å"Salem Witch Trials†). With spectral evidence not admitted, twenty-eight of the last thirty-three witchcraft trials ended in acquittals (â€Å"Salem Witch Trials†). In May of 1693, all remaining accused or convicted witches were released from prison (â€Å"Salem Witch Trials†). By the time witch-hunt ended nineteen convicted witches were executed (â€Å"Salem Witch Trials†). And at least four accused witches had died in prison (â€Å"Salem Witch Trials†). Then one man was pressed to death by stones (â€Å"Salem Witch Trials†).About one or two hundred other persons were arrested and imprisoned on witch craft charges and two dogs were executed as suspected accomplices of witches (â€Å"Salem Witch Trials†). Works Cited Linder, Douglas O. â€Å"An Account of Events in Salem,† Famous American Trials: Salem Witch Craft Trials of 1692. Sep 2009. Web. 15 Apr 2010. â€Å"Salem Witch Trials. † Wikipedia. Wikipedia Foundation. Inc. 12 Apr 2010. Web. 15 Apr 2010. â€Å"Matthew 6:9-13, The Lord’s Prayer. † Holy Bible: King James Version. Holman Bible Publishers. 1973. 26 Apr 2010.

A Personality Development Theory Applied in Choosing Career Essay

This paper attempts to look at the Big Five, a personality development theory that is used in studying the dimension of one’s personality. The Big Five model is a product of empirical research and at present, the most accepted approach among psychologists in studying personality traits. 1 The five factors are known as OCEAN- openness, conscientiousness, extraversion, agreeableness and neuroticism. 2 It is said that theses possession of these traits may be stable for over 45 years that can start from early adulthood. 3 Parts and portion of traits are also heritable genetically. In addition, the traits are result of adaptation to the environment. Generally, these factors are viewed as universal and have been found in languages of different nations. Knowing where one’s place in the dimension can be very useful for one’s improvement and realization of skills, talents and abilities. The Case Dave has long been dreaming of putting up his own business. After several years of working in the job which he definitely did not like, he finally decided to pursue his dream. At this point in time, Dave was not sure of what enterprise will best fit his personality. Applying the Big Five Personality Development Theory, we carefully examine Dave’s personality in order to see what are his dominant traits and characteristics, his weaknesses and   1Buss, D. M. (1996). Social adaptation and five major factors of personality. In J. S. Wiggins (Ed. ), The five-factor model of personality: Theoretical perspectives (pp. 180-207). New York: 2Guilford. Soldz, S. , & Vaillant, G. E. (1999). The Big Five personality traits and the life course: A 45-year longitudinal study. Journal of Research in Personality, 33, 208-232 3Buss, D. M. (1996). Social adaptation and five major factors of personality. In J. S. Wiggins (Ed. ), The five-factor model of personality: Theoretical perspectives (pp. 180-207). New York: unleashed talents if ever to be able to fit it with the kind of business that will best suit him. This is for the purpose of finding the better enterprise that will complement to his overall personality. This is important because the idea of putting up a business is much of a risk to take and Dave cannot afford not to be successful in his new chosen field after leaving his long time job. This is somehow a prelude to the future of his business because as the business and manger and owner to be, he will direct the efficiency and effectiveness of his business. To start with, traits and characters must be identified. In this case Dave provided us with some of his characteristics that are assumed to be relevant in helping him determine the best business for him. Dave mentioned that he was previously a paralegal and he had worked with attorneys and several clients in their law firm. He claimed that he often acts as a leader and in fact he formerly managed a staff. He added that he posses various leadership qualities though if given a choice, he would rather work alone. Applying the Big Five Personality Earlier, it has been mentioned that Dave, if given the chance will rather work alone, hence, we can conclude that he is an introvert. But since, he had longed desire to have his own business, it is inevitable to work with other person or to a group of person. By the word business, he is going to put up an organization. Building an organization means continuous interaction with other people and all the accompanied activities will operate in a relational or in a dynamic manner. Example of introversion is being independent and often being quiet. Introvert people prefer to do things by themselves and refuses to be helped by others and also disregard group activities because they see themselves more productive if they are alone. They are also the type who minimizes social involvement, in other words they prefer less socialization. Some of them are deliberately shy and some extreme cases found depression. Given that Dave wanted to put up his business and he also wanted to be alone if possible, I think the best business that will suit him is a coffee shop. Maintaining a coffee shop is not that difficult. Three to four people can be able to organize the business accordingly. If he will put up a coffee shop, he can be the one at the counter or he can hire another person to do that and all he needs to do is to supervise. Unlike his previous job wherein he had staff to lead to and attorneys and clients to deal with, a coffee shop business will put him in less trouble. If he has a passion in bartending, he can be the one to do it and all he needs to face is the waiter who will be passing the orders to him. As for agreeableness, undoubtedly, Dave has established leadership qualities necessary to operate a business. It will not be hard for him to deal with his employees to be, since he had his fair share on this matter on his preceding occupation. On the other hand, Dave showed did not mention about openness but I think it is safe to say that he is an open person because even if he has introvert qualities he bear with his colleagues for a long period of time and he tried to be a good leader and showed exceptional qualities of leadership in the law firm. Another proof of his openness is his likeness to have his own business. This means that he is open for new changes in his life to happen. Meanwhile, Dave has finally become assertive departing from his old reticent way. This is because at last he had the courage to left his job and starts a new with a business. Lastly, after evaluating the four factors of the Big Five, we can conclude that Dave is in the mid of imaginative and conventional dimension since he had let several years to past before actually realizing his wants, nevertheless he also posters creativity side as he was able to think of another form of occupation. In this activity, I learned the importance of knowing your personal traits and characteristics. Being familiar with your limitations and advantages may help you in various ways, likewise, it can help you understand others as well as to deal with them accordingly. Being acquainted with these traits will improve your relationship to other people and most of all it will contribute a lot in realizing your own potential and developing you as a person. References About. com Website (2008). The â€Å"Big Five Personality Model. Retrieved on January 17, 2008 from http://psychology. about. com/od/personalitydevelopment/a/bigfive. htm Buss, D. M. (1996). Social adaptation and five major factors of personality. In J. S. Wiggins (Ed. ), The five-factor model of personality: Theoretical perspectives (pp. 180-207). New York: Guilford. Soldz, S. , & Vaillant, G. E. (1999). The Big Five personality traits and the life course: A 45-year longitudinal study. Journal of Research in Personality, 33, 208-232