Un Chien Andalou by Salvador Dali and Luis Bunuel Essay

Un Chien Andalou by Salvador Dali and Luis Bunuel - Essay Example The purpose of paper "Un Chien Andalou by Salvador Dali and Luis Bunuel" is to investigate how the corresponding philosophical position of postmodernism influences the aesthetic values of the work of art, the film Un Chien Andalou. Surrealism, which is a part of the philosophy of postmodernism is defined as psychic automatism in its pure state, by which one expresses verbally by means of the written word, or in another manner, the actual functioning of thought. Surrealism is dictated by the thought, in the absence of any control exercised by reason, and devoid of aesthetic or moral concern. Surrealism is based on the belief in the superior reality â€Å"of certain forms of previously neglected associations, in the omnipotence of dream, in the disinterested play of thought†. It ruins completely all other psychic mechanisms and substitutes itself for them, while solving all the main problems of life. The postmodern philosophy challenges any clear and concise process of identific ation and definition as a part of rationality. Postmodernism rejects common sense and accessibility, scientific reason, philosophical logic, clarity or precision. On the other hand, postmodernism seeks to grasp those elements that escape these processes of definition, and â€Å"celebrates what resists or disrupts them†. A plurality of definitions has now come to describe postmodernism’s multifaceted nature. Examples of postmodern art relate to fracturing, fragmenting, indeterminacy and plurality. Postmodernism is the style of our age.

Example a Level Psychology Experiment Essay Example for Free

Example a Level Psychology Experiment Essay Hypothesis – there will be a significant positive relationship between the scores on a memory test and scored on a test to predict your chance of being a millionaire Null Hypothesis – there will be no significant relationship between scores on a memory test and scores on a test measuring the chances of becoming a millionaire and any relationship is due to chance Method: Design – the method of the experiment was a correlational study; this was used in order to see whether there was a relationship between the scores on a memory test and scores on a millionaire test. The experiment used co variables, which were the score on the memory test and score on the millionaire test. Controls – in order ensure the test was reliable the extraneous variables needed to be controlled. Standardised instructions were used as a control to give all participants the same instructions during the experiment, which meant that the experimenter did not affect the communication of the instructions by changing them for each participant which reduces the amount on experimenter bias. A further control that was used was using anonymous data by assigning each participant a number to record that data on a table, rather than using individual’s names. Participants – the target population for the experiment were young people in the Gosport area of each gender. The sampling method was an opportunity sample of 10 students aged 17-18 both males and females (2 males and 8 females) at Bay House Sixth Form from an A Level Psychology class and the researcher was a Psychology teacher at Bay House Sixth Form. Apparatus and Materials – the materials used for the experiment were a list of 34 words created by the researcher that were projected onto the board, paper and pens provided for the participants to record the number of words they remembered, a watch to time the one minute period of remembering and writing down the words, an online questionnaire to measure likelihood of becoming a millionaire at bbc.co.uk/science/humanbody/surveys/millionaire1/index.shtml Procedure – the participants were firstly given an explanation of the research and what the study would entail for them. They were them given the equipment they required to complete the memory test whilst remaining anonymous and were given standardise instructions by the researcher of how to complete the test and the rules of the research. The participants were then shown the list of 34 words to memorise by projecting the list on the board and where given one minute to memorise as many words as possible. The words were then hidden and the researcher projected instructions to the participants to write down all the words they remembered and they were given one minute to do so. The number of words memorised were recorded by the researcher by assigning each participant with a number and they stated out loud their score. The participants were then asked to move to a computer room to complete an online survey to test their likelihood of becoming a millionaire, after they completed the questionnaire the participants had to record their score next to their memory score on a board. The participants were then debriefed by the researcher. Ethics – there were few ethical issues in the experiment as informed consent was gained by the researcher to ensure the participants were given instructions and the aim of the research. Therefore, there was no deception in the research and so the integrity of the study was intact during the memory and millionaire tests. Furthermore, all the participants were over 16 and so there was no need for the researcher to obtain parental consent for the study. Participants were also given the right to withdraw before and during the research, therefore the participants were not pressured to take part or complete the study if they were not comfortable with the terms of the research or what the data was being used for. However, there may be ethical issues regarding the wellbeing of the participants during the research as the study may have caused stress or anxiety in the participants when completing the memory or millionaire tests because they may feel the pressure to do well in each test, although the research was anonymous and so this may have reduced the amount of stress caused by the study. Scatter Graph for Data: The scatter graph shows that there is a weak negative correlation between memory test scores and millionaire test scores, which means that it does not necessrily prove our hypothesis that there will be a significant postive relationship between the two co variables. Therefore, the hypothesis needs to be rejected and the null hypothesis can be accepted as the null hypotehsis reflects what our results show on the scatter graph. The graph can also help identify outliers, as the partipant that scored significantly higher on the memory test and lower on this millionaire test could be regarded as an outlier as it does not follow the pattern of the other data found from the research. Evaluation: Design – the design that was used in this research was correlational, which is good as allows us to identify if there is a relationship between two co variables as well as allowing research to be conducted that cannot be done in a lab experiment as is would not be viable. However, correlational studies do not show cause and effect between the two co variables and so it cannot be stated that having a good memory will cause a person to become a millionaire in the future as it could just as easily be that being a millionaire causes a person to have a good memory. Sample – the sample that as used in this study was very small, as only 10 people took part in the research; also the participants were psychology students. Therefore due to the small sample and the specific type of participant the results may not be able to be generalised to the wider population. Furthermore, gender may have been an issue with the sample as there were only 2 males, whereas there were 8 females, therefore there was not an equal mix of each gender and so the results cannot be generalised. Tests – the tests that were used in the study were a memory test created by the researcher and an online questionnaire to predict that chance that the participants would have of becoming a millionaire. The memory test was good as it used standardised instructions, meaning that the test was more reliable as the same instructions were shown to all participants at the same time, which reduces the amount on researcher bias and means that participants can query any confusion they have. Moreover, the standardise instructions mean that there is high control in the research and so the results are reliable. The millionaire test may have had some issues as the closed questions that were used may have not provided an applicable answer, resulting in participants answering questions incorrectly which may have an effect on the results.

Numerical Differential Equation Analysis Package

Numerical Differential Equation Analysis Package The Numerical Differential Equation Analysis package combines functionality for analyzing differential equations using Butcher trees, Gaussian quadrature, and Newton-Cotes quadrature. Butcher Runge-Kutta methods are useful for numerically solving certain types of ordinary differential equations. Deriving high-order Runge-Kutta methods is no easy task, however. There are several reasons for this. The first difficulty is in finding the so-called order conditions. These are nonlinear equations in the coefficients for the method that must be satisfied to make the error in the method of order O (hn) for some integer n where h is the step size. The second difficulty is in solving these equations. Besides being nonlinear, there is generally no unique solution, and many heuristics and simplifying assumptions are usually made. Finally, there is the problem of combinatorial explosion. For a twelfth-order method there are 7813 order conditions! This package performs the first task: finding the order conditions that must be satisfied. The result is expressed in terms of unknown coefficients aij, bj, and ci. The s-stage Runge-Kutta method to advance from x to x+h is then where Sums of the elements in the rows of the matrix [aij] occur repeatedly in the conditions imposed on aij and bj. In recognition of this and as a notational convenience it is usual to introduce the coefficients ci and the definition This definition is referred to as the row-sum condition and is the first in a sequence of row-simplifying conditions. If aij=0 for all i≠¤j the method is explicit; that is, each of the Yi (x+h) is defined in terms of previously computed values. If the matrix [aij] is not strictly lower triangular, the method is implicit and requires the solution of a (generally nonlinear) system of equations for each timestep. A diagonally implicit method has aij=0 for all i There are several ways to express the order conditions. If the number of stages s is specified as a positive integer, the order conditions are expressed in terms of sums of explicit terms. If the number of stages is specified as a symbol, the order conditions will involve symbolic sums. If the number of stages is not specified at all, the order conditions will be expressed in stage-independent tensor notation. In addition to the matrix a and the vectors b and c, this notation involves the vector e, which is composed of all ones. This notation has two distinct advantages: it is independent of the number of stages s and it is independent of the particular Runge-Kutta method. For further details of the theory see the references. ai,j the coefficient of f(Yj(x)) in the formula for Yi(x) of the method bj the coefficient of f(Yj(x)) in the formula for Y(x) of the method ci a notational convenience for aij e a notational convenience for the vector (1, 1, 1, ) Notation used by functions for Butcher. RungeKuttaOrderConditions[p,s] give a list of the order conditions that any s-stage Runge-Kutta method of order p must satisfy ButcherPrincipalError[p,s] give a list of the order p+1 terms appearing in the Taylor series expansion of the error for an order-p, s-stage Runge-Kutta method RungeKuttaOrderConditions[p], ButcherPrincipalError[p] give the result in stage-independent tensor notation Functions associated with the order conditions of Runge-Kutta methods. ButcherRowSum specify whether the row-sum conditions for the ci should be explicitly included in the list of order conditions ButcherSimplify specify whether to apply Butchers row and column simplifying assumptions Some options for RungeKuttaOrderConditions. This gives the number of order conditions for each order up through order 10. Notice the combinatorial explosion. In[2]:= Out[2]= This gives the order conditions that must be satisfied by any first-order, 3-stage Runge-Kutta method, explicitly including the row-sum conditions. In[3]:= Out[3]= These are the order conditions that must be satisfied by any second-order, 3-stage Runge-Kutta method. Here the row-sum conditions are not included. In[4]:= Out[4]= It should be noted that the sums involved on the left-hand sides of the order conditions will be left in symbolic form and not expanded if the number of stages is left as a symbolic argument. This will greatly simplify the results for high-order, many-stage methods. An even more compact form results if you do not specify the number of stages at all and the answer is given in tensor form. These are the order conditions that must be satisfied by any second-order, s-stage method. In[5]:= Out[5]= Replacing s by 3 gives the same result asRungeKuttaOrderConditions. In[6]:= Out[6]= These are the order conditions that must be satisfied by any second-order method. This uses tensor notation. The vector e is a vector of ones whose length is the number of stages. In[7]:= Out[7]= The tensor notation can likewise be expanded to give the conditions in full. In[8]:= Out[8]= These are the principal error coefficients for any third-order method. In[9]:= Out[9]= This is a bound on the local error of any third-order method in the limit as h approaches 0, normalized to eliminate the effects of the ODE. In[10]:= Out[10]= Here are the order conditions that must be satisfied by any fourth-order, 1-stage Runge-Kutta method. Note that there is no possible way for these order conditions to be satisfied; there need to be more stages (the second argument must be larger) for there to be sufficiently many unknowns to satisfy all of the conditions. In[11]:= Out[11]= RungeKuttaMethod specify the type of Runge-Kutta method for which order conditions are being sought Explicit a setting for the option RungeKuttaMethod specifying that the order conditions are to be for an explicit Runge-Kutta method DiagonallyImplicit a setting for the option RungeKuttaMethod specifying that the order conditions are to be for a diagonally implicit Runge-Kutta method Implicit a setting for the option RungeKuttaMethod specifying that the order conditions are to be for an implicit Runge-Kutta method $RungeKuttaMethod a global variable whose value can be set to Explicit, DiagonallyImplicit, or Implicit Controlling the type of Runge-Kutta method in RungeKuttaOrderConditions and related functions. RungeKuttaOrderConditions and certain related functions have the option RungeKuttaMethod with default setting $RungeKuttaMethod. Normally you will want to determine the Runge-Kutta method being considered by setting $RungeKuttaMethod to one of Implicit, DiagonallyImplicit, and Explicit, but you can specify an option setting or even change the default for an individual function. These are the order conditions that must be satisfied by any second-order, 3-stage diagonally implicit Runge-Kutta method. In[12]:= Out[12]= An alternative (but less efficient) way to get a diagonally implicit method is to force a to be lower triangular by replacing upper-triangular elements with 0. In[13]:= Out[13]= These are the order conditions that must be satisfied by any third-order, 2-stage explicit Runge-Kutta method. The contradiction in the order conditions indicates that no such method is possible, a result which holds for any explicit Runge-Kutta method when the number of stages is less than the order. In[14]:= Out[14]= ButcherColumnConditions[p,s] give the column simplifying conditions up to and including order p for s stages ButcherRowConditions[p,s] give the row simplifying conditions up to and including order p for s stages ButcherQuadratureConditions[p,s] give the quadrature conditions up to and including order p for s stages ButcherColumnConditions[p], ButcherRowConditions[p], etc. give the result in stage-independent tensor notation More functions associated with the order conditions of Runge-Kutta methods. Butcher showed that the number and complexity of the order conditions can be reduced considerably at high orders by the adoption of so-called simplifying assumptions. For example, this reduction can be accomplished by adopting sufficient row and column simplifying assumptions and quadrature-type order conditions. The option ButcherSimplify in RungeKuttaOrderConditions can be used to determine these automatically. These are the column simplifying conditions up to order 4. In[15]:= Out[15]= These are the row simplifying conditions up to order 4. In[16]:= Out[16]= These are the quadrature conditions up to order 4. In[17]:= Out[17]= Trees are fundamental objects in Butchers formalism. They yield both the derivative in a power series expansion of a Runge-Kutta method and the related order constraint on the coefficients. This package provides a number of functions related to Butcher trees. f the elementary symbol used in the representation of Butcher trees ButcherTrees[p] give a list, partitioned by order, of the trees for any Runge-Kutta method of order p ButcherTreeSimplify[p,,] give the set of trees through order p that are not reduced by Butchers simplifying assumptions, assuming that the quadrature conditions through order p, the row simplifying conditions through order , and the column simplifying conditions through order all hold. The result is grouped by order, starting with the first nonvanishing trees ButcherTreeCount[p] give a list of the number of trees through order p ButcherTreeQ[tree] give True if the tree or list of trees tree is valid functional syntax, and False otherwise Constructing and enumerating Butcher trees. This gives the trees that are needed for any third-order method. The trees are represented in a functional form in terms of the elementary symbol f. In[18]:= Out[18]= This tests the validity of the syntax of two trees. Butcher trees must be constructed using multiplication, exponentiation or application of the function f. In[19]:= Out[19]= This evaluates the number of trees at each order through order 10. The result is equivalent to Out[2] but the calculation is much more efficient since it does not actually involve constructing order conditions or trees. In[20]:= Out[20]= The previous result can be used to calculate the total number of trees required at each order through order10. In[21]:= Out[21]= The number of constraints for a method using row and column simplifying assumptions depends upon the number of stages. ButcherTreeSimplify gives the Butcher trees that are not reduced assuming that these assumptions hold. This gives the additional trees that are necessary for a fourth-order method assuming that the quadrature conditions through order 4 and the row and column simplifying assumptions of order 1 hold. The result is a single tree of order 4 (which corresponds to a single fourth-order condition). In[22]:= Out[22]= It is often useful to be able to visualize a tree or forest of trees graphically. For example, depicting trees yields insight, which can in turn be used to aid in the construction of Runge-Kutta methods. ButcherPlot[tree] give a plot of the tree tree ButcherPlot[{tree1,tree2,}] give an array of plots of the trees in the forest {tree1, tree2,} Drawing Butcher trees. ButcherPlotColumns specify the number of columns in the GraphicsGrid plot of a list of trees ButcherPlotLabel specify a list of plot labels to be used to label the nodes of the plot ButcherPlotNodeSize specify a scaling factor for the nodes of the trees in the plot ButcherPlotRootSize specify a scaling factor for the highlighting of the root of each tree in the plot; a zero value does not highlight roots Options to ButcherPlot. This plots and labels the trees through order 4. In[23]:= Out[23]= In addition to generating and drawing Butcher trees, many functions are provided for measuring and manipulating them. For a complete description of the importance of these functions, see Butcher. ButcherHeight[tree] give the height of the tree tree ButcherWidth[tree] give the width of the tree tree ButcherOrder[tree] give the order, or number of vertices, of the tree tree ButcherAlpha[tree] give the number of ways of labeling the vertices of the tree tree with a totally ordered set of labels such that if (m, n) is an edge, then m ButcherBeta[tree] give the number of ways of labeling the tree tree with ButcherOrder[tree]-1 distinct labels such that the root is not labeled, but every other vertex is labeled ButcherBeta[n,tree] give the number of ways of labeling n of the vertices of the tree with n distinct labels such that every leaf is labeled and the root is not labeled ButcherBetaBar[tree] give the number of ways of labeling the tree tree with ButcherOrder[tree] distinct labels such that every node, including the root, is labeled ButcherBetaBar[n,tree] give the number of ways of labeling n of the vertices of the tree with n distinct labels such that every leaf is labeled ButcherGamma[tree] give the density of the tree tree; the reciprocal of the density is the right-hand side of the order condition imposed by tree ButcherPhi[tree,s] give the weight of the tree tree; the weight (tree) is the left-hand side of the order condition imposed by tree ButcherPhi[tree] give (tree) using tensor notation ButcherSigma[tree] give the order of the symmetry group of isomorphisms of the tree tree with itself Other functions associated with Butcher trees. This gives the order of the tree f[f[f[f] f^2]]. In[24]:= Out[24]= This gives the density of the tree f[f[f[f] f^2]]. In[25]:= Out[25]= This gives the elementary weight function imposed by f[f[f[f] f^2]] for an s-stage method. In[26]:= Out[26]= The subscript notation is a formatting device and the subscripts are really just the indexed variable NumericalDifferentialEquationAnalysis`Private`$i. In[27]:= Out[27]//FullForm= It is also possible to obtain solutions to the order conditions using Solve and related functions. Many issues related to the construction Runge-Kutta methods using this package can be found in Sofroniou. The article also contains details concerning algorithms used in Butcher.m and discusses applications. Gaussian Quadrature As one of its methods, the Mathematica function NIntegrate uses a fairly sophisticated Gauss-Kronrod-based algorithm. The Gaussian quadrature functionality provided in Numerical Differential Equation Analysis allows you to easily study some of the theory behind ordinary Gaussian quadrature which is a little less sophisticated. The basic idea behind Gaussian quadrature is to approximate the value if an integral as a linear combination of values of the integrand evaluated at specific points: Since there are 2n free parameters to be chosen (both the abscissas xi and the weights wi) and since both integration and the sum are linear operations, you can expect to be able to make the formula correct for all polynomials of degree less than about 2n. In addition to knowing what the optimal abscissas and weights are, it is often desirable to know how large the error in the approximation will be. This package allows you to answer both of these questions. GaussianQuadratureWeights[n,a,b] give a list of the pairs (xi, wi) to machine precision for quadrature on the interval a to b GaussianQuadratureError[n,f,a,b] give the error to machine precision GaussianQuadratureWeights[n,a,b,prec] give a list of the pairs (xi, wi) to precision prec GaussianQuadratureError[n,f,a,b,prec] give the error to precision prec Finding formulas for Gaussian quadrature. This gives the abscissas and weights for the five-point Gaussian quadrature formula on the interval (-3, 7). In[2]:= Out[2]= Here is the error in that formula. Unfortunately it involves the tenth derivative of f at an unknown point so you dont really know what the error itself is. In[3]:= Out[3]= You can see that the error decreases rapidly with the length of the interval. In[4]:= Out[4]= Newton-Cotes As one of its methods, the Mathematica function NIntegrate uses a fairly sophisticated Gauss-Kronrod based algorithm. Other types of quadrature formulas exist, each with their own advantages. For example, Gaussian quadrature uses values of the integrand at oddly spaced abscissas. If you want to integrate a function presented in tabular form at equally spaced abscissas, it wont work very well. An alternative is to use Newton-Cotes quadrature. The basic idea behind Newton-Cotes quadrature is to approximate the value of an integral as a linear combination of values of the integrand evaluated at equally spaced points: In addition, there is the question of whether or not to include the end points in the sum. If they are included, the quadrature formula is referred to as a closed formula. If not, it is an open formula. If the formula is open there is some ambiguity as to where the first abscissa is to be placed. The open formulas given in this package have the first abscissa one half step from the lower end point. Since there are n free parameters to be chosen (the weights) and since both integration and the sum are linear operations, you can expect to be able to make the formula correct for all polynomials of degree less than about n. In addition to knowing what the weights are, it is often desi

To Kill a Mockingbird by Harper Lee :: To Kill a Mockingbird Essays

Analysis of Major Characters Scout - Scout is a very unusual little girl, both in her own qualities and in her social position. She is unusually intelligent (she learns to read before beginning school), unusually confident (she fights boys without fear), unusually thoughtful (she worries about the essential goodness and evil of mankind), and unusually good (she always acts with the best intentions). In terms of her social identity, she is unusual for being a tomboy in the prim and proper Southern world of Maycomb. One quickly realizes when reading To Kill a Mockingbird that Scout is who she is because of the way Atticus has raised her. He has nurtured her mind, conscience, and individuality without bogging her down in fussy social hypocrisies and notions of propriety. While most girls in Scout's position would be wearing dresses and learning manners, Scout, thanks to Atticus's hands-off parenting style, wears overalls and learns to climb trees with Jem and Dill. She does not always grasp social niceties (she tells her teacher that one of her fellow students is too poor to pay her back for lunch), and human behavior often baffles her (as when one of her teachers criticizes Hitler's prejudice against Jews while indulging in her own prejudice against blacks), but Atticus's protection of Scout from hypocrisy and social pressure has rendered her open, forthright, and well meaning. At the beginning of the novel, Scout is an innocent, good-hearted five-year-old child who has no experience with the evils of the world. As the novel progresses, Scout has her first contact with evil in the form of racial prejudice, and the basic development of her character is governed by the question of whether she will emerge from that contact with her conscience and optimism intact or whether she will be bruised, hurt, or destroyed like Boo Radley and Tom Robinson. Thanks to Atticus's wisdom, Scout learns that though humanity has a great capacity for evil, it also has a great capacity for good, and that the evil can often be mitigated if one approaches others with an outlook of sympathy and understanding. Scout's development into a person capable of assuming that outlook marks the culmination of the novel and indicates that, whatever evil she encounters, she will retain her conscience without becoming cynical or jaded. Though she is still a child at the end of the book, Scout's p erspective on life develops from that of an innocent child into that of a near grown-up.

Mr. Sun

Module Code: PM002 Class/Group: Group C Module Title: Research Design and Critique Assessment: Full Research Proposal Assignment Title: An investigation into the factors that influence the Glaswegian public’s choice of car. Student ID Number: 2059626 Date of Submission: November 29th, 2012 An investigation into the factors that influence the Glaswegian public’s choice of car. Rationale The number of automobiles had risen to over 1 billion vehicles all the world in 2010, which is 20 times more than this number in 1986(Sousanis, John,2011).Car plays a indispensable role in today's society, according to a survey from World Bank(2011), the number of ownership of motor vehicles per 1,000 people is more than 500 in most of developing countries, especially for Monaco, the number was 908(World Bank Data,2009). Although cars have become more and more commonplace, but the cars are still expensive commodity, also there is no doubt that the final decisions are usually made after ca reful consideration when people purchasing a car(Kathuria, Singla,2012). At the same time, as the vehicle types supplied to be chosen by consumers have become more and more various.When consumer facing with abundant of choices, they become more and more confused and irresolute. With the segmentation of automobile market, the factors that affect the public' car choices are more and more diversified. According to Couton et al. (2006), various studies have applied hedonic price modeling to show that price variation among new cars can be explained by differences in key product characteristics such as horsepower, engine capacity, speed, and safety features. However, these measurable variables may not be the main explanatory factors which will influence the choice of consumers.Based on the above mentioned content, this research will focus on the decisive factors which will impact the public's final choice of car, especially in the Glasgow area due to investigations and studies in the fiel d will be carried out and conducted in this city. Its results would probably benefit to car dealers and consumers. Especially for car manufacturers, they can according to consumer preferences to redesign and improve vehicles to gain better market performance. 1. What are the choices the public have when buying a car? 2. What are the main factors influencing public’s choices? . What variables affect these factors? Annotate Bibliography Banerjee,S. (2010) ,Study on Consumer Buying Behavior During Purchase of a Second Car , Journal of Marketing & Communication ,6 (2),4-13. This essay describes that for different types of automobiles, the main factors affect consumer’s purchase is slightly different in choosing a particular brand is always based on the different set of consumers towards various preference parameter. For different market segments of vehicle, dimensions are different. A successful car brand has had to accept and adopt these dimensions.In addition, the author also pointed out that there are many common factors influence the public's choice between consumers to buying a second car and purchasing the first one, but there are some obvious differences between them. For example, functional level factor such as car efficacy and usefulness are main concerns for second car buyers. Moreover, this article also mentioned that a high level of investment in advertising and promotional activities may not be able to guarantee a high percentage of repeat purchase. However, a long-term stable customer relationship will probably increase the probability of second time purchase.This journal is effectively to analysis interrelationship between consumer’s first car and second car, and common facts which seem to influence the public’s purchase behavior. The survey uses a probability sampling approach conducted with the passenger car owners in India with 525 samples. However, in this article, the author does not mention the relationship and impo rtance between satisfaction of customers on the second-hand value of the first car and loyalty for choosing the second-hand car, because a high level of satisfaction, may bring referral and repeat purchase.Randol E. Bucklin, S. Siddarth, Jorge M. Silva-Risso,(2008), â€Å"Distribution Intensity and New Car Choice†,JOURNAL OF MARKETING RESEARCH, Vol. XLV, 473-496. This journal demonstrate that the relationship between 4S shops distribution intensity of cars and brand new car buyers’ choices in the U. S. automobile market. Different from price, effect of advertising, promotional activities and other factors, distribution intensity changes relatively slow, but the distribution intensity will be affected some variables, thereby might affect decisions of consumers buying cars.Additionally, this article used information on the U. S car sales transactions gave by the Power Information Network, which included the accurate geographic locations of consumers and dealers. Non-prob ability sampling method was used in 55 4S shops as a samples. Dealer accessibility, dealer concentration and dealer spread would determine distribution intensity and then will largely affect the choice of the people for the car brands.This journal is relevant to the topic of this research, firstly it provides information about what factors will influence the public’s choice of car, secondly it shows how the three main variables influence the distribution intensity of each brand, so that influence the public’s choice of car. However, this study focuses on only the distribution intensity about car dealers, makes no attempt to differentiate between various different types of car, and the conclusion might not suitable for the niche car brands. Beside this, the author might overlook the fact that distribution intensity ontributes to high-end car brands. Dharmaraj,C. , Clement,S. J. ,(2010). Brand Preference Factors of Passenger Cars: An Empirical Assessment, Indiana Univers ity Press, The IUP Journal of Brand Management, 7(3),19-33. This article mainly analyzes the factors which will influence consumer's automotive brand preference. According to the author's study, performance of passenger cars are considered as the most important factor which might dominate consumer's preference, especially for male consumers, but economic abilities are the bases of the preference.In addition, the marketing communication strategy of a car will also largely affect the overall decisions of consumers. In conclusion, the comprehensive strength of a car, such as safety factor, industrial design, stability, scientific and technological content, durability, daily use cost, re-sale value , fuel consumption, comforts and so on, each of them is factor influence people ‘s preference and choice of car. This study is highly relevant to the topic of this research and demonstrates most of factors that will influent consumer purchase preference comprehensively and systematicall y.Although this survey collected data using questionnaires from 712 car buyers/owners by simple random sampling, there is not any variables about the respondents are addressed. In addition, the author offers no explanation for the distinction between Indian car market and developed countries’ market, the simple random sampling method was conducted in a midsize Indian city. Therefore, it is slightly possible that the survey result might not apply for city of Glasgow. Baltas,G. , Saridakis,G. 2009),†Brand-name effects, segment differences, and product characteristics: an integrated model of the car market†, Journal of Product & Brand Management, 18(2), 143 –-151. This article discusses that price of car is a main factor influence the public’s choice, and the price structure of new car market is determined by automobile characteristics, brand effects, and segment differences. A hedonic price experimental model is designed and implemented that includes b rand-name heterogeneity and functional characteristics.In addition, another extensive dataset model is applied to support the brand effects and hypotheses of segment differences. According to these two models, in mainstream car market, the functional characteristics determines automobile prices largely , however in high-end car market, incremental value is added to a car because its brand value , so the connotation of the brand value decide the price of prestige brands cars in large extent. The findings of this article include relevant information to this research. Firstly, it is a great probability that price of car is one of key facts which influence the public’s choice.This article demonstrates that there are at least three reasons determine the structure of automobile prices, and analyzes the variables and decisive factors of prices in mainstream segments and high-end segments respectively. However, the research focuses on many of the variables affecting the price of car and does not take into account other factors such as the industrial design of a car and the impact of marketing strategies. At the same time, mentioned in the text, the implicit brand value will affect car prices, thereby affecting consumer’s choice, but it is possible that the brand price is difficult to be quantified accurately.Kathuria,L. M. , Singla,V. ,(2012) Purchase of Pre-Owned Small Cars in India: An Exploratory Study, The IUP Journal of Marketing Management, 11(2),63-75. This study highlights that the main factors impacting the buying choice of second hand small vehicle were purchasing power constraint, high cost-effective, improve driving skills, desire for car , high resale price, good quality of after-sales service, brand public praise and easy to maintenance. Additionally, families who want to buy new four-wheelers to replace old two-wheelers should be seen as a new market segment might be targeted for selling cars.This article contributes to understand differen t and similar factors between people buying a new small car and pre-owned car. Nevertheless, the article was just focus on small vehicle with a specification requirements of length? 4 meters and with an engine displacement? 1,500 cubic centimeters (cc) for diesel and petrol, therefore, the universality of the research results might have certain limitations. Methodology As can be seen from previous studies and related sources, the factors affecting people's choice can be divided into two parts to analysis respectively.The one part is factors that influence people to buy a new car and the other is factors that influence people to choose a used car. Moreover, the new car dealers and used car markets are also often separated. Therefore, an explanatory study to illustrate the relationship between the consumer preferences and purchase factors by using a quantitative method is essential. In addition , the relationship between these two parts, as well as the positive and negative effects of factors of two parts would be explored with exploratory study concluded by a qualitative method.In modern societies, the number of car owners is very numerous, so within a short period of time to collect the data information from a large population base which is very important and not very easy. Although a case study strategy could be used to explore a contemporary phenomenon in its real life context, but it may take more time and lack breadth which makes it hard to generalize results (Saunders et al. 2009: 141-154). Beside this, survey data usually comes from standardizing academic investigating behaviors and tools, so that might make results more authoritative and reliable.Therefore, survey is a suitable research strategy for this research. According to Bryman (2012) points out that â€Å"quantitative research may sometimes be untrusted because the data can be artificial and spurious†. Because of there is a very numerous number of car owners, so a non-probability sampling would be used in this research. As here are almost 700,000 people who lived in the city of Glasgow, that means the sample size might bigger, a questionnaire is a data collection technique in which each person responds to the same set of questions, so questionnaire is more suitable for this research.Although the non-standardised interviews as a method is good for demonstrating the reasons for the decisions and attitudes of research participants (Saunders et al. 2009, 361), it would take too much time, also human and material resources. Ethic issues are defined as a situation or problem that needs people or organization to make a choice between options that must be evaluated as wrong (unethical) or right (ethical)(Business Dictionary,2012).According to the British Sociological Association(2004:2), the social research projects are designed and conducted, ethical issues are necessary to be taken into consideration. In this research, the non-maleficence which contains physical and indire ct harm is the cornerstone of all the ethical issues in the research (Saunders, el at. 2007: 181). In addition, the violation personal privacy and the protection of confidentiality may be the potential ethical issues.Maximum extent to avoid the occurrence of these ethical concerns, before the implementation of the access section of research, questionnaire participants will be informed: firstly,the purpose of this research, their participation is valuable, the results of the research may contribute to R & D and sales of new cars so that they can have a more suitable vehicle and a better car user experience; Secondly, respondents participate in this research follow the principles of voluntary and informed consent, whenever and wherever they can withdraw(Saunders et al, 2009:193); Thirdly, participants do not have to worry about their personal information will be faced with rick of leakage, because the questionnaire are anonymous.In addition, as car is a expensive commodity, questions on questionnaire about personal income and household economic situation of participants should be avoided, so as not to violate their privacy. Beside this, most of purchase of cars are family behavior, taking into account the special circumstances of some families, such as divorce, therefore the marriage status should avoid being asked, so as not to cause discomfort of participants. As Golafshani(2003:598) points out that the reliability is to ensure the consistency of research data collection and analysis. The risk of collecting data may do harmful to research reliability mainly relies on participants.According to Bell(2010:151), participants may finish the questionnaires inaccurately because of many reasons such as bad mood or time limited. If the participants are too excited or in a hurry, there is a small possibility that they fill the questionnaire patiently that would result in the data lacking of reliability, thereby affecting the consistency of collecting data. To solve this problem, use of internet-mediated questionnaires may be more effective, because of the respondents could complete the online questionnaire whenever and wherever they would like. The length of the questionnaire and the use of professional vocabulary may also are potential factors which may influence the research reliability.Advice from Bellk(2006:325), questionnaire is designed no more than two pages may contribute to increasing the quality and completeness. In addition, there are many specialized vocabulary in automotive sector, such as turbocharged and dual-rotor engine, that would confused participants. Therefore, common and usual words should be used as far as possible. According to cook and campbell(1979), the validity is defined as â€Å"best available approximation to the truth or falsity of a given inference, proposition or conclusion†. Firstly, The non-probability sampling will be applied in this research, due to the characteristics of this method, the non-probabilit y sampling will cause a certain threat to validity.Moreover, in the process of collecting data, there is possibility that the instrumentation may change so that influencing the results of this research. Word Count: 2278. References: Andersson, H. (2005), â€Å"The value of safety as revealed in the Swedish car market: an application of the hedonic pricing approach†, The Journal of Risk and Uncertainty, Vol. 30 No. 3, pp. 211-39. Baltas,G. , Saridakis,C. (2009), Brand-name effects, segment differences, and product characteristics: an integrated model of the car market, Journal of Product & Brand Management, 18(2),pp. 143 –-151. Belk,R. (2006), Handbook of Qualitative Research Methods. Northampton: Edward Elgar. pp. 322. Bell, J. (2010).Doing your research project, 5th edition. Berkshire: Open University Press. pp. 148-152. British Sociological Association,(2004), Statement of Ethical Practice for the Sociological Association. pp. 2-7. Bryman,A. , (2012). Social Research Method, Fourth Edition, Oxford: Oxford University Press Business Dictionary, Ethical Issue, Retrieved 21 November 2012 from http://www. businessdictionary. com/definition/ethical-issue. html Couton,C. , Gardes,F. And Thepaut,Y. (1996),Hedonic prices for environmental and safety characteristics and the Akerlof effect in the French car market. Applied Economics Letters, Vol. 3, pp. 435-40. Dharmaraj,C. , Sudhahar, C. J. ,(2010).Brand Preference Factors of Passenger Cars: An Empirical Assessment, Indiana University Press, The IUP Journal of Brand Management, 7(3),pp. 19-33. Golafshani,H. (2003),Understanding Reliability and Validity in Qualitative Research,The Qualitative Report, 8(4). PP. 597-607. http://www. nova. edu/ssss/QR/QR8-4/golafshani. pdf Kathuria,L. M. , Singla,V. ,(2012) Purchase of Pre-Owned Small Cars in India: An Exploratory Study, The IUP Journal of Marketing Management. 11(2). pp. 63-75. Reis, H. J. , Silva,S. ,and J. M. C. (2006), Hedonic price indices for new passe nger cars in Portugal (1997-2001), Economic Modelling, Vol. 23, pp. 890-908. Randol,E. , Bucklin,S. , and Siddarth, Jorge M.Silva-Risso,(2008), Distribution Intensity and New Car Choice, Journal of Marketing Research, Vol. 45(3), pp. 473-496. Saunders,M. , Lewis,P. , and Thornhill,A. (2009), Research Methods for Business Students. Fifth Edition. Essex: Prentice Hall. Sousanis, and John,(2011), World Vehicle Population Tops 1 Billion Units, Wards Auto. Retrieved 17 Nov. 2012,From http://wardsauto. com/ar/world_vehicle_population_110815 Banerjee, S. (2010) ,â€Å"Study on Consumer Buying Behavior During Purchase of a Second Car† , Journal of Marketing & Communication ,6 (2),pp. 4-13. White, R. (2004), How people buy cars, Admap, February, pp. 15-17. White, R. (2006), Advertising cars, Admap, July/August, pp. 14-15.

The Manhatten Project essays

The Manhatten Project essays There are many people who believe Japan bombed pearl harbor because they felt threatened when the united states spread its influence over the Philippines. However in bombing pearl harbor they would start World War Two. Because the U.S. wanted to ultimately win the war they needed some sort of a super weapon. Hence the Manhattan project was put into action. The Manhattan project was solely devoted to creating a nuclear weapon that would win the war hands down. The first thing that the U.S. needed to do was to assemble a team of scientists. The U.S. recruited six renowned scientists, each with their special talents. The first scientist was Neils Bohr, he is responsible for the idea that fission was possible making the atomic bomb a plausible idea. The second scientist was named Glen Seaborg. Seaborg was the first to discover plutonium-239 one of the possible fuels for the atomic bomb. The third scientist was Earnest Lawrence. Lawrence devised a way to attain uranium-235 another candidate for the fuel in an atomic bomb. The fourth scientist, Leslie Groves would be responsible for creating a more efficient way of producing Uranium-235. The fifth scientists name was Enrico Fermi. Fermi discovered that Fission could be sustained in a chain reaction, this would give the atomic bomb its great power.(Bracchini, 3) The last scientist was Robert Oppenheimer. Oppenheimer would be the director of the Manhattan project, he was involved in it every step of the way. The first problem with the atomic bomb was the ability to find a fuel that would behave the way that was needed for a fission reaction, and the ability to acquire it in decent amounts. There were two fuels proposed Uranium-235 ,proposed by Neils Bohr, and Plutonium-239, proposed by Glen Seaborg. Uranium-235 was a good candidate because it has the ability to continue fission once it is started. However to get Uranium-235 they needed Uranium ore which also contains ...