M1A3 - Organizatinal Behavior Research Paper Example | Topics and Well Written Essays - 750 words

M1A3 - Organizatinal Behavior - Research Paper Example In addition, mental models and shared vision become the third and fourth parts of the process respectively. The former are the personal assumptions within an individual about the company while the latter is the common goal shared by all members of staff9 (Sessa & London, 2006). Lastly, the team learning aspect becomes the fifth key pillar in this organization that is mainly the collective pooling of individually learnt aspects drawn from other members of staff (Marquardt, 2011). Objectively, this paper will give the name of a company in which transformation will aim at by including the intended broad principles. Further, it will include recommendations to the chosen learning organization that may include motivation, team management, culture, ethics, and empowerment. The learning organization chosen would be General Motors Corporation. Brief overview of General Motors General Motors is a name commonly associated to remarkable car brands in the world. Arguably, GM is the leading multi- state car manufacturer with its headquarters in Detroit, Michigan in the United States. Globally, it has establishments in a hundred and fifty seven countries spread across several continents. It has over two hundred thousand employees under its wing by either employing them directly or indirectly. GM has four regional clutters namely, GM South America, GM Europe, GM North America, lastly GM International Operations. Each of these segments is charged with organizing their designated world regions in sales, production, marketing, and development of products. The fifth and key pillar of the General Motors Company is the General Motors Financial that is in charge of the primary financial matters of the company. However, in June 2009 General Motors started experiencing financial strain (Kolb, 2011). The government of the United States intervened through the Chapter 11 accord in which the government-sought to protect the Company’s assets after bankruptcy declarations (Weston, 201 2). With this move, General Motors shareholders could not access their assets in Asia and Europe during this crisis. Remarkably, it experienced a relisting to the stock markets in 2010 following a successful public offering of preferred shares. Sequentially, the U.S Treasury let go of thirty-five percent of its shareholding rights to a minimum of twenty-six percent after the 2010 initial public share offer. Treasury had acquired this stake when it sought to save the company from economic plummet. GM as a transformed organization In order for GM to make substantial transformation, a number of aspects have to come in to play. For a start, the main reason that led to the economic fall of this market giant was the hefty allowances that it paid its managers during that time. The managers of the company across the globe took home unnecessary large pay, which the company struggled to effect. Therefore, the transformation process for this company would start by reevaluating the pay for its managers to suit the current economic times. Ideally, it is not morally up right to pay people heftily when a company as it its dismal performance. Secondly, the Company experienced economic struggle because of the high amounts of money that they injected in to the pension scheme for their retired

Computer Fraud And Crimes :: essays research papers fc

Computer Fraud and Crimes In the world of computers, computer fraud and computer crime are very prevalent issues facing every computer user. This ranges from system administrators to personal computer users who do work in the office or at home. Computers without any means of security are vulnerable to attacks from viruses, worms, and illegal computer hackers. If the proper steps are not taken, safe computing may become a thing of the past. Many security measures are being implemented to protect against illegalities. Companies are becoming more aware and threatened by the fact that their computers are prone to attack. Virus scanners are becoming necessities on all machines. Installing and monitoring these virus scanners takes many man hours and a lot of money for site licenses. Many server programs are coming equipped with a program called "netlog." This is a program that monitors the computer use of the employees in a company on the network. The program monitors memory and file usage. A qualified system administrator should be able to tell by the amounts of memory being used and the file usage if something is going on that should not be. If a virus is found, system administrators can pinpoint the user who put the virus into the network and investigate whether or not there was any malice intended. One computer application that is becoming more widely used and, therefore, more widely abused, is the use of electronic mail or email. In the present day, illegal hackers can read email going through a server fairly easily. Email consists of not only personal transactions, but business and financial transactions. There are not many encryption procedures out for email yet. As Gates describes, soon email encryption will become a regular addition to email just as a hard disk drive has become a regular addition to a computer (Gates p.97-98). Encrypting email can be done with two prime numbers used as keys. The public key will be listed on the Internet or in an email message. The second key will be private, which only the user will have. The sender will encrypt the message with the public key, send it to the recipient, who will then decipher it again with his or her private key. This method is not foolproof, but it is not easy to unlock either. The numbers being used will probably be over 60 digits in length (Gates p.98-99). The Internet also poses more problems to users. This problem faces the home user more than the business user. When a person logs onto the Internet, he or she may download a file corrupted with a virus.

Use of a Redox Indicator to show Dehydrogenase Activity

Triphenyl tetrazolium chloride (also known as T.T.C) is an example of an artificial hydrogen acceptor. It is a redox indicator which is colourless when oxidised, however when reduced, it produces a red, insoluble precipitate called ‘formazans'. T.T.C can therefore be used to investigate the enzyme activity of dehyrogenase enzymes by showing a colour change when they are present. The purpose of this experiment is to see what effect temperature has on the activity of dehydrogenase enzymes within yeast cells. Materials/Apparatus: * Actively respiring yeast suspension. This is prepared by adding 10g of dried yeast to 1dm3 of distilled water, followed by mixing in 50g of glucose. This mixture should be allowed to stand for 24 hours before the experiment takes place. * Tiphenyl tetrazolium chloride is used as a redox indicator to investigate the activity of dehydrogenase enzymes when yeast suspension is exposed to different temperatures. * Distilled water for the preparation of the yeast suspension. * Test tubes to place the mixture of yeast and T.T.C. * Test tube rack to allow the test tubes to stand upright in the water baths. * Incubator to allow enzyme activity to occur at different temperatures * Syringes to accurately measure the right amount of yeast and T.T.C needed for each solution. * A Glass rod to evenly distribute the cells in the solution after the T.T.C has been added. * Crushed ice to allow the dehyrogenase activity to take place at 10degrees. * Beakers for the yeast suspension to be prepared in. * Thermometer to measure the temperature of the water bath containing the ice cubes. * Stopwatch to measure the time taken for the solution to change colour. NOTE: The colour change is completed once the solution has turned a ‘salmon pink' colour. Allow all solutions to reach the same colour before removing them from the water baths. Method: Prepare a solution of yeast cells by adding 10g of dried yeast to 1dm3 of distilled water, followed by mixing in 50g of glucose. This mixture should be allowed to stand for 24 hours before the experiment takes place. Once the yeast suspension has been allowed to stand for 24 hours, the froth should be removed and discarded. Set up a water bath by adding ice cubes to cold water, until the water has reached 10degrees. Continue to measure the temperature with a thermometer ensuring that the temperature is maintained. Set up separate incubators at 30, 40, 50 and 60 degrees. Using a syringe, place 5cm of yeast suspension into three separate test tubes and place in the incubator. Leave for several minutes and then add 0.5cm of T.T.C into each solution and place them back into the incubator set at 30degrees. Start the stopwatch immediately. Observe carefully for any colour changes that have developed. When the colour change has taken place, take the test tubes out of the incubator and note down the time taken for the colour change to take place. Repeat this procedure at 20, 40, 50 and 60 degrees. To measure the dehydrogenase activity at 20 degrees, carry out this procedure at room temperature. Table of results: Temperature (degrees) Time taken for colour change to occur (minutes) 10 No change 20 52.11 30 26.12 40 10.08 50 4.22 60 4.43 A bar graph has been produced to portray these results so that a comparison can clearly be seen. The graph has been drawn on graph paper. Conclusion: The results from this experiment indicate that temperature has a definite affect on the activity of dehydrogenase enzymes. The graph shows that as the temperature increases, the time taken for the solution to change colour decreases. This shows that dehyrogenase enzymes work faster at a higher temperature as there was no colour change when the T.T.C was added to the yeast suspension at 10 degrees. The temperature at which the dehydrogenase enzymes worked at their quickest was 50 degrees. This indicates that 50 degrees is the optimum temperature for the enzyme activity to take place as the colour change took slightly longer when placed in a water bath set at 60 degrees. This may be due to the fact that some of the dehydrogenase enzymes could have been denatured due to the high temperature. However, it is not quite clear whether 50 degrees is the optimum temperature for the enzyme activity to take place because this experiment took place using a limited amount of temperature ranges. If this investigation was to be repeated, a wider range of temperatures could be used so that an optimum temperature could be established. Overall, the results from this experiment support the hypothesis and therefore have provided successful and sufficient data which have confirmed the predictions that were made prior to the investigation taking place.

Binomial Table for n7, n8 and n9

A binomial random variable provides an important example of a discrete random variable.   The binomial distribution, which describes the probability for each value of our random variable, can be determined completely by the two parameters: n   and p.   Here n is the number of independent trials and p is the constant probability of success in each trial.   The tables below provide binomial probabilities for n 7,8 and 9.   The probabilities in each are rounded to three decimal places. Should a   binomial distribution be used?.  Ã‚   Before jumping in to use this table, we need to check that the following conditions are met: We have a finite number of observations or trials.The outcome of each trial can be classified as either a success or a failure.The probability of success remains constant.The observations are independent of one another. When these four conditions are met, the binomial distribution will give the probability of r successes in an experiment with a total of n independent trials, each having probability of success p.  Ã‚   The probabilities in the table are calculated by the formula C(n, r)pr(1 - p)n - r where C(n, r) is the formula for combinations.   There are separate tables for each value of n.   Each entry in the table is organized by the values of p and of r.   Other Tables For other binomial distribution tables we have n 2 to 6, n 10 to 11. When the values of np  and n(1 - p) are both greater than or equal to 10, we can use the normal approximation to the binomial distribution.   This gives us a good approximation of our probabilities and does not require the calculation of binomial coefficients.   This provides a great advantage because these binomial calculations can be quite involved. Example Genetics has many connections to probability.   We will look at one to illustrate the use of the binomial distribution.   Suppose we know that probability of an offspring inheriting two copies of a recessive gene (and hence possessing the recessive trait we are studying) is 1/4.   Furthermore, we want to calculate the probability that a certain number of children in an eight-member family possesses this trait.   Let X be the number of children with this trait.   We look at the table for n 8 and the column with p 0.25, and see the following: .100.267.311.208.087.023.004 This means for our example that P(X 0) 10.0%, which is the probability that none of the children has the recessive trait.P(X 1) 26.7%, which is the probability that one of the children has the recessive trait.P(X 2) 31.1%, which is the probability that two of the children have the recessive trait.P(X 3) 20.8%, which is the probability that three of the children have the recessive trait.P(X 4) 8.7%, which is the probability that four of the children have the recessive trait.P(X 5) 2.3%, which is the probability that five of the children have the recessive trait.P(X 6) 0.4%, which is the probability that six of the children have the recessive trait. Tables for n 7 to n 9 n 7 p .01 .05 .10 .15 .20 .25 .30 .35 .40 .45 .50 .55 .60 .65 .70 .75 .80 .85 .90 .95 r 0 .932 .698 .478 .321 .210 .133 .082 .049 .028 .015 .008 .004 .002 .001 .000 .000 .000 .000 .000 .000 1 .066 .257 .372 .396 .367 .311 .247 .185 .131 .087 .055 .032 .017 .008 .004 .001 .000 .000 .000 .000 2 .002 .041 .124 .210 .275 .311 .318 .299 .261 .214 .164 .117 .077 .047 .025 .012 .004 .001 .000 .000 3 .000 .004 .023 .062 .115 .173 .227 .268 .290 .292 .273 .239 .194 .144 .097 .058 .029 .011 .003 .000 4 .000 .000 .003 .011 .029 .058 .097 .144 .194 .239 .273 .292 .290 ;268 .227 .173 .115 .062 .023 .004 5 .000 .000 .000 .001 .004 .012 .025 .047 .077 .117 .164 .214 .261 .299 .318 .311 .275 .210 .124 .041 6 .000 .000 .000 .000 .000 .001 .004 .008 .017 .032 .055 .087 .131 .185 .247 .311 .367 .396 .372 .257 7 .000 .000 .000 .000 .000 .000 .000 .001 .002 .004 .008 .015 .028 .049 .082 .133 .210 .321 .478 .698 n 8 p .01 .05 .10 .15 .20 .25 .30 .35 .40 .45 .50 .55 .60 .65 .70 .75 .80 .85 .90 .95 r 0 .923 .663 .430 .272 .168 .100 .058 .032 .017 .008 .004 .002 .001 .000 .000 .000 .000 .000 .000 .000 1 .075 .279 .383 .385 .336 .267 .198 .137 .090 .055 .031 .016 .008 .003 .001 .000 .000 .000 .000 .000 2 .003 .051 .149 .238 .294 .311 .296 .259 .209 .157 .109 .070 .041 .022 .010 .004 .001 .000 .000 .000 3 .000 .005 .033 .084 .147 .208 .254 .279 .279 .257 .219 .172 .124 .081 .047 .023 .009 .003 .000 .000 4 .000 .000 .005 :018 .046 .087 .136 .188 .232 .263 .273 .263 .232 .188 .136 .087 .046 .018 .005 .000 5 .000 .000 .000 .003 .009 .023 .047 .081 .124 .172 .219 .257 .279 .279 .254 .208 .147 .084 .033 .005 6 .000 .000 .000 .000 .001 .004 .010 .022 .041 .070 .109 .157 .209 .259 .296 .311 .294 .238 .149 .051 7 .000 .000 .000 .000 .000 .000 .001 .003 .008 .016 .031 .055 .090 .137 .198 .267 .336 .385 .383 .279 8 .000 .000 .000 .000 .000 000 .000 .000 .001 .002 .004 .008 .017 .032 .058 .100 .168 .272 .430 .663 n 9 r p .01 .05 .10 .15 .20 .25 .30 .35 .40 .45 .50 .55 .60 .65 .70 .75 .80 .85 .90 .95 0 .914 .630 .387 .232 .134 .075 .040 .021 .010 .005 .002 .001 .000 .000 .000 .000 .000 .000 .000 .000 1 .083 .299 .387 .368 .302 .225 .156 .100 .060 .034 .018 .008 .004 .001 .000 .000 .000 .000 .000 .000 2 .003 .063 .172 .260 .302 .300 .267 .216 .161 .111 .070 .041 .021 .010 .004 .001 .000 .000 .000 .000 3 .000 .008 .045 .107 .176 .234 .267 .272 .251 .212 .164 .116 .074 .042 .021 .009 .003 .001 .000 .000 4 .000 .001 .007 .028 .066 .117 .172 .219 .251 .260 .246 .213 .167 .118 .074 .039 .017 .005 .001 .000 5 .000 .000 .001 .005 .017 .039 .074 .118 .167 .213 .246 .260 .251 .219 .172 .117 .066 .028 .007 .001 6 .000 .000 .000 .001 .003 .009 .021 .042 .074 .116 .164 .212 .251 .272 .267 .234 .176 .107 .045 .008 7 .000 .000 .000 .000 .000 .001 .004 .010 .021 .041 .070 .111 .161 .216 .267 .300 .302 .260 .172 .063 8 .000 .000 .000 .000 .000 .000 .000 .001 .004 .008 .018 .034 .060 .100 .156 .225 .302 .368 .387 .299 9 .000 .000 .000 .000 .000 .000 .000 .000 .000 .001 .002 .005 .010 .021 .040 .075 .134 .232 .387 .630