Saturday, October 5, 2019
The Beer Industry in the United States Term Paper
The Beer Industry in the United States - Term Paper Example Throughout the world, brewing or the preparation of beer exists as an activity that even offers financial gains. As a point of fact, the United States of America is the leader in terms of beer production (Kirin Holdings, 2009). Not including the home brewers, United States has an estimate of 1700 breweries having the Anheuser - Busch Inc., the MillerCoors Brewing Corporation and the Pabst Brewing Company consecutively as its top three giant companies in terms of sales (Brewers Association, 2010). Nonetheless, despite the case that America comes to be number one in the production of beer around the world, it lags behind a number of countries, in particular, those in Europe, in terms of consumption making US ranked only second to China in total consumption and only 16th in the world in per capita consumption (Kirin Holdings, 2009). ... Third, it endeavors to understand what beer is in the American context. Fourth, it also seeks to define the structure of the American beer industry through looking at the top brewing companies and the craft breweries or microbreweries. Last, it aspires to take a particular look on the brewing at the domestic level. The need to explore the beer industry in American context can backed up by the reason that beer has always been part of the American culture. Beer has held its prominence to almost everyone existing in the world. Likewise, it is of interesting position that the United States is the leader of beer production. However, the beer industry in United States is not known to most of us. That is what this paper wants to shed light on. The significance of this paper is that it contributes to an understanding of what the beer industry looks like in the American context. Review of Related Literatures This part of the paper will first present a general idea of the available literatures about the beer industry in the United States. This review aims to offer a cursory outlook at how the range of presented data is sought to address the problem of this paper. In order to grasp a deeper understanding of the beer industry in the context of United States, this section is categorized into the following segments namely: a) A Brief Historical Account of Beer Industry in United States, b) The Beer Industry and the Different Types of Beers, c) The Beer Terminology in the United States, d) The Structure of the American Beer Industry, e) An Overview of Top 3 Brewing Companies in the United States, f) A Look at the Microbreweries, and g) Understanding Home Brewing. Meanwhile, it is deemed important to note that the reviewed books, articles and other materials
Friday, October 4, 2019
Educational Enquiry Assignment Example | Topics and Well Written Essays - 1750 words - 1
Educational Enquiry - Assignment Example The outcome so far has been disappointing, though it may be that cognitive psychologists and neuroscientists will soon produce something finer.†Hargreaves argues current educational research is neither adequately cumulative nor relevant to teachers’ practical concerns for it to initiate the contribution required. He draws a contrast between the role of research in education, and role to the practice of medicine by means of evidence based medicine as a model. He claims few teachers use psychology, sociology, philosophy, and history. This makes the teachers work more effective (Reynolds & Trinder, 1997, pg 56) Hargreaves is not very explicit about the form he believes educational research should take. He is neglective of strict methodological problems that are faced by educational researchers. He seems to view the procedure of developing cumulative knowledge about the outcome of different pedagogical methods directly. The use of a standard in judging current educational re search that assumes direct and instrumental form of the relationship. Hargreaves argues educational research has failed to provide practitioners with the required knowledge about pedagogical strategies work, and those that do not ensure competence of the practitioner; he claims that it is not only terms of practical skills but familiarity with practice relevant to the research. While Hammersley points out that it is the language Hargreaves uses implies a commitment to a method that many would deem positivist that it values research that emulates the scientific approach.Hammersley challenges the assumption by arguing this type of evidence is effective in improving practice on the basis that scientific methods, and... This essay approves that the goals of learning in a social constructive perspective differ, and learning is characterized by the subjective reconstruction of society means, and models by carrying out negotiations of meaning in social interaction. Its focus is on interaction within the local setting because they are viewed as automatically related. It is noted that neither an individual learner’s activity nor the local micro culture can be understood without the consideration of the other. These changes the research that is learning compared with a cognitive constructionist research.Constructionism is associated with qualitative data where as sociocultural approaches the original data collection is qualitative because the focal point is on interactions, and dynamic. Learning opportunities arise but research is done on experiences and changes entailed. Many social cultural studies focus on learning as a transformation of identity to forefront the personal characteristics, and ha ve little to say about the system. This report makes a conclusion that learning technology research is presently dominated by a paradigm that divides the research into two types qualitative, and quantitative. The division is normal science in learning, and has provided an agreement that has permitted researchers to shun disagreements over fundamentals, and an outline for standard research training. The standard structure is under pressure from developing research methods that are relevant to learning. Educational research fails to supply a cumulated body of concrete knowledge about the effectiveness, and efficiency of different methods. May be paradigm could finally be resolved in the natural sciences, because the outcome of research was unreliable.
Thursday, October 3, 2019
Organization behavior Essay Example for Free
Organization behavior Essay 1.) Critically review learning theory. Learning theory is a routine that is carried out on a daily basis in order to have an experience in various fields of work. There are several theories that explain learning theory. One of the theories is Maslow’ Hierarchy of needs which explains that, when the need level is satisfied, it ceased to become a motivator and fulfillment of higher level is the next goal. (Francesco and Gold, 2005).The second theory is Hertzberg’s motivation hygiene theory which states that, employees’ growth and esteem needs are driven by the motivation factors, responsibility and achievement. This wills results into a job satisfaction at work place. (Francesco and Gold, 2005). The third theory is McClelland’s learned needs theory which explains that needs is learned through Childhood environment, social norms and assists in the understanding of individual motivation (Francesco and Gold, 2005). The last theory is ERG theory which contributes on Maslow theory. It has three categories of needs. Existence which is the physical and psychological need, relatedness which is the need to share ideas with others and feel secure and growth is the need to achieve to fulfill of self esteemed needs. The learning theories have assisted very many people at work place especially the people who are working in the organizations. (Francesco and Gold, 2005). There have been several philosophers who have tried to explain what is learning. Various theories of learning have been therefore have been discussed. Learning theories are the ideas about why or how changes occur. There has been a theory on the behaviorist orientation to learning .This theory is from theorist such as Thorndike, Pavlov and skinner. (Smith, 1999).This theory argues that people change their behavior from the kind of learning they get. People according to the theory are stimulated by the environment they live in. When people live in a good environment they learn to be good and vise versa. Behaviorists argue that people can be taught to produce behavior change in the desired direction. An educator trying to teach people to change should arrange environment of the person to get the desired response. People can learn through skill development and training as well as behavior according to behaviorists. (Smith, 1999). There is another theory on cognitive orientation to learning. Cognitivist such as Piaget, Bruner and Gagne believe that learning process is an internal mental process. Internal mental process includes receiving insight about information, processing the information, memorizing it and finally making a perception. (Smith, 1999).That is how the learning process is according to Cognitivist. People according to the cognitivist learn through the internal cognitive structuring. This structure helps people develop skills and capacity to learn better. Cognitive structure if followed can help a person develop intelligence learning and memory as function of age as well as learning how to learn. Whichever the theory that is followed learning is an important aspect of human being and people should strive to learn more to improve themselves. (Saljo,1979) 2. Positive and negative reinforcements, punishment and extinction in shaping behavior There are many types of positive reinforcements that Godot can use. First Godot can reward excellent behavior. This is normally a very effective method of reinforcing good behavior. (Verddelho, 1999)The implication here is that when an employee is rewarded for good performance, the employee feels motivated to do even better the next time. In the end this will lead to an improved performance which is good not just for the individual but also for the organization. The second positive reinforcement is recognition. Recognition here implies acknowledging excellent behavior of good work. In this case the meaning for this is that supervisors like Godot can simply acknowledge and commend a well performing employee like Diane. Recognition of good work has the implication of having positive congratulatory words for a job that has been well accomplished. This can simply be achieved by a simple word of mouth like ‘well done.’ Unknown to many supervisors, this simple act of recognition normally has a very positive effects on the motivation of an employee of an individual and can result in positive work behavior. (Francesca Gold, 2008) The use of incentives is another positive reinforcement that can result in improved positive behavior. In this case incentives can be in form of extra or bonus pay for exemplary performance. Incentives can go along way in improving an individual’s performance on the job. (Francesca Gold, 2008)This is more so if an individual is capable of making more money by displaying good performance method. In this case the issue of a tip was a good gesture to the employees since those with exemplary performance would feel encouraged to do more. One negative way of reinforcing behavior is through harshness. In this case, the employees will only perform well because of fear of reprimand by Godot and not because they feel a compelling need to work hard. This is a negative reinforcement because positive organization behavior should be forced but should come spontaneously. The implication here is that employees should not be coerced into behaving well but rather positive behavior should just come naturally. This therefore means that these employees should not work hard because of fear of being reprimanded by the supervisor but instead should do so because of a need to do so. (Dwyer, 2005) 3. Discuss the impact of these reinforcements and punishment has on behavior and on Diane’s behavior specifically Reinforcement theory suggests that a reinforcement/reward and punishment of certain behavior will most likely result in a repeat of that particular behavior. That is to say that if behavior is not recognized or appreciated chances of it being repeated will be low. . (Francesco Gold, 2005) However, the consequence of reinforcement is determined by whether it is negative or positive. Positive reinforcement will motivate employees and result in behavior that will increase an organization’s output and the opposite is true. . (Francesco Gold, 2005) Diane works hard and her output is great. However, when she breaks a plate one day, Godot yells at her and even makes her pay for the broken plate and the cost of cleaning up the messed caused. Diane has been waiting all along for recognition but she only gets a negative reinforcement for her good output. As it is, it is better to motivate people for the right reasons rather than to punish them for the wrong reasons. (Skinner, 1957). Diane is punished for the wrong reason and fails to get reinforced for the right reasons. Negative reinforcement is not likely to motivate individuals and therefore this will in turn lead to them slacking in their work and thus the overall out put will be low. (Skinner, 1957). It is because of this that she relaxes when carrying out her duties. Reinforcement suggests that behavior determines outcome and a person will be motivated to seek reinforcement and avoid punishment. When she got a chance to work at a fancy French restaurant at Sydney known La Maison, Diana a University Student could not spend her vacation at her parents home Queabeyan, since she needed money to change on her diet during her next semester . (Skinner, 1957). Ready to prove how good she was, she would balance several plates on her arm and the customers would compliment her on her efficient service, but one day she dropped a bowl of bouillabaisse appetizer on the carpet and hurriedly went for sponge while apologizing but Godot shouted at her that he would deduct $24.95, $20 from her pay to compensate for appetizer and cost of cleaning rugs respectively, out of anger and confusion, next day Diana slowed down to avoid recurrence of same incident thus she carried no more than two dishes at a time thus slugging her tips down (Skinner, 1957). Q4 .Effectiveness of hourly pay rates and tips as a method for reinforcing desired behaviors. Tips and hourly rates have been used as a mode of payment against other methods of payment like piece rate and monthly payments. There is a big variation in the effectiveness of the methods. When hourly rate is used as the mode of payment, it has been found to have an effect on quality and speed. These two factors are the one that determine the output of any activity (Encina, 2000). Payment in work acts as an incentive to the worker. Whenever the worker is paid well he will work well and poor pay means poor jobs. It is noted that the worker will always optimize what he has at hand. When he is paid hourly he will do he/her work according to the hour he is being paid. This will spoil the consistency (Encina , 2000). When tips are added to the hourly rate, the worker will optimize on the tips and he/she will improve on his hourly work to get more tips. Things are different when hourly pay is made without the tips as there will be no motivation. Paying on an hourly rate together with the tips may improve on speed but destroy the quality. The use of tips and hourly rate need a balance as what motivate the worker are the conditions of work. Diane on his part was being motivated tips (Encina , 2000). Apart from the pay, there are other factors that motivate worker during their work. Recognition during work is another factor that lender the effectiveness of work. A worker may improve his effectiveness due to tips. This will improve his/her efficiency. When a worker feels that his efficiency has improved, he will look for recognition. Payment on hourly rates can improve the behaviors of the worker but additional inputs like recognition should be added. Diane opted to work in the right he will perfect the work and in return he will be recognized to get a better pay. For the behavior of an hourly paid worker to improve, incentives have to be added. This will help in improving the efficiency of work and altitude towards work will improve (Schildkraut 2003). The behavior of workers depends very much on the pay and recognition. As much as workers are paid hourly tends to improve the output, their behavior depends much on the supervision. Workers will always maximize on their man hours instead of employers manpower (Schildkraut 2003). Therefore to ensure that the behavior of the worker is good requires supervision. The supervision should not oppress the worker but instead it will help improve his behavior. The role of the supervisor for hourly paid worker is to help the worker to improve in his productivity. Though the supervisor will optimize on production, it should not destroy the quality of the work (Schildkraut 2003). In conclusion hourly rates can improve on the worker behavior but proper measures should be put in place to ensure that the objectives of the company or organization are met. The measure can include recognition after improvement, controlled supervision, and improvement after achievement. There should be no mistake of paying hourly for organization benefit but it should be to the worker (Schildkraut 2003).
Lazy, Decision Tree classifier and Multilayer Perceptron
Lazy, Decision Tree classifier and Multilayer Perceptron Performance Evaluation of Lazy, Decision Tree classifier and Multilayer Perceptron on Traffic Accident Analysis Abstract. Traffic and road accident are a big issue in every country. Road accident influence on many things such as property damage, different injury level as well as a large amount of death. Data science has such capability to assist us to analyze different factors behind traffic and road accident such as weather, road, time etc. In this paper, we proposed different clustering and classification techniques to analyze data. We implemented different classification techniques such as Decision Tree, Lazy classifier, and Multilayer perceptron classifier to classify dataset based on casualty class as well as clustering techniques which are k-means and Hierarchical clustering techniques to cluster dataset. Firstly we analyzed dataset by using these classifiers and we achieved accuracy at some level and later, we applied clustering techniques and then applied classification techniques on that clustered data. Our accuracy level increased at some level by using clustering techniques on datas et compared to a dataset which was classified without clustering. Keywords: Decision tree, Lazy classifier, Multilayer perceptron, K-means, Hierarchical clustering INTRODUCTION Traffic and road accident are one of the important problem across the world. Diminishing accident ratio is most effective way to improve traffic safety. There are many type of research has been done in many countries in traffic accident analysis by using different type of data mining techniques. Many researcher proposed their work in order to reduce the accident ratio by identifying risk factors which particularly impact in the accident [1-5]. There are also different techniques used to analyze traffic accident but its stated that data mining technique is more advance technique and shown better results as compared to statistical analysis. However, both methods provide appreciable outcome which is helpful to reduce accident ratio [6-13, 28, 29]. From the experimental point of view, mostly studies tried to find out the risk factors which affect the severity levels. Among most of studies explained that drinking alcoholic beverage and driving influenced more in accident [14]. It identified that drinking alcoholic beverage and driving seriously increase the accident ratio. There are various studies which have focused on restraint devices like helmet, seat belts influence the severity level of accident and if these devices would have been used to accident ratio had decreased at certain level [15]. In addition, few studies have focused on identifying the group of drivers who are mostly involved in accident. Elderly drivers whose age are more than 60 years, they are identified mostly in road accident [16]. Many studies provided different level of risk factors which influenced more in severity level of accident. Lee C [17] stated that statistical approaches were good option to analyze the relation between in various risk factors and accident. Although, Chen and Jovanis [18] identified that there are some problem like large contingency table during analyzing big dimensional dataset by using statistical techniques. As well as statistical approach also have their own violation and assumption which can bring some error results [30-33]. Because of these limitation in statistical approach, Data techniques came into existence to analyze data of road accident. Data mining often called as knowledge or data discovery. This is set of techniques to achieve hidden information from large amount of data. It is shown that there are many implementation of data mining in transportation system like pavement analysis, roughness analysis of road and road accident analysis. Data mining techniques has been the most widely used techniques in field like agriculture, medical, transportation, business, industries, engineering and many other scientific fields [21-23]. There are many diverse data mining methodologies such as classification, association rules and clustering has been extensivally used for analyzing dataset of road accident [19-20]. Geurts K [24] analyzed dataset by using association rule mining to know the different factors that happens at very high frequency road accident areas on Belgium road. Depaire [25] analyzed dataset of road accident in Belgium by using different clustering techniques and stated that clustered based data can extract better information as compared without clustered data. Kwon analyzed dataset by using Decision Tree and NB classifiers to factors which is affecting more in road accident. Kashani [27] analyzed dataset by using classification and regression algorithm to analyze accident ratio in Iran and achieved that there a re factors such as wrong overtaking, not using seat belts, and badly speeding affected the severity level of accident. METHODOLOGY This research work focus on casualty class based classification of road accident. The paper describe the k-means and Hierarchical clustering techniques for cluster analysis. Moreover, Decision Tree, Lazy classifier and Multilayer perceptron used in this paper to classify the accident data. Clustering Techniques Hierarchical Clustering Hierarchical clustering is also known as HCS (Hierarchical cluster analysis). It is unsupervised clustering techniques which attempt to make clusters hierarchy. It is divided into two categories which are Divisive and Agglomerative clustering. Divisive Clustering: In this clustering technique, we allocate all of the inspection to one cluster and later, partition that single cluster into two similar clusters. Finally, we continue repeatedly on every cluster till there would be one cluster for every inspection. Agglomerative method: It is bottom up approach. We allocate every inspection to their own cluster. Later, evaluate the distance between every clusters and then amalgamate the most two similar clusters. Repeat steps second and third until there could be one cluster left. The algorithm is given below            X set A of objects {a1, a2,à ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦an}            Distance function is d1 and d2            For j=1 to n           dj={aj}          end for          D= {d1, d2,à ¢Ã¢â€š ¬Ã‚ ¦..dn}         Y=n+1         while D.size>1 do -(dmin1, dmin2)=minimum distance (dj, dk) for all dj, dk in all D -Delete dmin1 and dmin2 from D -Add (dmin1, dmin2) to D -Y=Y+1          end while K-modes clustering Clustering is an data mining technique which use unsupervised learning, whose major aim is to categorize the data features into a distinct type of clusters in such a way that features inside a group are more alike than the features in different clusters. K-means technique is an extensively used clustering technique for large numerical data analysis. In this, the dataset is grouped into k-clusters. There are diverse clustering techniques available but the assortment of appropriate clustering algorithm rely on the nature and type of data. Our major objective of this work is to differentiate the accident places on their frequency occurrence. Lets assume thatX and Y is a matrix of m by n matrix of categorical data. The straightforward closeness coordinating measure amongst X and Y is the quantity of coordinating quality estimations of the two values. The more noteworthy the quantity of matches is more the comparability of two items. K-modes algorithm can be explained as:                             d (Xi,Yi)=                   (1)                Where           - (2) Classification Techniques Lazy Classifier Lazy classifier save the training instances and do no genuine work until classification time. Lazy classifier is a learning strategy in which speculation past the preparation information is postponed until a question is made to the framework where the framework tries to sum up the training data before getting queries. The main advantage of utilizing a lazy classification strategy is that the objective scope will be exacted locally, for example, in the k-nearest neighbor. Since the target capacity is approximated locally for each question to the framework, lazy classifier frameworks can simultaneously take care of various issues and arrangement effectively with changes in the issue field. The burdens with lazy classifier incorporate the extensive space necessity to store the total preparing dataset. For the most part boisterous preparing information expands the case bolster pointlessly, in light of the fact that no idea is made amid the preparation stage and another detriment is that lazy classification strategies are generally slower to assess, however this is joined with a quicker preparing stage. K Star The K star can be characterized as a strategy for cluster examination which fundamentally goes for the partition of n perception into k-clusters, where every perception has a location with the group to the closest mean. We can depict K star as an occurrence based learner which utilizes entropy as a separation measure. The advantages are that it gives a predictable way to deal with treatment of genuine esteemed attributes, typical attributes and missing attributes. K star is a basic, instance based classifier, like K Nearest Neighbor (K-NN). New data instance, x, are doled out to the class that happens most every now and again among the k closest information focuses, yj, where j = 1, 2à ¢Ã¢â€š ¬Ã‚ ¦ k. Entropic separation is then used to recover the most comparable occasions from the informational index. By method for entropic remove as a metric has a number of advantages including treatment of genuine esteemed qualities and missing qualities. The K star function can be ascertained a s: K*(yi, x)=-ln P*(yi, x) Where P* is the likelihood of all transformational means from instance x to y. It can be valuable to comprehend this as the likelihood that x will touch base at y by means of an arbitrary stroll in IC highlight space. It will performed streamlining over the percent mixing proportion parameter which is closely resembling K-NN sphere of influence, before appraisal with other Machine Learning strategies. IBK (K Nearest Neighbor) Its a k-closest neighbor classifier technique that utilize a similar separation metric. The quantity of closest neighbors may be illustrated unequivocally in the object editor or determined consequently utilizing blow one cross-approval center to a maximum point of confinement provided by the predetermined esteem. IBK is the knearest-neighbor classifier. A sort of divorce pursuit calculations might be used to quicken the errand of identifying the closest neighbors. A direct inquiry is the default yet promote decision blend ball trees, KD-trees, thus called cover trees. The dissolution work used is a parameter of the inquiry strategy. The rest of the thing is alike one the basis of IBL-which is called Euclidean separation; different alternatives blend Chebyshev, Manhattan, and Minkowski separations. Forecasts higher than one neighbor may be weighted by their distance from the test occurrence and two unique equations are implemented for altering over the distance into a weight. The qua ntity of preparing occasions kept by the classifier can be limited by setting the window estimate choice. As new preparing occasions are included, the most seasoned ones are segregated to keep up the quantity of preparing cases at this size. Decision Tree Random decision forests or random forest are a package learning techniques for regression, classification and other tasks, that perform by building a legion of decision trees at training time and resulting the class which would be the mode of the mean prediction (regression) or classes (classification) of the separate trees. Random decision forests good for decision trees routime of overfitting to their training set. In different calculations, the classification is executed recursively till each and every leaf is clean or pure, that is the order of the data ought to be as impeccable as would be prudent. The goal is dynamically speculation of a choice tree until it picks up the balance of adaptability and exactness. This technique utilized the Entropy that is the computation of disorder data. Here Entropy is measured by:                                 Entropy () =                                  Entropy () = Hence so total gain = Entropy () Entropy () Here the goal is to increase the total gain by dividing total entropy because of diverging arguments by value i. Multilayer Perceptron An MLP might be observed as a logistic regression classifier in which input data is firstly altered utilizing a non-linear transformation. This alteration deal the input dataset into space, and the place where this turn into linearly separable. This layer as an intermediate layer is known as a hidden layer. One hidden layer is enough to create MLPs. Formally, a single hidden layer Multilayer Perceptron (MLP) is a function of f: YIà ¢Ã¢â‚¬ ’YO, where I would be the input size vector x and O is the size of output vector f(x), such that, in matrix notation                                                F(x) = g(ÃŽÂ ¸(2)+W(2)(s(ÃŽÂ ¸(1)+W(1)x))) DESCRIPTION OF DATASET The traffic accident data is obtained from online data source for Leeds UK [8]. This data set comprises 13062 accident which happened since last 5 years from 2011 to 2015. After carefully analyzed this data, there are 11 attributes discovered for this study. The dataset consist attributes which are Number of vehicles, time, road surface, weather conditions, lightening conditions, casualty class, sex of casualty, age, type of vehicle, day and month and these attributes have different features like casualty class has driver, pedestrian, passenger as well as same with other attributes with having different features which was given in data set. These data are shown briefly in table 2 ACCURACY MEASUREMENT The accuracy is defined by different classifiers of provided dataset and that is achieved a percentage of dataset tuples which is classified precisely by help of different classifiers. The confusion matrix is also called as error matrix which is just layout table that enables to visualize the behavior of an algorithm. Here confusing matrix provides also an important role to achieve the efficiency of different classifiers. There are two class labels given and each cell consist prediction by a classifier which comes into that cell. Table 1 Confusion Matrix                             Correct Labels Negative Positive Negative TN (True negative) FN (False negative) Positive FP (False positive) TP (True positive) Now, there are many factors like Accuracy, sensitivity, specificity, error rate, precision, f-measures, recall and so on. TPR (Accuracy or True Positive Rate) = FPR (False Positive Rate) = Precision = Sensitivity = And there are also other factors which can find out to classify the dataset correctly. RESULTS AND DISCUSSION Table 2 describe all the attributes available in the road accident dataset. There are 11 attributes mentioned and their code, values, total and other factors included. We divided total accident value on the basis of casualty class which is Driver, Passenger, and Pedestrian by the help of SQL. Table 2 S.NO. Attribute Code Value Total        Casualty Class Driver Passenger Pedestrian 1. No. of vehicles 1 1 vehicle 3334 763 817 753 2 2 vehicle 7991 5676 2215 99 3+ >3 vehicle 5214 1218 510 10 2. Time T1 [0-4] 630 269 250 110 T2 [4-8] 903 698 133 71 T3 [6-12] 2720 1701 644 374 T4 [12-16] 3342 1812 1027 502 T5 [16-20] 3976 2387 990 598 T6 [20-24] 1496 790 498 207 3. Road Surface OTR Other 106 62 30 13 DR Dry 9828 5687 2695 1445 WT Wet 3063 1858 803 401 SNW Snow 157 101 39 16 FLD Flood 17 11 5 0 4. Lightening Condition DLGT Day Light 9020 5422 2348 1249 NLGT No Light 1446 858 389 198 SLGT Street Light 2598 1377 805 415 5. Weather Condition CLR Clear 11584 6770 3140 1666 FG Fog 37 26 7 3 SNY Snowy 63 41 15 6 RNY Rainy 1276 751 350 174 6. Casualty Class DR Driver PSG Passenger PDT Pedestrian 7. Sex of Casualty M Male 7758 5223 1460 1074 F Female 5305 2434 2082 788 8. Age Minor 1976 454 855 667 Youth 18-30 years 4267 2646 1158 462 Adult 30-60 years 4254 3152 742 359 Senior >60 years 2567 1405 787 374 9. Type of Vehicle BS Bus 842 52 687 102 CR Car 9208 4959 2692 1556 GDV GoodsVehicle 449 245 86 117 BCL Bicycle 1512 1476 11 24 PTV PTWW 977 876 48 52 OTR Other 79 49 18 11 10. Day WKD Weekday 9884 5980 2499 1404 WND Weekend 3179 1677 1043 458 11. Month Q1 Jan-March 3017 1731 803 482 Q2 April-June 3220 1887 907 425 Q3 July-September 3376 2021 948 406 Q4 Oct-December 3452 2018 884 549 Direct Classification Analysis We utilized different approaches to classify this bunch of dataset on the basis of casualty class. We used classifier which are Decision Tree, Lazy classifier and Multilayer perceptron. We attained some result to few level as shown in table 3 Table 3 Classifiers Accuracy Lazy classifier(K-Star) 67.7324% Lazy classifier (IBK) 68.5634% Decision Tree 70.7566% Multilayer perceptron 69.3031% We achieved some results to this given level by using these three approaches and then later we utilized different clustering techniques which are Hierarchical clustering and K-modes. Figure 1 Direct classified Accuracy Analysis by using clustering techniques In this analysis, we utilized two clustering techniques which are Hierarchical and K-modes techniques, Later we divided dataset into 9 clusters. We achieved better results by using Hierarchical as compared to K-modes techniques. Lazy Classifier Output K Star: In this, our classified result increased from 67.7324 % to 82.352%. Its sharp improvement in result after clustering. Table 4 TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class 0.956 0.320 0.809 0.956 0.876 0.679 0.928 0.947 Driver 0.529 0.029 0.873 0.529 0.659 0.600 0.917 0.824 Passenger 0.839 0.027 0.837 0.839 0.838 0.811 0.981 0.906 Pedestrian IBK: In this, our classified result increased from 68.5634% to 84.4729%. Its sharp improvement in result after clustering. Table 5 TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class 0.945 0.254 0.840 0.945 0.890 0.717 0.950 0.964 Driver 0.644 0.048 0.833 0.644 0.726 0.651 0.940 0.867 Passenger 0.816 0.018 0.884 0.816 0.849 0.826 0.990 0.946 Pedestrian Decision Tree Output In this study, we used Decision Tree classifier which improved the accuracy better than ear
Wednesday, October 2, 2019
Duels :: Essays Papers
Duels "This is the excellence of Court: take away the ladies, duels and the ballets and I would not want to live there." - A. d'Aubigne, Baron de Foeneste, Il, 17 Duels and the act of dueling is something that has characterized not only the imagination of historians and modern warfare enthusiasts, but also the minds of writers and readers of literature for years. The numerous literary variations on the theme of dueling are enough of an indication of its importance, and the fascination with the act continues to increase. However, dueling is more than a literary climax or a plot twist; duels have been being fought for centuries and are actually derivatives of many medieval practices. The word duel has several predecessors, depending on which history is being referenced. The most common form of the word is derived from the German word Duell, which is a derivative of the Latin word duellum. Duellum is a combination of the Latin words bellum and duo, which connotes a war between two. This simple definition seems to be the most common and the most recognizable. Historian Francois Billacois states that a duel is "a fight between two or several individuals (but always with equal numbers on either side), equally armed, for the purpose of proving either the truth of a disputed question or the valour, courage and honour of each combatant (Billacois, 5)." Historian Ute Frevert concurs, but points out that duels, especially in the modern era, were "no mock fights, but serious passages at arms in which the opponents risked their lives and which could result in serious injury, or even death (Frevert, 11)." Most contemporary historians believe that the modern version of the duel developed out of three medieval institutions: the feud, the judicial duel and the knightly tournament. The belief that dueling was derived from these three events is often referred to as the continuity theory. Feuds in the medieval period occurred when people attempted to settle disputes and exact revenge for insults through "private vengeance," rather than by going to the authorities and entrudting them to settle the matter. Judicial duels, on the other hand, were official acts, during which both parties (the plaintiff and the defendant) fought their grievances out on the battle field with swords in front of a judge.
Tuesday, October 1, 2019
Similarities Between Hurston’s Novels, Seraph on the Suwanee and Their Eyes Were Watching God :: Compare Comparison Essays
Similarities Between Hurston’s Novels, Seraph on the Suwanee and Their Eyes Were Watching God Seraph – page 153 So, calling soothingly to Earl, Jim started from the south border of the sink hole and began to pick along to where Earl stood braced between two great cypress trees. Earl’s face was cold and unrecognizing. Jim caught hold to vines and shrubs to keep from slipping off the precarious footing into the water, and said nice things to Earl and kept going. He was a good half way along the dangerous route when Earl stepped forth and leveled the rifle and took aim. Eyes – page 184 He steadied himself against the jam of the door and Janie thought to run into him and grab his arm, but she saw the quick motion of taking aim and heard the click. Saw the ferocious look in his eyes and went mad with fear as she had done in the water that time. She threw up the barrel of the rifle in frenzied hope and fear. Hope that he’d see it and run, desperate fear for her life. But if Tea Cake could have counted costs he would not have been there with the pistol in his hands. No knowledge of fear nor rifles nor anything else was there. He paid no more attention to the pointing gun than if it were Janie’s dog finger. She saw him stiffen himself all over as he leveled and took aim. The fiend in him must kill and Janie was the only thing living he saw. Ms. Hurston’s two books Seraph on the Suwanee and Their Eyes Were Watching God are remarkably similar in many aspects, and I believe that these two passages exemplify that likeness. These two scenes take place just before a person is shot, Tea Cake in Eyes, and earl in Seraph. Janie kills (shoots) Tea Cake because he contracted rabies during the hurricane by a dog, and Earl was killed (shot) by several of the townsmen because he tried to rape Lucy Ann. Both Tea Cake and Earl were rendered mad and lost all humanity due to their circumstances. Both men needed help long before someone realized they could possibly be a danger to themselves or others. Both men were killed for attacking a woman, even though they clearly could not control themselves. Jim and Janie both try to help, but ultimately fail.
Centripetal Force Lab Activity
Centripetal Force Lab Activity Analysis: 1. A) Average Percent Difference: 50g: (values expressed in newtons) Step 1: Calculate the average value of the two variables Average Value= Value 1+ Value 2 /2 = 0. 49+ 0. 61/2 = 1. 1/2 = 0. 55 Step 2: Calculate the difference between the two variables Difference= Value 2- Value 1 = Fc- Fg = 0. 61- 0. 49 = 0. 12 Step 3: Calculate % difference % difference= difference of the variables / average of the variables x 100 = 0. 12/ 0. 55 x 100 = 21. 81% 100g: (values expressed in newtons)Step 1: Calculate the average value of the two variables Average Value= Value 1+ Value 2 /2 = 0. 98+ 1. 84/2 = 2. 82/2 = 1. 41 Step 2: Calculate the difference between the two variables Difference= Value 2- Value 1 = Fc- Fg = 1. 84- 0. 98 = 0. 86 Step 3: Calculate % difference % difference= difference of the variables / average of the variables x 100 = 0. 86/ 1. 41 x 100 = 60. 99% 150g: (values expressed in newtons) Step 1: Calculate the average value of the two var iables Average Value= Value 1+ Value 2 /2 = 1. 47+ 2. 19/2 = 3. 66/2 = 1. 83Step 2: Calculate the difference between the two variables Difference= Value 2- Value 1 = Fc- Fg = 2. 19- 1. 47 = 0. 72 Step 3: Calculate % difference % difference= difference of the variables / average of the variables x 100 = 0. 72/ 1. 83 x 100 = 39. 34% 200g: (values expressed in newtons) Step 1: Calculate the average value of the two variables Average Value= Value 1+ Value 2 /2 = 1. 96+ 2. 66/2 = 4. 62/2 = 2. 31 Step 2: Calculate the difference between the two variables Difference= Value 2- Value 1 = Fc- Fg = 2. 66- 1. 96 = 0. 70 Step 3: Calculate % difference difference= difference of the variables / average of the variables x 100 = 0. 70/2. 31 x 100 = 30. 30% 250g: (values expressed in newtons) Step 1: Calculate the average value of the two variables Average Value= Value 1+ Value 2 /2 = 2. 45+ 3. 57/2 = 6. 02/2 = 3. 01 Step 2: Calculate the difference between the two variables Difference= Value 2- Valu e 1 = Fc- Fg = 3. 57- 2. 45 = 1. 12 Step 3: Calculate % difference % difference= difference of the variables / average of the variables x 100 = 1. 12/ 3. 01 x 100 = 37. 20% Average % difference: = Sum of all 5 averages/5 21. 81+ 60. 99+ 39. 34+ 30. 30+ 37. 20/ 5 = 189. 64/ 5 = 37. 92% B) Slope Calculations (Graph is displayed on a separate sheet) 50g: Slope= Rise/Run = 0. 61/0. 49 = 1. 25 100g: Slope= Rise/Run = 1. 84/0. 98 = 1. 877 150g: Slope= Rise/Run = 2. 19/1. 47 = 1. 489 200g: Slope= Rise/Run = 2. 66/1. 96 = 1. 357 250g: Slope= Rise/Run = 3. 57/2. 45 = 1. 457 After calculating the slope of each section of the graph (each section corresponds to a certain mass used in the lab activity) it is evident that it varies from it’s expected value by a great amount.The expected value of the slope was 1 as the rise and the run were supposed to be equal. However in our case the rise and the run varied greatly and therefore because they were different numbers the slope did not turn o ut to be 1 (the only way to get a slope of 1 is if both the numerator and denominator are equal, as a number divided by itself is always 1 and a number divided by a different number can never equal 1). 2. Yes the data collected did verify the equation Fc=42Rmf2. This is because the only varying value in this case â€Å"f†, had a direct relationship with the value of Fc.The only other values that had to be determined in this lab was the radius and the mass of the rubber stopper but they were constant variables (constant at 0. 87m and 12. 4g respectively) meaning that they had no varying effect on the value of Fc. For there to be a relationship between Fc and 42Rmf2 when the value of any of the variables changes the value of Fc has to change as well Because the value of â€Å"f†had a direct relationship with the value of Fc, when the value of â€Å"f†changed the value of Fc changed as well. In this particular case when the value of â€Å"f†grew so did th e value of Fc.For example, during the 50g test the frequency was 1. 2Hz and the Fc was 0. 61N, and during the 100g test the frequency was 2. 08Hz and the Fc was 1. 84N. This shows that as the frequency increases so does the Fc acting on the system. This therefore shows the relationship between Fc and 42Rmf2. 3. A) When the string was pulled down and the stopper was still spinning, the stopper started spinning at a faster rate (took less time to complete 1 cycle around the trip) B) This happens simply because the radius is being shortened.Because the stopper on the end of the string is moving around the horizontal circle at a constant speed it is therefore being acted upon by a constant net-force. In this case the net-force acting upon it (the stopper) is Fc, therefore because it is Fc acting upon it, the force can be calculated by the formula 42Rmf2 as that is equal to Fc. In this case because the string with the stopper on the end was being pulled down this means that the radius of the entire circle was decreasing (less string= smaller distance= smaller radius).In that formula if the radius is smaller that means that the centripetal force will be larger. In this case that larger the centripetal force acting on the rubber stopper, the faster the rubber stopper rotates around the horizontal circle. C) The laws of conservation of energy state that the total energy in the system stays the same but simply takes on different forms (kinetic and potential being examples). Therefore this case is not contrary to the laws of conservation of energy simply because when the radius is decreasing the rubber stopper speeds up.In the laws of conservation of energy when an object is speeding up the object is gaining kinetic energy. However in this case while the stopper is speeding up the hanging mass (along with some of the string) is falling to the ground. From a conservation of energy perspective when an object loses height it loses potential energy. Therefore in this case t he object at the top gains kinetic energy while the mass loses potential energy. Because of this energy transfer no energy is lost in the system as hen the object is losing potential energy the other object in the same system is gaining kinetic energy, therefore the energy stays the same. D) In figure skating the skaters do the exact same thing as what was done in this lab experiment. In order to spin faster they bend low (get low to the ground) and tuck their arms and legs in. This causes them to spin much faster than they were originally spinning and follows the same principles that the rubber stopper experiment followed. When they get low they lose potential energy but getting low causes them to tuck in (tuck in their legs and arms) and ultimately have a smaller radius.This smaller radius causes them to have a much greater centripetal force and ultimately causes them to spin faster and causes them to gain kinetic energy. This follows the laws of conservation of energy as when the y lose potential energy they gain kinetic energy (theoretically no energy lost- only transferred) Sources of Error: In this particular lab activity there were not very many potential sources of error simply because it was not as complicated an activity as many others. Therefore all errors that were made were simply human measurement errors.The main source of error in this lab activity was measuring the period/frequency. This was a challenge simply because the person measuring had to do many different things in a very small amount of time. That one person was responsible for firstly choosing a spot along the path of the horizontal circle to begin the measurement from, then that same person had to start the watch during the very small time frame in which the rubber stopper passed by that specific point on the circle. From there the person had to count the stopper pass by 5 times and stop the watch when it passed by the 5th time.This made it very difficult to get a completely accurate measurement for the period and the frequency, as it was very difficult to get an exact measurement of that time period. These slight miscalculations of the frequency caused the calculation of the centripetal force to be slightly wrong as well because the calculation of centripetal force depended on the frequency. This is evident because our â€Å"Fg†and â€Å"Fc†calculations are way off, as they were supposed to be nearly the same number as Fg= Fc. – X-axis= Fc – Y-axis= Fg – point 1= 50g – point 2= 100g – point 3= 150g – point 4= 200g – point 5= 250g Data: Mass of stopper: 12. 4g Radius of Rotation: 87cm Mass of suspended masses| Time for 5 cycles| Period (T)| Frequency (f)| FgFg=mhg| FcFc=42Rmf2| 50g| 4. 2s| 0. 84| 1. 2Hz| 0. 49N| 0. 61N| 100g| 2. 44s| 0. 48| 2. 08Hz| 0. 98N| 1. 84N| 150g| 2. 23s| 0. 44| 2. 27Hz| 1. 47N| 2. 19N| 200g| 1. 99s| 0. 4| 2. 5Hz| 1. 96N| 2. 66N| 250g| 1. 65s| 0. 34| 2. 9Hz| 2. 45N| 3. 57N|
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