Anita and Meena in Anita and Me and Piggy and Ralph in Lord of the Flies Essay

In both Anita and Me and Lord of the Flies, the characters have especially a similar kind of kinship. The two individuals in the companionship are not on a similar level when they are together. For instance, in Anita and Me, Meena is never observed as increasingly better than Anita and Piggy is never observed as better than Ralph. In spite of this reality, the peruser can clearly tell that both Meena and Piggy are all the more mentally better than Anita and Ralph. These two kinships in the two books experience changes as occurrences happen, for example, the framing of Jack’s clan in Lord of the Flies and when Anita’s different companions relinquish her. These progressions truly power both Anita and Ralph, the more better of the two companionships than depend vigorously on the second rate of the fellowships, Piggy and Meena for passionate help. In the event that these pieces of the two books were taken a gander at in detail, the peruser would see that both Ralph and Anita normally feel as though they can't proceed and Meena and Piggy are generally the individuals who help them through their troublesome occasions and offer help. Toward the start of Anita and Me, Anita is depicted as a significant alluring little youngster and one with the ability to have authority over individuals, ‘Anita was the undisputed ‘cock’ of our yard†¦her foghorn voice, profane demeanor †¦ demonstrated she was hefting enough testosterone around to procure the title†¦she had the essence of an irritated seraph, colossal green eyes, light hair and a twisting mouth†¦Ã¢â‚¬â„¢ Here we can see that Anita is portrayed as the ‘undisputed ‘cock’ of our yard’. This represents how she is a ground-breaking young lady. The way that highlights, for example, light hair, huge green eyes and a face of a seraph are taken after in her recommend that she is a serious other-worldly, great and pleasant little youngster. This, be that as it may, is negated by her ‘foghorn voice’ and ‘foul mouth’. This gives us how she isn't generally what she appears and that despite the fact that she may look intense and incredible, she truly is a significant desolate, pitiful young lady. On the off chance that we contrast Anita’s appearance with Meena’s, we see an enormous distinction: ‘†¦the winter coat, the scabbed knees, my difficult nine-year old face†¦ not on the grounds that I was excessively youthful or seriously dressed, it was something different, something about me so offputting, so unimaginable†¦.’ Meena portrays herself as ugly. When contrasted with Anita, we can see that it is reality. Anita’s portrayal gives her development and prevalence while Meena’s cause her to appear as though she is a little ‘nine-year old’ young lady who doesn't have a similar development and predominance as Anita. It is a lot of equivalent to Lord of the Flies with Piggy and Ralph’s kinship. Toward the start, our initial introduction picked up of Ralph is of an athletic and alluring little fellow: ‘He was mature enough, twelve years and a couple of months, to have lost the unmistakable stomach of childhood†¦you could see since he may make a fighter, to the extent width and weight of shoulders went, purchase there was a mellowness about his mouth and eyes that declared no devil’ Here Ralph is depicted in an alluring manner and he, as Anita, can be depicted in two distinct ways: right off the bat as a ‘boxer’ which depicts Ralph as a solid, influential man while the ‘mildness about his mouth and eyes’ shows that he is as yet a blameless wonderful little youngster. Similarly Anita is companions with Meena in Anita and Me, Ralph is companions with Piggy. Piggy is fundamentally the same as Meena. Both Piggy and Meena are not as alluring as Ralph and Anita. Along these lines, they are viewed as insuperior to them: ‘The exposed law breakers of his knees were full, gotten and scratched by thorns’ Here Piggy’s depiction doesn't depict him as either extremely alluring or intriguing. The way that his knees are scratched by thistles causes Piggy to appear to be very youthful as you typically envision small kids to have scratched knees. Ralph is depicted as very nearly a man. This is another manner by which Piggy is portrayed as substandard compared to Ralph. Another manner by which Ralph and Anita are depicted as more noteworthy than Piggy and Meena are the point at which they meet. When Anita converses with Meena just because, she expect that she is increasingly better than Meena. ‘†¦then grabbed the pack off me and started leaving as she ate’ When Anita meets Meena, she grabs a pack of desserts from her and begins to leave. She expects Meena to follow and I find amazing that Meena assumes this is fine and she feels glad to follow her a couple of paces behind. Meena feels favored to be in Anita’s organization. Anita understands this and utilizations it for her potential benefit. Anita has used to understanding that she is typically the pioneer of a gathering and that she can apply a great deal of intensity. Ralph additionally accept that he is unrivaled when he meets Piggy. At the point when Piggy asks what Ralph’s name is, Ralph doesn't restore the motion: ‘The fat kid stood by to be asked his name thus purchase this proffer of associate was not made’ Here Ralph is depicted as very egotistical. We can see that Ralph clearly feels here and there better than this fat kid and along these lines he feels that he isn't a similar level as him. All through the book, Ralph has a specific measure of prevalence over Piggy and utilizations it frequently. At the point when Jack, Simon and Ralph investigate the island just because and Piggy requests to come, Ralph embaraces him by declining to allow him to come. The equivalent is done in Anita and Me, as Anita is all through the book progressively better than Meena. Both Meena and Piggy don't have a place with the gatherings that are shaped in the books. Piggy is very intelligent and adult for the games played and he is the oddball of the gathering, as is Simon. Meena too doesn't fit in to Anita’s gathering. She depicts herself as ‘too youthful for Anita’s thought and unreasonably old for the children’. In any event, when she joins Anita’s gathering, she some o f the time feels strange. Towards the finish of the two books, both Anita and Ralph find that they need bolster when their dear companions leave them. In Anita and Me, Anita encounters this when her mom leaves: ‘†¦she consistently appeared to be more seasoned than her companions. However, when I saw her sitting alone on the recreation center swings, from a separation, her folded face and slouched shoulders transformed her quickly into a little old woman. When Anita’s mother and the artist leave her, Anita feels discouraged and forlorn. Meena, in spite of the fact that she is irate with her, feels frustrated about Anita and attempts to solace and bolster her. This shows how Meena, despite the fact that she is depicted as insuperior to Anita, is the more steady of the two young ladies. Meena has two cherishing guardian who take care of her well and an infant sibling while Anita lives with her inconsistent mother who is scarcely there for her and a dad who sees her infrequently. Ralph additionally ends up in this circumstance when the young men partition into two gatherings and Ralph is left distinctly with Simon and Piggy: ‘Piggy’ ‘Uh?’ ‘What are we going to do?’ Piggy took a gander at the conch. ‘You could-‘ ‘Call an assembly?’ Ralph snickered pointedly as he gave the signal and Piggy glared. Here we can see the distinction in Ralph’s method of conversing with Piggy. We can see that Ralph has no clue about what to do since Jack has made his own clan. Ralph now finds that he is depending on Piggy to help and bolster him through his period of scarcity. Ralph likewise utilizes the word ‘we’ rather than ‘I’. This shows how Ralph feels that both him and Piggy are presently in their own clan. It likewise shows how Ralph is leaving Piggy alone on a similar level as him by utilizing ‘we’. Another route, wherein the fellowship of Anita and Meena and the kinship among Ralph and Piggy are comparative, is the closure of the kinship. Toward the finish of the book, Meena understands that Anita isn't the individual she ought to be a closest companion with: ‘I don’t give a hurl what your sister [Anita] does, Tracey. Yow can reveal to her that from me.’ Now, Meena has understood that Anita has not regarded her just as she ought to have been dealt with. Meena takes in this from warming up to two others who treat her well and are genuine companions. When Meena comes back from medical clinic, she doesn’t associate a lot and is glad doing things alone. Anita, desirous of her satisfaction and the adoration she gets from her folks, sends compromising notes to her in the expectation of startling Meena. Now we can truly observe that Meena is better than Anita as Anita attempts to try to try to feel progressively prevalent and make sure about by undermining her. The equivalent occurs in Lord of the Flies. After Piggy has kicked the bucket, Ralph acknowledges what an incredible companion Piggy truly was to him and the amount he needs him when Jack’s clan betray him: ‘And in them, with dingy body, unwiped nose, Ralph sobbed for the finish of guiltlessness, the murkiness of man’s heart, and the fall through the demeanor of the valid, insightful companion called Piggy. Here we can see that Ralph’s picture of a solid, amazing youngster is lost and we consider him to be a youthful na㠯⠿â ½ve kid rather than Piggy who is depicted as ‘wise’. Previously, Piggy was depicted as a fat, asthmatic kid who was substandard compared to Ralph however now we see that Piggy is better than Ralph. Ralph understands this and feels terrible in the manner he rewarded Piggy. Towards the finish of the two books, the characters who were depicted as unrivaled: Anita and Ralph think that its difficult to adapt when Meena and Piggy leave them. In Anita and Me, when Meena breaks the fellowship among herself and Anita, Anita starts to find that she is envious of Meena and sends her undermining notes. Also in Lord of the Flies, when Piggy passes on, Ralph thinks that its difficult to adapt. He is disregarded all and is abdominal muscle

The Medieval Era Essay Example | Topics and Well Written Essays - 1000 words

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This Whole System is wrong Case Study Example | Topics and Well Written Essays - 250 words

This Whole System isn't right - Case Study Example Any businesses or associations that disregard those measures are in danger of being rebuffed seriously through the forcing of approvals and other corrective measures. These approvals may prompt the blacklist of our items and therefore a decrease in gainfulness. Aside from the danger presented by sanctions, you know that our organization has manufactured a decent name for itself as a result of its quality items and great advertising. The issue of poor working conditions in our production lines in China may very well wreckage up our notoriety and put us at loggerheads with governments and clients. I accept this is a value that would be unreasonably high for the organization to pay, considering we can found changes in those processing plants and improve it for laborers there. Simultaneously, I might want to request a second glance at the periods of a portion of the laborers in those industrial facilities. Kid work is unlawful, and the work of underage specialists might be impeding to our desires for development and long haul plans. I trust in your capacity to make the correct call since you have substantiated yourself as a capable, visionary and businesslike pioneer. Let us attempt to offset our aspirations with our strategies, in light of the fact that occasionally the end doesn't as a rule legitimize the

Predictive Analytics How to Forecast the Future

Predictive Analytics How to Forecast the Future One of the most popular features of Big Data is predictive analytics. Far from the latest business buzzword, predictive analytics is a set of techniques that have become fundamental to the business strategies of many household name brand firms, such as Netflix, Google, and Amazon. These firms, and many others, dominate their respective markets, due in large part to the significant use of predictive analytics.Predictive analytics is a form of business intelligence gathering, the strategic business use of which is powerful enough to upend an industry. Driven by the tremendous-revenue generating potential of predictive analytics, more firms are investing in the necessary infrastructure, such as data storage and processing hardware and software and both database administrators and data analysts. As they do so, predictive analytics tools and techniques, grow in sophistication and refinement.Moreover, as more firms adopt predictive analytics, and incorporate it into their existing strategi es, they fuel its widespread adoption, as competitors must adopt it or risk losing significant market share. © Shutterstock.com | ImageFlowIn this article, we will cover 1) the definition of predictive analytics; 2) discuss data analysis; and 3) the types of predictive analytics; as well as cover 4) using predictive analytics; 5) the benefits of predictive analytics; 6) the risks of predictive analytics; and 7) a real-life example of a firm using predictive analytics.WHAT IS PREDICTIVE ANALYTICS?Predictive analytics is an assortment of statistical and mathematical techniques used to predict the probability of future events occurring. Fundamentally, statisticians and data scientists combine and standardize a variety of historical datasets to develop correlative statistical models that firms, research organizations, and even governments use to forecast a wide range of phenomena.The field’s origins lie in the beginnings of the computer age in the 1940s, specifically with the U.S. government’s use of computational models during World War II. Notable examples include the development of the Kerrison Predictor in 1940, which automated anti-aircraft weapon targeting, and the use of computer simulations by the Manhattan Project to determine the probable results of nuclear chain reactions in 1944.Just as computers and computing technology have grown exponentially since then, so too has the field of predictive analytics. In 2012 alone, technology users generated 2.5 exabytes of data per day â€" an estimated three-quarters of which is text, audio, or video messages. That’s a lot of data for firms to leverage, and with data storage prices and space requirements having shrunk exponentially since the 1940s (indeed, from even a decade ago), the adoption of predictive analytics is an increasingly cost-effective proposition â€" if not, exactly a simple one.Eric Siegel answers eight questions about predictive analytics DATA ANALYSISIn addition to either developing the necessary infrastructure in-house to leverage predictive analytics, or outsourcing their business intelligence ga thering, a firm must determine what questions they will use predictive analytics to answer. Predictive analytics, whether done externally or internally, is costly in terms of time and labor, as the answers to these questions are the result of intensive research, involving multiple datasets with many variables.It is important for data scientists to be able to link and visualize datasets in order to interpret them better. While computers have gotten faster and better at processing vast amounts of data, human insights lie at the root of the answers to Big Data questions. It is also important to understand that the answers to predictive analytics are, for the most part, correlative, not causative, by nature. This means that data scientists are looking at the probability of an event based on the event happening under similar conditions. A failure to understand the deeper underlying reasons â€" the causes â€" of the event, can lead to inaccurate predictions.TYPES OF PREDICTIVE ANALYTICSTh ere are several types of predictive analytics methods, including predictive modeling, design analysis and optimization, transaction profiling, and predictive search.Predictive ModelingWhen most laypeople discuss predictive analytics, they are usually discussing it in terms of predictive modeling. Indeed, predictive modeling is at the heart of predictive analytics, and has been popularized in science fiction as well as by the financial services industry.It involves mathematically modeling associations between variables in historical data, in order to predict or forecast the likelihood of a future event. Commonly used in the financial services industry to predict the behavior of capital markets, predictive analytics is increasingly being used for sales and revenue forecasting, dynamic pricing, online recommendation systems, strategic planning, and other business areas requiring decision-making about the future.Predictive modeling yields the probabilities of event occurrences based on previous event occurrences; as such there is no guarantee that a desired event will occur (or conversely an undesired event will fail to occur). Understanding this can reduce overreliance on the models.Decision analysis and optimizationDecision analysis and optimization is a subfield of predictive analytics that deals with reducing the uncertainty inherent in decision-making. Specifically, it involves aspects of a decision, and/or multiple decisions to determine the one likely to yield the most success. Firms often use decision analysis and optimization in functional areas, such as supply chain management to ensure the firm’s decisions maximize revenue and result in a firm achieving and/or exceeding other key performance goals.For example, a distribution chain optimization problem might involve determining the ideal mix of online and brick-and-mortar retailers to use to achieve a target revenue goal. Using SAS Analytics, IBM SPSS Modeler, another popular predictive modeling applic ation suite, or internal proprietary software, a data scientist can import multiple datasets (such as historical wholesale prices, local and online retailers, distribution costs by distribution method, and more), build models, and test and retest results.Transaction profilingTransaction profiling involves aggregating and filtering information from transactions involving enterprise software. These can include, but are not limited to, credit card transactions on an online retailer’s website, and logins to a proprietary social network; there are often isolated datapoints. This subfield involves standardizing this data and clustering it with relevant data in ways that can allow a firm to create predictive models of transactional data.Predictive searchPredictive search, fundamentally, involves creating algorithms that take one set of inputs and finds a particular output. However, the increasing sophistication, and in some cases, the incompleteness, of inputs requires algorithms that re turn the best possible answer.To illustrate this, consider two co-workers. The first asks the second for a restaurant suggestion for a business lunch. The second can make the recommendation based on their knowledge of the first co-workers personal preferences, likes/dislikes, and knowledge of the area. A search engine, hypothetically, has realms of data to make a strong recommendation, such as the user’s geographic location, online mentions of personal preferences.Further, the second co-worker might immediately realize, that the first co-worker might actually need a vegetarian restaurant for this particular meeting. Predictive search also involves deep dives into multiple datasets to provide you with a personalized output that gets at the underlying reason for your input. Ideally, a search query might “recognize” that the restaurant recommendation is likely for a particular meeting on your online calendar, further “recognize” that the client is a vegetarian, and return res taurants that fit this need. Predictive search developments will harness more and more data in assessing the best possible answer to return.USING PREDICTIVE ANALYTICSPredictive analytics can be used for a variety of business strategies, and has even give rise to many business models, such as search, search advertising, and recommendation engines. Firms must determine the costs and benefits of developing the in-house capabilities to do this, or outsourcing their Big Data needs to a third-party market research firm. Both approaches have time, cost and labor benefits and drawbacks for any firm; however, with other firms increasingly using predictive analytics, each firm will have to map its Big Data strategy now or in the near future. Once a strategy has been determined, the firm must determine what insights will best inform their strategy and then use predictive analytics to obtain them.BENEFITS OF PREDICTIVE ANALYTICSPredictive analytics benefit any decision by providing executives, managers and other decision-makers with the tools to make the best possible decision. Some applications include, but are not limited to predictions of customer purchasing likelihood, for use in targeted marketing and upselling; sales and revenue forecasting; optimize marketing channel, supply chain, distribution chain, and manufacturing optimization; and new product development.Really, there are no limits to the potential applications of predictive analytics for optimization and forecasting. Even scientific organizations and governments have begun to invest in the resources necessary to leverage predictive analytics.RISKS OF PREDICTIVE ANALYTICSThere are several risks to using predictive analytics, though most stem from overreliance on this set of tools. Executives and managers must understand that predictive analytics involves probabilities and correlation, which are not absolute. Data scientists must strive to filter out all of the noise from datasets to ensure accurate and replic able modeling results. They must further strive to present these results as actionable insights with risk parameters for each choice.Asking the wrong questionsAwash in reams of data, it is critical that firms ask the right questions. Predictive analytics is most efficient when used to determine the answer to a narrow inquiry, such as the likelihood of customer A to buy product X at time Y for price Z, rather than the likelihood of customers buying product X (as might be asked by a layman). Further, data scientists must be able to test assumptions and pivot quickly from erroneous ones. For example, if a question involves the impact of a marketing technique on sales â€" one favored by the CEO and widely assumed to have a significant impact, and later studies determine it has no effect, the data scientist must be able to assess the remainder of the question freely.Data scientists must take the general questions that may come from executives and managers and extract the root business ne ed. To fulfill this need, they must use the data to create appropriate recommendations by determining the appropriate datasets, filter out extraneous information, build models, and test and retest them.Bad dataData scientists must be aware that not all data is accurate, arrive at an estimate of bad data, and correct for it in their studies. Data can be bad for any number of reasons, including self-reporting errors, corrupted files, poorly phrased questions, incomplete data aggregation, and poor standardization methods.It is critical that data scientists quickly recognize and filter bad data from their data sets. They must also make sure they do not create bad data themselves â€" for example through an imperfectly calculated transformation function. Further, they must take the time to improve aggregation and standardization methods to limit the collection of bad data. Without reasonably accurate data, data scientists cannot build predictive analytics models whose assumptions will hol d.Complexity and unpredictabilityBig Data is messy, consisting of everything from social media mentions to traffic camera images to website logs. Predictive analytics, being a set of statistical techniques, requires all data to be standardized and quantified. Quantifying non-numeric data has its own risks and creates uncertainty.Further, data is unpredictable, especially dynamic data. A model that accurately forecasts future events could be thrown into disarray by a sudden unanticipated cascade of events, which were not initially estimated. Such was the case in 2007, when the majority of financial services firms failed in incorporate the possibility of sudden credit defaults, which triggered a series of other events that prior to 2007 would have been improbable.Privacy and securityMany privacy advocates find such data usage invasive and alarming. There is something inherently intrusive about firms collecting information about individuals in order to predict their behavior. Advocacy efforts include lobbying for limitations to data collection types, amounts and methods in nations across the globe. Executives and data managers must be aware of the ever-changing Big Data regulatory landscape.Privacy is a huge concern for another reason â€" security. Hackers target data storage devices and facilities for financial gain, ideological reasons, and thrills. With many nations holding firms at least partially responsible for the damage caused by loss of secured data, firms must ensure they keep up-to-date with the latest data security measures. If they outsource their data analysis to a business intelligence vendor, they are likewise compelled to ensure that the business intelligence vendor secures the firm’s data appropriately.CASE STUDY © pixabay | WikiImagesPredictive analytics are a major source of competitive advantage for Amazon, so much so that Amazon has taken market share from many brick and mortar retailers across the U.S., and even other parts of the world. Amazon uses predictive analytics to power its recommendation algorithms that help the retailing giant upsell, as well as to make its distribution system more efficient.Amazon provides site visitors with product recommendations based on your viewing history. As that viewing history grows, Amazons algorithms, using the increased data, create increasingly useful and accurate recommendations. The firm also offers discounted pricing, and/or package deals in order to entice you upsell, as well as premium pricing when demand is high and inventory is low.Beyond Amazon’s on-screen predictive analytics applications, the retailer has begun to ship products in advance of customer orders, based on the results of its predictive models. Amazon filed a patent on a †œmethod and system for anticipatory package shipping” in 2012, designed to increase the efficiency of its distribution chain. By harnessing this method during peak volume periods, such as the holidays, Amazon, whose predictive analytics models have already demonstrated a high probability of accuracy, can ensure that it has the inventory on hand to distribute and that goods are distributed beforehand, minimizing customer dissatisfaction.Amazon’s use of predictive analytics has been instrumental in its dominance of the online retail space in the U.S., in which it is the market leader as of 2014, with net sales of nearly $60 billion.