Cause and Effect Essay Example | Topics and Well Written Essays - 750 words - 3

Cause and Effect - Essay Example The mass media which includes advertisements, television shows, and magazines plays a huge role in shaping the perception of the society on the perfect body size and shape. Recognizing the profit motive of business organizations, advertisements are often geared as one of the most important ways of developing brand image and elicit positive response from buyers. Women are not exempted from the effects of these advertisements and are ready prey of marketers trying to sell products to them. In an article entitled Body Image and Marketing, it is estimated that women an average woman is exposed to around 400 to 500 advertisements each day and that 69% of the girls surveyed agree that the magazines' standard of perfection affects their perception of beauty. Through the use of skinny models in advertising nice clothes, sexy lingerie, makeup, and jewelry, ordinary women come to the bitter realization that she should be thin in order to be perfectly beautiful like these models. Beauty is equa ted with thinness and any woman that is over size six is considered to be fat and ugly. Noting that the size of an ordinary American woman is far from these skinny girls in fashion magazines, the media adversely affects the individual's body image. Young women feel left out and depressed because they cannot live up to the standards of the society. This cultivates a culture of low self esteem, a feeling of being unattractive, and a strong desire to be model thin. The negative body perception brings about the obsession to be thin through strict dieting. The obsession to thinness motivates women to perfect their body through any possible way. The most common resort is through dieting and starvation which often leads to eating disorders. The Mayo Clinic defines eating disorder as "a broad group of serious conditions in which you are so preoccupied with food and weight that you can often focus on little ease." In addition to this, it is estimated that 90% of individuals with eating disorders are motivated by the obsession to be as skinny as magazine models. The prevalence of anorexia nervosa and bulimia nervosa in the American society has been overwhelming as women starve themselves in order to lose weight. However, these young women do not stop dieting at any weight because they consider people's remarks of their thinness as compliments. The grim reality is the fact that pursuing the thin ideal can be fatal. These eating disorders do not only r ob people of their time, energy, and focus but can lead to fatal illnesses. Technological advancements have ushered us to an era where our physique can be altered through the use of modern equipments. Women do not have to be "imperfect" and "ugly" but are given the chance to redefine their bodies through cosmetic surgery. This technological advancement can help women get rid of the unwanted body fat in no time. It is irrefutable that women who are discontented with their body size are very much willing to undergo cosmetic surgery in order to change their appearance. In the United States, it is reported that 57% of American population has had liposuction while 47% had tummy tuck. These cosmetic surgeries are seen to be motivated by the desire

Negative Feedback Essay Example for Free

Negative Feedback Essay Negative feedback is the process by which the outputs tend to reduce the inputs, which causes the system to stabilize. It is found in many functions that organisms carry out on a daily basis, most notably homeostasis. Negative feedback can be seen during the menstrual cycle, during days 5-10 oestrogen levels slowly rise, this increase causes the release of the hormones FSH and LH to be inhibited. Also in low concentrations progesterone has a negative feedback effect on FSH, this means that more follicles cant be stimulated during the second half of the cycle. Moreover, once levels of progesterone are high, it inhibits the production of the hormone LH, meaning the corpus luteum is no longer stimulated to make progesterone, so it could be said that progesterone effectively turns itself off unless pregnancy occurs. Negative feedback can also be witnessed in childhood growth, the pituitary gland produces thyroid stimulating hormone, which is used to create the materials necessary to grow. The amount of TSH produced is controlled by negative feedback. Homeostasis, or the maintenance of a constant internal environment is also mainly controlled by negative feedback. For example, thermoregulation works because if the temperature falls below normal the body will initiate a response such as shivering, however as the temperature returns to normal the corrective mechanism will be reduced. Similarily negative feedback also controls Osmoregulation, if osmoreceptors detect change in the concentration of the blood, for example it may be too high the hormone ADH is released which targets the distal convulated tubule, causing it to become more permeable to water so that more is reabsorbed into the blood, reducing the concentration. Negative feedback is also a crucial part of controlling blood glucose levels, for example if the islets of Langerhans detect a fall in blood glucose levels, the alpha cells release glucagon, which in turn activates the conversion of glycogen to glucose within cells so it can be released in to the blood. As the levels of glucose in the blood increase the amount of glucagon released will decrease until it reaches normal. Negative feedback is an essential process that helps regulate many of the processes that enable us to live.

Forecasting Ensemble Empirical Mode Decomposition

Forecasting Ensemble Empirical Mode Decomposition Introduction This chapter introduces the background of time series and the importance of forecasting. The  motivation behind the project is elaborated and finally the aims and objectives are given. 1.1 Background Time series can be defined as a sequence of observations or measurements that are taken  at equally spaced timed interval (Xu, 2012). Hence, it is a stochastic process and can be  expressed as (Xu, 2012): x(t) = xi; i = 1; 2; :::;N: (1.1) Some examples of time series data include yearly profit, monthly recorded temperature,  hourly electrical consumption. Time series are classified into two categories mainly the stationary time series and  non stationary time series. Stationary time series consist of data which remain fixed irrespective  of the whereabouts. A stationary process is one where the mean, variance  and autocorrelation do not vary with time (Nau, 2014). For example, the financial stock  change of Mauritius remains constant in Mauritius as well as in any other place in the  world. Non stationary time series on the contrary involve data that keeps changing over  time. For instance, if we consider meteorological data of Mauritius, the data collected are  varied considerably from region to region as well as accordingly throughout the year. For  example, we have more rainfall over regions on the Central Plateau compared with the  coastal regions as demonstrated by Figure (1.1) which illustrates the variation of rainfall  collected for Mauritius over distinct regions from 1960 1990.while figure 1.2 shows theà ‚  difference in signal data between the two classes of time series. All meteorological data  including temperature, wind speed, solar irradiance irradiance, sea pressure and many  more weather parameters similar to rainfall have variations both in time and location. Hence, we can conclude that meteorological data are non stationary in nature. Figure 1.1: Distribution of rainfall for Mauritius for the year 1961-1990 Source:http://unfccc.int/resource/docs/natc/maunc1/chap1/chapter1.htm Figure 1.2: Difference between stationary and non stationary series , Source:http://en.wikipedia.org/wiki/Stationaryprocess Time series modeling is a vast field of research. The analysis of time series signals can  be extrapolated to meet demands of analytical results and predicting results in various  fields, such as : Economical Climatological Biological   Financial and others Due to its implementation in various fields, continuous research are been done in order to  design model for forecasting with better accuracy and efficiency. The behaviour of time  series is governed by four main aspects namely trend, seasonal variation, cyclic variation  and random variation (Xu, 2012). Trend of time series can be pictured as the evolution of  the series over time and hence gives the forthcoming pathway of the data. Hence, trend  analysis is very efficient in predicting extensive behaviour of data. Phonetically, a general  assumption in most time series techniques is that the data are stationary. Transformation  of non stationary to stationary is often done to manipulate the data for analysis. Forecasting is of high precedence in application of time series as it can predict future  events based on past events, specially when using in the field of limited resources. Forecasting  may be classified as a prediction, a projection or estimate of a future activity. In  fact, we have two types of forecasting methods namely qualitatively and quantitatively. Qualitative methods are non mathematical computations whereas quantitative methods  are rather objective methods based on mathematical computations. 1.2 Motivation We belong to a world of success in which one of the leading factor to success is our ability  to predict the result of our choices making all of us in a way or another forecasters. Climate consists of one of the major applications of forecasting. Over years, newer and  better models are been investigated so as to improve forecasting accuracy as much as  possible. Investigating weather parameters is highly necessary so as to be able to predict  weather situations which are required in various fields such as aviation, shipping,  oceanography and agriculture. Moreover, it is helps to evade weather hazards. Mauritius  has being confronted to drastic changes in weather conditions recently. We have  already a weather station which is deploying its best methods for weather forecasting  but is unable to predict accurately unexpected changes in weather, for example the recent  flash flood in March 2013 or one of the most worst drought that stroke Mauritius  in 2002. Therefore, in order to prevent further incidents or life taking calamities, it is of  high importance to have accurate and early predictive models in order to take preventive  measures to make sure that the population is safe well before such events occur. This  project comprises of investigating a different method for forecasting meteorological data. Throughout this project we will be dealing with time series models based of data which  has been collected over years and try to foresee future events based on the fundamentals  patterns confined within those data. The most commonly used forecasting model for time series was the Box Jenkins  models (ARIMA and ARMA models) (Peel et al., 2014). They are non-static models that  are beneficial in forecasting changes in a process. Many models have further been developed  among which is listed the Hilbert Huang Transform (Huang and Shen, 2005). Since climate data are of nonlinear and non-stationary nature, Hilbert Huang Transform  is capable of improving accuracy of forecast since most previous traditional methods  are designed for stationary data while this method is efficient in both cases. On the other  hand, recognizing all the advantages of Artificial Neural Network, it is of no surprise that  this methodology has gained so much interest in the this field of application. ANN have  proven to be more effective, compared to other traditional methods such as Box-Jenkins,  regression models or any other models (Khashei and Bijari, 2009) as a tool for forecasting. Both successful models mentioned however carries their own associated percentage  error. As a means to minimize error, both models can be combined to give rise to a new  hybrid model with better performance capabilities. 1.3 Aims And Objectives 1. In this project, the aim is to develop a combined model from two completely different  computational models for forecasting namely Ensemble Empirical Mode Decomposition  and Artificial Neural Network so as to improve accuracy of future  predictions of time series data. 2. EEMD will be adopted as the decomposition technique to obtain a set of Intrinsic  Mode Functions (IMF) and residual for meteorological time series data for Mauritius  signal while ANN will be the forecasting tool which will take as input parameters  the non obsolete IMFs. The results obtained will be compared with real data in  order evaluate the performance of the model. The idea is to reduce error associated  with each model when employed separately as both models possess their own skill  in determining trend in complex data. 3. Eventually, the model will be applied to forecast meteorological data mainly rainfall  from MMS and wind speed from studies conducted by fellow colleagues. 1.4 Structure of Report   1. Chapter 2 consists of a literature review on the models and their applications 2. Chapter 3 introduces Ensemble Empirical Mode Decomposition and validate the  EMD model. 3. Chapter 4 introduces the Artificial Neural Network and validate the network. 4. Chapter 5 present the results from application of EEMD to meteorological data. The  EEMD-ANN hybrid model is also introduced and validate. Finally the following is  applied to rainfall and wind speed data. 5. Chapter 6 presents the conclusion and the future work.

Problems With Leadership In IT :: Information Technology Essays

IT Leadership Paper As we go into the second part of the first decade of the new millennium the field of Information Technology has turned into a very important aspect of our lives. New and improved technologies are starting to become part of our lives without us realizing what this means. No one can argue that our lives have been simplified and improved by the technological gadgets, that over time we have been able to develop. But underneath the beauty of a product or a service lays the challenges and responsibilities many companies faced in order to be able to provide us with a service or to create a product. It is believed, that in many business, organizations or institutions the Information Technology Department is the area or the field that causes the most problems. One of the biggest reasons for which people tend to make this assumption or statement is that it has been hard to assimilate and integrate such a volatile department into the structure an organization had. People forget that having an IT department is a recent addition to the structural environment of a company or an institution. It was until computer became popular that people started to consider creating a department that would take care and provide technological solutions to the different issues that arouse due to the new technologies being implemented. Therefore it has to be taken into consideration that IT is an area that has not been fully developed to the extent of being completely incorporated into the traditional structure of a company. One of the reasons for which it has been tough to fully integrate the IT department is because of its constant change and need to stay up to date with the emerging technologies. When I say that it has not been fully integrated or incorporated I mean that we have not been able to understand the complexity of this assimilation in order to be able to get the best results. And by best results, I mean being able to come up with the best solution that arise with every situation and problem that comes up. It has been so hard to get out of this transitory situation due to the fact of communication problems. Companies and institutions took the wrong approach when the technologies started to emerge. Many top executives decided that it would be better to let the people that knew about computers and programming to focus in the technological area.

A Difficult Decision

Decision making is one of the hardest things a human being can do for themselves. The decisions people make, they do to either better themselves or worsen themselves. Decision making could be: what college someone’s going to or making a big change in their lives like moving. Others would make a bad decision, whether it’s ignoring good advice or going as far as doing drugs. Some people would make decisions because of the situation there in and is an impulse decision, but sometimes it turns out good.Decision MakingFor four and a half years, I thought my mother was actually starting to change, but, in reality I misled myself into believing something that wasn’t true. I was a responsible student, worked and thought everything was going well and where I wanted it to be. The last few months that I lived with my mother and step-father, everything went spiraling into complete and total chaos. The last week, was the worst and best thing that has ever happened for me, and also the most difficult decision of my life. The start of everythingIn July of 2013, my life started to spiral out of control, I just graduated high school and was looking for work consistently. My mother insisted that I pay rent, every week until I could find my own place to live; at first I didn’t mind that, I just needed to find a job and fast. I luckily found a job a month later, I was doing very well there and then I got second job. I wasn’t making that much money and my mom kept insisting that the rent I’m supposed to pay, is more than the last time.I barely made the amount she wanted me to pay a week, so I tried to find other things I could possibly do to make more money; I was out of luck. My mom and I started to argue a lot over the littlest things. I was trying to get myself out of that house as soon as possible, the living conditions were very bad no matter what I would  try to do, no food, filthy house. I was never home to do any of the cleaning be cause I was always at work or on my spare time see the people I really care about; but, it started to get worse.Getting worseAs the months started to drag on bye, my living situation became increasingly worse. I had found out that my mother and her husband were doing drugs. I really didn’t appreciate the way they used me and thought I was stupid enough to believe they weren’t doing anything. They started accusing me of stealing things, and doing things that I never even thought about doing. One day my mother called the police on me and she had told the police officer that I had attacked her. This was the most hurtful thing imaginable, my own mother, calling the police on me.What happened was, I wanted the money I had lent to her back ($50), I needed the money for some food, she kept resisting giving me my money back; I saw the money laying on the counter, so I went and got it and she, repeatedly kicked me in the stomach and bit me. Never once, would I ever put a hand o n my mother. I’m really glad I had people in my life that would take care of me whenever I needed a helping hand.The evictionThe last week that I saw my mother was the day I received an eviction notice from her, right before Christmas. I was very hurt and I had no idea what to do, I’ve never been in this situation before; I was scared. She said that I didn’t do anything around the house to help, so I had to go. I Know why she evicted me and I hope that someday that she will get the help that she needs. A couple of days after I had received the eviction notice, I found a place to live. It was not an easy move, but it was the best choice I have ever made. On December 7, 2013 I said my final goodbyes to my mother and have not seen or mumbled a single word to her, since that day.Where I wentPeople, who I’m not even related to, treated me just like their family. I call her my aunt Tonya; she has been sheltering me since that day. She offered me a place to stay , and I took the offer. The difficult decision was that I had to move an hour and a half away, from my family and friends. They all understood, and want me to succeed in life, and show people I will make something of myself and prove the people that told me I couldn’t wrong. Now,  I’m focusing better on school, and plan to succeed.

North American Free Trade Agreement and Company Fruit

1. Why did many textile jobs apparently migrate out of the United States in the years after the establishment of NAFTA? Jobs migrated out of the United States because where the average labor for US was $10 to $12 an hour compared to rates in Mexico at $10 to $12 a day. For example, the company Fruit of the Loom Inc. would benefit more and increase their revenue by paying their employee’s less to perform the job. It is also stated that NAFTA was credited with helping crease increase political stability in Mexico. So this could be another reason for the Jobs migrating out of the United States. 2. Who gained from the process of readjustment in the textile industry after NAFTA? Who lost? Due to this readjustment in which the United States jobs migrated to Mexico had a major effect on workers in the textile mills in the United States. But indeed had a great benefit on consumers in the US. It stated that employment in textile mills dropped from 478,000to 239,000, employment in apparel plummeted from 858,000 to 296,000. This shows that a great amount of workers were left empty handed searching for new employment. But on the other hand, this adjustment made it more reasonable for people like myself. Due to textiles moving to Mexico, prices dropped on clothing. Now it makes it easier for consumers to buy clothing at a cheaper price rather than spending a lot of money just to do so. This shows that the market will grow because people can and willing to spend more money at the cheaper rate. In this case, Mexico and U.S will benefit. Mexico would increase jobs as low cost production moves south. And U.S will increase a prosperous market and lower the prices for consumers from goods produced in Mexico. Especially when prices are at a discounted rate. 3. With hindsight, do you think it is better to protect vulnerable industries such as textiles, or to let them adjust to the painful winds of change that follow entering into free trade agreements? What would the benefits of cost of protection be? What would the costs be? I have a two-sided opinion on this matter. I feel that in a way we should protect industries such as the textiles because jobs would be lost and wages levels would decline tremendously in the United States and Canada. Mexican workers would emigrate north and pollution would increase due to Mexico’s more lax standards. And also Mexico would also lose its sovereignty which is not an important factor. But on the other hand, we shouldn’t protect because this would prosper in the market & benefit the consumers.