The Impact of the World Trade Organisation (WTO) on the Sustainability Essay

The Impact of the World Trade Organisation (WTO) on the Sustainability of Competitiveness of the Petrochemical Industry in Saudi Arabia - Essay Example o establish the rules to harmonize the rule associate with the chemical industry in Saudi Arabia (Al-Alamy, 2003; Zuhd, 2005l; Al Zuhd, 2005; Al-Sadoun, 2008). According to the GATT says Yu, this â€Å"Harmonize System would ensure greater ability for countries to monitor and protect the values of tariff concession† (Yu, 2008, p. 8). It ensures that there is only One General Rules to be applied to all Members but these rules also cover the specific commitments made by all members. For example, in the part one of the agreement concession called the Most Favored Nation Tariffs, there is a clause known as â€Å"bonded tariff† or the maximum tariff the members should levy. If the tariff levied is higher than the maximum tariff stated, the country has to compensate other parties for the excess amount, but it is based on line-by-line according to national nomenclatures of the time when concession took place. How the tariff is calculated? It is based on the description of the product base rate duty before any tariff, rate of bonded tariff, implementation period, initial negotiation, and other duties and charges (Robinson, 2004; Al-S adoun, 2008). In his paper to the World Bank, Saudi Arabia and WTO in the light of Mena Experience, (http://worldbank.org/idf/ndf3/papers/global/Al-Sahlawi.pdf.) Professor Al-Sahlawi, indicates that compare to the growth of manufacturing industry in the global market, the growth of petrochemical industry in the Middle East and Arab Regions is considered slow. He suggests that it is important to follow Egypt and Morocco to improve petrochemical industry and make it to be more competitive than the manufacturing industry in the global market or to create petrochemical products as export merchandise. In terms of joint ventures in the petrochemical industry, Al-Salawi (ibid) claims that furthering the process of privatization eases the process of foreign direct investment such as in Egypt where the rate of foreign direct investment has increased

Eassy Essay Example | Topics and Well Written Essays - 750 words

Eassy - Essay Example According to Bardhan and Dwight, several factors can endear a company to outsource its business operations. Off shore labor laws, wage laws and tax laws favor the idea of outsourcing as there is almost a certainty of maximizing profits through low cost labor. Workers abroad are often willing to work longer hours for less pay than those in the United States where there could also be a lack of expertise in certain areas of business process. In addition to this, the company is offered a great diversity of skill and kept abreast with emerging technology from various ends of the globe that go a long way in improving service delivery and quality of service. Further still, the company that off shores acquires global status and recognition in the world market which is a huge plus in its operations. This in addition to the fact that off shoring some business operations actually enables a company to shift focus to other crucial areas of the business is of unparallel value to the company growth and development (Bardhan and Dwight, 22). However endearing these advantages of outsourcing may be, several negative implications and concerns also lie in wait, both to the company and the United States economy. The good that may result from the endeavors of outsourcing almost always serves the interest of the top brass of the company only. Little or nothing at all of the entire benefits actually trickles down to the middle level workers or the public in general. Quality concerns arise as a result of outsourcing. As long as the outsourcing contracts are honored, the outsourcing company may tend to be motivated by profit to decrease expenses and condone shoddy work. The employees of the company may not also have the loyalty to the business, a trait that is often priceless in ensuring success of the operations. Workers may change jobs or move to greener pastures whenever they please or sense danger leaving the company in turmoil and incurring further costs as such with hiring and rec ruitment as cited by Cromie (54). There are also numerous hidden costs that arise from off shoring business functions. For instance, the process requires the hiring of a lawyer(s) and signing of contracts. Major budgetary loopholes appear for exploitation to the detriment of the company and anything not covered in the contract will be basis for the company to pay the additional charges. Other regulations regarding this exploit also seem to secretly add to costs of operations. Failure to pay wages strictly as stipulated can result in several implications like fines, back pay awards or even disqualification from filing any further H1B1 visa petitions for future workers. There is the risk of a company exposing some of its important and confidential operation information to third parties as a result of off shoring. This may render the company weak in the face of any arising or already established competition. The company also loses management control of outsourced business functions lea ving several critical decision makings out of their hands. Some of these outside decisions may at times lead to bankruptcy. Language barrier and other cultural issues often come to play a negative role during outsourcing, majorly resulting in breakdowns in communication and even rejection of certain company products and services on cultural and religious grounds. Other problems include inappropriate categorization of responsibilities causing mayhem and a complete eyesore (Bergsten,

Topshop Transactional website Essay Example for Free

Topshop Transactional website Essay Topshop is a transactional website that specifically sells merchandise like clothes, shoes and accessories. The audience intended to shop at Topshop is varied between teenagers and middle aged women around 35 years old. As I navigated around the Site I believe that it would fulfil the needs of the audience with its wide variety of merchandise. Site Structure The homepage of Topshop is well decorated and brightly colour to obtain the customers attention. The site is also updated every week to keep the site fresh and original looking. As you can see from the screenshot above there is a list of options down the left hand side of the site navigating you to Shop products and to the different shop information. The first listing on the column is Shop by which takes the customer to the different categories of stock e. g. Fashion Tops, basic Tops, Dresses etc. Also on the main column there are a number of options to click upon Shop By When clicked upon shop by opens a new window showing the different items of merchandise a customer can purchase New Shows you the items recently added to the Topshop website and what you can purchase in the Topshop store Collections collections shows the customer the different type of styles i. e. Punk, Office etc and different types of designer that design for Topshop e. g. Celia Birtwell Topshop Boutique This option also shows you different types of designers but the designers are not house hold names but are well known e. g. Richard Nicoll and Markus Lupfer. The TEE shop the tee shop shows the different style of plain t-shirts you can purchase in every colour so that if youre looking for a simple top it will be easily found. The denim shop this includes all denim items the Topshop provide e. g. Jeans. Jackets etc TOPSHOP mini Topshop mini is a new selection on this website this consist of many various items supplied for small babies. Topshop info when this option is chose this brings the customer to whatever information they are seeking for example Return policy or Postage and Package cost. Also located in the options in the left hand side is Topshop info when clicked upon this brings the viewer to various options like Store Locator, Contact us, policies or Services and Help. Topshop site is well laid out and simple to navigate around, the Information like Privacy is straightforward to find and will make customers more relaxed about purchasing over the internet. However there are no search facilities or site map which might be a downfall to Topshop success but as you continue viewing this site the customer will realise that the site is well laid out and therefore there will be no need for these functions. The arrangement throughout the site is simple enough to follow for someone who can use computers but for a newcomer or older person this site would take a lot of time and effort to get adjusted to. The services and help are clearly marked in the Topshop site and its easy to use, simple layout of the information is well documented and is very useful. The domain name of the site is Topshop which I consider to be really memorable as it is short but appealing. As Topshop is a renowned site if entered into any search engineer e. g. Google, it will locate the site. When entered the site is quick and effortless to load which is a huge advantage also as no password is needed to enter the Topshop website any user is aloud to access it. The Topshop website is in keeping with the corporate identity and the Topshop font on the website is the same as the Topshop high street store and the corporate design on the sales bag.

Estimating Reservoir Porosity: Probabilistic Neural Network

Estimating Reservoir Porosity: Probabilistic Neural Network Estimation of Reservoir Porosity Using Probabilistic Neural Network Keywords: Porosity Seismic Attributes Probabilistic Neural Network (PNN) Highlights: Porosity is estimated from seismicattributes using Probabilistic Neural Networks. Impedance is calculated by using Probabilistic Neural Networks inversion. Multi-regression analysis is used to select input seismic attributes. Abstract Porosity is the most fundamental property of hydrocarbon reservoir. However, the porosity data that come from well log are only available at well points. Therefore, it is necessary to use other methods to estimate reservoir porosity. Interpolation is a simple and widely used method for porosity estimation. However, the accuracy of interpolation method is not satisfactory especially in the place where the numbers of wells are small. Seismic data contain abundant lithology information. There are inherent correlations between reservoir propertyand seismic data. Therefore, it ispossible to estimate reservoir porosity by using seismic data andattributes. Probabilistic Neural Network is a neoteric neuralnetwork modelbased on statistical theory.It is a powerful tool to extract mathematic relation between two data sets. For this case, it has been used to extract the mathematic relation between porosity and seismic attributes. In this study, firstly, a seismic impedance volume is calculated b y seismic inversion. Secondly, several appropriate seismic attributes are extracted by using multi-regression analysis. Then, a Probabilistic Neural Network model is trained to obtain mathematic relation between porosity and seismic attributes. Finally, this trained Probabilistic Neural Network model is applied to calculate a porosity data volume. This methodology could be used to find advantageous areas at the early stage of exploration. And it is also helpful for the establishment of reservoir model at the stage of reservoir development. 1. Introduction In recent years, clear advances have been made in the study and application of intelligent systems. Intelligent system is a powerful tool to extract quantitative formulation between two data sets and has begun to be applied to the petroleum industry (Asoodeh and Bagheripour, 2014; Tahmasebi and Hezarkhani, 2012; Karimpouli et al., 2010; Chithra Chakra et al., 2013). There are inherent correlations between reservoir properties and seismic attributes (Iturrarà ¡n-Viveros and Parra, 2014; Yao and Journel, 2000). Therefore, it ispossible to estimate reservoir porosities by using seismic data and attributes. Previous studies have proved that it is feasible to estimate reservoir porosity by using statistical methods and intelligent systems (Na’imi et al., 2014; Iturrarà ¡n-Viveros, 2012; Leite and Vidal, 2011). Probabilistic NeuralNetwork (PNN) is a neoteric neural network model based on statistical theory. It is essentially a kind of parallel algorithm based on the minimum Bayesian risk criterion (Miguez, 2010). It is unlike traditional multilayer forward network that requires an error back propagation algorithm, but a completely forward calculation process. The training time is shorter and the accuracy is higher than traditional multilayer forward network. It is especially suitable for nonlinear multi attributes analysis. For this case, PNN has good performance on unseen data. In this study, the propounded methodology is applied to estimate the porosity of sandstone reservoir prosperously. 2. Probabilistic Neural Network PNN is a variant of Radial Basis Function networks and approximate Bayesian statistical methods, the combination of new input vectors with the existing data storage to fully classify the input data; a process that similar to human behavior (Parzen, 1962). Probabilistic Neural Network is an alternative type Neural Network (Specht, 1990). It is based on Parzen’s Probabilistic Density Function estimator. PNN is a four-layer feed-forward network, consisting of an input layer, a pattern layer, a summation layer and an output layer (Muniz et al., 2010). Probabilistic NeuralNetwork is actuallya mathematical interpolation method, but it has a structure of neural network. It has better interpolation function than multilayer feed forwardneural network. PNN’s requirement of training data sample is as same as Multilayer Feed Forward Neural Network. It includes a series of training sample sets, and each sample corresponds to the seismic sample in the analysis window of each well. Suppose that there is a data set of n samples, each sample consists of m seismic attributes and one reservoir parameter. Probabilistic Neural Network assumes that each output log value could be expressed as a linear combination of input logging data value (Hampson et al., 2001). The new sample after the attribute combination is expressed as: (1) The new predicted logging values can be expressed as: (2) whereà ¯Ã‚ ¼Ã… ¡ (3) The unknown quantity D(x, xi) is the â€Å"distance† between input point and each training sample point. This distance is measured by seismic attributes in multidimensional space and it is expressed by the unknown quantity ÏÆ'j. Eq. (1)and Eq. (2) represent the application of Probabilistic Neural Network. The training process includes determining the optimal smoothing parameter set. The goal of the determination on these parameters is to make the validation error minimization. Defining the kth target point validation result as follows: (4) When the sample points are not in the training data, it is the kth target sample prediction value. Therefore, if the sample values are known, we can calculate the prediction error of sample points. Repeat this process for each training sample set, we can define the total prediction error of training data as: à £Ã¢â€š ¬Ã¢â€š ¬Ãƒ £Ã¢â€š ¬Ã¢â€š ¬ à £Ã¢â€š ¬Ã¢â€š ¬(5) The prediction error depends on the choice of parameter ÏÆ'j. This unknown quantity realizes the minimization through nonlinear conjugate gradient algorithm. Validation error, the average error of all excluded wells, is the measure of a possible prediction error in the process of seismic attributes transformation. The trained Probabilistic Neural Network has the characteristics of validation minimum error. The PNN does not require an iterative learning process, which can manage magnitudes of training data faster than other Artificial Neural Network architectures (Muniz et al., 2010). The feature is a result of the Bayesian technique’s behavior (Mantzaris et al., 2011). 3. Methodology The data sets used in this study belong to 8 wells (consisting of W1 to W8) and post-stack 3D seismic data in Songliao Basin, Northeast China. The target stratum is the first member of the Cretaceous Nenjiang Formation that is one of the main reservoirs in this area. In this study, the main contents include seismic impedance inversion, attributes extraction, training and application of PNN model. The flow chart is shown in Fig. 1. Fig. 1. The flow chart of this study 3.1 Seismic impedance inversion This section is to calculate a qualified 3D seismic impedance data volume for porosity estimation. The attributes are gathered from both seismic and inversion cube. The phase of input 3D seismic data is close to zero at the target stratum. The data have good quality in the entire time range without noticeable multiple interference. T6 and T5 are the top and bottom of reservoirs, respectively. T6-1 is an intermediate horizon between T6 and T5 (Fig. 2 (b)). This data volume covers an area of approximately 120 km2. The structure form of reservoir in this area is a slope. There are two faults in the up dip direction of slope (Fig. 2 (a)). (a) (b) Fig. 2. (a) T6 horizon display. (b) An arbitrary line from seismic data, line of this section is shown in (a). Seismic datacontain abundant information of lithology andreservoirs property. Through seismic inversion, interface type of seismic datacan beconverted intolithology type of loggingdata, which could be directlycompared withwell logging (Pendrel, 2006). Seismic inversionbased on logging data takes full advantage of large area lateral distribution ofseismic data combined with using the geologicaltheory. It is an effective method to study the distribution anddetailsof reservoirs. PNN inversion is a neoteric seismic wave impedance inversion method. There is mapping relation between synthetic impedance from well log data and seismic traces near well. In PNN inversion method, this mapping relation will be found and a mathematical model will be built up by training. The concrete steps of PNN inversion are as follow (Metzner, 2013): (1). Build up an initial reservoir geological model. The control points of model are defined by a series of different depth, velocity and density data. (2). Neural Network model establishment and training. At this step, a PNN model is built up and trained. The training and validation error of trained PNN should be minimized. The trained PNN model includes the mathematical relation between synthetic impedance by well log data and seismic traces near well. (3). Calculation of impedance by applying the PNN model to seismic data volume. PNN inversion method takes full advantage of all the frequency components of well log data, and has good anti-interference ability. PNN inversion will not reduce resolution in inversion process, and there is no error accumulation. Final results of inversion are displayed in Figs. 3, 4, 5 and Table 1. Fig. 3. Cross plot of actual impedance and predicted impedance Fig. 4. Cross Validation Result of Inversion. Correlation=0.832, Average Error=546.55[(m/s)*(g/cc)] Fig. 5. Arbitrary line from inversed impedance data volume. Base map is shown in the figure lowerleft. Table 1 Numerical analysis of inversion at well locations 3.2 Seismic attributes selection by using multi-regression analysis Multi-regression analysis is a mathematical method which is used to analysis the relationship between one dependent variable and several independent variables (Hampson et al., 2001). The basic principle is that although there is no strict, deterministic functional relation between dependent variables and independent variables can try to find the most appropriate mathematical formula to express this relation. Multi-regression analysis can be used to solve the following problems: (1). Determine if there is correlation between certain variables. If it exists, find a suitable mathematical expression between them. (2). According to one or several variable values, predict the value of another variable, and calculate the forecast accuracy. (3). Factor analysis. For example, in the common effects of many variables for a variable, find out the most important factors, the secondary important factors, and the relationship between these factors. In the multi-regression analysis method, prediction error of N attributes is always less than or equal to N-1 attributes. Adding attributes means to use higher polynomial to fit curve. We can calculate the prediction error of each polynomial. This prediction error is equal to the root mean square error between real values and predicted values. With the increase of polynomial order, the prediction error decreases. But when we use overhigh order polynomial to fit curves, the existing data may fit well, but the interpolation or extrapolation over boundary would be fitting badly. This problem is called over-trained. In this study, the data would be divided into training data set and validation data set. The training data set is used to determine the correlation coefficient, and the validation data set is used to compute the validation error. If a high order polynomial fit the training data set well, but fit the validation data set badly. It means that the order of polynomial is too high. In this section, multi-regression analysis method is used to find the most suitable seismic attributes. As illustrated in Table 2, the training error gradually reduces with the increasing number of attributes, but when the number of attributes increases to four, validation error will rise. So, the best set of seismic attributes should contain three attributes that are the first three attributes in Table 2. The first three attributes are Inverted Impedance, Average Frequency and Filter 35/40-45/50. The most significant seismic attribute is Inverted impedance. Those attributes yield useful information about the lateral changes in lithology and porosity (Chopra and Marfurt, 2005). Furthermore, the training error for them is less than 3% that shows the exactness of results. It should be noted that PNN is a kind of nonlinear method, so the aforementioned attributes can be used as input for porosity prediction by PNN. (Kadkhodaie-Ilkhchi et al., 2009) Table 2 The result of multi-regression analysis for porosity estimation 3.3 Porosity estimation using PNN The main purpose of this section is to establish an optimum PNN model. The inputs of this model are three selected attributes in the previous section. In order to highlight the advantages of Probabilistic Neural Network in porosity estimation, another four algorithms have been used. Another four algorithms are single attribute analysis, multi-regression analysis, Multi-layer Feed Forward Network (MLFN) and Radial Basis Function (RBF). The training and validation results are shown in Table 3. According to the results, PNN algorithm gives less training and validation error. As seen from Table 3, the correlation coefficient of training result could reach 0.915, which is considered as a high correlation coefficient. It is higher than multi-regression analysis method (the correlation coefficient of multi-regression analysis is 0.844) and other methods. According to the numerical validation results, PNN method for porosity estimation is more accurate than others in this case. In the final of this section, the analysis for creating an optimum PNN model was done (Table 3 and Fig.6). Table 3 The training and validation results of neural networks Fig. 6. Cross plot of predicted porosity versus actual porosity 4. Results and Discussion We have demonstrated the application of Probabilistic Neural Networkto reservoir porosity estimation from seismic attributes. Two mathematical tools have been used: multi-regression analysis and PNN method. In the section of seismic impedance inversion, a qualified inverted impedance data volume has been calculated (Fig.3). In the section of seismic attributes selection, multi-regression analysis has been used to find appropriate seismic attributes (the first three attributes of Table 2). Those seismic attributes come from 3D seismic data volume and inverted impedance data volume. The optimal model is built up by PNN with proper trend and minimization of error. We have demonstrated this methodology on a set of 8 wells log data. The correlation coefficient of training data set could reach 0.915, which is considered as a high correlation coefficient (Fig.6). The well W5 is not used in training. It is used to validate the result of porosity estimation. The correlation coefficient of validation result could reach 0.881, which means that this methodology is reliable. The estimated porosity of W5 is displayed in Fig.7. After the establishment of an optimum PNN model for porosity estimation, we apply this model to all seismic data volume. Then, a porosity data volume could be calculated (Figs.8, 9). In Fig.9, an ancient river could be seen in the rectangle with higher porosity than elsewhere in the region. This is consistent with the law of geology. which shows, from one aspect, that the Probabilistic Neural Network is a reliable tool for porosity estimation. This method is an effective way to create an acceptable porosity data volume. 5. Conclusions We have demonstrated that the estimation of reservoir porosity from seismic attributes and inversion impedance using PNN method. In this study, two mathematic tools have been used: multi-regression analysis and PNN method. At attributes selection stage of this study, three attributes have been selected. At the porosity estimation stage, a PNN model has been established and trained. The training and validation correlation coefficient between predicted porosity and actual porosity could reach 0.915 and 0.881, respectively. The profile of estimated porosity shows that porosity variation in vertical direction is approximately increasing from bottom to the top and can be verified at well locations. The results indicate that PNN is a reliable method for porosity estimation. And it has obvious advantages in estimation accuracy compared with conventional methods such as multi-regression analysis and Multi-layer Feed Forward Network. The proposed methodology can be used to estimate porosity from seismic data. This methodology could reduce drilling risks and improve the success rate of exploration at the early stage of reservoir exploration. And it also could provide an acceptable porosity data volume which could be used to build reservoir geological model at the stage of reservoir development.

Why Do Children Talk To Themselves? :: Psychology Psychological Papers

Why Do Children Talk To Themselves? Whether you are a parent, teacher, child care giver, or a child observer you may have noticed that many children talk to themselves. Laura Berk reports that, â€Å"private speech can account for 20-60 percent of the remarks a child younger than 10 years makes† (78). Why do children do this? Does it benefit the child as Vygotsky would say, or is it just that the child is making egocentric remarks that play no positive role in normal cognitive development as Piaget would claim? I am going to be looking at the differences between Vygotsky’s and Piaget’s points of view. Then, I will look at Laura Berk’s findings in her article, â€Å"Why Children Talk to Themselves.† I will also talk about other findings concerning this topic. Jean Piaget and Lev Vygotsky were developmental psychologists interested in the origins and processes of cognitive development. These two psychologists disagreed sharply on the role that private speech played in one’s cognitive development. Vygotsky called this private speech while Piaget called it egocentric speech. Piaget observed the activities of three to eight year old kindergarten children, and discovered such uses of speech as verbal repetitions of another individual, monologues during an activity, and non-reciprocal remarks in collective settings. In these instances their speech was not directed towards other individuals. In Piaget’s mind these patterns of speech showed evidence of egocentrism, a sign of cognitive immaturity, and an inability to share the perspective of another individual. However, he argued, as the children grow older they socialize increasingly more with others, and their speech becomes communicative. Their speech moves away from being self- to other-oriented, a sign that they are able to adopt the perspectives of others. A child overcomes egocentrism by beginning to think critically and logically, causing egocentric speech to fade away. Vygotsky believes that a child’s cognitive development originates in socialization activities, and then goes through a process of increasing individuation. He argued that self-directed speech did not show any cognitive immaturity, but did show some form of development. He claims that private speech represents a functional differentiation in the speech of a child, or that a child begins to differentiate between speech that is directed towards the others and speech that is self-directed.

the time is now :: essays research papers

If... he has committed murder, he must die. In this case, there is no substitute that will satisfy the legal requirements of legal justice.There is no sameness of kind between death and remaining alive even under the most miserable conditions, and consequently there is no equality between crime and the retribution unless the criminal is judicially condemned and put to death." Immanuel Kant. About 2000 men, women, and teenagers currently wait on America's "Death Row." Their time grows shorter as federal and state courts increasingly ratify death penalty laws, allowing executions to proceed at an accelerated rate. It's unlikely that any of these executions will make the front page, having become more and more a matter of routine in the last decade. Indeed, recent public opinion polls show a wide margin of support for the death penalty. But human rights advocates continue to decry the immorality of state-sanctioned killing in the U.S., the only western industrialized country that continues to use the death penalty. Is capital punishment moral? Capital punishment is often defended on the grounds by the government, that society has a moral obligation to protect the safety and the welfare of its citizens. Murderers threaten this safety and welfare. Only by putting murderers to death can society ensure that convicted killers do not kill again. Second, those favoring capital punishment contend that society should support those practices that will bring about the greatest balance of good over evil, and capital punishment is one such practice. Capital punishment benefits society because it may deter violent crime. While it is difficult to produce direct evidence to support this claim since, by definition, those who are

Anorexia Nervosa

Anorexia Nervosa is one of the most common eating disorders highly prevalent among the teenagers.   Anorexia Nervosa is a condition which affects individuals who are looking for perfection of their body shape, but which has devastating psychological and the physiological effects on the individual. It is usually characterized by extreme low body weight and distortion of the body image.Most of those who become anorexic have an obsessive fear of gaining excess weight resulting to various voluntary easting disorders including starvation, purging, excessively engaging in physical exercises to create a negative energy balance, and other measures like diet pills or the use of diuretic drugs.The condition has also shown a gender dimension where female adolescents are mostly affected although research shows that about 10% of anorexia condition has been diagnosed in males.  The condition comes with various neurobiological, psychological, and sociological effects which may lead to the death of the victim. While individuals may have an obsessive fear of gaining weight, anorexia nervosa may have severe negative effects more than what can be attributed to being overweight.What is anorexia nervosa?While the battle ranges on fighting the rising case of overweight, there are more efforts directed on the opposite direction.   The rising cases of anorexia nervosa especially among our teenagers has been a matter of concern   to health experts promoting action on some social events like modeling competition which promote anorexic conditions especially one teenage girls (Ellison, 1999).Anorexia nervosa, which is simply referred to as anorexia, is eating disorder which affects individuals who have obsessive fear of becoming overweight.   It is a psychological disorder which goes beyond eating disorder. Apart from fearing begin overweight, it is also an unhealthy way of trying to cope with various emotional problems, perfectionism and the desire to have control.Anorexic indi viduals usually equate themselves with how thin they are.   While it starts out as a simple way to diet, the condition may spill out of control and become chronic therefore difficult to overcome (Simpson, 2002).  Anorexic individual tend to maintain a body weight that is far below their normal body mass index, which is a ratio of individual height and weight, and which is used to assess the weight status of individuals.   In some extreme cases, individuals becoming skeletally thin although due to psychological disorder they think they are still fat and therefore continue losing more weight. This extreme thinning comes with various health effects including psychological and physiological effects.Causes of anorexia nervosaAlthough there is no known cause of anorexia nervosa, it is postulated that biological, psychological and social cultural factors at play which leads to development of the condition.  Ã‚   Let us look at these factors.Biological predisposition is one of the l eading factors which lead to development of the condition. Research has found out that teenagers with parents or older siblings who have developed the condition are at a higher risk.   This may indicate a genetic link to the development of the condition (Ellison, 1999). Studies of twins have been used to support this possible genetic link.There is a probability that individual have genetic component towards perfectionism, sensitivity and perseverance which are traits associated with the condition.   However, there is no evidence that serotonin, which is the hormone associated with depression, has a role in development of the condition.Psychological factors have been explored far and wide.   It is postulated that it is possible that people with anorexic individuals have psychological and emotional characteristics which may predispose the individual to the condition.These individuals tend to have obsessive-compulsive personality traits which may influence them to stick to a stri ct diet despite their continued hunger (Ellison, 1999).   They may also have an extreme drive to perfectionism.For social cultural factor, research has found out that the modern western culture reinforces the desire to have a thin body.   The media has created the desire to have waif-like images of models and actors who become role model for the teens.   Peer pressure may also have a factor to play (Simpson, 2002).How does Anorexia Nervosa evolve?Anorexia nervosa is a chronic condition which evolves in different stages.   An understanding of these stages is important to assist physicians to identify the most appropriate intervention that will be appropriate intervention.The first stage is the identification of weight problem, which is an obsessive problem although the individual may not be overweight. At this stage the individual begin dieting. The stage may last four to six months. The mind of the victim is occupied with the need to lose weight and control the body. Close f riends and family members are helpless to the victim.The next stage is the stagnation stage. At this juncture, the weight loss reaches its bottom and the individual cannot lose more weight (Lucas, 2005).   This is a long period which is usually filled with frustrations individuals want to lose more weight which they cannot and at the same time they are not ready to gain weight.The third stage is regaining of weight.   In this period, the individual fails to gain more control of her body as body cells respond to starvation. This is usually one of the most terrible periods for the individual as one cannot have more control of the body.The individual may have bulimic episodes but continued weight gain makes one frustrated and unhappy which is followed by self hate and sometimes depression (Lucas, 2005). The individual seems to improve physically but psychologically feels incompetent setting in the paradox of anorexia.The last stage is confronting the reality.   At this stage, the individual is physically correct and their weight become normal again and has no more bulimic episodes or if present they are less intense. At this stage, individuals are able to accept themselves but with help from counselors, friends, and family members. (Lucas, 2005)