Economics for Business Essay Example | Topics and Well Written Essays - 1500 words

Economics for Business - Essay Example Since China was the biggest importer of the Australian rock lobsters, this industry would lose a significant amount of its revenues in the face of such a prohibition. The Australian Fishing Authorities had also requested the National Government to negotiate these trade restrictions with the Chinese Government. This would save their business from being affected (The Telegraph, 2010). 2. The Chinese economy was the largest buyer of the rock lobster exports of Australia. When the Chinese Government prohibited the Australian lobsters from being imported into the country in November 2010, this came as shocking news to the Australian fishing community. With the exclusion of its biggest export destination, the rock lobster industry in Australia was sure to suffer huge financial losses. The fishing communities in the Victorian province, Western and Southern Australia specialized in the trading of rock lobsters and were expected to be significantly affected as a result of the Chinese ban. The Australian fishing authorities feared that the country’s fishing community would be compelled to sell their catch at very low prices in the market. This in turn would result in lower revenues for these indigenous people. Thus, Australia’s fishing industry was set to be badly hit by China’s import restrictions. ... This ban was expected to generate a host of impacts on the Australian economy, the Chinese markets as well as the international trading community. First of all, the Australian fishing communities were expected to be adversely affected by the Chinese prohibition. The fishing communities hailing from the Victorian province, the eastern and southern Australia specialize in the trading and exporting of rock lobsters. They would witness a sharp decline in the revenues earned from the lobster export. The price of lobsters in Australia’s domestic market was expected to fall, as the demand for lobsters to be exported would reduce. This again would affect the profits of the Australian fishing communities, who would have to sell their lobster hauls at much lower prices to the local citizens. While China had implemented a ban on the import of the Australian rock lobsters, the nation continued to import lobsters from New Zealand and South Africa. If this resulted in a decline in the total quantity of lobsters imported in the Chinese economy, this would lead to a rise in lobster prices in the Chinese markets. However, if the country kept its import quantity the same as before by importing more lobsters from New Zealand and South Africa, then the internal lobster prices would not rise. Finally, there was an opportunity for New Zealand and South Africa to gain from the Chinese ban. Even after it had stopped importing Australian rock lobsters, China continued to import is seafood from New Zealand and South Africa. Both these countries could witness an increase in their lobster exports if China decided to import extra lobsters to replenish the missing imports from Australia. This would result in increased export revenues for both these nations (Herald Sun, 2010). The

Journal III Research Paper Example | Topics and Well Written Essays - 1000 words

Journal III - Research Paper Example In support of his emphasis on the imagination, Lefebvre elucidated why people relate serial killing to cannibalism, but did not refer to films depicting such themes. Hence, he wanted to establish facts out of the imagination, not through the actual thematic presentation but by way of the figure, which represents â€Å"the poetic and imaginary logic,† from where an individual draws his or her conception of the work (Lefebvre 43). The film Monster by Patty Jenkins relates to the reading since it is a story about a lesbian woman, who was compelled to become a prostitute to survive, and later on became a serial killer because of her uncontrollable hatred to men. Aileen, the prostitute turned serial killer, became a victim of her own memory and lived a life of misery because of her past – the terrible past that left her yearning for love – the love that she found in Selby. Aileen justified her wrongdoings by deeming them as acts of survival with her life already being torn apart. In the end, despite all the sacrifices, despite earning the money she needed for her and Selby to survive, Aileen was again left with no one by her side and was betrayed. Aileen was jailed and executed for being a serial killer, and now there was no point to life for her. In a major way, there is this connection between the serial killer in Monster and the serial killer that Lefebvre described in his written piece. The serial killer in Lefebvre’s description was motivated to kill for no reason, and Aileen killed some of her victims only because of anguish and despair and not because of hatred to the individual being victimized. Therefore, she had no substantial reason whatsoever to kill some of her victims. In another way, the film Party Monster by Fenton Bailey and Randy Barbato also forms a connection with Lefebvre’s idea of the modern day serial killer, which is to him the â€Å"locus memoriae of cannibalism† (58). Although there was no serial ki lling involved, the film showed how the imagination of Michael Alig brought him to the world of drug addiction and to committing a crime that ended his position as the leader of the fabulous and popular group party kids. Michael Alig was blinded by fame and was drowned by it. All he wanted was to be himself and to show what he could do, but he ended up struggling to prove something. Cannibalism or serial killing may not be the theme of the film but it leaves a message in regard to the significance of loving oneself and to staying away from things that could bring harm to oneself and to others. Drugs turned out to be the reason why the party kids were insanely partying, and drugs were also the cause of the lead character’s downfall. There may be no cannibalism or serial killing, but the act of drug addiction may be likened to the act of killing oneself slowly every single day for no justifiable reason. A person addicted to drugs is like feeding on his own meat, nourishing it w ith medicine that brings it to bereavement. The metaphor â€Å"queers that kill† was formed because of homosexuality. Homosexuality is viewed by some as a termite eating beneath the surface to inflict damage on the foundations of society. Some people deem homosexuals as a menace to society, which have to be killed, or be forced to live life the way it

Moral luck Essay Example | Topics and Well Written Essays - 1250 words

Moral luck - Essay Example Concepcion (2002) describes the "standard view of responsibility" whereby "it is unjust to hold a person morally responsible for that which she did not control. Agents deserve to be morally appraised or held liable only for that which they controlled" (455). The problem this poses is that at some level it can always be argued that a situation was outside of a person's control. There will invariably be some uncontrollable factor that, when joining the confluence of other factors over which a person did have control, it can be argued was the cause of any given scenario. For example, if a person driving a vehicle strikes a child who suddenly runs into the road, it might be argued that the person could have been paying closer attention and thereby braked sooner, or should have been driving more slowly. On the other hand it could be argued that a reasonable person under the circumstances could not have predicted the child running into the road, and therefore this was just bad luck and the driver should not be held responsible. This epitomizes the concept of moral luck. To what extend does a random, uncontrollable occurrence relieve a person of moral responsibility for a harm done Concepcion goes on to argue that accepting the standard view of responsibility "is tantamount t... never morally responsible, show that luck is not ubiquitous or at least that ubiquitous luck is not moral, or show that ascriptions of responsibility can retain justice despite the omnipresence of luck" (455). The implication is that it does not make sense to avoid assigning responsibility simply because luck played a role in the moral dilemma, since luck will virtually always be a factor to some degree. This would logically mean that nobody is ever morally responsible for anything. Paradox of Moral Luck and Moral Responsibility The paradox, as Thomas Nagel (1979) describes it, is that individual moral responsibility is possible even though luck is ubiquitous. We hold a person responsible for actions taken within a scenario he/she did not bring about. It is possible for a person to be morally blameworthy even though the bulk of the circumstances bringing about a scenario were entirely outside of that person's control. Degrees of control are virtually irrelevant, as proximate causation of uncontrollable variables fails to offer any relief from a person's moral responsibility for actions taken within any given situation. Consider the case of a soldier who voluntarily enlists in the U.S. Marine Corps in the hopes of being deployed in Iraq to fight in the war on terror. This person, after months of grueling patrols in Baghdad watching his fellow marines get killed by improvised explosive devices, decides to take revenge by entering the nearest civilian residence and shooting up the women and children inside. Then consider the kid who had his sights set on college but was instead drafted into the U.S. Army during the Vietnam War and becomes so worn down by the war and numbed to killing that he partakes in a massacre of civilians at Mai Lai. The former soldier chose to

American History - 12 Essay Example | Topics and Well Written Essays - 1750 words

American History - 12 - Essay Example †¢ The Civil Rights Act (1968) banned racial discrimination and desegregation. †¢ The Voting Rights Act (1965) banned discriminatory methods of denying suffrage to African Americans †¢ Medicare was created to offset the costs of health care for the nation's elderly. †¢The Fair Housing Act (1968) provided funds to construct low-income housing †¢ The Elementary and Secondary Education Act provided major funding for American public schools. †¢ The National Endowment for the Arts and Humanities used public money to fund artists and galleries. †¢ Congress tightened environmental controls with stronger Air and Water Quality Acts. †¢ Johnson secured several gains before Conservatives gained control of Congress. 2. _ Roe vs. Wade (1973) ______ †¢ Historic Supreme Court decision on abortion in the USA. †¢ Struck down an 1857 Texas statute that made abortion illegal except where the life of the mother was in danger. †¢ The Court ruled that the right to terminate a pregnancy is part of a woman's constitutional right to privacy under the Fourteenth Amendment. †¢ The State cannot regulate the right to abortion during the first trimester of pregnancy. †¢ All abortions to be performed only by licensed physicians under medically safe conditions. †¢ In the second trimester abortions are subject to State regulations regarding qualifications and licenses of the physicians. †¢ In third trimester, abortions legal only to save the health and life of the mother. †¢ Rejected contention that life is present from conception. †¢ Defined the rights of the fetus as emerging when it can survive independently outside the womb.   †¢ Set the terms of the abortion debate for decades to come. 3. ____Equal Rights Amendment_____ †¢ Holds that â€Å"Equality of rights under the law shall not be abridged by the United States or by any State on account of sex.† †¢ First proposed in Congress by the National Women's Party in 1923.   †¢ Approved by the House in 1970. †¢ Approved by the Senate in 1972. †¢ Opposed by social conservatives, such as Phyllis Schlafly, envisioning a threat to traditional family structure. †¢ Support spearheaded by the National Organization for Women. †¢ Ratified by thirty-five of the necessary thirty-eight states by 1977. †¢ Granted extension by Congress until June 30, 1982 †¢ Reintroduced in Congress in July 1982. †¢ The ERA is still not a part of the US constitution 4. __The Vietnam War________ †¢ Theater of the Cold War and the longest war in American history. †¢ US supports the anti-communist government of South Vietnam, led by the unpopular Ngo Dinh Diem. †¢ The communist Viet Cong, supported by the North’s Ho Chi Minh, unleashes guerilla war in the South. †¢ Diem is overthrown by a military coup in 1963, and assassinated with tacit US approval. †¢ The Gulf of Tonkin Re solution, passed in 1964, escalates the war and the involvement of US troops. †¢ Despite decades of hostilities, billions of dollars, and nearly 60,000 American casualties, the United States fails to achieve its objectives. †¢ Mounting casualties and media reports turn American public opinion decisively against the war †¢ The Paris Peace Accords of 1972 end the Vietnam War †¢ President Nixon signs a ceasefire in January 1973 that formally ends the hostilities. †¢ In 1975, Ho Chih Minh’s communist forces from the north overrun the south and unify the nation.   5. National Organiza

Social Identity Theory Essay Example for Free

Social Identity Theory Essay Although there are some members of the IMML group that are as foreign to us as the BBAs, we feel more comfortable working with IMMLs because they have the same behavioural characteristics as us, as well as sharing common goals. Tajfel suggests that a reason for this is the need to treat the teams values as our own. Examples of this include: the aim of finding a placement or business school in our respective countries, with a well respected degree in a relatively unique course from a renowned university. Concerns about placements cross language boundaries, and unites all IMMLs. A group thinks and behaves in characteristically design team ways3. This supports the social identity theory of when IMML first attended the Organizational Behaviour lecture of semester 2 with Kate McArdle. Throughout the past 2 years it has become acceptable to enter the management classes late, strolling slowly to the seat and even talking or waving to friends while the class has already started. However, when Kate McArdle started teaching the course this norm was not acceptable to her. By trying to change the group norm (apologising if late), IMML had temporarily modified their behaviour: the number of students being late was reduced, and if they were late they apologised out of courtesy to the entire class. However, when Foster Fei started teaching the class in Week 5, IMML reverted back to their initial behaviour. We can thus say that in this case it is difficult, almost impossible, for the minority to impose a change on a majority, especially if that norm has had time to evolve for 2 years. If a group majority were to accept the new group norm, they must all conform to the new norm. In summary we can say that because the majority of the IMML course was not convinced of the necessity of being on time, Kate McArdle only changed the group norm temporarily. * FIMML. GIMML. SPIMML. According to Tajfel in order to evaluate their own opinion and abilities, individuals not only compare themselves to other individuals with whom they interact, but also compare their own group with similar and distinct, out groups. 4 After the exam results were released, one characteristic of the members in FIMML was that they then tried to compare their results with others in their small seminar group, and secondly in a more general way against the other language groups. This comparison produced concrete (if biased i. e. ask the people you know will have the results to back up your stereotypes and our assertions of other groups) evidence to reinforce the stereotypes of the other groups and emphasizes our opinion of our group being superior. This minimises the perceived difference in the in-group (seminar B) and maximises the difference from the out-group (IMML as a whole) Cross theory example One student, Harry*, who joined group B at its formation was fluent in French. At the start of the year he spoke often in class, offered answers almost constantly and talked very quickly. This was not very well received by the group at first. The student realised this and decided to answer only when no one else did. According to Tajfel: this was so that others could have a chance. Harry sacrificed his own interests for the good of the group as a whole. This shows how Harry moved from thinking individually (personal identity), doing what benefited him, to feeling and thinking as a representative of a group. This also complies with Aschs theory about how groups influence individual attitudes and behaviour. Observing norms is of such benefit to us that we are prepared to suppress any personal desires and are thus willing to limit our individual freedom and abide by them5. Harry therefore supports both the theory of Tajfel as well as Aschs. As a second example of this we refer to an IMML Spanish student, Steve*, who came into the IMML French group in the second year. The French group felt that the new student was violating pivotal norms of our group. Such norms were: speaking a lot during the classes; criticising teachers; and overtly expressing his opinions without taking others opinions into consideration. His norms could either derive from the norms he had learned in his first year in the Spanish group, or due to his individual differences that he has gained through his culture, since his French nationality is exceptional to that of the group. After being in the French IMML group for a few weeks, there was tension between the new student and his language group, whereas the seminar group B didnt seem affected by the new presence because the new student was not in the same group. This example disproves Aschs theory. The new student didnt conform to the group pressure put upon him. In our opinion, his judgement and actions were not affected, even when the group vocalised their general disagreement to him being there. Neither did the group break up as a result of this. * Small (formal informal) Groups In this section of the essay we will use the example of Steve again because he is relevant for this part as well. However, we will do this in the context of small informal or formal groups looking at it from a whole different angle. As long as individuals see themselves as more important than the group, then the latter cannot function effectively. 6 Steve joining the French group lowered our self-awareness and heightened our group awareness. The group has had no influence over his behaviour and showed animosity to him by for example sighing or laughing when he talks. This is the case even now, 6 months after his coming into FIMML. He did not adapt to the norm of the group. Perhaps this insinuates that Steve does not see IMML as one of the groups that form his social identity. In this way he would not be influenced by the group, conform to it or attempt to convert the group to his way of thinking as it bears little or no importance on his self concept, and furthermore his social identity. In not recognising IMML as a key part of this social identity he does not feel that it is necessary to conform to the behaviour. Steves action supports Tajfels theory as mentioned above. This could be due in part to our perception of his elitist attitude, where he feels superior to the rest, stopping the group from functioning. Group members even left his class. For us to conform to him, we feel that he would have to conform to us first. We could perhaps propose therefore that established groups do not conform to newcomers, but rather newcomers conform to previously established groups. When he joined seminar group A, he changed the group norms. He had an outspoken personality and his loud confident attitude contradicted the previously quiet oral class. At first this resulted in others leaving the group, but the remaining students still did not conform to him and so eventually he was forced to leave. As soon as he left, the other group members returned.

Data Mining Techniques in Airline Industry

Data Mining Techniques in Airline Industry Purpose and Scope All around the world, the airline industry could be described in few words, which is intensely competitive and dynamic. The airline industry generates billions of dollars every year but still has a cumulative profit margin of less than 1%1. Many Airlines are trying to recover from deep debt. The reasons for these are multifold- fuel prices, high cyclicality and seasonality, fierce competition, high fixed costs and many other issues related to security and passengers safety. To ensure for the best economic outcome, Airline companies are trying with their most creative asset data. Data used in conjunction with data mining techniques allows comprehensive intelligent management and decision-making system. Achieving these benefits in a timely and intelligent manner may help in resulting lower operating costs, better customer service, market competitiveness, increased profit margin and shareholder value gain. This purpose of this paper is to demonstrate the applications of data mining techniques on multiple aspects of airline business. For example, to predict the number of domestic and international airline passengers from a specific city/airport, to dynamically price the tickets depending on seasonality and demand, to explore the frequent flyer database to prepare for CRM implementation, to makes the operational decisions about catering, personnel, and gate traffic flow, to assist the security agencies for secure and safe flights for the passenger specially after 9/11 incident. Predict the Number of Passenger by applying Data Mining Technique Forecasting is critical to any business for planning and revenue management, especially in the Airline industry, where a lot of planning is required to buy/lease new aircrafts, to hire crew members, to find the new slots in busy airports and to get the approvals from many aviation authorities. In the case of Air travel, lot of seasonality and cyclicality involved. Passengers are more likely to fly to some destinations based on the time of the year. Business travelers are likely to travel weekdays than weekends. Early morning and evening flights are desired by business travelers who want to accomplish a days work at their destination and return the same day. To forecast the number of passenger, artificial neural network (ANN) can be used. The purpose of a neural network is to learn to recognize patterns in a given data. Once the neural network has been trained on samples of the given data, it can make predictions by detecting similar patterns in future data. The growth factors which might influence the air travel demand depend on several things. Mauro Calvano2 in his study of transport Canada aviation forecast 2002-2016 considered 12 major socio-economic factors as follows: GDP Personal Disposable income Adult Populations US economic Outlook Airline Yield Fleet/route structure/Average Aircraft Size Passenger Load factors Labor cost and productivity Fuel cost/Fuel efficiency Airline cost other than Fuel and Labor Passenger Traffic Allocation Assumptions New technology Factors 1 to 5 are related demand side of the forecast Factors 6 to 10 are related to operations and supply side Factors 10 and 11 represent the structural changes This historical data is called the estimation set. A fraction of the overall available data is reserved for validating the accuracy of the developed forecast model. This reserved data set is called the forecasting set because no information contained in it is used in any form during the development of the forecast model. The data in the forecasting set are used for testing the true extrapolative properties of the developed forecast model. The estimation set is further divided into a training set and a testing set. Information in the training set is used directly for the determination of the forecast model, whereas information in the testing set is used indirectly for the same purpose. Figure1: Forecasting Process Model For a given ANN architecture and a training set, the basic mechanism behind most supervised learning rules is the updating of the weights and the bias terms, until the mean squared error (MSE) between the output predicted by the network and the desired output (the target) is less than a pre-specified tolerance. Neural networks are can be represented as layers of functional nodes. The most general form of a neural network model used in forecasting can be written as: Y = F [H1 (x), H2 (x), à ¢Ã¢â€š ¬Ã‚ ¦. , Hn (x)]+ u Where, Y is a dependent or output variable, X is a set of input/ influencing variables, F Hs are network functions, and u is a model error. This input layer is connected to a hidden layer. Hs are the hidden layer nodes and represents different nonlinear functions. Each node in a layer receives its input from the preceding layer through link which has weights assigned, which get adjusted using an appropriate learning algorithm and the information contained in the training set. Figure2: ANN Architecture Abdullah Omer BaFail3 did the study to forecast the number of airline passenger in Saudi Arabia. He selected the most influencing factors to forecast the number of domestic passengers in the different cities of Saudi Arabia. For Dhahran he selected factors like: Oil gross domestic product for last 6 years, private non-oil gross domestic product, Import of goods and services for last 10 years, and population size for last 2 years. The domestic and international actual and forecasted number of passengers for the city of Dhahran for the years 1993 through 1998 is shown below. Forecasts underestimated the actual travel. The Mean Absolute Percentage Error (MAPE) for domestic travel is about 10%, while for international travel is about 3%. Figure3: Forecasting results from Abdullah Omer BaFail3 The take away from the Abdullah Omer BaFail3 for me is that the efficient forecasting model can be invented using ANN if we using the right influencing indicators. In this study some indicators which influence are oil gross domestic product and per capita income in the domestic and international sectors. In view of the fluctuating nature of the passenger usage of airline services in Saudi Arabia, certain suggestions were made. Most of these recommendations were in order to improve the flexibility of the system to the fluctuations in demand and supply. Hub and spike model was also suggested as solutions in certain sectors to increase the flexibility in adjusting their capacity allocations across markets as new information about demand conditions become available. Application of Data Mining technique to predict the Airline Passengers No-show Rates Airlines overbook the flights based on the expectation that some percentage of booked passengers will not show for each flight. Accurate forecasts of the expected number of no-shows for each flight can increase airline revenue by reducing the number of perishable seats (empty seats that might otherwise have been sold) and the number of involuntary denied boardings at the departure gate. Typically, the simplest way is to go for average no-show rates of historically similar flights, without the use of passenger-specific information. Lawernce, Hong, Cherrier4 in their research paper predicted the no-show rates using specific information on the individual passengers booked on each flight. The Airlines offer multiple fares in different booking class. The number of seats allocated to each booking class is driven by demand for each class, such that revenue is maximized. For example, few seats can be kept on hold for the last-minute travelers with high fares and number of seats sold in lower-fare classes earlier in the booking process. Terms and conditions of cancellation and no-show also vary in each class. The no-shows results in lost revenue if the flight departs with empty seats that might otherwise have been sold. Near accurate forecasts of the expected number of no-shows for each flight are very much desirable because the under-prediction of no-shows leads to loss of potential revenue from empty seats, while over-prediction can produce a significant cost penalty associated with denied boardings at the departure gate and also create customer dissatisfaction. In the simplest model, the overbooking limit is taken as the capacity plus the estimated number of no-shows. Bookings are offered up to this level. No-shows numbers are predicted using time-series methods such as taking the seasonally weighted moving average of no-shows for previous instances of the same flight. Figure4: No-show trend over days to departure Source: Lawernce, Hong, Cherrier4 The simple model does not take account of specific characteristics of the passengers. Lawernce, Hong, Cherrier4 in his study used classification method, similarly Kalka and Weber5 at Lufthansa used induction trees to compute passenger-level no-show probabilities, and compared their accuracy with conventional, historical-based methods. I tried to summarize Lawernce, Hong, Cherrier4 approach and results briefly below. Whenever a ticket is booked the Passenger Name Records (PNRs) is generated and all the passenger information is recorded. The PNR data includes, for each passenger, specifics of all flights in the itinerary, the booking class, and passenger specific information such as frequent-flier membership, ticketing status, and the agent or channel through which the booking originated. Each PNR is also specified whether the passenger was a no-show for the specified flight. In the simplest model the mean no-show rate over a group of similar historical flights is computed. The mean in turn used to predict the number of no-shows over all booking classes. The passenger-level model given by can be implemented using any classification method capable of generating the normalized probabilities. The PNR records are partitioned into segments, and separate predictive models are developed for each segment. In the passenger-level modeling we characterize each using the PNR details. Let Xi; i = 1à ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦..I denote I features associated with each passenger. Combining all features yields the feature vector X = [X1à ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Xi ] Each passenger, n = 1à ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦.N, booked on flight m is represented by the vector of feature values xmn = [xmn, 1à ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦ xmn, ià ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦.. xmn, I ] We know the predicted no-show rate from the historical model; it is assumed the passenger inherits the no-show rate. The passenger level predictive model is then stated as follows: given a set of class labels cmn a set of feature vectors xmn and a cabin level historical prediction  µmhist predict the output class of passenger n on flight m: P(C = cmn |  µmhist , X= xmn ) We are specifically interested in the no-show probability, cmn = NS, and write this probability in the simplified form P(NS |  µmhist , xmn ) The number of no-shows in the cabin is estimated as à ¢Ã‹â€ Ã¢â‚¬Ëœ P(NS |  µmhist , xmn ) The summing of probabilities for each passenger in the cabin, gives no-show rate for the cabin. An analogous approach can also be used to predict no-show rates at the fare-class level. Lawernce, Hong, Cherrier4 compare results computed using the historical, passenger-level, and cabin-level models. The models were built using approximately 880,000 PNRs booked on 10,931 flights, and evaluated against 374,900 PNRs booked on 4088 flights. The figure shows a conventional lift curve computed using the three different implementations of the passenger-level model. Figure 5: Gain Charts Source: Lawernce, Hong, Cherrier4 Each point on the lift curve shows the fraction of actual no-shows observed in a sample of PNRs selected in order of decreasing no-show probability. The diagonal line shows the baseline case in which it is assumed that the probabilities are drawn from a random distribution. The three implementations of the passenger-level model identify approximately 52% of the actual no-shows in the first 10% of the sorted PNRs. This is one of the way the Airlines can incorporate data mining models incorporating specific information on individual passengers can produce more accurate predictions of no-show rates than conventional, historical based, statistical methods. Application of Data Mining technique to Strategies Customer Relationship Management In the current time most of the industries using frequency marketing programs as a strategy for retaining customer loyalty in the form of points, miles, dollars, beans and so on. Airlines are a big fan of this Kingfishers Kingmiles, Jet Airways Jet Privilege, American Airlines AAdvantage, Japan Airlines Mileage Bank, KrisFlyer Miles etc. they all seemed to have carved their own identities. Frequent Flyer Program presents an invaluable opportunity to gather customer information. It helps to understand the behavioral patterns, unveil new opportunities, customer acquisition and retention opportunities. This helps Airlines to identify the most valuable and the appropriate strategies to use in developing one-to-one relationships with these customers. The objective of data mining application over the frequent flyer customer data could be many, but ideally it is as follows: Customer segmentation Customer satisfaction analysis Customer activity analysis Customer retention analysis Some of the examples in each category are: Classify the customers into groups based on sectors most frequently flown, class, period of year, time of the day, purpose of the trip. Which types of customers are more valuable? Do most valuable customers receive the value for money? What are the attributes and characteristics of the most valuable customer segments? What type of campaign is appropriate for best use of resources? What are the opportunities to up-selling and cross-selling, for example hotel booking, upgrade to next class, credit card, etc. Design packages or grouping of services Customer acquisition. Yoon6 designed a database knowledge discovery process consisting of five steps: selecting application domain, target data selection, pre-processing data, extracting knowledge, and interpretation and evaluation. This study refers to the Yoon process to deal with three mining phases, including the pre-process, data-mining, and interpretation phases for airlines, as illustrated in figure below. Figure 6: database knowledge discovery process Source: Yoon6 Some straightforward solution can be implemented that can also be scaled-up in future like K-means, Kohonen self-organizing networks and classification trees. In the case of K-means algorithm, it is applied on customer data, assigning each to the closest existing cluster center. The K- means model is run with different cluster number until K-means clusters are well separated. In the case of classification trees (C5.0), we derive a simple rule set to uniquely classify the complete database. Again, we have to generate the attributes, resulting from the sequence of flight segments. The accuracy of the forecast for each segment is provided by balancing the training set according to equally sized clusters. We regulate the number of subsequent rules, while determining a minimal numbers of records given within each subgroup. Maalouf and Mansour7 did the study based on 1,322,409 customer activities transactions and 79,782 passengers for a period of 6 years. They prepared Data based on Z-Score Normalization and ran the multiple queries and transformed the data to create the clustering input records. They used K-means and O-Cluster algorithms. The result generated by clustering provides customer segmentation with respect to important dimensions of customers needs and value. The table below is the result is a summary of the profile produced by k-means clustering that includes: revenue mileage, number of services used, and customer membership period. Figure 7: Clustering result on Airline Customer Data Source: Maalouf and Mansour7 The results generated by k-means clustering are used as a basis for the association rules algorithm. Two different scenarios have been applied. The first scenario is based on Financial, Flight, and Hotel activities with 1,896 records. The second scenario is based on the flight activities especially the sectors, with 1,867 records. Figure 8: Association rules for best customer activities Source: Maalouf and Mansour7 Some of the take way from Meatloaf and Mansour7 study. Clustering using k-means algorithm generated 9 different clusters with specific profile for each one. From the cluster analysis it can be found which are the best customer clusters (higher mileage per passenger) than other clusters. Need a retention strategy for these clusters. Cross Selling strategies can be formulated between the clusters (for example between: 15 and 11; 13 and 17 because they are close in services value. The cluster analysis provides an opportunity for the airline to produce more revenue from a customer. For example, the airline could apply an up-selling strategy by selling a higher fare seat depending on the clusters. From the cluster analysis Airline may adopt an enhanced strategy for customers in clusters in order to increase services usage and revenue mileage per passenger. Plan for marketing campaign or special offers by analysis through association rules, for example, the customers using the Flight and Financial services never use the Hotel Services and the customers using the Flight and Hotel services never use the Financial Services. By analyzing the services used in different clusters, Airline can characterize services integration. It enables the airline to serve a customer the way the customer wants to be served. Application of Data Mining Application technique to understand the Impacts of Severe Weather Severe weather has major impacts on the air traffic and flight delays. Appropriate proactive strategies for different severe-weather days may result in improvement of delays and cancellations. Thus, understanding en-route weather impacts on flight performance is an important step for improving flight performance. Zohreh and Jianping8 in their study proposed a framework for data mining approach to analysis of weather impacts on Airspace system performance. This approach consists of three phases: data preparation, feature extraction, and data mining. The data preparation phase includes the usual process of selection of data sources, data integration, and data formatting. Figure 9: Framework proposed by Zohreh and Jianping8 He used three data sources: Airline Service Quality Performance (ASQP), Enhanced Traffic Management System (ETMS), and National Convective Weather Forecast (NCWF) supplied by National Center for Atmospheric Research. He used NCWF data from April through September 2000 to represent the severe weather season. These data-sets included the scheduled and actual departure and arrival times of each flight of ten reporting airlines, tail number, wheels off/on times, taxi times, cancellation and diversion information, planned departure and arrival times, actual departure and arrival times, planned flight routes, actual flight routes, and cancellations, flight frequencies between two airports, intended flight routes between two airports, flight delays, flight cancellations, and flight diversions. The image segmentation phase resulted in a set of severe-weather regions. Then for each of these regions, a set of weather features and a set of air traffic features are extracted. A day is described by a set of severe-weather regions, each having a number of weather and traffic features. As a result of this study it was found that there is strong correlation of blocked flights, #of bad weather regions, bad weather airports, blocked distance, bad weather longitude, by pass distance, bad weather latitude, # of bad weather pixels with flight performance. Similarly the clustering algorithms (like K-means) can be applied. The expectation is that the same clusters have similar weather impacts on flight performance. Zohreh and Jianping8 generated clusters for the entire airspace It was found that a cluster with worse weather almost always had bad performance. The clusters with large percentage of blocked flights, bypass distance, and blocked distance had a worse performance. These results were promising and showed that days in a cluster have similar weather impacts on flight performance Other data mining approach which can be applied is Classifications. Application of Classification can help us discover the patterns/rules that have significant impact on the flight performance. Discovered rules may be used to predict if a day is a good or a bad performance day based on its weather. For example Rule for Good: if %BlockedFlights and BypassDistance then Good (n, prob) There can be different ways where we can apply data mining approach to analysis of weather impact on airline performance. It seems to be that results obtained from clustering and classifications were very meaningful for airline and passengers to plan ahead. Application of Data Mining techniques to ensure safety and security of Airlines passenger The reaction of the terrorist attack on 26/9 and 11/9 resultant in increase Security at airports: It ends up allowing only ticketed passengers past the security gates, screen carry-on luggage more carefully for possible weapons. The question is whether these steps could have avoided the attacks, the people involved in the attack had legitimate tickets, and carrying box cutters and razor blades (like in any other normal person would do). The uncommon was the combination of their characteristics, like none were U.S. citizens, all had lived in the U.S. for some period of time, all had connections to a particular foreign country, all had purchased one-way tickets at the gate with cash. With the amount of data available about the passenger during ticketing, the can be reviewed to characterize relevant available passenger information. Given a passengers name, address, and a contact phone number, various data bases (public or private) can identify the social security number (SSN), from which much information will be readily available (credit history, police record, education, employment, age, gender, etc.). Since there is large number of characteristics available on both individual passengers, it will be important to identifying signals within the natural variability or noise. If predicted wrong, this may lead to either falsely detaining an innocent passenger or failing to detain a plane that carries a terrorist. The airlines already collect much data on various flights. When the data come in the form of multiple characteristics on a single item, exploratory tools for multivariate data can be applied, such as classification, regression trees, multivariate adaptive regression splines/trees. The security of the air transportation can be improved substantially through modern, intelligent use of pattern recognition techniques applied to large linked databases. Similarly Data mining techniques can be used for the Safety of the passenger. An air safety office plays a key role in ensuring that an aviation organization operates in a safe manner. Currently, Aviation Safety offices collect and analyze the incident reports by a combination of manual and automated methods.. Data analysis is done by safety officers who are very familiar with the domain. With Data mining one can find interesting and useful information hidden in the data that might not be found by simply tracking and querying the data, or even by using more sophisticated query and reporting tools. In a study done by Zohreh Nazeri, Eric Bloedorn, Paul Ostwald10 it was found that finding associations and distribution patterns in the data, bring important inside. The other finding is Linking the incident reports to other sources of safety related data, such as aircraft maintenance and weather data, could help finding better causal relationships. SumMRry Business Intelligence through efficient and appropriate Data mining application can be very useful in the Airline industry. The Appropriate action plans from the data mining analysis can result in improved customer service, help generating considerable financial lift and set the future strategy.

Factors for Successful Behaviour Change Interventions

Factors for Successful Behaviour Change Interventions Critically assess the key elements that contribute to successful behaviour change interventions. The need for change Everyone wants a long and healthy life although some young people claim that they do not wish to be old. This is because of the negative connotations of old age. It is also a flippant opt-out of taking responsibility. The rationale behind a healthy style of living is unassailable. So why is it difficult to get people to change their ways? For many people, simply getting them to change at all is a major obstacle. As Niccolo Machiavelli observed more than 500 years ago,[1] â€Å"It must be considered that there is nothing more difficult to carry out, nor more doubtful of success, nor more dangerous to handle, than to initiate a new order of things.† It is not only innate mule-like conservatism that hampers change. There are also positive attributes to the status quo. The tobacco industry has long attempted to portray smoking as cool, sophisticated and sexy. To the rational person, spending vast amounts of money to ruin your health and smell like an old ashtray cannot be cool, sophisticated or have any trace of sex appeal. Over many decades Hollywood has connived to portray smoking in a positive light and this does have at least a subliminal effect.[2] Simply giving people information about the risks of adverse lifestyles and the benefits of changing their ways is not enough. The relationship between smoking and lung cancer has been public knowledge since the report of the Royal College of Physicians on Smoking and Health in 1963 although the association was first suggested in 1950[3] and the association with other diseases in 1956.[4] There has to be a sustained approach with a solid theoretical background. Models for change The two theories that are most commonly employed in health promotion are the Transtheoretical Model (TTM) and the Health Belief Model (HBM). Both are most commonly employed to produce changes in health promotion but the TTM model can be used to produces changes in other fields including industry. Both these models focus on the decision making process of the individual. Other models include the Theory of Reasoned Action (TRA) and the Theory of Planned Behaviour (TPB). All four have been reviewed by NICE.[5] Transtheoretical Model The Transtheoretical Model was devised in the 1980s. It sees five stages of action. Stage 1 is the pre-contemplative stage. It is defined as having no intention to change in the next six months. To use smoking as an example, the person may not appreciate the true level of risk. They may be aware that there is adverse publicity about tobacco. Perhaps some people around them are giving up but they have not taken it seriously enough to think of doing so themselves. Any desire to change is still at a subconscious level. Stage 2 is the contemplative level. It is defined as intention to change in the next six months although it may take longer. The people involved are aware of the benefits of changing but they are also acutely aware of the challenges. They must decide the balance between costs and benefits. Stage 3 is the preparation phase. There is intention to take action in the near future and preparation is in hand. The people involved have already taken some significant steps to prepare for the change. Action is planned for the next month or so. With regard to smoking, they may have discussed it with other people. They may have sought out support groups and discussed prescription of nicotine replacement therapy (NRT) or bupropion. They may have set a date to quit. Stage 4 is action. The people involved have modified their thinking and behaviour. In this case, they have actually stopped smoking. Stage 5 is maintenance and focuses on sustaining the success. It is important to ascertain that the people involved do not relapse into the old behaviours and are confident of maintaining the new ones. They may focus on money saved, feeling healthier and enjoying food more. It is also necessary to address negative aspects such as possible weight gain. Health Belief Model The Health Belief Model is quite popular amongst healthcare professionals. It was developed in the 1950s in the USA to examine why there was a low uptake of a screening programme for tuberculosis. There are five steps: The first is perceived susceptibility. It relates to an individual’s feeling of how likely he is to suffer from a condition. It may run in the family or affect a friend. Many people seem to regard themselves as immune to certain illnesses. We all know the smoker who refuses to give up because he knows someone who smoked all of his life without any overt problem. Perceived seriousness is the next step. This is similar to susceptibility. Does the patient see lung cancer as curable? Does he see other smoking related diseases as manageable? The third step is perceived benefits and barriers. Patients weigh up the benefits against the costs of taking action. This means implication rather than financial costs. Fear of being excluded from his group of smoking friends may be a barrier. Uptake of cervical smears may be impaired by potential embarrassment. Self efficacy is step four. It is sometimes called health motivation. It describes how a person sees the ability to change behaviour. If a person thinks that he is unable to stop smoking this is a barrier. The fifth stage is cues to action. This is the trigger that initiates change. It may be an intervention from a health professional, an illness or a life event such as a new baby. Becoming pregnant can be a strong cue for women. Curtailing smoking Smoking cessation is a particularly relevant area to analyse. Smoking is the greatest avoidable contributor to ill health and premature death. Everyone knows of the dangers although perhaps they choose to underestimate them. Even young people still take up the habit. A Cochrane review found limited support for the effectiveness of multi-component interventions in the community to help prevent the uptake of smoking in young people.[6] Smoking tends to be most prevalent in deprived communities. A NICE public health guidance called â€Å"Identifying and supporting people most at risk of dying prematurely† focused mainly on smoking cessation and the provision of statins as being cost effective and clinically effective.[7] Both NICE[8] and CKS[9] have extensively reviewed the evidence and made recommendations with regard to smoking cessation. There are also plenty of Cochrane reviews.[10] NICE regards those of lower social class and pregnant women as a priority. Before the dangers of smoking were publicly known there was no difference in smoking habits between social classes. Now there is a distinct gradient[11] and it is said that smoking accounts for a significant amount of the decreased health and increased mortality through the social classes.[12] This is known as health inequalities. Key elements in changing behavior The first element of change has to be to implant in the individual’s mind that there is a need for change. With regard to smoking this may come in many ways. Public health messages often preach the wisdom of quitting. Health issues are raised whenever tax is increased and the price of cigarettes rises. This does reduce consumption.[13] Possibly milestones in life such as a 40th or 50th birthday may spur consideration of one’s health. Pregnancy is often a strong incentive to quit and both partners should do so to allow the baby a smoke free home. A Cochrane review was unimpressed at the evidence that getting both of a couple to quit together increased the success rate.[14] However, lack of evidence of efficacy and evidence of lack of efficacy are not the same and it does seem a good idea. The matter may arise during a consultation with a health professional. This may be when reviewing a directly relevant disease such as diabetes, coronary heart disease, hypertension, ast hma or COPD or it may simply be brought up as it comes to light on the health promotion template. It has been shown that if GPs simply raise the issue during a consultation this can have an effect.[15] NICE suggests that people who are not ready to quit should be asked to consider it and to seek help in the future. Some people fail to understand the concept of risk. They need it explained in terms that they can understand. There is no certainty that a smoker will die of a disease related to his habit nor that a non-smoker will have a long and healthy life. It is useful to have some simple figures. About 1 in 5 non-smokers die before 65 years old compared with 2 in 5 smokers. Half of all smokers die of a smoking related disease. If you do the National Lottery in the hope of winning the jackpot the chance of doing so is 1 in 14 million. For young people who cannot imagine being as old as 30, a different approach is needed. Smoking accelerates the aging process in arteries, in the lungs, in bones and in the skin. For those who fear old age, they are bringing it on. Money, fitness and smelling fresh are also positive attributes. Nicotine is highly addictive.[16] There are other components to the urge. There is the ritual of lighting up and something to do with the hands as well as the image. All these must be addressed to help the individual to cope. Many people find it helpful to set a date to quit. This allows for some counselling before the event and for such matters as NRT or bupropion to be discussed.[17] The forum in which this is done may well be a smoking cessation clinic within primary care. It is usually nurse led. There may be one-to-one counselling but groups may also be helpful.[18] The members give each other mutual support and tips for how to cope. Having brought the person to the point of quitting it is important to give support through the potentially difficult time ahead. In the early days motivation is high. This must be sustained. Congratulate the person on the achievement. Make him feel good about himself. Reinforce the positive aspects of quitting. Some people collect the money that they would have spent on tobacco each day and put it towards their holiday. NRT can give a slow release background level of nicotine in the blood to help ameliorate withdrawal. It is very important that the individual does not smoke. Inhalation of nicotine gives a rapid surge and this surge contributes greatly to the addictive mechanism. Some people test that their patients have been abstinent by using a carbon monoxide meter.[19] Carbon monoxide is present in tobacco smoke and it binds to haemoglobin with 210 times the affinity of oxygen. Therefore, carbon monoxide is released slowly for a while after smoking. At what stage has a person successfully quit? Is it after a week, a month or 6 months? There is a joke that goes, â€Å"Giving up smoking is easy. I’ve done it many times.† This emphasises the great problem of recidivism. It is a problem with smoking, alcohol and drug abuse. Those who counsel drug addicts and alcoholics continue for a long time after abstention to ascertain that it continues. Alcoholics Anonymous will invite people to their meetings even years after they last had a drink. They know how precarious the position is. People who stopped smoking may restart 6 months, a year or even a couple of years after they quit. It is often said that ex-smokers are the most intolerant of the smell of tobacco smoke and this is good. People may choose to start again in times of stress or crisis. A typical scenario is when out drinking. Smokers often try to undermine those who have successfully quit. Perhaps they emphasise their own inadequacy. â€Å"Go on. Just have one. It will do you good!† are the sort of thing that alleged friends say. In the words of Alcoholics Anonymous, â€Å"One is too many and 100 is not enough.† One night out drinking can lead to complete reversion. This is less likely to happen now that smoking in pubs and bars is illegal but it is still a risk and should be discussed in counselling. Forewarned is forearmed. Reflection on learning in health promotion It is easy enough to read and learn the theories of health promotion but putting them into practice is another matter. There will always be surprises and there will always be areas to learn. No isolated incident comes to mind but there are a number of issues that have emerged with accumulated experience. Any reasonable person will look at the health issues involved and will conclude that the healthy way of life is the logical option. This applies particularly to not smoking. However, not everyone is reasonable and this includes highly educated people. Matters that are not much emphasised in health promotion advice and the literature are peer pressure and denial. It used to be said that the prevalence of smoking was higher amongst nurses at the completion of their training than at the start although the true incidence of smoking amongst nurses is uncertain.[20] There is no doubt that peer pressure within a school of nursing is high. Stress is also given as a reason for smoking.[21] However, stress is a subjective experience and it may be used as an excuse to conform to peer pressure. Practice what you preach is a common proverb. An obese healthcare professional who advises weight loss or one who advises cessation of smoking whilst be known to indulge personally, lacks credibility. However, the â€Å"sinner† is also less likely to attempt health promotion.[22] Nurses who smoke are less likely to believe the compelling arguments about the dangers of smoking.[23] This is unlikely to represent an objective scepticism about the evidence but simply denial. If all this applies to nurses, it is unsurprising to find that it is at least as true when dealing with patients. People have to be ready to change. The mere noting of the fact that a patient smokes and the raising of eyebrows is another cue for it to sink in. There is no point in trying to pressurise the person who is not yet ready but leave an invitation to return when the time is right. What makes a person believe? It is not the level of evidence. People will find all sorts of excuses to doubt the overwhelming evidence about smoking or to pretend that it does not apply to them. On the other hand it seems much easier to convince people that the MMR vaccine causes autism when there is not a shred of evidence to support the allegation.[24] The convenience of a belief is important. We must lead by example. The â€Å"sinner† as a â€Å"preacher† is unconvincing although the reformed â€Å"sinner† may be more credible. It is important not to appear as sanctimonious with a â€Å"holier than thou† attitude. We need to show empathy with those we try to help. This applies not just to the physical addiction to nicotine but to the demands of peer pressure. The latter is especially important for young people. It is very easy for the non-smoker to see only negative images of smoking. Ask the patient to make a list of all the good things about smoking and all the bad things about it. A similar technique is used when counselling drug abusers. There must be something positive about taking drugs or no one would do it. Similarly, there are positive aspects to smoking. This shows that there is empathy towards the positive aspects. It also allows the patient to see the balance and to believe that he is making his own decision. He is not being coerced or bullied. It is also important to be realistic about the negative attributes of smoking cessation. Honesty gives credibility. It is often not so much the nicotine addiction that is a problem. This wanes with time. It is the gain in weight.[25] Weight gain is much more visible than damage to lungs or arteries. Many teenage girls say that they smoke to aid weight control. In fact, starting to smoke as a teenager probably does not help at all whilst the later in life that smoking is stopped the more marked weight gain is likely to be. Exercise rather than smoking is much healthier and much more effective. Nicotine has a nauseating effect and so when it is withdrawn there is likely to be increased appetite. Food now tastes better and some people suck sweets to occupy their mouth. It is important to discuss the matter. Weight gain is bad for health but unless the gain is enormous the benefit of smoking cessation will greatly outweigh the dangers of weight gain. If the patient decides to start smoki ng again the result will be a fat smoker. The weight will not melt away. Health promotion is a very personal matter. It requires a relationship on a one-to-one basis. It requires trust and respect. The health promoter must be seen as an honest broker rather than an evangelist. This requires empathy and it requires respect of the patient too. It is an important and difficult decision and he needs help and support. 1 [1] Niccolo Machiavelli. The Prince. 1532. Translator: W. K. Marriott http://www.sonshi.com/machiavelli.html [2] Tickle JJ, Sargent JD, Dalton MA, Beach ML, Heatherton TF. Favourite movie stars, their tobacco use in contemporary movies, and its association with adolescent smoking. Tob Control. 2001 Mar;10(1):16-22. http://www.ncbi.nlm.nih.gov/pubmed/11226355 [3] Doll R, Hill AB. Smoking and carcinoma of the lung; preliminary report. Br Med J. 1950 Sep 30;2(4682):739-48. [4] Doll R, Hill AB. Lung cancer and other causes of death in relation to smoking; a second report on the mortality of British doctors. Br Med J. 1956 Nov 10;2(5001):1071-81. [5] Behaviour change: Taylor et al models review. NICE 2006. http://www.nice.org.uk/nicemedia/pdf/Behaviour_Change-Taylor_et_al-models_review_tables_appendices.pdf [6] Sowden A, Stead L. Community interventions for preventing smoking in young people. Cochrane Database of Systematic Reviews 2002, Issue 3. Art. No.: CD001291. http://www.cochrane.org/reviews/en/ab001291.html [7] NICE. PH15 Identifying and supporting people most at risk of dying prematurely: guidance. September 2008. http://www.nice.org.uk/nicemedia/pdf/PH015Guidance.pdf [8] NICE. Smoking cessation. March 2006 http://www.nice.org.uk/guidance/index.jsp?action=byIDo=11375 [9] CKS Library. Smoking cessation. 2007. http://www.cks.library.nhs.uk/smoking_cessation [10] Cochrane Collaboration. http://www.cochrane.org/reviews/index.htm [11] Jefferis BJ, Power C, Graham H, Manor O. Changing social gradients in cigarette smoking and cessation over two decades of adult follow-up in a British birth cohort. J Public Health (Oxf). 2004 Mar;26(1):13-8. http://www.ncbi.nlm.nih.gov/pubmed/15044567 [12] Jha P, Peto R, Zatonski W, Boreham J, Jarvis MJ, Lopez AD. Social inequalities in male mortality, and in male mortality from smoking: indirect estimation from national death rates in England and Wales, Poland, and North America. Lancet. 2006 Jul 29;368(9533):367-70. http://www.ncbi.nlm.nih.gov/pubmed/11226355 [13] Leverett M, Ashe M, Gerard S, Jenson J, Woollery T. Tobacco use: the impact of prices. J Law Med Ethics. 2002 Fall;30(3 Suppl):88-95. http://www.ncbi.nlm.nih.gov/pubmed/12508509 [14] Park E-W, Schultz JK, Tudiver F, Campbell T, Becker L. Enhancing partner support to improve smoking cessation. Cochrane Database of Systematic Reviews 2004, Issue 3. Art. No.: CD002928. DOI: 10.1002/14651858.CD002928.pub2. http://www.mrw.interscience.wiley.com/cochrane/clsysrev/articles/CD002928/frame.html [15] Smoking cessation guidelines for health professionals: an update. Health Education Authority. West R, McNeill A, Raw M. Thorax. 2000 Dec;55(12):987-99. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmedpubmedid=11083883 [16] Russell MA. The nicotine addiction trap: a 40-year sentence for four cigarettes. Br J Addict. 1990 Feb;85(2):293-300. Review. http://www.ncbi.nlm.nih.gov/pubmed/2180512 [17] Guidance on the use of nicotine replacement therapy (NRT) and bupropion for smoking cessation. NICE technology appraisal no. 39 (2002). www.nice.org.uk/TA039 [18] Stead LF, Lancaster T. Group behaviour therapy programmes for smoking cessation. Cochrane Database of Systematic Reviews 2005, Issue 2. Art. No.: CD001007. http://www.cochrane.org/reviews/en/ab001007.html [19] Middleton ET, Morice AH. Breath carbon monoxide as an indication of smoking habit. Chest. 2000 Mar;117(3):758-63. http://www.ncbi.nlm.nih.gov/pubmed/10713003 [20] The incidence of smoking amongst nurses: a review of the literature. Rowe K, Clark JM. J Adv Nurs. 2000 May;31(5):1046-53. Review. http://www.ncbi.nlm.nih.gov/pubmed/10840237 [21] Rowe K, Macleod Clark J. Why nurses smoke: a review of the literature. Int J Nurs Stud. 2000 Apr;37(2):173-81 http://www.ncbi.nlm.nih.gov/pubmed/10684959 [22] McKenna H, Slater P, McCance T, Bunting B, Spiers A, McElwee G. Qualified nurses smoking prevalence: their reasons for smoking and desire to quit. J Adv Nurs..2001 Sep;35(5):769-75. http://www.ncbi.nlm.nih.gov/pubmed/11529979 [23] The effect of training on knowledge and opinion about smoking amongst nurses and student teachers. Elkind AK. J Adv Nurs. 1988 Jan;13(1):57-69. http://www.ncbi.nlm.nih.gov/pubmed/3372886 [24] Bandolier Extra. MMR vaccination and autism. http://www.jr2.ox.ac.uk/bandolier/Extraforbando/MMRextra.pdf [25] Filozof C, Fernà ¡ndez Pinilla MC, Fernà ¡ndez-Cruz A. Smoking cessation and weight gain. Obes Rev. 2004 May;5(2):95-103. http://www.ncbi.nlm.nih.gov/pubmed/15086863