Problems With Hmos Essays - Health In The United States,

Problems With Hmo's Many employees must designate a health plan through their employer. These days, as HMOs (health maintenance organizations) and managed care plans continue to proliferate, that means a choice between bad and worse. As employees line up in the lunch-room for a process called open enrollment, they may be surprised to learn that managed care rates have gone up ? again. The mirage that managed care is cheaper care is finally fading. And, for the first time in years, employees may also have the promise of free choice in medicine in the form of a new method of financing health care. Consumers are already aware of horror stories involving HMOs, but cheap rates persuaded many that managed care is less expensive. Recent rate hikes are proving otherwise. Many patients must go out of network for crucial care. Co-payments are rising. It's little wonder why. As HMO executive Randall Crenshaw, chief medical officer for Cariten Healthcare of Tennessee, recently told the Wall Street Journal, more man aged care patients are becoming frequent fliers; they over utilize health care and drive costs up. The deterioration of managed care stems from a basic economic principle: health care subsidized by government and rationed by bureaucrats is doomed to failure. Canada's socialized medical system, which designates knee replacement an elective, is sending patients scurrying across the border and national health care in the United Kingdom restricts heart transplants to anyone under age 55. Managed care in America is no exception. Congress made health insurance premiums fully tax deductible to employers covering employees' health care in 1942. This discouraged individuals from buying insurance for themselves and encouraged employers to offer benefits. Eventually, employees came to regard health care as an entitlement provided by the employer. In 1965, Congress created Medicare. Seniors were forced into the free-for-all of Medicare, personal responsibility was replaced by paternalism, and, predictably, unrestricted health care for older Americans lead to frenzy of spending by patients and doctors. Those who had clamored for Medicare argued that, since the state subsidizes seniors' medical care, the state ought to pay for everyone's health care. In an act of pragmatism, President Nixon proposed the HMO Act, which Congress passed in 1973. The law gave millions of dollars to HMOs, which, until then, had constituted a small portion of the market. HMOs multiplied rapidly with the new federal giveaways. Managed care, now including PPOs, mushroomed. Employers initially perceived managed care plans as cheaper than traditional fee-for-service insurance. Gradually, they stopped offering a choice of health plans, making individual policies more expensive. HMOs' penetration of the industry had been subsidized into existence. Government had instituted managed care. Today, while overall quality of patient care remains the best in the world, doctors practice medicine in an increasingly intricate web of rationing and regulations: Physicians are stripped of professional autonomy. As patients wander the maze of managed bureaucracy, costs rise and quality deteriorates. Every American dependent on a third party for health coverage is a potential victim of managed care. And state sponsored management of medicine is growing. Last year, Congress and the President increased regulations on the medical profession with the Kennedy Kassebaum legisla tion. This year, Congress approved the President's $24 billion in kiddie care state subsidies. A few politicians (Senotors Kennedy and Wellstone), demand national health care on the grounds that health care is a right. Is health care a right? The Declaration of Independence protects the inalienable right to life, liberty and the pursuit of happiness; it does not guarantee happiness or health care. The right to act in your self-interest and provide necessities for yourself means you have the right to choose. A proper solution adopts and protects this right. Fortunately, Congress has the power to preserve the freedom of choice in medicine: they can vote to expand medical savings accounts (MSAs). Approved for a limited test last year, MSAs are used in conjunction with high deductible insurance. Contributions are tax-deductible and money earns interest; funds may be used to cover medical expenses ? including mental health, vision and dental care ? tax-free. MSAs, offered by Merrill Lynch, Time Insurance and Wells Fargo, among others, allow employees to boost savings. MSAs offer patients an escape from managed care and

The Killer Angels Essay

The Killer Angels Essay The novel The Killer Angels, by Michael Sharra is the story of the Battle of Gettysburg. On July 1, 1863 the union army and the confederate army fought the largest battle of the American Civil War. The book is based on real life people who were a part of the Civil War. The main characters were Gen. Robert Lee from the confederate army, Gen. James Longstreet, and Col. Joshua Chamberlain from the union army. The period covered in this book is Jun. 30-Jul 3, 1863. It takes place in a small town called Gettysburg, Pennsylvania. The main cause of the war was because the confederate army wanted to secede from the rest of the states. Most of the story was from the perspective of the confederate leaders, so we were able to really capture what the south was really thinking throughout this whole book. The story for the most part is told in the 3rd person. The confederates controlled Seminary Ridge and the union army controlled Cemetery Hill, Cemetery Ridge, Culp’s Hill, Little Round Top , and Big Round Top. Shaaras book very well tells the story of two great armies, both motivated and fighting for what each believes is a great and just cause, as they come together to fight one of the biggest battles ever fought in the world. This is a great novel of leadership, hardship, and history. Michael Shaara was born in 1928 in Jersey City, New Jersey. He graduated from Rutgers and it was during his college years that he realized he wanted to become a writer. In his early thirties, Shaara published several award-winning science-fiction short stories in the most popular pulp magazines of the day. He then began to write straight fiction and published numerous short stories in Playboy, Redbook, and Cosmopolitan. After finding old letters from his great-grandfather, a member of the 4 th Georgia infantry who had been wounded at Gettysburg, The 1966 trip was followed by another trip in 1970 and seven years of work on the manuscript. In 1974, The Killer Angels was published. It does not make the Confederate side look bad, but instead, without being pro-south, shows the reader that the key ingredient that made the Confederate Army one of the great armies of all times was the fact that the Confederates truly believed in the justice of their cause. He also published a Korean War nov el, called Broken Place, in which the main character seeks to be free of his demons and finds freedom through boxing. This book is based on the words of the men themselves, their letters, and other documents. He said that he avoided historical opinions. He also said he condensed some of the action and cut out some of the minor characters for brevity, but has not changed any facts. He also says he changed some of the language because back then men spoke in windy phrases, so he updated it. The story basically begins with a spy coming to Longstreet and telling him he has seen the union army nearby. Longstreet is surprised at this because J.E.B. Stuart was supposed to be keeping tabs on them. So, his plan was to go north and towards Gettysburg to cut them off. Meanwhile, union Col. Joshua Chamberlain wakes up to find out he has an extra 100 men to add to his regiment, the 20th Maine. They had caused a mutiny, so they were sent down to Chamberlain. They were really tired and hungry and they didn’t really want to fight anymore. Since Chamberlain is a very good speaker, he goes up and gives a very encouraging speech and all the soldiers except for 6 joined the 20th Maine. Gen. Buford of the union army rides into Gettysburg and sees that the confederates are nearby. He then gathers together his men to position them in the hills. The reason for that was he knew that getting the high ground was crucial because if they got the high ground it would be easier to fight the confederates than fighting them below. On the second day Lee wakes up to realize that Stuart still hasn’t come back yet. Since Stuart isn’t there, he doesn’t know where the union army is. He then has a meeting with Longstreet saying that he thinks they should plan defensive tactics to win, but Lee want to attack them and go straight at the union army. Lee didn’t want to go into the defensive mode because if he did it would look like he was retreating and he didn’t want that. The battle begins with the confederates attacking the union army. Buford end having to hold them off until Reynolds and the rest of his troops come. Reynolds then dies and later Lee comes right in the middle of the fight. After the that 1st battle he union army retreated up to the hills and set up cannons and dug places where they could be blocked from the fighting. Longstreet starts to get scares because he knows that the union army has good defensive position in the hills. This doesn’t at all help their stra tegy of swinging southeast to attack the union forces. Meanwhile, Lee is mad at Ewell for not doing what he said and taking Cemetery Hill and Culp’s Hill. On July 2, Chamberlain moves his regiment to Gettysburg. On his way they come across a slave who escaped and Chamberlain asks Kilrain what he thinks of black people. Kilrain believes u should judge the person by their action instead of judging them by their race. He says he doesn’t understand how they can look into the eyes of a man and make a slave of him and still quote the bible. Back in Gettysburg, Lee’s men want to attack the union army from their 2 flanks to try and weaken their line, but Longstreet still wants to move southeast to DC. Lee however doesn’t agree with him and Longstreet is forced to attack the flank. When Chamberlain finally arrives he and his troops are placed on Little Round Top. Him and his men hold down the hill for a long time but eventually they run out of bullets and Chamberl ain orders a bayonet charge on the confederates. They used a tactic, which was to scream while they were running down the hill to scare the confederates. The union army retained control over Little Round Top, and the confederates lost a lot of men. Later on that night J.E.B. Stuart comes back, and Lee scolds him for not being there to help locate the union army. He rationalizes that since he weakened the two outer flanks the next goal was to bust through the middle of their line. If he could charge through the middle then he could split the army in two. On July 3, Chamberlain moves all his men to the middle of the line because he thought there wouldn’t be much fighting. Longstreet then once again tries to convince Lee that it would be wise to swing southeast toward DC but he says no again. He is intent on attacking the union. So, Longstreet goes along with it and puts Pickett in charge of the assault on the union army. The confederates then shoot guns and cannons at the union to weaken the middle line. The confederated don’t do much damage though because they ended up shooting too high and the cannon balls went overtop of the line. As the confederate troops come across the field the Union fires full force with their cannons, which made big holes in the confederate line and kills many of their troops. Then they come even closer and they kill many more with their guns. Even after they get almost destroyed, Lee’s wounded men beg him to attack again. Then the confederate’s retreat and the battle ends. I felt this was an excellent book. I like the fact that the author made it so that you could focus in on one character a chapter because it helped you to be able to get into the thoughts and moods of the central character of the chapter. Also, I felt the characters in the story were well represented, especially the characters of Lee and Chamberlain. Lee was a very good commander who made really good decisions until the end. He strongly believed in the traditional ways of fighting but he still recognized some of Longstreet’s ideas. Lee’s confidence in his army is what lead to the disaster of Pickett’s Charge. Chamberlain throughout the story is put in a bad position because his brother serves in the army and he might have to send him into battle one day. He’s also a very good public speaker and he shows this when he motivates the 2nd Maine to fight on behalf of the union army or also called the 20th Maine.

Research Paper Introduction_1 Essay Example | Topics and Well Written Essays - 250 words

Research Paper Introduction_1 - Essay Example As such, all the aerospace logistics companies, Kuehne+Nagel included, have to morph constantly with respect to the varying trends to survive. Specifically, the market structure affects the business model of a company that has to be compatible with the prevailing market conditions. Kuehne+Nagel is precisely struggling to manage cost positioning that is just a part of the whole issue of market structure. The problem is partly contributed to by the customers who use cost as their tool for choosing an aerospace logistics company, and partly by the presence of several companies who have to scramble for the few clients (Francois, 2012 p. 1). With its traditional business model which did not pay much attention to cost positioning issues, Kuehne+Nagel found itself in financial mess when it had to lower its prices in order to come into equilibrium with that of the market. However, reducing the costs acted as a double-edged sword, drawing clients in but stretching the operations of the compan y. The only solution was to devise such cost-friendly solutions like shared warehousing among

Irs and orgainzed crime Research Paper Example | Topics and Well Written Essays - 750 words

Irs and orgainzed crime - Research Paper Example , permanence, hostility or threat of violent behaviour, prohibited ventures, legal business penetration, fraud, and lack of principles (Finklea, 2010, p. 2). The structure, however, is not rigid but is hierarchical. In the United States, organized crime has held its roots for a long time but came to be popular in the 20th century. This was with the penetration of Italian mafia, Russian mafia, Japanese Yakuza and Chinese Tongs that the federal government battled with for most of the century (Finklea, 2010). IRS was established with a mission to offer Americans taxpayers with excellent service through assisting them to comprehend and comply to their tax obligations, as well as enforce the law with integrity and fairness to all (Internal Revenue Service, 2012). Under this mission, the service has a criminal investigations (CI) department. The department seeks to serve the American public by investigating potential criminal violations of the internal revenue code and related financial crimes. The crimes that the IRS investigates include tax evasion, money laundering and Bank Secrecy Act laws. It is only the IRS that has the authority to investigate potential criminal violations of the Internal Revenue Code despite there being other agencies, which deal with the same crimes (Internal Revenue Service, 2012.). For these reasons, the IRS criminal investigation works on a global scale. The federal bureau of investigation attempts to put a stop to organized crime from all over the world. To counter the problem of financial crime, the IRS’s criminal investigations department is divided into three interdependent divisions: Legal Source Tax Crimes; Illegal Source Financial Crimes; and Narcotics Related and Counterterrorism Financial Crimes (Internal Revenue Service, 2012). The service, since the 1919 has attempted, succeeded and failed in investigating organized crime that include narcotics and money laundering. One of the earliest cases is that of Al Capone in the

Analysis of SIF Intrinsic Dynamics

Analysis of SIF Intrinsic Dynamics Abstract. It was recently found that spectral solar incident flux (SIF) as a function of the ultraviolet wavelengths exhibit 1/fà ¯Ã¢â€š ¬Ã‚ ­type power-law correlations. In this study, an attempt was made to explore the SIF intrinsic dynamics versus a wider range of wavelengths, from 115.5nm to 629.5nm. It seemed that the intermittency of SIF data set was very high and the revealed DFA-n exponents were very close to unity. Moreover, the power spectral density was better fitted algebraically than exponentially with exponent very close to unity. Eliminating the fitting of three different types of Planck formula from SIF data set, scaling exponents very close to unity were derived, indicating that the 1/f scaling dynamics concern not the Planck’s law but its variations. 1. Introduction As it is well known, electromagnetic radiation is continuously emitted by every physical body. This emitted radiation is adequately described by Plancks law near thermodynamic equilibrium at a definite temperature. There is a positive correlation between the temperature of an emitting body and the Planck radiation at every wavelength. As the temperature of an emitting surface increases, the maximum wavelength of the emitted radiation increases too. Smith and Gottlieb (1974) re-examined the subject of photon solar flux and its variations versus wavelength and showed that variations in the extreme ultraviolet (UV) spectrum and in the X-ray of solar flux may reach at high orders of magnitude causing significant changes in the Earth’s ionosphere, especially during major solar flares (Kondratyev et al. 1995; Kondratyev and Varotsos 1996; Alexandris et al. 1999; Melnikova 2009; Xue et al. 2011). Simon (1978) examined the solar irradiance fluxes from 120 to 400 nm and suggested that the internal scaling properties of solar radiation during the eleven-year cycle is still unknown, as the cause of the available solar data discrepancies can not be attributed to solar activity. Solanki and Unruh (1998) proposed simple models of the total solar irradiance variations versus wavelength showing that variations on solar flux are mainly caused by magnetic fields at the solar surface. Solar observations may be reproduced by a model of three parameters: the quiet Sun, a facular component and the temperature stratification of sunspots. Tobiska et al. (2000) developed a forecasting solar irradiance model, called SOLAR2000, covering the spectral range of 1–1,000,000 nm. Using this tool, the authors attempted to describe solar variation versus wavelength and through time from X-ray through infrared wavelengths, in order to predict the solar radiation component of the space environment. Very recently, Varotsos et al. (2013a,b) suggested the existence of strong persistent long-range correlations in the solar flux fluctuations versus UV wavelengths. More precisely, by applying the detrending fluctuation analysis (DFA) to the initial SIF versus UV wavelengths data set power-law correlations of the type 1/f, which is omnipresent in nature, was found. In the present study, we examine whether the scaling feature of 1/f noise is apparent in a wider spectrum of SIF, namely for wavelengths (WL) between 115.5 and 629.5nm. 2. Data and analysis As mentioned just above solar incident flux data for WL ranging from 115.5nm to 629.5nm with a step of 1nm were employed. The spectrophotometric data of spectral extraterrestrial solar flux have been taken from the book by Makarova et al. (1991) (see also Makarova et al. 1994; Melnikova and Vasilyev 2005). Fig. 1a depicts SIF values for the wavelength range of 115.5-629.5nm. The principal feature shown in this figure is the existence of non-stationarities into the solar spectral distribution and the strong upward trend. The detrending of this data set was accomplished by applying linear as well as polynomial best fit. To eliminate the non-stationarities of this data set and to detect the intrinsic self-similarity, we applied the well-known DFA method (Peng et al. 1994; Weber and Talkner 2001; Varotsos 2005; Skordas et al. 2010). Furthermore, we calculated the power spectrum for the initial SIF-WL data set, using Fast Fourier Transform (FFT) algorithm as well as the maximum entropy method (MEM) of the TISEAN package (http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.1/index.html). For comparison reasons, we used the Planck formula taking into account the effective temperature obtained from NASA (http://nssdc.gsfc.nasa.gov/planetary/factsheet/sunfact.html). A brief description of DFA-tool may be given as follows: Consider the SIF data set x(i) of length N which is integrated over WL. In more detail, to integrate the data, we calculate the à ¯Ã‚ ¬Ã¢â‚¬Å¡uctuations of the N observations x(i) from their mean value xave, notably: x(i) xave. Therefore, the integrated data set, y(i), is consisting of the following points: y(1) = [x(1) − xave], y(2) = [x(1) − xave] + [x(2) − xave], . . . , y(i) = (1) We split the integrated data set into non-overlapping boxes of equal length, Ï„. In each box, a best polynomial local trend (of order n) is à ¯Ã‚ ¬Ã‚ tted in order to detrend the integrated profile (by subtracting the locally fitted trend). The root-mean-square fluctuations Fd (Ï„) of this integrated and detrended profile is calculated over all scales (box sizes). The detrended fluctuation function F is defined by: k = 0, 1, 2,†¦, , (2) where z(i) is a linear least-square fit to the Ï„ data contained within a box (Kantelhardt et al. 2002). In case the signals involve scaling, a power-law behavior for the root-mean-square à ¯Ã‚ ¬Ã¢â‚¬Å¡uctuation function Fd (Ï„) is observed: Fd (Ï„) ∠¼ Ï„a (3) where ÃŽ ± is the scaling exponent, a self-afà ¯Ã‚ ¬Ã‚ nity parameter that represents the long-range power-law correlation (Ausloos and Ivanova 2001). It is worthy of note that a is a generalized Hurst exponent (H) being equal between them for the case of a fractional Gaussian noise (FGN) data set, where 0 1. For a fractional Brownian motion (FBM) data set (1 2) the relation between these exponents is H = a − 1 (Ausloos and Ivanova, 2001). For uncorrelated data, the scaling exponent is ÃŽ ± = 0.5. An exponent ÃŽ ± ≠  0.5 in a certain range of Ï„ values implies the existence of long-range correlations in that time interval. If 0ÃŽ ±ÃŽ ± ≠¤1.0 (and if again the data set is nonintermittent), then persistent long-range power-law correlations prevail (the case ÃŽ ± = 1 corresponds to the so-called 1/f noise) (Weber and Talkner 2001). Finally, the scaling properties of SIF-WL data set were also studied using Haar analysis (Lovejoy and Schertzer, 2012a,b). According to Haar analysis the variation of X parameter fluctuations ΔX with scale Δt can be defined using the â€Å"generalized† qth order structure function Sq(Δt) = ÃŽ §(Δt)q >, where the symbol stands for ensemble averaging. In a scaling regime, Sq(Δt) ≈ ΔtÃŽ ¾(q), where the exponent ÃŽ ¾(q)= qH K(q) and K(q) indicates the scaling intermittency (satisfying K(1) = 0). 3. Discussion and results Varotsos et al. (2013a) studying the high-resolution observations of SIF reaching the ground and the top of the atmosphere, suggested that SIF versus ultraviolet WL exhibit 1/fà ¯Ã¢â€š ¬Ã‚ ­type power-law correlations. This result was derived by applying the DFA method on the SIF dataset obtained from the Villard St. Pancrace station of the Lille University of Sciences and Technology and was based on the slope (i.e., 1.02 ±0.02) of the log – log plot of the root mean square fluctuation function of SIF versus the WL segment size Ï„. In order to further establish the power-law scaling mentioned before, Varotsos et al. (2013b) employed the two criteria suggested by Maraun et al. (2004), notably the rejection of the exponential decay of the autocorrelation function and the constancy of â€Å"local slopes† in a certain range towards the low frequencies. The results showed that the power-law fit on the power spectral density (of the detrended SIF-WL versus logÏ„) was much better than exponential one, for scales higher than Ï„ ≈ 1.4 nm, while the method of local slopes exhibited â€Å"constancy† with constant threshold (a ≈ 0.92) at the same range, Ï„ > 1.4 nm. Therefore, the two criteria of Maraum et al. (2004) were satisfied ensuring the 1/fà ¯Ã¢â€š ¬Ã‚ ­type power-law correlations in SIF versus WL suggested by Varotsos et al. (2013a). In the present study, the scaling dynamics of a wider spectrum of SIF-WL data set was studied, for wavelengths between 115.5 and 629.5nm. Firstly, DFA-n seemed to can take care of the trends revealing a DFA-exponent close to unity (after DFA-2), as shown in Fig. 1b. In the following we plotted the power spectral density (using FFT) of the linearly detrended SIF data set. The derived power spectral density showed that the power-law fitting gives coefficient of determination, R2, higher than the exponential one (see Fig. 2a). This was confirmed by F-test, at 95% confidence level. From the other hand, the DFA-1 exponent was 1.24, while by applying the multiple DFA on the linearly detrended SIF data, the derived exponents ranged from 0.94 to 1. Similar results were derived when we repeated the above mentioned analysis for the polynomially (of 6th degree) detrended SIF data set. The derived power spectral density was, once more, better fitted algebraically than exponentially (a result which was confirmed by F-test, at 95% confidence level), while the linearly DFA-exponent was 1.01 (see Fig.2b). In other words, it seemed that no detrending is needed for the studied data set. Next, to resume our results we analysed the initial SIF-WL data set by using Haar analysis (Lovejoy and Schertzer, 2012a,b). According to Haar analysis, as also mentioned in the Section 2, the variation of SIF fluctuations versus wavelength Ï„ can be defined using the â€Å"generalized† qth order structure function Sq(Ï„) = RF(Ï„)q >, for which it holds that in a scaling regime Sq(Ï„) ≈ Ï„Î ¾(q), where the exponent ÃŽ ¾(q)= qH K(q) and K(q) illustrates the scaling intermittency (satisfying K(1) = 0 and ÃŽ ¾(1) = H). Fig. 3b shows that the intermittency of SIF data set is very high (C1=0.20), hence the RMS exponent = ÃŽ ¾(2)/2=0.60 is quite different from the q=1 exponent (H) and the data are far from Gaussian. In the classical quasi-Gaussian case, K(q) = 0 so that ÃŽ ¾(q) is linear. More generally, if the field is intermittent – for example if it is the result of a multifractal process – then the exponent K(q) is generally non linear and co nvex and characterizes the intermittency. The physical significance of H is thus that it determines the rate at which mean fluctuations grow (H > 0) or decrease (H Ï„. According to Fig. 3a,b, the exponent ÃŽ ¾(2) of the structure function equals to zero (at scales below 10 nm), a fact which means that the power spectrum exponent ÃŽ ²=1+ÃŽ ¾(2) equals to 1 (1/f structure). On the other hand, at larger scales, the exponents ÃŽ ¾(2) and ÃŽ ² seem to equal to 1.2 and 2.2, respectively. To clarify this aspect, we calculated the power spectrum for the initial SIF-WL data set, using the MEM. Then, we plotted in Fig. 4a the power spectral density versus frequency and we compared these results with 1/f and 1/f2.2 structure, following the exponent ÃŽ ¾(2)= 1.2 shown in Fig. 3. Interestingly, the results lied in between these two lines. Furthermore, we calculated the power spectrum for the initial as well as for the polynomially (of 6th and 7th degree) detrended SIF data set, using again the MEM. We plotted in Fig. 4b the power spectral density versus WL and the corresponding algebraically fitting in the range from 10 to 100 nm. In the latter fitting we found exponent very close to unity. However, Varotsos et al. (2013a) tried to formulate the above-shown finding, i.e., that the solar spectral irradiance obeys 1/f power-law as a function of UV wavelength, using the well-known Plancks law: which, in the limit of small wavelengths tends to the Wien approximation: , where I(Ï„,ÃŽ ¤) is the amount of energy emitted at a wavelength Ï„ per unit surface area per unit time per unit solid angle per unit wavelength, T is the temperature of the black body, h is Plancks constant, c is the speed of light, and k is Boltzmanns constant. By applying the DFA method on the various values of I(Ï„,ÃŽ ¤) Varotsos et al. (2013a) showed that the calculated I(Ï„,ÃŽ ¤) values do not obey the 1/fà ¯Ã¢â€š ¬Ã‚ ­type scaling. In an effort to clarify this aspect, we firstly applied on the initial SIF data set a fitting that was based on the Planck formula: Planck(Ï„)= and the derived exponents were a1 = 6799.86 ± 45.39(0.6674%) and b1= 2754.38  ± 15.85 (0.5755%) (see Fig. 5). Next, we applied the formula: p(Ï„)= which releases the exponent in front of the Bose-Einstein distribution giving a possible explanation for the different distribution of photons. The derived exponents were a= 3250.17  ± 90.65 (2.79%), b= 4456.26  ± 121.3 (2.72%) and c= 8.81914  ± 0.2589 (2.94%). Our last step was to use the Planck formula taking into account the effective temperature obtained from NASA. However, since hc = 1.986à ¯Ã†â€™-10-25 Jm and k = 1.38à ¯Ã†â€™-10-23 J/K, the Planck formula gives ÃŽ ¤sun = 5224.3 K while the p(Ï„) formula gives ÃŽ ¤sun = 3229.5 K. Furthermore, the Wien displacement law: Ï„max à ¯Ã†â€™-T = b, where Ï„max = 450 nm is the peak wavelength and b = 2.898à ¯Ã†â€™-10−3mK is a constant of proportionality called Wiens displacement constant, gives ÃŽ ¤sun = 6440K. The analysis based on Planck and Wien laws gives ÃŽ ¤sun = (5224 + 6440) / 2 K = 5832 K which is less than 1% from the effective temperature obtained from NASA. Finally, the application of DFA method on the detrended SIF data, eliminating the fitting of Planck, p(Ï„) and Plank(Ï„, Tef) formula, gave again DFA-exponents very close to 1 (ranging from 1.01 to 1.08) as shown in Fig. 6. All the previous discussion indicates that the 1/f scaling dynamics observed in solar flux concerns not the Planck’s law but its variations. 4. Conclusions The main conclusions of the present survey were: DFA-n applied on the initial SIF data set revealed DFA-exponents very close to unity (after DFA-2). Power spectral density for the linearly detrended SIF data set showed that the power-law fitting gives coefficient of determination, R2, higher than the exponential one, while DFA-1 exponent was 1.24 and DFA-n exponents ranged from 0.94 to 1. Similar results were extracted when we repeated the above mentioned analysis for the polynomially (of 6th degree) detrended SIF data set and it seemed that no detrending is needed for the studied data set. To resume our results we analysed the initial SIF-WL data set by using Haar analysis. As ÃŽ ¹t was derived, the intermittency of SIF data set was very high and the data were far from Gaussian. At scales below 10 nm, the power spectrum exponent ÃŽ ² was almost 1 (1/f structure), while at larger scales, the exponents ÃŽ ¾(2) and ÃŽ ² seemed to equal to 1.2 and 2.2, respectively. The results of the power spectral density for the initial SIF-WL data set (using the MEM) versus frequency seemed to lie in between 1/f and 1/f2.2 structure. The power spectral density versus WL for the initial as well as for the polynomially (of 6th and 7th degree) detrended SIF data set (using again the MEM) revealed also exponent very close to unity. Eliminating the fitting of three different types of Planck formula from SIF data set, scaling exponents very close to unity were derived Finally, the 1/f scaling dynamics observed in solar flux concerns not the Planck’s law but its variations.

Macbeth :: essays research papers

The play is about a man called Macbeth who on his journey home meets three witches who for tell his future as first becoming thane of Cawdor and, later king. Macbeth instantly sercomes to the witches and believes what the witches say. When Duncan announces is successor as his son Malcolm is dream of becoming king through honesty are dashed. Lady Macbeth quickly comes up with a plan to make Macbeth king, by killing king Duncan and making his son Malcolm flee in hope to save his own life. The plan is followed through Duncan dies and Malcolm flees for his life leaving Macbeth as king. But the guilt is too much and he soon starts to see the goast of King Duncan, meanwhile his wife goes insane with guilt and kills herself. Malcolm army soon attacks on Macbeth and Macduff kills Macbeth. Malcolm was made king.Shakespeare use of witches in act 1 scene 3 and act 4 scene 1 is highly significant, I also believe that they say a lot about Shakespeare and how he would of thought. The main reasons though that I feel the witches roles were so significant is because Shakespeare wanted to please the king who was interested in demonology, to expose Macbeths mind the witches sort of showed his conscious escaping. Also to show human weakness or temptation, which gives the audience, a lot to listen for.The witches appearing in Act 1 had a huge affect on the story line. Even before we meet the witches we know the next few minuets will be very important, because of the significant weather change. You notice it has become very dark and tense this instantly makes the audience aware of their surroundings. When you do meet the witches you notice a good use of pathetic fallacy because the sky is dark and gray and so is the mood of the characters.When he meets the witches they tell him of his future as King Macbeths response is the expected yer right its more of a great! He doesn’t seem to need to know how? Or why? Or even how do you know? Which suggests that he may already have had these ambitions he doesn’t seem to question the witches as if he already thought it out minuet by minuet and he doesn’t need any more answers.I also believe that Lady Macbeth shared the same ambitions as her husband because when Macbeth tells her of the witches prophesies she also doesn’t questions whatthe witches say which can suggest she is a witch or that she also has an ambitions of her husband becoming king.

The Red Cross

Kevin Smith The Red Cross is an International organization that thrives on donations and the kindness of others to succeed. This Red Cross supports military families, disaster relief, health and safety training along with education, and blood drives that save thousands of lives yearly. The Red Cross started In the year 1881 and was mainly used to help wounded soldiers from the battlefield. â€Å"The global Red Cross has evolved into the world's largest humanitarian network- the only non-governmental organization with specific irresponsibleness outlined In the Geneva conventions†(RedCross, 2013). The Red Cross has established itself in the cyber community to succeed. With online donations exceeding 36 million dollars the Red Cross has been able to far exceed anything It did In the years prior to the Internet. The Red cross relies on its volunteers as its strength to work donation sites for all actions the organization participates in. The challenges the Red Cross faces today is the ability to keep enough volunteers on hand to run the organization. While the Red cross has full time members like any business this organizations success comes from those willing to help. The challenges the Red Cross faces In the media environment include many things. System hacking can be seen as hackers will set up take websites that look like the Red Cross web page. This in turn turns out to be a scam and people will give donations to a hacker Instead of the Red Cross. There can be other challenges out there for this organization that would include government policy. Foreign policy. ND legislative issues that can be strain the mission of the Red Cross. The Instant access the Red Cross has to the world has helped this organization in ways that are unimaginable. The Instant media environment has created an unlimited amount of people it can reach throughout the world. The money raised on dally donations Is lox the amount today ads It was In the uses. This is due to the instant access the world has to the Red Cross. The Red Cross uses the social media during disasters because this is the fastest and most effective way to reach a large number of Individuals.The Instant media environment has done nothing but help the Red Cross succeed. I mentioned earlier that volunteer are a challenge for the Red Cross, but with social media and instant media the Red Cross as been able alleviate some of the stress as they can collect for volunteers over the internet The Red Cross will use multiple tactics to get its message out to the community. The Red Cross will gain its target audience by associating someone within the organization to speak or Interact with the community that Is In question.This tactic is used by military recruiters and it tends to work well with inner city communities as well as communities with language barriers. The Red Cross has volunteers trot all walks tot elite so it would only be natural to use those people trot that community to spread the Red C ross message too particular group of immunity. The internal messaging of the employees Is that of care for the community and to give security to those in need The mission stays the same in the Red Cross and that is to provide care to all walks of life that may need it.The message stays the same internally throughout the world for the Red Cross as well. The Red Cross is not the largest humanitarian organization by doing things the wrong way. The internal structure is sound and solid and provides excellent support to all communities throughout the world. This is done with compassion, time, and hard work on behalf of the employees and the thousands of volunteers within the organization. I researched the Red Cross for numerous hours and found that it takes compassion and dedication too cause to make this organization successful.The Red Cross can be unified with many different events and by anybody willing to put in the time and energy to do so. I recently was a part of the Golf for Life rally which was to raise money and blood donations for the Red Cross. There are other events that happen all over the United States on a daily basis in which blood and money is raised for this organization. This is not only going on in the United States, but all over the world. If a person was to look up how to do an event on the Red Cross website it would show them exactly not only to go about doing it, but also show them ways it can be successful.