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25: J2EE Design Patterns Optimizing code to use a fast lane reader involves throwing out some of the other lessons in this book. As with almost every optimization, you start by doing things the proper way and then find ways to circumvent them so that your code performs better (or more correctly in line with expectations of the customer). You have two ways of implementing this design pattern: via direct access to the data access objects that you normally use in the bean implementation, or via a session bean that does direct database queries. In either case, you are throwing away the entity bean middleware layer and returning raw data. For this reason, there's no real example code to show you for this pattern: You are just using existing code in a different way.
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public MyStuff getStuff() { return stuff; } public void setStuff(MyStuff stuff) { this.stuff = stuff; } public void setPageContext(PageContext pageContext) { super.setPageContext(pageContext); }
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In this chapter, you will learn to do the following: Appreciate the need for scripting elements in JSP Identify the types of scripting elements Use declarations Declare variables and methods Use scriptlets Use conditional constructs in scriptlets Use expressions Use scripting elements to create a table directly in a JSP Use scripting elements to create a transaction page
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moment of private deliberation. Alternatively, it may in some instances extend to discussions with friends and family, or even to acquiring information through paying closer attention to relevant news in the media and elsewhere. Waterton and Lievesley (1989) report evidence for just such an effect from qualitative interviews with respondents to the 1983 1986 British Social Attitudes Panel. These processes of public and private deliberation may, in due course, lead individuals to adopt a different attitudinal position than would have been the case in the absence of the initial interview. Fishkin s experiments with Deliberative Polling methodology convincingly demonstrate that information and deliberation can result in sizeable net and gross opinion change amongst random samples of the general public (Fishkin, 1997). Goodin and Niemeyer (2003) show, furthermore, that much of the opinion change induced via deliberation can come through private internal reflection, without any subsequent social or interactional element. Thus, the very process of administering attitude questions may be sufficient to engender some degree of attitude change, through stimulating respondents to reflect more closely on the subject matter of the questions (Jagodzinski et al., 1987). Yet, it is not only shifts in marginal distributions that may result from this stimulus effect. Even in the absence of attitude change, respondents may come to hold their attitudinal positions more strongly and with greater consistency in relation to other idea elements in their belief systems as a whole (Sturgis et al., 2005). So, increases in information, reflection and deliberation should lead to more considered, stable attitudes that have been arrived at through rational, preference-based judgement, as opposed to being constructed, more or less randomly, on the spot. An empirical expectation resulting from the CS model is, therefore, that indicators of attitude strength or crystallization should increase between the first and subsequent waves of the panel.
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QoS-Enabled Middleware for MPEG Video Streaming Table 14.1 QoS-GFS levels QoS Level 5 4 3 2 1 Transmitted Frame(s) 12 10 8 6 4 Skipped Frame(s) 0 2 4 6 8 GoP Pattern IBBPBBPBBPBB IBBPB.PBBPB. IB.PB.PB.PB. IB.P..PB.P.. I..P..P..P..
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The problem with VaR is not just that historical episodes from time to time destroy its reputation. The poor thing never had a chance, on account of the mathematical structure with which it was endowed. VaR is calculated by multiplying the sigma (standard deviation, or volatility) of the given nancial positions, times the size of the positions, by a number representing a certain degree of statistical con dence. For instance, in the case of a 95 percent con dence interval you would multiply sigma by the number 1.65. If you want 99 percent con dence, you multiply sigma by 2.33. Thus, according to the Normal probability distribution that rules in VaR-land, the probability of any deviation beyond three sigmas is basically zero. In fact, VaR would likely ignore such freakishness as they would not fall under the interval (the probability in any given day of a 3-sigma event is less than 0.15 percent, or about once every 750 trading days; this is roughly equivalent to a 99.9 percent con dence, or 3.2-sigma event, which allows for one off-radar major loss every four years). That is, the theoretical maximum loss derived from VaR is mathematically constrained to be no larger than around 3 times sigma times the size of the position. The VaR radar will not, by de nition, register a gure higher than that. Unfortunately, in the real nancial world, 3-sigma and more events take place quite often, certainly more than once every three years and de nitely more than once every 125 years (the probability of 4-sigma events, 99.995 percent con dence, according to the Normal curve) or every 14,000 years (5-sigmas, 99.999 percent). In August 2007, the markets witnessed 25-sigma events, several days in a row. According to the Normal distribution, that is impossible, and thus hopelessly unforeseeable by its VaR offspring. Real in real life, utterly unreal in theoretical life. If the probability of a 5-sigma event is equivalent to a single occurrence since the last Ice Age, the chances of a (single) 25-sigma event are way smaller than once since Big Bang (12 to 14 billion years ago). So no need to dwell too much on the theoretical odds for a series of consecutive such happenstances. Someone has compared them to winning a high-stakes lottery more than 50 times in a row. Since VaR limits are destined to be breached as havoc inevitably strikes the markets, VaR paradoxically becomes a tool that becomes less relevant just as it claims to be offering increased accuracy. Going from a 95 percent interval to a 99 percent, or even 99.9 percent, won t do much in terms of preventing breaches (remember that anything beyond 3-sigma, a pretty habitual sight in
Identify the HTML elements of the page. Identify the JSP tags to be included in order to display the hit count. Write the code for the JSP page. View the JSP page.
where (u, v; ) is the joint density at point (u, v) of two jointly Gaussian random variables with zero expectation, variance 1, and covariance . Now standard algebra shows that
Step Three: Evaluate Online Conduit Strategies
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