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EIRP of satellite Free-space loss Satellite pointing loss Off-contour loss Excess attenuation rainfall Gaseous absorption loss Polarization loss Terminal pointing loss Isotropic receive level TerminalG/ T Sum Boltzmann s constant (dBW) C/ N 0
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The motor hotel has an offer from a soft drink vending company to install vending machines at no cost to the motor hotel. The vending company would collect the sales revenue (forecast to be as above for the next several years) from the soft drink machines, paying the motor hotel a commission of 10 percent on that revenue. Customers would help themselves to both soft drinks (by inserting cash in the machine) and ice (which would be free), thus eliminating the labor cost. An ice machine would have to be purchased by the motor hotel at a cost of $7,000. It would have a five-year life and a trade-in value at the end of that time of $1,000. Use straight-line depreciation. Annual maintenance and operating costs of the ice machine are estimated to be $100 per year. The motor hotel is in a 30 percent tax bracket. a. Calculate the payback period. b. Calculate the ARR.
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self-directedness as assessed using the Temperament and Character Inventory and the 5-HTTLPR allele (Hamer, Greenberg, Sabol, & Murphy, 1999). To date, a self-report measure of personality that has no genetic in uence has not been identi ed (Plomin & Caspi, 1998). The quali cation should be added that heritability studies have relied largely on self-report measures alternative methods of assessment may yield different results. However, this was not the case with the few studies using other methods (Heath, Neale, Kessler, Eaves, & Kendler, 1992; Riemann, Angleitner, & Strelau, 1997). Riemann and colleagues (1997), for example, reported a twin study conducted in Germany and Poland that compared assessments of the ve factors using self-report questionnaires with peer ratings. Estimates of heritability based on self-report were similar to those reported by other studies. The peer ratings also showed evidence of heritability, although estimates were lower than those obtained from self-reports. Multivariate genetic analyses showed that the same genetic factors contributed to self-report and peer ratings. These results suggest that ndings of a heritable component to all self-report measures are likely to generalize to other methods of measurement. Evidence of heritability alone, however, is not suf cient to justify the use of behavioral genetic criteria to clarify trait structure. It is possible that environmental factors that account for about 50% of the variance have a substantial effect on trait covariation. If this were the case, the nding that traits are genetically related would be of less value in clarifying personality structure. The evidence, however, suggests that the phenotypic structure of traits closely parallels the underlying genetic architecture (Livesley, Jang, & Vernon, 1998; Loehlin, 1987) a point that is discussed in detail later in this chapter. It should be noted, however, that information about heritability merely explains the variance in a single trait as opposed to the covariance between traits. Such information has limited value in explicating personality structure. As Turkheimer (1998) argued, all individual differences in behavior are heritable and . . . the very ubiquity of these ndings make them a poor basis for reformulating scientists conceptions of human behavior (p. 782). Nevertheless, information on heritability forms the foundation for understanding of the etiology of personality. The major contribution of behavior genetics to understanding personality structure, however, comes from multivariate genetic analyses that elucidate the genetic structure underlying multiple traits (Carey & DiLalla, 1994). Multivariate analyses extend univariate analysis of the genetic and environmental in uences on a trait to evaluate genetic and environmental components of the covariation between two or more traits (DeFries & Fulker, 1986). It is this generate data matrix barcode
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Similarly, we can prove that Election( 1/2 j 1 ) and Election( 1/2 j ) succeed in electing a leader with probability at least 1 e 1/2 and 2e 2, respectively. Therefore, Elec2 tion(Di) fails to elect a leader with probability at most 1 This completes the proof. Let D i = Di Di Di be an infinite sequence, where denotes the concatenation of sequences. For example, D 2 = 1 , 1 , 1 , 1 , 1 , 1 , . . . . Suppose that every station knows the 2 4 2 4 2 4 upper bound u of the number n of the station. Since Election(D log u ) elects a leader with probability at least 1 from Lemma 3.2, t times iteration of Election(D log u ) fails 2 to elect a leader with probability 1/2t. Also, the t times iteration runs in t log u time slots. Therefore, we have: Lemma 3.3 An oblivious protocol Election(D log u ) elects a leader in log f log u time slots with probability at least 1 1/f for any f 1. 10.3.3 Oblivious Leader Election for Scenario 3 Let V = v(1), v(2), . . . be a nondecreasing sequence of positive integers such that 1 v1 v2 holds. For such sequence V, let P(V) = Dv(1) Dv(2) Dv(3) be the infinite sequence of probabilities. For example, if V = 1, 2, 3, . . . , then P(V) = D1 D2 D3 = 1 1 1 1 1 1 , , , , , , . . . . We are going to evaluate the performance Election(P(V)) for various 2 2 4 2 4 8 sequences V. For a sequence V = v(1), v(2), . . . , let l(V) denote the minimum integer satisfying v[l(V)] log n . In other words 1 v(1) v(2) < v[l(V)] log n v[l(V) + 1] v[l(V) + 2] 1 1/4 1 1 e 1 e 1/2 (1 2e 2) < . 4 2 2
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